Targeting “undruggable” cancer proteins: pharmacological challenges and emerging strategies

Introduction

Precision oncology, empowered by the multi-omics era, has enabled an unprecedented mapping of the molecular pathways underlying cancer initiation and progression. Advanced genomics, transcriptomics, proteomics, and metabolomics technologies systematically uncover the central and recurrent oncogenic drivers across diverse tumor types (1-3). However, this wealth of biological insight exposes a critical disconnect: many of the most physiopathologically relevant targets remain inaccessible to conventional pharmacological modalities, falling into the category of so-called “undruggable” proteins.

The term undruggability refers to proteins that, despite their pivotal roles in tumor biology, exhibit structural or functional characteristics that make them exceptionally difficult to modulate with traditional small-molecule inhibitors. This category includes transcription factors (such as MYC), scaffold proteins, members of the small guanosine triphosphatase (GTPase) family [including Kirsten rat sarcoma viral oncogene homolog (KRAS)], and many phosphatases. Major pharmacological barriers include the absence of deep hydrophobic pockets; large, flat, and dynamic interaction surfaces; intrinsically disordered domains; challenging intracellular localization; and the difficulty in obtaining stable high-resolution structures for rational drug design. Overcoming these limitations is not merely an academic pursuit but a relevant clinical necessity, as these proteins often represent central nodes in signaling networks essential for tumor cell survival and proliferation (4).

This landscape has driven a paradigm shift in drug discovery. Traditionally, the process began with a bioactive molecule, after which its target was identified. Today, the sequence is reversed: the molecular target is defined first through detailed tumor profiling, and only then are tailored therapeutic agents developed to engage it. This fundamental shift both demands and fuels the development of innovative pharmacological strategies that surpass simple occupancy of an enzymatically active site, opening a new and promising battleground in the fight against cancer (5,6).

In this context, the present review aims to map and critically analyze the leading innovative strategies that are redefining the boundaries of what is considered “druggable” in oncology. Specifically, we seek to (I) systematize the conceptual foundations and recent advances of the most promising approaches to target classically undruggable proteins, including targeted protein degradation (TPD), protein-protein interaction (PPI) inhibition, nucleic acid-based therapies, covalent inhibitors, allosteric modulation, and the use of artificial intelligence (AI) in drug design; (II) discuss the clinical translation of these approaches, highlighting success stories, lessons learned from failures, and persistent challenges related to efficacy, safety, and resistance; and (III) propose an integrated perspective on how these diverse strategies may converge, synergize, and ultimately reshape the standard of care in precision oncology. Thus, this review aims not merely to catalog emerging technologies, but to contextualize them as necessary and disruptive responses to a central challenge posed by modern cancer biology: transforming biologically crucial targets into pharmacologically vulnerable ones.

Emerging strategies to target “undruggable” proteins

TPD: proteolysis-targeting chimeras (PROTACs) and molecular glues

TPD has emerged as a transformative paradigm in modern drug discovery, enabling the reduction of protein abundance rather than modulating the enzymatic output of disease-relevant proteins. Unlike classical small-molecule inhibitors that rely on occupancy-driven mechanisms and require persistent engagement with well-defined active sites, degraders operate through event-driven catalysis, co-opting the ubiquitin-proteasome system (UPS) to redirect E3 ligases toward selected substrates and induce their polyubiquitination and proteasomal elimination (7-11). This shift from functional blockade to targeted removal has expanded therapeutic possibilities for proteins previously deemed undruggable, particularly in oncology, where many oncogenic drivers lack accessible ligandable pockets or display structural constraints that limit conventional inhibition. Within the diverse modalities that constitute the TPD landscape, two mechanistically distinct platforms stand out as the most advanced: heterobifunctional degraders, exemplified by PROTACs, and monovalent molecular glues, which stabilize neo-PPIs between an E3 ligase and its target (12-18). Together, these technologies have redefined the boundaries of druggable biology, offering a powerful and versatile strategy to eliminate pathogenic proteins across a broad range of cellular pathways.

Historical development and conceptual framework of PROTACs and molecular glues

The conceptual basis of PROTACs was established in 2001, when Sakamoto and colleagues described chimeric molecules that tethered a target protein to the Skp1-Cullin-F-box (SCF) E3 ligase complex, inducing ubiquitination and degradation of the target (19-23). Early PROTACs were largely peptidic and suffered from poor cell permeability and pharmacokinetics. Subsequent optimization identified fully small-molecule PROTACs capable of achieving catalytic, in vivo target knockdown and provided proof of principle that this approach could be drug-like (12,24,25). In parallel, clinical experience with immunomodulatory drugs (IMiDs; thalidomide, lenalidomide, and pomalidomide) revealed that they function as molecular glue degraders by binding the E3 ligase cereblon (CRBN) within the CRL4^CRBN complex, which in turn reprograms substrate specificity to promote degradation of neo-substrates such as Ikaros family zinc finger 1/3 (IKZF1/3) and CK1α (26-29). Structural studies of CRBN-ligand-substrate ternary complexes and, later, DDB1- and CUL4-associated factor 15 (DCAF15)-binding sulfonamides (e.g., indisulam) provided a generalizable mechanistic framework for small molecules that stabilize PPIs and induce neomorphic substrate recognition by E3 ligases (30-32). These converging lines of evidence, optimization of heterobifunctional degraders and mechanistic elucidation of clinical molecular glues, catalyzed the rapid expansion of TPD as a distinct therapeutic modality.

Molecular mechanisms of TPD

PROTACs are heterobifunctional small molecules composed of three elements: a ligand for the protein of interest (POI), a ligand for an E3 ubiquitin ligase, and a chemical linker that orients the two proteins in a productive configuration (8,33). Upon simultaneously binding the POI and the E3 ligase, a PROTAC induces formation of a ternary complex that positions the substrate within reach of the E2–3 ubiquitination machinery. This induced proximity promotes polyubiquitination of the POI and its subsequent degradation by the 26S proteasome (34,35). Because the PROTAC is not consumed during the process, a single molecule can catalytically degrade multiple copies of the target, enabling potent, event-driven biological effects with sub-stoichiometric engagement (9).

A defining feature of PROTAC function is that the sum of binary affinities does not simply dictate the formation of a ternary complex. Cooperative interactions, either positive or negative, between the POI, E3 ligase, and PROTAC strongly influence degradation potency, selectivity, and susceptibility to the hook effect (34,36,37). Structural and biophysical analyses have demonstrated that PROTACs can stabilize neo-protein-protein interfaces not present in the absence of the degrader, accounting for the remarkable selectivity achievable even among highly homologous proteins. From a design perspective, the identity of the recruited E3 ligase profoundly shapes degradation outcomes (36,38,39). Although over 600 human ligases exist, only a few have been broadly exploited, with CRBN and von Hippel-Lindau (VHL) being the most widely used due to their well-characterized ligandability and favorable cellular expression (40-43). Expanding the E3 ligase repertoire, including DCAF15, inhibitor of apoptosis proteins (IAPs), and tissue-restricted ligases, represents a fundamental opportunity to increase tissue specificity and overcome resistance (30,41,44,45).

Molecular glues achieve targeted degradation through a distinct mechanism. Rather than bridging two proteins via a linker, molecular glues are monofunctional small molecules that stabilize or create PPIs between an E3 ligase and a substrate. They typically bind the E3 ligase and remodel its surface, enabling recognition of proteins that would not naturally be recruited for ubiquitination. This remodeling can involve subtle alterations in hydrogen bonding networks, stabilization of transient hydrophobic patches, or induction of conformational states that expose new interaction surfaces. The result is a composite neo-interface that allows the ligase to ubiquitinate the recruited substrate. Unlike PROTACs, molecular glues do not require a high-affinity ligand for the target protein (16,17,46-50). Their degradative activity emerges from induced structural complementarity, enabling access to proteins traditionally considered undruggable, including transcription factors, scaffolding proteins, and intrinsically disordered proteins.

The IMiDs represent the foundational clinical success of molecular glues. Their activity in myelodysplastic syndromes and multiple myeloma derives from CRBN binding, which reshapes the substrate-binding pocket of the CRL4^CRBN ligase to degrade the transcription factors IKZF1 and IKZF3, key regulators of lymphoid differentiation. This degradation has a strong correlation with clinical response and represents one of the clearest examples of ligand-induced neosubstrate recruitment in humans. The clinical efficacy of IMiDs in hematologic malignancies thus constitutes indirect validation of the molecular glue concept (16,46,51,52). More recently, aryl sulfonamides, such as indisulam, have been shown to rewire DCAF15 to degrade RNA binding motif protein 39 (RBM39), highlighting the broader potential of molecular glues to modulate diverse cullin-RING ligases (17,30,31). Although many molecular glues were initially discovered serendipitously, systematic strategies are now available, including chemoproteomics, phenotypic screening coupled with clustered regularly interspaced short palindromic repeats (CRISPR)-based target deconvolution, and rational design approaches that identify molecules capable of enhancing weak pre-existing interactions or generating entirely new interfaces (47,53).

A direct comparison of the molecular architecture, mechanistic principles, and pharmacological properties of PROTACs and molecular glues highlights fundamental differences. PROTACs are large, heterobifunctional molecules that physically tether the target protein to an E3 ubiquitin ligase through two distinct ligands connected by a chemical linker, whereas molecular glues are conventional small molecules that induce or stabilize PPIs without an explicit linker. PROTACs require a ligandable pocket on the POI and allow modular tuning of selectivity by varying ligands and linkers, while molecular glues do not rely on POI ligandability and instead remodel the E3 ligase surface to drive substrate recruitment. Pharmacokinetically, molecular glues more often display favorable oral drug-like properties, whereas PROTACs frequently demand extensive optimization of polarity, rigidity, and susceptibility to efflux to achieve adequate systemic exposure. In oncology, PROTACs have performed particularly well when high-affinity ligands exist, but catalytic inhibition is insufficient or prone to resistance, whereas molecular glues have been most successful when substrate engagement is primarily dictated by ligand-induced reprogramming of the E3 ligase (8,54).

Clinical translation of PROTACs and molecular glues in oncology

The first PROTAC to reach clinical trials was bavdegalutamide (ARV-110), an oral degrader of the androgen receptor (AR) (55-58). Evaluated in metastatic castration-resistant prostate cancer, ARV-110 has shown antitumor activity in heavily pretreated patients, including those harboring clinically relevant AR ligand-binding domain mutations such as T878A and H875Y (58,59). Phase I/II data indicate measurable prostate-specific antigen (PSA) declines and objective responses in selected mutation-defined subgroups, providing the first human proof-of-mechanism for PROTAC-mediated receptor degradation (60,61). A second landmark candidate, vepdegestrant (ARV-471), is an estrogen receptor (ER) degrader developed for ER-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer (ER⁺/HER2⁻). Preclinical studies demonstrated robust ER degradation, tumor regression, and synergistic efficacy when combined with cyclin-dependent kinase (CDK)4/6, phosphoinositide 3-kinase (PI3K), or mechanistic target of rapamycin (mTOR) inhibitors (62). In a phase III trial of vepdegestrant versus Fulvestrant, an ER-antagonist, in patients with estrogen receptor 1 (ESR1)-mutated, ER⁺/HER2⁻ advanced breast cancer, median progression-free survival improved from 2.1 to 5.0 months, but not in the full patient population (63,64). The safety profile of vepdegestrant was manageable, with grade ≥3 adverse-event rates similar to Fulvestrant, supporting its potential as a novel PROTAC-based therapeutic option, but requiring additional information regarding the molecular determinants of response.

Beyond nuclear receptors, PROTACs have been developed against Bruton’s tyrosine kinase (BTK), breakpoint cluster region-Abelson 1 fusion (BCR::ABL1), CDK9, bromodomain-containing protein (BRD)4, mouse double minute 2 homolog (MDM2), FMS-like tyrosine kinase 3 (FLT3), and anaplastic lymphoma kinase (ALK), targets where conventional inhibitors often fail due to resistance mutations, lack of ligandable pockets, or essential scaffolding functions (65-77). For example, BTK degraders remain active against C481S variants resistant to covalent inhibitors (65,78), while BRD4 degraders induce more complete suppression of oncogenic transcriptional programs than bromodomain and extra-terminal domain (BET) traditional inhibitors (79,80). The degraders’ ability to eliminate mutant or truncated proteins lacking inhibitor binding sites provides major therapeutic advantages in heterogeneous tumors. Despite their progress, off-target degradation can arise from unexpected ternary complexes, and resistance may develop through downregulation of E3 ligases, mutations at the POI-ligase interface, or rewiring of signaling networks (8,81,82). These barriers have motivated parallel exploration of alternative proximity-inducing strategies, especially molecular glues.

Building on IMiDs, next-generation cereblon E3 ligase modulators (CELMoDs), such as iberdomide (CC-220) and mezigdomide (CC-92480), were rationally engineered to enhance substrate affinity, increase degradative potency, retain or augment immunomodulatory effects, and are now in clinical evaluation for hematologic malignancies (83-86). Iberdomide has shown strong degradation of IKZF1/3 with superior potency relative to lenalidomide (83,87,88), and phase I/II trials in relapsed/refractory multiple myeloma demonstrated deep responses even in heavily pretreated patients, including those patients refractory to IMiDs (83,84). Mezigdomide induces more efficient CRBN structural remodeling, enabling stronger recruitment of neosubstrates; early clinical results report rapid reductions in tumor burden and durable responses when combined with dexamethasone (85,86). These clinical observations provide compelling evidence that ligase reprogramming by molecular glues can overcome therapy resistance, preserve efficacy in adverse-risk genetic backgrounds, and expand the therapeutic window of existing drug classes.

Indisulam, E7070, and related sulfonamides exemplify molecular glues acting through the substrate receptor DCAF15. Although initially characterized for cytostatic activity, chemoproteomic studies revealed that indisulam induces DCAF15-mediated degradation of the cancer-associated splicing factor RBM39 (30,89). In preclinical models, RBM39 degradation leads to widespread intron retention, splicing defects, and apoptosis in cancers dependent on spliceosome integrity, including subsets of acute myeloid leukemia (AML) and solid tumors (90-92). Although clinical trials with indisulam yielded mixed results (93-95), partly due to lack of biomarker-based patient selection, modern understanding of its glue mechanism has revived interest, and current strategies aim to identify molecular signatures predictive of RBM39 dependency (89,96,97). Second-generation aryl sulfonamides have been optimized to enhance DCAF15 engagement and improve substrate selectivity. Preclinical studies have demonstrated that these compounds induce potent and preferential degradation of RBM39, leading to widespread splicing dysregulation and antiproliferative effects in AML and acute lymphoblastic leukemia (ALL) (17,30,89,98-100). Among DCAF15-recruiting aryl sulfonamides, E7820 has now entered biomarker-selected phase II testing in patients with splicing factor mutant myeloid malignancies, where on-target RBM39 degradation and associated splicing changes have been demonstrated (95,101). Earlier clinical experience with indisulam and related sulfonamides in solid tumors and AML, combined with emerging translational data implicating DCAF15 and RBM39 as key determinants of sensitivity, is now guiding the design of biomarker-driven trials rather than purely unselected cohorts (93,94).

Additional molecular glues have broadened the therapeutic landscape by enabling the degradation of targets previously inaccessible to small-molecule inhibition (87). Compounds derived from CC-885 analogs induce GSPT1 degradation, resulting in potent antitumor effects through disruption of translation termination, with pronounced sensitivity observed in AML models (18,102-104). Structure-guided screening has also yielded BRD9-directed glues, which selectively eliminate this SWI/SNF chromatin-remodeling subunit and demonstrate robust preclinical efficacy in cancers dependent on BRD9-containing complexes (105-107). Cyclin K-directed molecular glues such as CR8 and HQ461 promote CDK12-complex degradation and downregulate DNA damage-response transcriptional programs, while genetic and pharmacologic studies of CDK12 loss demonstrate enhanced sensitivity to DNA-damaging agents and poly(ADP-ribose) polymerase (PARP) inhibition in hormone receptor (HR)-deficient or ‘BRCA-like’ tumors, supporting the concept of CDK12-complex degraders as potential synthetic-lethal strategies in this setting (108-111). Importantly, next-generation engineered glues now target transcription factors with intrinsically disordered regions (112-114). Collectively, molecular glues demonstrate how subtle chemical perturbation of E3 ligases can dramatically alter substrate specificity, enabling targeted degradation of proteins that remain inaccessible to conventional inhibition strategies. Their translational progress, from IMiDs to CELMoDs and emerging ligase-reprogramming scaffolds, highlights a powerful therapeutic paradigm capable of addressing resistance-prone oncogenic drivers and expanding the druggable proteome “degronome” in a clinically validated manner. An overview of clinical evidence for emerging PROTAC- and molecular glue-driven therapies targeting traditionally undruggable proteins in cancer is illustrated in Table 1. A mechanistic comparison of PROTAC-mediated and molecular glue-induced TPD is illustrated in Figure 1.

Figure 1 Mechanistic comparison of PROTAC-mediated and molecular glue-induced targeted protein degradation. (A) PROTAC-mediated ternary complex formation. PROTACs are heterobifunctional small molecules composed of two distinct ligands connected by a flexible chemical linker. One ligand binds the POI, while the other recruits an E3 ubiquitin ligase (e.g., VHL, CRBN). This architecture facilitates induced proximity between the POI and the E3 ligase, bypassing the need for a native evolutionary interaction. (B) Molecular glue-induced E3 surface remodeling and neo-interaction. Unlike PROTACs, molecular glues are monovalent small molecules that bind to the E3 ligase surface, acting as “remodelers”. Binding induces a conformational change or fills a surface cavity to create a “composite” neo-interface. This allows the E3 ligase to recognize and sequester a “neo-substrate” (POI) that lacks intrinsic affinity for the ligase in its native state. (C) Convergence on the UPS and catalytic recycling. Both modalities converge on the 26S proteasome pathway through event-driven catalysis. The E3 ligase complex facilitates the transfer of ubiquitin from an E2 enzyme (not shown) to the POI, forming a polyubiquitin chain. This signal directs the POI to the 26S proteasome for ATP-dependent unfolding and degradation into constituent amino acids. Crucially, the degrader (PROTAC or molecular glue) is not consumed in the reaction; it dissociates from the complex after ubiquitination, allowing it to function sub-stoichiometrically through multiple catalytic cycles (recycled degrader). Original figure created by the authors using the Gemini tool (https://gemini.google.com/). CRBN, cereblon; E3, E3 ubiquitin ligase; POI, protein of interest; PROTAC, proteolysis-targeting chimera; UPS, ubiquitin-proteasome system; VHL, von Hippel-Lindau.

In synthesis, while the PROTAC field is rich with preclinical innovation, the clinical pipeline is currently led by vepdegestrant (ARV-471) for ER⁺/HER2⁻ breast cancer, having demonstrated the first phase III victory for a PROTAC (64). This success positions ER degraders as the modality with the highest immediate potential for regulatory expansion and routine clinical use. In the molecular glue arena, the next-generation CELMoDs, particularly mezigdomide (CC-92480) and iberdomide (CC-220), represent the most advanced candidates, showing clear efficacy in IMiD-refractory multiple myeloma and are poised to become new standards of care in relapsed/refractory settings (84,86).

PPI inhibitors: blocking critical interactions in cancer pathways

PPI orchestrate virtually every aspect of cell signaling, transcription, and cellular homeostasis, and many oncogenic drivers act not as enzymes but as hubs within densely connected interaction networks (170-172). Early structural and thermodynamic analyses emphasized that PPI interfaces are typically large, relatively flat, and conformationally dynamic, which led to the long-standing dogma that PPI were undruggable with conventional small molecules (173-176). Pioneering conceptual and biophysical works demonstrated, however, that many interfaces contain discrete hotspot residues and pre-organized pockets that can be recognized by carefully designed ligands, overturning the notion of universal undruggability and establishing the existence of druggable microenvironments within PPI surfaces (175,177-179). In parallel, cancer-centric network efforts, such as the OncoPPi project and the associated OncoPPi Portal, have mapped thousands of tumor-relevant interactions, thereby reframing PPI as a systematic and navigable space for target discovery and pathway rewiring in oncology (180,181).

This conceptual shift has been reinforced by translational proof-of-concept programs targeting well-defined oncogenic PPI. In the intrinsic apoptosis pathway, it was demonstrated that the B-cell lymphoma 2 (BCL2) homology domain 3 (BH3)-mimetic ABT-737 can bind the canonical components of anti-apoptotic BCL2 family members and induce tumor regression in vivo, providing one of the first clear demonstrations that intracellular PPI can be drugged with small molecules in preclinical cancer models (182). In another proof-of-concept, it was identified that nutlin-class antagonists occupy the p53-binding pocket of MDM2, activating p53 signaling and suppressing tumor growth in xenograft models (183). Subsequent clinical development of venetoclax, a highly BCL2-selective BH3 mimetic, and of multiple MDM2 inhibitors has validated BCL2 family and MDM2-p53 interactions as tractable therapeutic nodes in hematologic malignancies (156,184-186). Altogether, these advances have transformed PPI from diffuse, ostensibly inaccessible surfaces into structured, strategically stratified targets for contemporary anticancer drug discovery, and have revealed a heterogeneous class of PPI modulators that includes disruptors and stabilizers, orthosteric and allosteric agents, and chemotypes ranging from fragments and small molecules to macrocycles and peptides.

Historical development and conceptual framework of PPI

Early work on PPI in drug discovery established the conceptual backdrop against which PPI were long regarded as undruggable targets. Large, shallow, and dynamic interfaces were considered poorly compatible with classical small-molecule binding, even though early analyses already pointed to the existence of localized hotspots that concentrate binding energy and could, in principle, be exploited by rationally designed ligands (173,175-177,187). Subsequent evidence framed PPI as central regulators of signaling and transcription, clarifying why conventional high-throughput screening often failed against these targets, and highlighting the need for structure-guided strategies and for mimicking extended secondary structures such as α-helices at protein interfaces (188-196). During the 2010s, this view shifted from pessimism to a more systematic framework in which PPI were classified according to mode of modulation, orthosteric versus allosteric, inhibitors versus stabilizers, and successful chemotypes were catalogued as a distinct pharmacological modality spanning small molecules, macrocycles, peptides, and antibodies (196-201). Analyses of scaffold properties and buried surface area further showed that effective PPI inhibitors tend to occupy larger, more three-dimensional chemical space and engage a broader interface than typical enzyme inhibitors, helping to define the physicochemical envelope of viable PPI-directed compounds (202-204).

In parallel, structural, computational, and experimental advances provided practical tools to distinguish tractable from recalcitrant PPI and to validate them as therapeutic targets in oncology. Docking-based workflows were adapted to “surf” PPI surfaces, identify clusters of hotspot residues, and guide ligand placement, while web-based resources, such as PocketQuery, were developed to extract and rank druggable residue clusters using interface-focused scores (205,206). At the network level, cancer-focused maps such as the OncoPPi interactome and portal used these concepts to prioritize the most promising PPI for target discovery and pathway rewiring (180,181). More recently, PPI-specific druggability classification schemes based on solvent exposure, pocket depth, and hydrophobicity have shown that ligand binding can itself carve deeper, more favorable cavities in cancer-relevant PPI, adding a dynamic dimension to target assessment (207). Seminal experimental studies then demonstrated that small molecules can disrupt disease-relevant PPI in vivo: inhibition of the MDM2-p53 interaction was shown to stabilize p53 and induce tumor regression in xenograft models, and BH3 mimetics targeting anti-apoptotic BCL2 family members proved capable of re-engaging mitochondrial apoptosis in hematologic malignancies (182,183,208,209). These convergent lines of evidence redefined PPI from static structural curiosities to dynamic, therapeutically actionable nodes in oncogenic signaling networks, paving the way for subsequent mechanistic and clinical developments.

Molecular mechanisms of PPI inhibition and modulation in oncogenic signaling

At the molecular level, PPI inhibitors act either by directly occupying interface hotspots or by allosterically reshaping one binding partner, which results in the impossibility of complex formation. Small-molecule modulators are often classified as orthosteric antagonists that mimic key side chains at the native binding epitope, and allosteric ligands that induce long-range conformational changes, frequently stabilizing inactive or non-productive states (182,198,199,201). Structural studies indicate that druggability is strongly influenced by interface topology: PPI with moderate buried surface area, enriched in localized hydrophobic pockets and charged hotspots, are more amenable to inhibition by conventional small molecules than very large, featureless contacts (187,210-213). Docking-guided mapping of these hotspots enables rational placement of pharmacophoric groups that compete for critical hydrogen bonds and π-π contacts at the interface (214,215). Complementary computational tools, such as web servers that identify clusters of interface residues with favorable “druggability scores” and convert them into three-dimensional pharmacophores, provide a practical route from structure to ligand hypotheses (205,216-218).

Peptide and peptidomimetic inhibitors operate through closely related, but often more “native-like”, mechanisms. Short peptides or constrained analogues can reproduce α-helices, β-strands, or loop motifs that dominate many oncogenic PPI, thereby competitively displacing the natural partner at the interface (193,196,219,220). Design strategies based on helix-stabilized, stapled, or β-hairpin peptidomimetics present key side-chain arrays with improved proteolytic stability and, in some cases, enhanced cell permeability, enabling the targeting of archetypal interfaces such as BCL2/BH3 and MDM2/p53 (221-225). Clinically advanced therapeutic peptides often exploit high-affinity, high-specificity binding to shallow grooves or extended scaffolds that are difficult for traditional small molecules to engage (161,226-229). An additional layer of control is provided by peptide-based covalent inhibitors, which position mild electrophiles toward nucleophilic residues at or near the interface, irreversibly locking transient complexes into a functionally off state and have the potential to overcome rapid dissociation or high local concentrations of endogenous ligands (230-236).

Canonical oncologic examples illustrate how these molecular mechanisms translate into pathway modulation and therapeutic effect. In the MDM2-p53 axis, nutlin-class antagonists were shown to occupy the hydrophobic cleft of MDM2 that recognizes the p53 α-helical transactivation motif, displacing p53, stabilizing the tumor suppressor, and triggering cell-cycle arrest and apoptosis in tumor xenograft models (183). These studies established that well-defined intracellular PPI can be selectively targeted in vivo, with clear pharmacodynamic readouts such as p53 accumulation and induction of p53-responsive genes. In mitochondrial apoptosis, the BH3 mimetic ABT-737 was reported to bind the BH3-binding groove of BCL2, B-cell lymphoma-extra large (BCL-xL), and B-cell lymphoma 2-like protein 2 (BCL-w) with high affinity, freeing pro-apoptotic BH3-only proteins to engage BCL-2-associated X protein (BAX)/BCL2 antagonist/killer (BAK) and drive mitochondrial outer-membrane permeabilization, cytochrome c release, and caspase activation (182). Subsequent work with the next-generation BH3 mimetics confirmed that highly specific groove binding can neutralize anti-apoptotic BCL2 family members, re-sensitize malignant cells to intrinsic apoptosis, and produce tumor regressions in preclinical models, thereby converting subtle, nanometer-scale perturbations at PPI surfaces into decisive cell-death signals in cancer (237-240). These examples demonstrate that mechanistically tailored PPI inhibitors, whether small molecules or peptides, may effectively rewire oncogenic signaling networks when the structural context, binding energetics, and cellular dependencies are appropriately aligned.

Clinical translation of PPI inhibitors in oncology: from mechanistic proof-of-concept to first-in-class therapies

Clinical development of PPI inhibitors has so far led to the approval of only a small subset of modulators, with several additional agents currently in early- or late-stage trials. Most clinically advanced compounds are canonical occupancy-driven inhibitors, rather than degraders, and selectively target well-defined oncogenic interfaces such as BCL2/BH3 or MDM2-p53. Preclinical work identified ABT-737 as a high-affinity BH3 mimetic that binds BCL2, BCL-xL, and BCL-w and induces regression of both solid and hematologic tumors in murine models, establishing BH3 mimetics as bona fide PPI inhibitors of anti-apoptotic proteins (182). Mechanistic studies showed that ABT-737 and its orally bioavailable analogue navitoclax (ABT-263) displace pro-apoptotic BH3-only proteins from anti-apoptotic BCL2 family members, thereby triggering BAX/BAK-dependent mitochondrial outer membrane permeabilization and caspase activation, particularly in tumors with high BCL2 and low myeloid cell leukemia 1 (MCL1) expression (208,237,241). Early clinical translation with navitoclax in solid tumors and lymphoid malignancies demonstrated clear on-target antitumor activity but also dose-limiting thrombocytopenia due to BCL-xL inhibition in platelets (157,242,243). These findings, together with additional mechanistic data, directly informed the rational design of venetoclax (ABT-199), a next-generation BH3 mimetic that preserves high BCL2 affinity while largely sparing BCL-xL, maintaining potent killing of BCL2-dependent cells in vitro and in xenografts while mitigating platelet toxicity in preclinical models (243,244).

Clinically, venetoclax translated this preclinical rationale into high response rates across B-cell malignancies and AML. In relapsed/refractory chronic lymphocytic leukemia (CLL), a phase I dose-escalation study reported an overall response rate of 79%, including 20% complete remissions (CRs), with a median progression-free survival of 15.4 months, confirming robust on-target activity in heavily pretreated patients (155,185,245,246). Venetoclax-based combinations with anti-CD20 antibodies and BTK inhibitors further deepened minimal residual disease negativity and enabled time-limited therapy, underpinning multiple regulatory approvals in CLL and related B-cell neoplasms and illustrating how BH3 mimetics can overcome chemotherapy resistance while selecting for characteristic resistance mechanisms such as upregulation of MCL1 or BCL-xL (247-252). In AML, preclinical work demonstrated that many primary samples and xenografts are exquisitely sensitive to low-nanomolar venetoclax, with BH3 profiling and genomic features such as isocitrate dehydrogenase 1/2 (IDH1/2) mutations predicting response (253-258). Venetoclax monotherapy increased composite complete remission [CR + CR with incomplete hematologic recovery (CRi)] rates in a subset of high-risk and elderly patients, supporting the phase III VIALE-A trial, in which venetoclax plus azacitidine improved median overall survival from 9.6 to 14.7 months and increased composite CR rates to roughly 66% versus 28% with azacitidine alone (156,259). Subsequent real-world cohorts and meta-analysis have confirmed meaningful response rates but often report inferior survival than in the VIALE-A trial, reflecting frailer and more heterogeneous populations and underscoring the need for refined patient selection and combination strategies (260-264).

The MDM2-p53 axis represents a major test bed for PPI-directed therapeutics. Preclinical studies with cis-imidazoline, called nutlins, and second-generation derivatives, such as idasanutlin, showed that small-molecule antagonists can occupy the hydrophobic p53-binding pocket of MDM2, disrupt the MDM2-p53 interaction, stabilize wild-type p53, induce p53 upregulated modulator of apoptosis (PUMA) and p21 expression, and produce robust antitumor activity in tumor protein p53 (TP53)-wild-type leukemias and solid tumors, particularly in combination with cytarabine or hypomethylating agents (183,265-269). Early-phase clinical studies of idasanutlin in relapsed/refractory AML suggested meaningful single-agent activity and chemosensitization, leading to the phase III MIRROS trial, in which 447 adults with relapsed/refractory AML were randomized to idasanutlin plus intermediate-dose cytarabine or placebo plus cytarabine (270-272). In the TP53-wild-type subset, the overall response rate improved to 38.8% versus 22.0% with cytarabine alone, but the primary endpoint of overall survival was not met [median 8.3 vs. 9.1 months; 95% confidence interval (CI) of hazard ratio: 0.81–1.45], CR rates increased only modestly (20.3% vs. 17.1%), and gastrointestinal toxicity, including high-grade diarrhea, was frequent and limited dose intensity (186).

To address limitations of small-molecule MDM2 antagonists, peptide and peptidomimetic PPI inhibitors have been advanced into early-phase oncology trials. Therapeutic peptides targeting PPI include agents directed at integrin receptors, chemokine receptors, and intracellular hubs, reflecting broader efforts to exploit high-affinity, high-specificity binding at extended protein surfaces (161,229,273-275). Within the p53 pathway, ALRN-6924 (sulanemadlin) is a stapled peptide designed to mimic the N-terminal transactivation domain of p53 and bind both MDM2 and mouse double minute X (MDMX), intending to restore p53 signaling in tumors where MDMX overexpression attenuates the effects of MDM2-selective inhibitors (229,276). In a first-in-human phase I trial, 71 patients with advanced solid tumors or lymphomas, enriched for TP53-wild-type disease, received intravenous ALRN-6924 on weekly or twice-weekly schedules; among 41 efficacy-evaluable TP53-wild-type patients treated at biologically active doses, the disease-control rate reached 59%, including two complete responses, two partial responses and 20 cases of durable stable disease, with six patients remaining on therapy for more than one year (161). Pharmacodynamic analyses showed activation of p53 transcriptional targets, and importantly, dose-limiting thrombocytopenia was not observed, in contrast to many small-molecule MDM2 inhibitors (277). Nonetheless, subsequent development of ALRN-6924 has shifted toward chemoprotection rather than direct antitumor use, and later-phase oncology trials have been constrained by myelosuppression and strategic considerations, illustrating both the promise and the challenges of peptide-based PPI modulation in the clinic (278-281).

Among PPI inhibitors, venetoclax stands alone as the undisputed clinical champion, having successfully navigated the challenges of on-target toxicity to become a cornerstone of therapy for CLL and AML (156,185). Its trajectory, from a rationally designed, highly selective BH3-mimetic to a globally approved drug, serves as the primary roadmap for developing future PPI inhibitors. The high potential of this target class is further underscored by revumenib, a menin-lysine methyltransferase 2A (KMT2A) inhibitor, which is demonstrating transformative activity in genetically defined leukemias and is on a fast track to regulatory approval (163).

Second mitochondria-derived activator of caspases (SMAC) mimetics represent another PPI-focused strategy in oncology, designed to antagonize X-linked inhibitor of apoptosis protein (XIAP) and cellular inhibitor of apoptosis protein 1/2 (cIAP1/2) by mimicking the N-terminal IAP-binding motif of SMAC/DIABLO, thereby relieving caspase inhibition, promoting cIAP1/2 degradation, and rewiring tumor necrosis factor alpha (TNF-α)-driven death signaling toward apoptosis or necroptosis (282-286). Preclinical data show that agents such as birinapant, LCL161, and GDC-0152 enhance death-receptor signaling and synergize with chemotherapy or kinase inhibitors across multiple tumor models (287-292). Clinically, phase I/II studies have consistently demonstrated on-target biochemical effects, rapid IAP down-modulation in blood and tumor biopsies, but only modest single-agent efficacy, with stable disease as the most frequent best response (293,294). In a phase II trial of birinapant in advanced epithelial ovarian and other solid tumors, objective responses were rare despite clear pharmacodynamic evidence of IAP degradation, and LCL161 monotherapy in myelofibrosis showed limited clinical benefit in early reports (166,295). Expanded results of a phase II study of weekly oral LCL161 in intermediate/high-risk myelofibrosis reported an overall response rate of approximately 30%, with improvements in anemia, spleen size, and constitutional symptoms burden, and a median overall survival not reached in 34 months of follow-up (166). Although encouraging in a heavily pretreated population, these results have not yet led to regulatory approval, and SMAC mimetics are now being explored in rational combinations with Janus kinase (JAK) inhibitors and immune checkpoint blockade, guided by mechanistic data indicating strong context dependence of TNF-α/IAP signaling (285,296-299). In contrast to BH3 mimetics, where biochemical binding, mitochondrial priming, and disease-specific dependencies supported biomarker-guided deployment, SMAC mimetics exemplify how compelling pharmacodynamic activity and early clinical signals may still fall short of achieving survival benefit and acceptable toxicity in unselected populations. Integrating structural pharmacology with system-level biomarkers to identify tumor contexts can be both safe and transformative for disrupting IAP-centered PPI inhibition.

Overall, the clinical trajectory of PPI-targeting agents in oncology underscores both the promise and the complexity of this modality. BH3 mimetics, such as venetoclax, have reached routine clinical practice because preclinical work enabled biomarker-guided patient selection and rational combinations. By contrast, MDM2/MDMX inhibitors and SMAC mimetics have demonstrated compelling pharmacodynamic and early efficacy signals but have faced challenges in achieving survival benefit and manageable toxicity in unselected populations. Future translational success for PPI inhibitors will likely depend on integrating structural pharmacology with system-level biomarkers, as demonstrated by BH3 profiling, TP53/MDM2/MDMX status, TNF-α/IAP signaling signatures, to identify the tumor contexts in which disrupting a given protein-protein interface can be both safe and transformative for patients. A synthesis of the available clinical data on emerging PPI inhibitors aimed at disrupting undruggable cancer targets is summarized in Table 1. A summary of the principles and therapeutic applications of targeting PPI via structural mimicry is illustrated in Figure 2.

Figure 2 Principles and therapeutic applications of targeting PPIs via structural mimicry. (A) Druggability landscape of protein interfaces. Schematic comparison of PPI topologies. Left: low-druggability interfaces characterized by shallow, featureless surfaces and conformationally dynamic boundaries with low solvent accessibility scores. Right: highly druggable interfaces presenting nuanced, pocketed topologies. The call-out box highlights hotspot residues—localized areas that contribute disproportionately to the binding free energy (ΔG)—which serve as critical anchoring points for pre-organized pockets targeted by molecular interventions. (B) Modalities of PPI inhibition and secondary structure mimicry. Orthosteric inhibition (left) involves direct competition at the native interface, often driven by extensive atomic-contact halos and specific electrostatic or π-π stacking interactions. Allosteric inhibition (top right) is achieved via ligand binding to a distal site, inducing a conformational shift that alters the primary binding interface. Bottom right: Strategies for secondary structure mimicry. Synthetic small molecules and hydrocarbon-stapled peptides are designed to spatially replicate the critical functional groups of a native α-helix (e.g., side chains at positions i, i+3, i+4), conferring structural rigidity and proteolytic stability. (C) Therapeutic targeting of the p53-MDM2 axis. Top: Structural basis of MDM2 inhibition by Nutlin-class antagonists. The small molecule occupies the MDM2 hydrophobic cleft, perfectly mimicking the insertion of key p53 residues (W23, L48, K48). Middle: Pharmacological displacement of the p53 α-helical transactivation motif prevents degradation, leading to p53 stabilization, accumulation, and nuclear localization. Bottom: Mechanism of ALRN-6924 (sulanemadlin), a stapled peptide therapeutic designed to structurally mimic the p53 helix, resulting in potent dual inhibition of both MDM2 and MDMX. (D) BH3-mimetics and the BCL2 apoptotic switch. Surface representation of the BCL2 hydrophobic groove occupied by the selective inhibitor venetoclax. The call-out details the precise molecular mimicry of the native BH3 domain's conserved residues by the small-molecule scaffold. Top right: The biological mechanism of action; venetoclax displaces pro-apoptotic BH3 proteins (such as BAX), which subsequently undergo oligomerization at the mitochondria to induce MOMP, cytochrome c release, and apoptosis. Bottom right: Side-by-side structural comparison illustrating the topological alignment between the native BH3 α-helix and the venetoclax chemical mimetic. Original figure created by the authors using the Gemini tool (https://gemini.google.com/). BAX, BCL-2-associated X protein; BCL2, B-cell lymphoma 2; BH3, BCL2 homology domain 3; MDM2, mouse double minute 2 homolog; MDMX, mouse double minute X; MOMP, mitochondrial outer membrane permeabilization; p53, tumor protein p53; PPI, protein-protein interaction.

Covalent inhibitors: designing irreversible binders for challenging targets

Historical development of the technique of covalent inhibitors

Covalent inhibitors, a class of drugs characterized by their ability to form a stable and long-lasting chemical bond with their target proteins, are experiencing a true renaissance in the field of drug discovery (300). Far from being a new concept, classic examples such as aspirin and penicillin have employed this mechanism of action for more than a century. However, for a long time, the development of such compounds was met with skepticism, primarily due to concerns regarding toxicity and undesirable off-target reactions (301). This perception changed with advances in structural biology, computational modeling, and chemoproteomics, which made it possible to design covalent inhibitors with high selectivity and safety (302). Consequently, compounds once deemed risky have become precise and powerful tools in the modern therapeutic arsenal. Their advantages, including high potency, prolonged duration of action, and the potential to overcome resistance mechanisms, have driven the resurgence of this class, particularly in oncology (303). Clinical success has validated the safety and efficacy of this approach, with multiple approved drugs that have transformed the treatment of several malignancies. Approximately 30% of currently marketed drugs act through a covalent mechanism, and more than 50 compounds are either on the market or in advanced stages of clinical trials, underscoring their growing acceptance and importance in contemporary medicine (300,301).

Historically, the initial covalent inhibitors emerged empirically. Aspirin, first marketed in 1899, and penicillin, discovered in 1928, are historical milestones that act through the formation of covalent bonds with their respective targets, cyclooxygenase (COX) and DD-transpeptidase, respectively (304). Throughout the 20th century, other important covalent drugs were introduced, including fluorouracil (1962), omeprazole (1988), and clopidogrel (1997) (305). Despite these early successes, the intrinsic reactivity of electrophilic groups led the pharmaceutical industry to prioritize reversible inhibitors. The turning point came over the past two decades, driven by advances that made it possible to identify specific nucleophilic residues and fine-tune warhead reactivity. This rational approach culminated in the development of the first targeted covalent inhibitors (TCIs). The Food and Drug Administration (FDA) approval of the TCIs afatinib [epidermal growth factor receptor (EGFR)] and ibrutinib (BTK) in 2013 consolidated the clinical feasibility of this strategy (306).

Mechanistic basis of covalent inhibitors

The mechanism of action of covalent inhibitors is fundamentally distinct from that of traditional reversible inhibitors. While the latter bind to and dissociate from their targets through weak and transient interactions, covalent inhibitors form a stable chemical bond, often irreversible, that “silences” the target protein for an extended period. These compounds consist of a molecular scaffold that confers selectivity and a reactive electrophilic group (“warheads”) (301). Acrylamides, nitriles, and epoxides are among the most common warheads, and cysteine is the main target residue due to the high nucleophilicity of its thiol group, although lysine, serine, threonine, and tyrosine can also be exploited (307). Acrylamides, which act as Michael acceptors, are widely used in clinically approved kinase inhibitors (308).

The binding occurs in two steps: (I) non-covalent recognition, in which the inhibitor fits reversibly into the active site, guided by weak interactions such as hydrogen bonds and hydrophobic forces, ensuring the correct positioning and orientation of the molecule; (II) covalent bond formation, when the warhead reacts with a nucleophilic residue, resulting in sustained inactivation of the protein (305). The covalent bond enables the targeting of undruggable proteins, such as those with shallow or flexible binding sites, and allows high selectivity by exploiting poorly conserved residues (309). However, the design of these drugs requires a balance between reactivity and selectivity. Overly reactive warheads may cause toxicity, while those with low reactivity may fail to form the bond (301). The irreversible nature also imposes challenges related to acquired resistance, which have been addressed through the development of next-generation inhibitors and dual or bivalent approaches capable of recognizing multiple sites on the target protein (302,310).

The development of covalent drugs follows a rational process divided into four main stages: target identification, hit identification, binding characterization, and optimization (311). In target identification, it is essential to select therapeutically relevant proteins that contain accessible nucleophilic residues, considering factors such as substrate, resistance, and “druggability” potential. Next, during hit identification, structure-based design, covalent docking, and electrophilic group orientation are employed to identify compounds capable of selectively reacting with the target residue. The binding characterization involves determining the mode and kinetics of binding, residence time, and distinguishing between reversible and irreversible interactions. Finally, the optimization phase aims to fine-tune the reactivity, selectivity, and residence time of the compounds to balance potency and safety (312-314).

Applications in oncology of covalent inhibitors

Covalent inhibitors have revolutionized cancer therapy by providing solutions to long-standing challenges and transforming the prognosis of patients with various malignancies. Their potent and durable inhibitory capacity is particularly advantageous in the context of high target protein turnover and the development of resistance, features commonly observed in tumor cells (308).

Several clinically approved agents exemplify the therapeutic potential of covalent inhibition in oncology. Among the first historical examples are the prodrugs 5-fluorouracil and gemcitabine, both pyrimidine nucleoside analogs that exert their antineoplastic effects through inhibition of thymidylate synthase and ribonucleotide reductase I, respectively, key enzymes involved in DNA synthesis and repair (315,316). An important milestone in this field was the FDA approval of bortezomib in 2003, a boronic acid dipeptide indicated for the treatment of multiple myeloma. Its mechanism involves covalent interaction with the 26S proteasome, leading to blockade of proteolytic activity, accumulation of misfolded proteins, and ultimately, apoptosis in malignant plasma cells (317).

Advancing along the timeline, in 2015, osimertinib, a third-generation EGFR-targeted compound, stood out for its superior efficacy against the T790M resistance mutation, establishing a new treatment standard in non-small cell lung cancer (NSCLC) for patients with refractory disease (318). Subsequently, neratinib, a covalent HER2 inhibitor, was approved in 2017, expanding the application of irreversible inhibition to solid tumors and consolidating this strategy in the treatment of HER2-positive breast cancer (319). Between 2018 and 2019, regulatory approvals were granted for additional covalent agents employing α,β-unsaturated carbonyl electrophilic motifs as reactive warheads. Within this period, approvals included dacomitinib, a broad-spectrum tyrosine kinase receptor inhibitor particularly effective in NSCLC with activating EGFR mutations (320); selinexor, a CRM1/Exportin-1 (XPO1) inhibitor that blocks nuclear export of tumor suppressors and induces apoptosis (321); and zanubrutinib, a covalent BTK inhibitor approved for multiple B-cell malignancies, including mantle cell lymphoma, CLL, marginal zone lymphoma, and Waldenström’s macroglobulinemia (322).

In the following years, the covalent strategy advanced toward targets previously considered undruggable. The development of KRAS^G12C inhibitors represented a watershed moment: sotorasib, approved in 2021 (323), and adagrasib, approved in 2022 (324), provided effective options for tumors driven by this specific mutation, marking a paradigm shift in precision oncology. During the same period, tepotinib received accelerated FDA approval, following its initial authorization in Japan in 2020, for the treatment of NSCLC with alterations in the mesenchymal-epithelial transition (MET) receptor (325). Also in 2022, the FDA approved futibatinib, a potent fibroblast growth factor receptor (FGFR) inhibitor, further expanding the repertoire of covalent small molecules available for targeted cancer therapy (326). In 2023, pirtobrutinib received accelerated approval for patients with CLL previously treated with BTK and BCL2 inhibitors (327). Most recently, in 2025, the FDA approved sunvozertinib, an EGFR inhibitor, for adults with NSCLC harboring EGFR exon 20 insertions who had progressed after platinum-based chemotherapy (328).

Currently, several covalent inhibitors are in preclinical and clinical stages of development for cancer treatment, reflecting the rapid progress and diversity of this therapeutic class. Among the compounds in the preclinical phase, notable examples include RM-018, RMC-8839, APG-1842, ERAS-3490, VRTX-126, and G12Si-5, which primarily target variants of KRAS. In phase I, compounds such as divarasib, MK-1084, M1823911, RMC-6291, BMF-219, FF-10101, SB-3826, and Werner syndrome adenosine triphosphate (ATP)-dependent helicase (WRN) are being evaluated; these act on different classes of proteins, including signaling oncogenes, kinases, and regulatory enzymes involved in cell survival. Phase II compounds, such as D-1553, H3B-6545, olafertinib, zipalertinib, glecirasib, and IBI351, have shown promising results, particularly in the context of KRAS, ERα, and EGFR mutations. Finally, the most advanced clinical candidates, oritinib, rezivertinib, and avitinib (Phase III), represent the most mature efforts of this strategy, primarily aimed at tumors with EGFR alterations.

The covalent inhibitor field has matured beyond kinases, with its most significant recent impact being the validation of KRAS^G12C as a druggable target. The approvals of sotorasib and adagrasib have fundamentally altered the treatment landscape for a subset of NSCLC and colorectal cancers, representing the pinnacle of this strategy’s potential to crack historically ‘undruggable’ problems (323,324). The rapid clinical advancement of next-generation inhibitors targeting other KRAS mutants (e.g., divarasib for G12C, zoldonrasib for G12D) suggests this class will continue to yield high-impact medical advances (Table 2).

Main relevant targets of covalent inhibitors in oncology

The success of covalent inhibitors depends on identifying protein targets that possess an accessible nucleophilic residue in a strategic position, allowing the formation of a stable and specific bond with the drug (333). Kinases are the most extensively explored targets due to their central role in cellular signaling and tumor proliferation (334). EGFR is one of the best-studied examples, with inhibitors forming a covalent bond with cysteine 797 (C797) located in the ATP-binding site (303). Similarly, BTK is irreversibly inhibited by compounds that bind to cysteine 481 (C481), blocking the signaling essential for the survival of malignant B cells (335). The HER2 receptor is another important target, being covalently inhibited through interaction with cysteine 805 (C805) (303). Among the most remarkable advances in the field is the development of covalent inhibitors for the KRAS^G12C oncoprotein, long considered undruggable due to its smooth surface and strong affinity for guanosine triphosphate (GTP). The identification of a reactive cysteine at position 12 (G12C), located in an inducible pocket near the binding site, enabled the design of molecules capable of trapping KRAS in its inactive state (336).

Although most currently approved covalent inhibitors target cysteine residues in kinases, the field has been rapidly expanding to include other nucleophilic amino acids, significantly broadening the spectrum of therapeutic targets (307). Emerging strategies exploit lysine residues in proteins such as CDK2 and PI3K (337,338), serine residues in targets such as COX (339) and hepatitis C virus proteases (340), threonine residues in the proteasome (the target of bortezomib), and tyrosine residues in proteins such as BCL6 and glutathione S-transferase pi 1-1 (GSTP1-1) (341,342), in addition to ongoing investigations involving glutamate, aspartate, and methionine (307). Furthermore, the covalent approach has been applied to protein classes beyond kinases, including epigenetic targets such as lysine-specific demethylase 1 (LSD1) (343), viral proteases (344), and PPI complexes, such as MCL1 and rat sarcoma viral oncogene homolog (RAS), which have traditionally been difficult to modulate with reversible small molecules (345,346).

The ability to design highly selective and long-lasting molecules has made it possible to target proteins previously considered inaccessible. Current research aims to optimize reactivity prediction, discover new selective warheads, and expand the number of proteins amenable to covalent inhibition. Despite challenges related to acquired resistance, combinatorial strategies and next-generation inhibitors are being developed to overcome these limitations (302,310). The use of predictive biomarkers is also expected to enhance patient selection and therapeutic monitoring, maximizing efficacy while minimizing adverse effects (308). Thus, covalent inhibitors have become established as a promising strategy in modern pharmacology, with the potential to transform the treatment of various oncological diseases and beyond.

Allosteric modulation: exploiting hidden or cryptic binding pockets

Allosteric modulation has emerged as a central approach in the development of anticancer therapeutics, particularly for proteins previously considered undruggable. Within this field, the exploration of hidden and cryptic binding pockets, binding sites that are concealed or induced by conformational changes, has revolutionized our ability to interrogate dynamic structural states of proteins and selectively disrupt oncogenic pathways (347). Contemporary structural biology, supported by molecular dynamics (MD), AI-based structural prediction (such as AlphaFold), biophysical assays, and advanced computational modeling, has made it possible to map previously invisible conformational transitions. These advances reveal dynamic allosteric sites that can be exploited to develop more selective, potent inhibitors that are less susceptible to resistance (348).

Within this framework, evidence from recent studies is integrated to illustrate how cryptic pockets are formed, exposed, or stabilized across different protein classes, including BCR::ABL1, sirtuin 6 (SIRT6), protein phosphatase, Mg²⁺/Mn²⁺ dependent 1D (PPM1D), Src homology 2 domain-containing protein tyrosine phosphatase 2 (SHP2), RAS-family GTPases, mTOR, and KRAS^G12D. Structural mechanisms, functional impacts, therapeutic applications, and implications for tumor resistance are discussed, including promising strategies for ex vivo platforms applied to cancer (349-354).

Asciminib represents a remarkable therapeutic advance in the treatment of chronic myeloid leukemia (CML), as it inhibits the BCR::ABL1 oncoprotein through a unique allosteric mechanism: it binds to the ABL1 myristoyl pocket, the so-called STAMP mechanism (specifically targeting the ABL myristoyl pocket), in contrast to classical inhibitors that compete for the ATP-binding site, thereby providing greater selectivity and the potential to reduce off-target effects (355,356). In the pivotal phase III ASCEMBL trial (NCT03106779), which included CML-chronic phase (CP) patients previously treated with ≥2 tyrosine-kinase inhibitors (TKIs), asciminib achieved a significantly higher major molecular response (MMR) rate than bosutinib (357). In addition, the safety and tolerability profile was favorable, with lower rates of grade ≥3 adverse events and fewer treatment discontinuations due to toxicity compared to bosutinib. Subsequent analyses, with approximately 4 years of extended follow-up, confirmed the sustained superiority of asciminib over bosutinib in both efficacy and safety at the 156-week cutoff, the MMR remained higher in the asciminib arm, reinforcing its durability (357,358). Phase 1 studies in heavily pretreated patients receiving asciminib monotherapy also demonstrated sustained antileukemic activity and an acceptable safety profile, with a median exposure of 5.9 years (359). For these reasons, asciminib not only expands therapeutic options for patients who are refractory or intolerant to multiple TKIs, but also emerges as a promising long-term maintenance strategy, combining durable efficacy with a lower toxic burden.

The study targeting SIRT6 represents a landmark in the functional identification of cryptic allosteric sites. By investigating SIRT6, the authors uncovered a pocket induced by interaction with nicotinamide adenine dinucleotide (NAD⁺), demonstrating that certain proteins only display pharmacologically relevant pockets when constrained into specific conformational states. The discovery of JYQ-42, an inhibitor with enhanced selectivity, not only validates the concept of a cryptic pocket but also shows that protein flexibility can be experimentally manipulated to reveal new therapeutic intervention targets (360).

Similarly, it was demonstrated that even AI-generated structural predictions, traditionally interpreted as static, may contain latent information on alternative conformational states. By investigating the phosphatase PPM1D and integrating AlphaFold predictions with MD simulations, it was shown that partially accessible states can evolve into functional pockets capable of accommodating allosteric inhibitors. This study sets highly relevant methodological precedents for hematologic cancers: proteins lacking resolved crystallographic structures, which are common in signaling pathways critical for leukemias, as also broadly present in solid tumors, can be analyzed under computational dynamics to reveal pharmacological targets that would otherwise remain unrecognized (354).

SHP2, a key phosphatase in RAS/mitogen-activated protein kinase (MAPK) signaling activation, provides another paradigmatic example. It has been shown that oncogenic mutations alter the equilibrium between closed and open states, exposing alternative allosteric pockets and modulating the response to inhibitors. Understanding these conformational states not only explains acquired resistance to allosteric inhibitors but also suggests new therapeutic opportunities in tumors with aberrant RAS pathway activation (349).

The concept of cryptic pockets was expanded by demonstrating that multiple GTPases (Ras, Rho, Rab) exhibit “switch II” pockets, traditionally associated with KRAS, whose broader existence had been underappreciated. This conceptual expansion indicates that allosteric modulation may constitute a therapeutic avenue for entire protein families, many of which play critical roles in the cytoskeleton, signaling, or vesicular trafficking, making them relevant targets in cancer (350).

The exploration of mTOR activating mutations as generators of new pockets (351) showed that such alterations can expose cryptically accessible regions. This suggests that the tumor mutational landscape itself can generate pharmacological opportunities, a particularly important point for heterogeneous tumors (361), where the coexistence of multiple clones may produce distinct conformational states of the same protein. Thus, heterogeneity, typically viewed as a barrier, may also serve as a source of new allosteric targets.

More recent studies on KRAS^G12D (352) exemplify the maturity of this field: inhibitors such as MRTX1133 can bind a switch II pocket that exists only in specific GTPase states, effectively “freezing” its activation cycle. The detailed understanding of interactions between the switch I region, switch II region, and the RAF-binding domain demonstrates that even highly oncogenic mutations can be neutralized through allostery, provided the pocket is appropriately exploited.

Collectively, these studies converge on a clear conclusion: the discovery and exploitation of cryptic pockets is no longer a biochemical curiosity but rather a central axis in the next generation of cancer therapies. Current evidence shows that the structural flexibility of cancer-relevant proteins contains pharmacologically actionable pockets that become accessible only under specific conditions, such as mutations, interactions, activation states, or even metabolic stimuli. This perspective fundamentally reshapes rational drug design, enabling highly precise therapies with improved molecular penetration, reduced toxicity, and greater ability to overcome acquired resistance. The allosteric strategy’s most compelling clinical validation comes from asciminib in CML. By successfully targeting the BCR::ABL1 myristoyl pocket, it has provided a highly effective and better-tolerated option for patients with resistance to multiple ATP-competitive TKIs, proving that allosteric inhibitors can achieve best-in-class status (357). This success establishes a strong precedent for exploiting other cryptic pockets, such as those being mapped in SHP2 and various GTPases, positioning allosteric modulators as a high-potential avenue for future therapies (349,350). The identification and modulation of allosteric and cryptic pockets in proteins relevant to cancer are detailed in Table 3.

Nucleic acid-based approaches: small interfering RNA (siRNA), antisense oligonucleotides (ASOs), CRISPR/Cas system

RNA-based therapies

The long-held notion that certain molecular targets are undruggable has been increasingly challenged by the development of RNA-based therapies. Grounded in advances in molecular biology, this therapeutic field benefits from the manipulation of genetic material itself, thereby bypassing limitations associated with targeting protein structures directly. The conceptual basis for its current application stems from the pioneering work of Aaron Klug (362), who recognized RNA as a molecule with complex folding patterns and specific functional roles. Major milestones that expanded the field include the first demonstration of RNA base pairing for therapeutic purposes in 1978 (363), the discovery of RNA interference in 1998 (364) and subsequent approval of the first ASO therapy (365), the characterization of the CRISPR/Cas system in 2012 (366), and the approval of the first siRNA-based therapy in 2016 (365).

RNA-based therapies are generally grouped into three categories: (I) compounds that bind endogenous RNAs to modulate their function; (II) therapies in which RNA act as a regulator of protein expression; and (III) strategies in which RNA serve as a guide molecule for nucleases to elicit permanent changes in genomic DNA (367). The present section addresses the three major classes with broad current application: siRNA, ASOs, and CRISPR/CRISPR-associated protein 9 (Cas9) genome editing.

siRNA

siRNAs represent the second most prominent class of oligonucleotides currently under clinical investigation (368). Defined as double-stranded oligonucleotides spanning 20–25 nucleotides in length, siRNAs act intracellularly in conjunction with the RNA-induced silencing complex (RISC) to mediate post-transcriptional silencing of specific messenger RNAs (mRNAs) (368). Their inhibitory activity depends on the initial processing of double-stranded RNA by the endoribonuclease Dicer, which releases the guide and passenger strands subsequently loaded into RISC (369). Once incorporated, the passenger strand is degraded by argonaute 2 (AGO2), while the guide strand hybridizes to its target mRNA, directing AGO2-mediated cleavage (369). Beyond cytoplasmic mRNA degradation, siRNA-RISC complexes may also promote chromatin remodeling and histone modification within the nucleus (370). Following target cleavage, RISC-bound siRNAs can dissociate and bind additional mRNA molecules, functioning catalytically (371).

The first clinical application of siRNA therapy was reported in 2010, involving silencing of the M2 subunit of ribonucleotide reductase in metastatic melanoma (372). These clinical efforts were built upon a foundation of pioneering preclinical work that had already established the feasibility of siRNA-mediated tumor suppression in vivo, using optimized delivery systems to target genes in murine models of cancer, thereby paving the way for human translation (373-375). The first siRNA drug approval occurred in 2018, when patisiran was authorized for the treatment of transthyretin-mediated hereditary amyloidosis (376). The global coronavirus disease 2019 (COVID-19) pandemic further accelerated RNA-based therapeutic development, leading to the emergency authorization of the first mRNA vaccines targeting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2020 (377). To date, five siRNA-based therapies have been approved by the FDA: patisiran, givosiran, lumasiran, inclisiran, and vutrisiran (378).

Key contributors to the success of siRNA therapeutics include chemical modifications that enhance stability and reduce immunogenicity. Unmodified siRNAs can activate immune responses through Toll-like receptor engagement (379) and exhibit poor bioavailability and limited cellular uptake (380). To address these barriers, strategies such as phosphorothioate backbone modification, nucleotide base alterations (e.g., 5-methyluridine; 5-propynyluridine), sugar modifications (2'-OME, FANA), and terminal modifications (2-thiouridine, dihydrouridine) have been widely applied (380).

Therapeutic performance has also improved through advanced delivery systems designed to optimize biodistribution, cellular uptake, and biocompatibility (381,382). Non-viral delivery platforms include lipid nanoparticles (LNPs), cationic polyplexes, exosomes, spherical nucleic acids (SNAs), and DNA nanostructures (378,381). Among viral vectors, adenoviruses and retroviruses remain notable for their efficient membrane permeability and relatively low toxicity (378).

In oncology, siRNAs have become essential experimental and translational tools (381). High-throughput siRNA libraries enable functional genetic screens that map oncogenic pathways (383), including loss-of-function studies based on reverse-transfection microarray platforms such as transfected cell array (384). siRNA-mediated silencing has also proven valuable in dissecting PPI networks, including the p53-MDM2 axis (385).

Further enhancements have emerged from combination strategies, demonstrating synergistic interactions between siRNA and immune checkpoint inhibitors [e.g., PD-L1 with anti-programmed cell death protein 1 (PD-1) antibodies], small-molecule inhibitors [e.g., KRAS siRNA with mitogen-activated protein kinase kinase (MEK) inhibitors], multi-target siRNA cocktails, and CRISPR-Cas9 technologies (386). Clinically relevant signaling pathways targeted in cancer trials include vascular endothelial growth factor (VEGF) and EGFR (387), BCL2 (NCT03020017), Casitas B-lineage lymphoma proto-oncogene B (Cbl-b) (NCT06172894), protein kinase N3 (PKN3) (NCT01808638), and polo-like kinase 1 (PLK1) (NCT01437007).

Among the completed clinical trials, the greatest number focused on silencing the CBLB gene in autologous T cells, leading to enhanced lymphocyte activation against solid tumors such as melanoma, hepatocellular carcinoma, pancreatic cancer, and colorectal cancer (NCT02166255; NCT03087591; NCT06172894). Silencing of PKN3 was also evaluated in solid tumors, delivered either in a liposomal formulation (NCT00938574) or in combination with gemcitabine (NCT01808638). Additionally, Phase 1 studies targeting mutant KRAS in pancreatic tumors employed the LODER (“LOcal DElivery Device”) system to achieve sustained intratumoral release, resulting in disease stabilization in a subset of patients (NCT01188785). In phase 1/2 studies, PDCD1 (PD-1) silencing following allogeneic bone marrow transplantation was explored as a strategy to enhance donor-derived T cell antitumor activity (NCT02528682). The remaining completed clinical trials are summarized in Table 4.

The anticipated expansion of siRNA therapeutics for undruggable cancer proteins is supported by their rapid design, durable activity, minimal genotoxicity relative to DNA-based gene therapies (377), and increasing precision driven by structural biology, AI, and modular chemistry (388).

ASOs

Within the scope of RNA-based therapeutics, ASOs currently represent the largest group of agents undergoing clinical evaluation (368). Structurally composed of a single RNA or DNA strand, ASOs were first tested therapeutically in 1978, demonstrating their ability to suppress Rous sarcoma virus replication in infected cell culture models (363). Subsequent chemical refinements broadened their structural diversity: phosphorodiamidate morpholino oligomers (PMOs) were introduced in 1989 (389), peptide nucleic acids (PNAs) in 1990 (390), and later developments included locked nucleic acids (LNAs) (391) and transcription factor “decoys” (392). Several ASOs are now approved for neuromuscular, metabolic, and hematological diseases, including mipomersen, defibrotide, nusinersen, inotersen, eteplirsen, golodirsen, viltolarsen, and casimersen (378).

Although grouped as a single pharmacological class, ASOs are subdivided according to their mechanism of action into RNase H-recruiting oligonucleotides, such as gapmers (e.g., mipomersen, inotersen, volanesorsen), and splicing-modulating ASOs, also referred to as occupancy ASOs (e.g., nusinersen, eteplirsen, golodirsen, viltolarsen, casimersen) (365). RNase H-recruiting ASOs allow the enzyme to recognize and cleave RNA-DNA heteroduplexes (393), a process in which AGO2 can contribute to guide strand stabilization (365,394). Splicing modulators bind to regulatory regions of pre-mRNA to alter spliceosome recognition of exons, promoting either exon inclusion or exclusion and resulting in gene upregulation or downregulation, respectively (365).

Recognizing these two distinct mechanisms of action, chemical modifications were strategically introduced to enhance the efficiency of one pathway while reducing activity through the other. First-generation ASOs incorporate phosphodiester backbone substitutions that enable RNase H recruitment (395), whereas second-generation ASOs contain 2'-O-methyl or 2'-O-methoxyethyl modifications, increasing target affinity while preventing RNase H activation (396). Finally, third-generation ASOs exhibit greater stability and improved pharmacokinetic properties relative to earlier designs, owing to extensive chemical modifications, such as LNAs, PMOs, and PNAs (397).

As with siRNAs, advances in structural optimization and delivery systems expanded clinical applications of ASOs, including in oncology. A distinguishing feature of ASOs relative to other RNA therapeutics is their potential to enhance gene expression under specific conditions (398), a particularly relevant feature for tumor suppressor gene restoration. Current strategies emphasize targeting non-enzymatic protein components, utilizing selective conjugates for tissue-specific delivery, and simultaneously inhibiting interconnected signaling pathways (399). Computational tools are increasingly employed to minimize off-target binding (400) and to reduce immune activation risks (365). These improvements support their use in monotherapy or in combination with established agents, including in refractory and late-line cancer therapy settings (396).

Completed clinical trials involving ASOs have targeted signaling pathways that overlap substantially with those explored in siRNA therapeutics. These include the RAS pathway (NCT00024648; NCT00024661; NCT03101839; NCT01159028) and the BCL2 family (NCT00543231; NCT01188785; NCT00017251; NCT00070083; NCT00005032; NCT00021749; NCT00004870; NCT00078234; NCT00059813; NCT00085228; NCT00017602; NCT00016263; NCT00024440). Additional ASO targets are STAT3 (NCT01839604; NCT02549651; NCT01839604; NCT02144051; NCT01563302), MAPK pathway components (NCT00487786; NCT01780545; NCT01120470; NCT00002592), and transforming growth factor beta (TGF-β)-associated pathways (NCT00471432; NCT00054106; NCT00258375; NCT04862767; NCT04504669)-detailed in Table 4.

Regarding numerical representativeness, the highest number of studies has focused on BCL2 inhibition using oblimersen (G3139), which has advanced to phase 3 clinical evaluation. Its use in solid tumors and hematologic malignancies demonstrated an acceptable tolerability profile and synergistic effects when combined with agents such as fludarabine and cyclophosphamide (NCT00024440), though without achieving a robust overall clinical benefit (401). In phases I and II, ASOs targeting CLU, a regulator of the TGF-β pathway (OGX-011), FOXP3, which is regulated by TGF-β (AZD8701), and TGF-β itself (TASO-001) have been investigated. Although these studies demonstrated biologically meaningful pathway modulation in solid tumors, as well as synergistic effects between OGX-011/docetaxel (NCT00258375) and between AZD8701/durvalumab (NCT04504669), they did not result in broad or consistent clinical efficacy. Similar patterns were observed for ASOs targeting Hsp27 (OGX-427) and c-MYB (G4460), both components of the MAPK pathway. These agents showed effective target inhibition in solid and hematologic tumors, acceptable safety profiles, and synergism between OGX-427/docetaxel (NCT01780545) and between G4460/chemotherapy combined with bone marrow transplantation (NCT00002592); yet clinical benefit remained inconsistent. Additional synergy studies identified the combination of a STAT3-targeting ASO (AZD9150) with MEDI4736 as promising in B-cell lymphomas (NCT02549651), highlighting the potential of ASO-based approaches to enhance the efficacy of other treatments when administered concomitantly.

Collectively, these strategies highlight both the adaptability and remaining challenges of ASO-based cancer therapies, including off-target effects, toxicity due to protein binding, and inefficient cytoplasmic delivery (399,402). Ongoing advances involve designing sequences that avoid immunostimulatory motifs and employ sugar, backbone, and base modifications to improve stability (365,403).

CRISPR/Cas9

Among the RNA-associated therapeutic platforms discussed, CRISPR/Cas9 genome editing currently holds the greatest visibility and breadth of application. Introduced into scientific literature in 2012 (404), its first clinical use occurred in 2016 in a study evaluating the safety of PD-1 knockout T cells for the treatment of refractory lung cancer (405). Since then, CRISPR/Cas systems have been investigated in disorders including Duchenne muscular dystrophy, cystic fibrosis, Wolfram syndrome, Leber congenital amaurosis, β-thalassemia, sickle cell disease, Huntington’s disease, and human immunodeficiency virus (HIV) infection (404). In oncology, major advances include PD-1-knockout T cells for metastatic lung cancer and chimeric antigen receptor T (CAR-T) cells targeting MUC1 with concurrent PD-1 deletion for advanced esophageal cancer (406).

CRISPR systems are categorized into three types: (I) systems using Cas3 to induce progressive DNA degradation; (II) systems based on Cas9, most widely used for genome editing; and (III) systems in which Cas10 targets DNA or RNA (404). Editing requires Cas9 to bind a single guide RNA (sgRNA) and a protospacer-adjacent motif (PAM) located downstream of the target sequence (404). DNA cleavage occurs approximately 3 base pairs upstream of the PAM sequence, mediated by Cas9 RuvC and HNH endonuclease domains, generating a double-strand break (404). The cell subsequently engages either non-homologous end joining (NHEJ) or homology-directed repair (HDR), resulting in gene disruption or precise sequence modification (407).

As with other gene-based therapies, delivery strategies vary by application. Viral vectors include adeno-associated virus (AAV), favored due to low immunogenicity and reduced carcinogenesis risk (404), as well as adenoviral, retroviral, and lentiviral vectors, which support efficient delivery and prolonged expression (408). Non-viral approaches employ biodegradable lipid and polymer nanocarriers (e.g., folate-conjugated liposomes, cationic nanoparticles), and exosomes for more selective tumor targeting (406,408).

CRISPR/Cas9 plays a central role in functional genomic mapping, oncogenic pathway modulation, and model generation. HDR-mediated repair enables development of genetically engineered mouse models (GEMMs and nGEMMs) (407), widely used in cancer research (409-411). Other variations of the technique that employ a catalytically inactive Cas9 protein (dCas9) enable transcriptional activation (CRISPRa) or repression (CRISPRi), used to map regulatory determinants of tumor resistance (412) and to modify chromatin acetylation or methylation states in promoter and enhancer regions (413), including ATM (414), BRCA1 (415), PTEN (416), and TP53 (417). There are also platforms such as SHERLOCK and DETECTR for the non-invasive detection of DNA and RNA mutations in liquid biopsies and for the early identification of cancer (418).

Although no CRISPR/Cas9-based therapies are yet approved in oncology, numerous early-phase clinical trials are underway. A substantial proportion involves CAR-T cell engineering, in which patient-derived T cells are genetically modified to express a CAR and re-infused (419). CAR-T cells targeting B-cell maturation antigen (BCMA) (NCT04244656), CD16 (NCT04035434), CD19 (NCT04557436), CD70 (NCT04502446; NCT04438083), and CLL-1 (NCT06128044) are currently being evaluated for hematologic malignancies (Table 4).

Parallel clinical efforts involve PD-1 knockout T cells in esophageal cancer (NCT03081715), multiple myeloma, melanoma, synovial sarcoma (NCT03399448), lung cancer (NCT02793856), and breast cancer (NCT05812326), which have demonstrated enhanced initial T-cell activation but insufficient durability due to tumor-induced immunosuppression. Another emerging target is WT1, a regulator of cellular growth, differentiation, and survival (420). CRISPR-mediated targeting of WT1 via AAV vectors in AML patients showed efficient editing (NCT05066165) but limited cell expansion and no tumor regression. Significant progress has also been made using CRISPR screens to detect disease-associated mutations, including TP53 alterations in ovarian cancer (NCT03606486) and immunotherapy-resistance mechanisms in pancreatic cancer, such as RIPK1 (NCT03681951).

Recent methodology innovations include AI-based prediction of on- and off-target editing events (418), exon-focused mutagenesis strategies to identify essential molecular dependencies (421), structural modification of Cas9 to mask immunogenic epitopes, and targeted editing within immune-privileged tissues (422).

Within nucleic acid-based therapies, the most immediate clinical impact is being driven not by gene editing, but by CAR-T cells engineered with CRISPR/Cas9. This approach has successfully addressed manufacturing and safety challenges, leading to numerous ongoing trials (e.g., targeting BCMA, CD19) with high complete response rates in refractory hematologic malignancies (Table 4). While siRNA and ASOs continue to face delivery hurdles in oncology, CRISPR-engineered cell therapies have already transitioned from an experimental concept to a clinically viable and highly potent treatment modality. The mechanistic landscape of nucleic acid-based therapies in oncology is described in Figure 3.

Figure 3 Mechanistic landscape of nucleic acid-based therapies in oncology. Progress in molecular biology has enabled the transition from traditional protein targeting to genetic material manipulation, addressing “undruggable” oncogenic drivers. (A) ASOs: single-stranded synthetic oligonucleotides that modulate gene expression via two primary mechanisms: recruitment of RNase H to cleave DNA-RNA hybrids (gapmers) or steric blocking of the splicing machinery to alter mRNA transcripts (splicing modulators). Unlike other platforms, ASOs can uniquely upregulate gene expression under specific conditions. (B) siRNA: Double-stranded RNAs are processed by Dicer and incorporated into the RISC. The guide strand directs AGO2 to execute site-specific mRNA cleavage. The catalytic nature of RISC enables sustained post-transcriptional silencing. (C) The CRISPR/Cas9 system facilitates targeted genome editing through the assembly of the Cas9 nuclease scaffold with a sgRNA. Upon identifying a specific protospacer-adjacent motif (PAM) downstream of the target DNA, the Cas9 complex induces an “open-complex” conformation. The HNH and RuvC endonuclease domains execute a targeted DSB approximately 3 base pairs (bp) upstream of the PAM. This genomic lesion triggers the cell’s endogenous repair machinery, bifurcating into two primary pathways: NHEJ, resulting in gene disruption (knockout), or HDR, which facilitates the engineering of CAR-T cells targeting hematologic and solid tumor antigens (e.g., BCMA, CD19, CD16, CD70, MUC1, CLL-1) and the knockout of the PDCD1 gene (PD-1 protein) to enhance antitumor activity. Unlike traditional siRNA or ASO approaches, CRISPR-engineered cell therapies represent a clinically viable and potent treatment modality for refractory malignancies. (D) Delivery challenges are addressed using advanced platforms, including lipid nanoparticles (LNPs) and viral vectors (AAV/lentivirus). Original figure created by the authors using the Gemini tool (https://gemini.google.com/). AAV, adeno-associated virus; AGO2, argonaute 2; ASO, antisense oligonucleotide; BCMA, B-cell maturation antigen; CAR-T, chimeric antigen receptor T-cell; Cas9, CRISPR-associated protein 9; CD, cluster of differentiation; CLL-1, C-type lectin-like molecule-1; CRISPR, clustered regularly interspaced short palindromic repeats; DSB, double-strand break; HDR, homology-directed repair; HNH, histidine-asparagine-histidine (nuclease domain); LNP, lipid nanoparticle; mRNA, messenger RNA; MUC1, mucin 1; NHEJ, non-homologous end joining; PAM, protospacer-adjacent motif; PDCD1, programmed cell death protein 1; RISC, RNA-induced silencing complex; RNase H, ribonuclease H; RuvC, resolvase-like nuclease domain; sgRNA, single guide RNA; siRNA, small interfering RNA.

AI & computational drug discovery: predicting structures and new binding sites and molecules

The History of the technique’s development

The evolution of computational and AI techniques has profoundly transformed the landscape of drug discovery. In the 1980s and 1990s, drug design relied largely on quantitative structure-activity relationship (QSAR) models and early docking simulations to predict binding affinities between ligands and protein targets. These models depended heavily on limited datasets and empirical assumptions about molecular interactions. By the early 2000s, the combination of X-ray crystallography, nuclear magnetic resonance (NMR), and computational chemistry gave rise to structure-based drug design (SBDD) and virtual screening pipelines. However, the approach remained confined to targets with well-defined active sites, primarily enzymes, receptors, and kinases (423).

The next revolution emerged from the convergence of big data, high-performance computing, and AI algorithms. Around 2015–2020, deep learning (DL) and machine learning (ML) architectures, particularly convolutional neural networks (CNNs), graph neural networks (GNNs), and generative adversarial networks (GANs), demonstrated their capacity to learn molecular features directly from data, enabling unprecedented prediction accuracy for structure, binding sites, and molecule generation (424,425).

A major inflection point occurred with AI-based protein structure prediction, exemplified by platforms inspired by AlphaFold and RoseTTAFold, which achieved near-atomic accuracy in predicting previously unresolved protein conformations (426). This breakthrough made it possible to model undruggable proteins such as transcription factors or disordered signaling molecules for which no crystallographic data existed (427). Currently, the field is moving toward autonomous discovery platforms, where AI systems integrate target identification, structure modeling, pocket detection, and de novo ligand generation in a single computational loop (428).

Operating principles of AI and computational drug discovery

The modern AI-driven drug discovery pipeline can be conceptualized as a sequence of interlinked computational processes, each enhanced by machine intelligence. These include protein structure prediction, binding-site identification, generative molecule design, and target prioritization through multi-omics integration (429).

The accuracy of protein structure and binding-site prediction has been significantly improved through the application of advanced AI models trained on vast structural databases, achieving a sub-ångström precision for most domains (426). Subsequent algorithms perform binding-site recognition, identifying potential ligandable regions. Techniques such as pocket geometry analysis, solvent mapping, and energy-based scoring reveal both orthosteric (active) and allosteric (regulatory) sites. Crucially, AI systems can predict cryptic sites, transient pockets that emerge only during protein dynamics or conformational transitions (430,431).

To extend beyond static crystal structures, advanced computational tools employ MD simulations and Markov state models, exploring conformational ensembles. AI methods trained on MD trajectories detect hidden druggable pockets and model flexible, disordered domains that were previously inaccessible (423,430). In the context of cancer proteins such as KRAS and MYC, such cryptic pocket detection has led to successful identification of induced-fit sites, allosteric or transient cavities exploitable for ligand binding (432).

Other AI’s generative models, such as ReLeaSE, Reinvent 4, and DiffDock, create novel small molecules conditioned on target-site geometry and desired pharmacological parameters (427). These models use variational autoencoders (VAEs), transformer networks, or reinforcement learning to propose molecules optimized for potency and selectivity. The compounds generated are then refined using AI-assisted docking and free-energy perturbation scoring (425). Recent evaluations show that AI-generated molecules exhibit significant structural novelty and drug-likeness (425,427).

By integrating genomic, transcriptomic, proteomic, and metabolomic data, AI-based multi-omics frameworks expose hidden regulatory hubs and generate robust rankings for drug-target prioritization. Correlating mutational landscapes with expression profiles and pathway dependencies enables algorithms to identify cancer-specific vulnerabilities that are subsequently structurally modeled and screened in silico (433). This system-level perspective transforms target discovery from a random to a data-driven process (434).

Within the oncology domain, AI is implemented as a predictor of tumor-specific neoantigens and T-cell receptor (TCR) binding affinities. Deep learning models trained on HLA-peptide datasets can predict peptide-MHC binding with high accuracy, accelerating vaccine design and adoptive T-cell therapies. Computational pipelines integrate tumor sequencing with AI antigenicity scoring to identify de novo peptide candidates for cancer immunotherapy (431,433). Additionally, AI-enabled structure modeling elucidates conformations of immune checkpoint receptors such as PD-1, PD-L1, and LAG-3, guiding small-molecule design (433).

Among the cancer undruggable targets, KRAS, MYC, and TP53 exemplify AI’s transformative role. AI modeling for KRAS revealed a switch-II pocket, leading to covalent inhibitors such as sotorasib and adagrasib (435). AI-driven in MYC simulations uncovered transient MYC-MAX pockets (432), and in TP53, its frameworks identify stabilizers that rescue mutant p53 conformations (309,432). Moreover, AI-based multi-omics integration aids in stratifying patients for targeted therapy (433,434). With the BCL2 family of survival proteins as their substrate for intracellular targeting, we conclude that peptide stapling and fragment-based drug discovery show promise to traverse the critical surface features of proteins that drive human cancer (436). AI’s role is not as a direct therapeutic but as an accelerator. Its highest potential for future medical use lies in its ability to unlock targets like KRAS and MYC, as detailed in Table 5. By predicting previously invisible pockets and generating novel chemical matter, AI platforms are de-risking the early stages of drug discovery. The most immediate medical impact will be the influx of AI-designed molecules, such as those for undisclosed targets, entering clinical trials in the coming years, fundamentally changing the productivity of the pharmaceutical pipeline.

Future directions & perspectives

Advances in TPD, PPI inhibitors, nucleic acid-based therapies, allosteric modulation, and AI are collectively reshaping the landscape of oncologic pharmacology. Nevertheless, for their full transformative potential to be realized, critical challenges must be addressed and new synergies strategically explored. Persistent key challenges include:

• Resistance: adaptive mechanisms, such as mutations within degrader interfaces, downregulation of E3 ligases, rewiring of signaling pathways, and alterations in ligand-binding sites, limit the durability of therapeutic responses. Proactive strategies, including next-generation agents and rational combination therapies, will be essential to mitigate resistance and sustain long-term efficacy.
• Safety and selectivity: concerns related to off-target degradation or covalent binding, the immunogenicity of oligonucleotide therapeutics, and idiosyncratic toxicities underscore the need for improved predictive toxicology models. Furthermore, the development of conditionally activated or tissue-selective therapies will be crucial to enhance therapeutic windows and minimize systemic risk.
• Delivery and accessibility: the inherent complexity of PROTACs, siRNA, and cell-based therapies presents substantial challenges in delivery and manufacturing, often accompanied by prohibitive costs. Progress in delivery platforms, such as nanocarriers and targeted conjugates, and the demonstration of superior clinical value will be necessary to ensure both therapeutic effectiveness and equitable patient access.
• Technological convergence and the integrated future: future progress will depend on synergistic integration across therapeutic modalities, with AI serving as the central unifying engine. AI-driven pipelines are poised to transform every stage of drug discovery, from target identification and prediction of dynamic protein conformations to de novo molecular design. Rational combinations, such as pairing targeted degraders with PPI inhibitors or coupling gene-based therapies with immunotherapies, hold promise for achieving deeper and more durable responses. Systematic mapping of the degradome (novel E3 ligase substrates) and cryptic pockets will further expand the pharmacologically accessible proteome.
• Reconfiguring precision oncology: these emerging strategies are shifting the paradigm from a mutation-centric framework to one focused on functional protein states (e.g., conformational landscapes, context-specific complexes). Precision medicine will be increasingly augmented by dynamic biomarkers, including real-time monitoring of protein degradation through liquid biopsies, enabling adaptive, state-responsive therapeutic regimens. Ultimately, the goal is to move beyond palliative control, leveraging catalytic, irreversible, or genome-editing mechanisms to pursue durable responses or functional cures in advanced malignancies.

Conclusions

In summary, the once-daunting paradigm of undruggable cancer proteins is being decisively dismantled by a new generation of pharmacological strategies (Figure 4). A comparative overview of emerging strategies for targeting undruggable cancer proteins is described in Table 6. From catalytic protein degradation and precise disruption of critical interactions to direct genetic modulation and AI-driven discovery, these innovative approaches are redefining the very boundaries of druggability. They translate deep biological insight into therapeutic action, moving beyond simple occupancy to event-driven elimination, state-specific inhibition, and root-cause genomic correction. While challenges in resistance, safety, delivery, and accessibility persist, they represent the next frontier for optimization rather than fundamental roadblocks. The future of precision oncology lies in the intelligent integration of these platforms, guided by AI and dynamic biomarkers, to deliver truly personalized, potent, and potentially transformative therapies. The journey from biologically crucial to pharmacologically vulnerable is well underway, promising a new era where even the most elusive oncogenic drivers can be effectively targeted for patient benefit.

Figure 4 The evolution of therapeutic modalities for historically “undruggable” cancer drivers. The schematic illustrates the expansion of the “druggable genome” through diversification of chemical biology tools, broadening the range of pharmacologically vulnerable targets. The five parallel strategies depicted, targeted protein degradation, PPI inhibition, covalent engagement, allosteric control, and nucleic acid-based therapeutics, provide complementary routes to overcome the structural and dynamic constraints that limit the occupancy of small molecules. In this model, artificial intelligence serves as an integrating layer that accelerates target-modality matching, de novo ligand and degrader design, cryptic pocket discovery, and biomarker-guided patient stratification. The resulting paradigm emphasizes precision control of oncogenic dependency through three non-mutually exclusive endpoints: (I) event-driven elimination of disease-sustaining proteins; (II) state-specific modulation of functional conformations and interaction networks; and (III) genomic correction or silencing at the causal information layer. Collectively, the schema highlights how multimodal, AI-augmented strategies can convert biologically essential yet previously non-actionable nodes into clinically tractable vulnerabilities, enabling tighter alignment between mechanism, genotype, and therapeutic outcome in precision oncology. Original figure created by the authors using the Gemini tool (https://gemini.google.com/). AI, artificial intelligence; ASO, antisense oligonucleotide; Cas9, CRISPR-associated protein 9; CRISPR, clustered regularly interspaced short palindromic repeats; KRAS, Kirsten rat sarcoma viral oncogene homolog; POI, protein of interest; PROTAC, proteolysis-targeting chimera; siRNA, small interfering RNA.

Acknowledgments

The authors would like to thank Gemini, Grammarly, and ChatGPT for assistance in improving the grammar and syntax of the text. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the published article.

Footnote

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2830/prf

Funding: This study was supported by the São Paulo Research Foundation (FAPESP) (Nos. 2024/21624-7, 2025/07801-6, 2023/12969-8, 2024/02796-1, 2022/11038-8, and 2023/12246-6). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior, Brasil (CAPES), Finance Code 001.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-1-2830/coif). J.A.M.N. serves as an unpaid editorial board member of Translational Cancer Research from August 2025 to September 2027. The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Cancer Genome Atlas Research Network. The Cancer Genome Atlas Pan-Cancer analysis project. Nat Genet 2013;45:1113-20. [Crossref] [PubMed]
2. Hasin Y, Seldin M, Lusis A. Multi-omics approaches to disease. Genome Biol 2017;18:83. [Crossref] [PubMed]
3. Hutter C, Zenklusen JC. The Cancer Genome Atlas: Creating Lasting Value beyond Its Data. Cell 2018;173:283-5. [Crossref] [PubMed]
4. Dang CV, Reddy EP, Shokat KM, et al. Drugging the 'undruggable' cancer targets. Nat Rev Cancer 2017;17:502-8. [Crossref] [PubMed]
5. Swinney DC, Anthony J. How were new medicines discovered?. Nat Rev Drug Discov 2011;10:507-19. [Crossref] [PubMed]
6. Eder J, Sedrani R, Wiesmann C. The discovery of first-in-class drugs: origins and evolution. Nat Rev Drug Discov 2014;13:577-87. [Crossref] [PubMed]
7. Luh LM, Scheib U, Juenemann K, et al. Prey for the Proteasome: Targeted Protein Degradation-A Medicinal Chemist's Perspective. Angew Chem Int Ed Engl 2020;59:15448-66. [Crossref] [PubMed]
8. Békés M, Langley DR, Crews CM. PROTAC targeted protein degraders: the past is prologue. Nat Rev Drug Discov 2022;21:181-200. [Crossref] [PubMed]
9. Zhao L, Zhao J, Zhong K, et al. Targeted protein degradation: mechanisms, strategies and application. Signal Transduct Target Ther 2022;7:113. [Crossref] [PubMed]
10. Yoon H, Rutter JC, Li YD, et al. Induced protein degradation for therapeutics: past, present, and future. J Clin Invest 2024;134:e175265. [Crossref] [PubMed]
11. Tsai JM, Nowak RP, Ebert BL, et al. Targeted protein degradation: from mechanisms to clinic. Nat Rev Mol Cell Biol 2024;25:740-57. [Crossref] [PubMed]
12. Sakamoto KM, Kim KB, Kumagai A, et al. Protacs: chimeric molecules that target proteins to the Skp1-Cullin-F box complex for ubiquitination and degradation. Proc Natl Acad Sci U S A 2001;98:8554-9. [Crossref] [PubMed]
13. Bondeson DP, Mares A, Smith IE, et al. Catalytic in vivo protein knockdown by small-molecule PROTACs. Nat Chem Biol 2015;11:611-7. [Crossref] [PubMed]
14. Winter GE, Buckley DL, Paulk J, et al. DRUG DEVELOPMENT. Phthalimide conjugation as a strategy for in vivo target protein degradation. Science 2015;348:1376-81. [Crossref] [PubMed]
15. Lu G, Middleton RE, Sun H, et al. The myeloma drug lenalidomide promotes the cereblon-dependent destruction of Ikaros proteins. Science 2014;343:305-9. [Crossref] [PubMed]
16. Krönke J, Udeshi ND, Narla A, et al. Lenalidomide causes selective degradation of IKZF1 and IKZF3 in multiple myeloma cells. Science 2014;343:301-5. [Crossref] [PubMed]
17. Bussiere DE, Xie L, Srinivas H, et al. Structural basis of indisulam-mediated RBM39 recruitment to DCAF15 E3 ligase complex. Nat Chem Biol 2020;16:15-23. [Crossref] [PubMed]
18. Hansen JD, Correa M, Alexander M, et al. CC-90009: A Cereblon E3 Ligase Modulating Drug That Promotes Selective Degradation of GSPT1 for the Treatment of Acute Myeloid Leukemia. J Med Chem 2021;64:1835-43. [Crossref] [PubMed]
19. Skowyra D, Craig KL, Tyers M, et al. F-box proteins are receptors that recruit phosphorylated substrates to the SCF ubiquitin-ligase complex. Cell 1997;91:209-19. [Crossref] [PubMed]
20. Zheng N, Schulman BA, Song L, et al. Structure of the Cul1-Rbx1-Skp1-F boxSkp2 SCF ubiquitin ligase complex. Nature 2002;416:703-9. [Crossref] [PubMed]
21. Xu L, Wei Y, Reboul J, et al. BTB proteins are substrate-specific adaptors in an SCF-like modular ubiquitin ligase containing CUL-3. Nature 2003;425:316-21. [Crossref] [PubMed]
22. Chamberlain PP, Hamann LG. Development of targeted protein degradation therapeutics. Nat Chem Biol 2019;15:937-44. [Crossref] [PubMed]
23. Dale B, Cheng M, Park KS, et al. Advancing targeted protein degradation for cancer therapy. Nat Rev Cancer 2021;21:638-654. [Crossref] [PubMed]
24. Sakamoto KM, Kim KB, Verma R, et al. Development of Protacs to target cancer-promoting proteins for ubiquitination and degradation. Mol Cell Proteomics 2003;2:1350-8. [Crossref] [PubMed]
25. Schneekloth AR, Pucheault M, Tae HS, et al. Targeted intracellular protein degradation induced by a small molecule: En route to chemical proteomics. Bioorg Med Chem Lett 2008;18:5904-8. [Crossref] [PubMed]
26. Dobrovolsky D, Wang ES, Morrow S, et al. Bruton tyrosine kinase degradation as a therapeutic strategy for cancer. Blood 2019;133:952-61. [Crossref] [PubMed]
27. Jiang B, Wang ES, Donovan KA, et al. Development of Dual and Selective Degraders of Cyclin-Dependent Kinases 4 and 6. Angew Chem Int Ed Engl 2019;58:6321-6. [Crossref] [PubMed]
28. Petzold G, Fischer ES, Thoma NH. Structural basis of lenalidomide-induced CK1alpha degradation by the CRL4(CRBN) ubiquitin ligase. Nature 2016;532:127-30. [Crossref] [PubMed]
29. Krönke J, Fink EC, Hollenbach PW, et al. Lenalidomide induces ubiquitination and degradation of CK1α in del(5q) MDS. Nature 2015;523:183-8. [Crossref] [PubMed]
30. Han T, Goralski M, Gaskill N, et al. Anticancer sulfonamides target splicing by inducing RBM39 degradation via recruitment to DCAF15. Science 2017;356:eaal3755. [Crossref] [PubMed]
31. Uehara T, Minoshima Y, Sagane K, et al. Selective degradation of splicing factor CAPERα by anticancer sulfonamides. Nat Chem Biol 2017;13:675-680. [Crossref] [PubMed]
32. Faust TB, Yoon H, Nowak RP, et al. Structural complementarity facilitates E7820-mediated degradation of RBM39 by DCAF15. Nat Chem Biol 2020;16:7-14. [Crossref] [PubMed]
33. An S, Fu L. Small-molecule PROTACs: An emerging and promising approach for the development of targeted therapy drugs. EBioMedicine 2018;36:553-62. [Crossref] [PubMed]
34. Roy MJ, Winkler S, Hughes SJ, et al. SPR-Measured Dissociation Kinetics of PROTAC Ternary Complexes Influence Target Degradation Rate. ACS Chem Biol 2019;14:361-8. [Crossref] [PubMed]
35. Liu X, Zhang X, Lv D, et al. Assays and technologies for developing proteolysis targeting chimera degraders. Future Med Chem 2020;12:1155-79. [Crossref] [PubMed]
36. Gadd MS, Testa A, Lucas X, et al. Structural basis of PROTAC cooperative recognition for selective protein degradation. Nat Chem Biol 2017;13:514-21. [Crossref] [PubMed]
37. Bondeson DP, Smith BE, Burslem GM, et al. Lessons in PROTAC Design from Selective Degradation with a Promiscuous Warhead. Cell Chem Biol 2018;25:78-87. [Crossref] [PubMed]
38. Zorba A, Nguyen C, Xu Y, et al. Delineating the role of cooperativity in the design of potent PROTACs for BTK. Proc Natl Acad Sci U S A 2018;115:E7285-92. [Crossref] [PubMed]
39. Wurz RP, Rui H, Dellamaggiore K, et al. Affinity and cooperativity modulate ternary complex formation to drive targeted protein degradation. Nat Commun 2023;14:4177. [Crossref] [PubMed]
40. Ishida T, Ciulli A. E3 Ligase Ligands for PROTACs: How They Were Found and How to Discover New Ones. SLAS Discov 2021;26:484-502. [Crossref] [PubMed]
41. Liu Y, Yang J, Wang T, et al. Expanding PROTACtable genome universe of E3 ligases. Nat Commun 2023;14:6509. [Crossref] [PubMed]
42. Vicente ATS, Moura SPSP, Salvador JAR. Synthesis, biological evaluation and clinical trials of Cereblon-based PROTACs. Commun Chem 2025;8:218. [Crossref] [PubMed]
43. Rodriguez-Gimeno A, Galdeano C. Drug Discovery Approaches to Target E3 Ligases. Chembiochem 2025;26:e202400656. [Crossref] [PubMed]
44. Ting TC, Goralski M, Klein K, et al. Aryl Sulfonamides Degrade RBM39 and RBM23 by Recruitment to CRL4-DCAF15. Cell Rep 2019;29:1499-1510. [Crossref] [PubMed]
45. Zhang X, He Y, Zhang P, et al. Discovery of IAP-recruiting BCL-X(L) PROTACs as potent degraders across multiple cancer cell lines. Eur J Med Chem 2020;199:112397. [Crossref] [PubMed]
46. Fischer ES, Böhm K, Lydeard JR, et al. Structure of the DDB1-CRBN E3 ubiquitin ligase in complex with thalidomide. Nature 2014;512:49-53. [Crossref] [PubMed]
47. Mayor-Ruiz C, Bauer S, Brand M, et al. Rational discovery of molecular glue degraders via scalable chemical profiling. Nat Chem Biol 2020;16:1199-1207. [Crossref] [PubMed]
48. Ege N, Bouguenina H, Tatari M, Chopra R. Phenotypic screening with target identification and validation in the discovery and development of E3 ligase modulators. Cell Chem Biol 2021;28:283-299. [Crossref] [PubMed]
49. Lier S, Sellmer A, Orben F, et al. A novel Cereblon E3 ligase modulator with antitumor activity in gastrointestinal cancer. Bioorg Chem 2022;119:105505. [Crossref] [PubMed]
50. Wei J, Feng W, Chen Q, et al. Rational discovery of molecular glue degraders based on block chemistry. Drug Discov Today 2025;30:104480. [Crossref] [PubMed]
51. List A, Dewald G, Bennett J, et al. Lenalidomide in the myelodysplastic syndrome with chromosome 5q deletion. N Engl J Med 2006;355:1456-65. [Crossref] [PubMed]
52. Richardson PG, Oriol A, Beksac M, et al. Pomalidomide, bortezomib, and dexamethasone for patients with relapsed or refractory multiple myeloma previously treated with lenalidomide (OPTIMISMM): a randomised, open-label, phase 3 trial. Lancet Oncol 2019;20:781-94. [Crossref] [PubMed]
53. King EA, Cho Y, Hsu NS, et al. Chemoproteomics-enabled discovery of a covalent molecular glue degrader targeting NF-κB. Cell Chem Biol 2023;30:394-402. [Crossref] [PubMed]
54. Eladl O. Molecular glues and PROTACs in targeted protein degradation: mechanisms, advances, and therapeutic potential. Biochem Pharmacol 2025;242:117297. [Crossref] [PubMed]
55. Neklesa T, Snyder LB, Willard R, et al. Abstract 5236: ARV-110: An androgen receptor PROTAC degrader for prostate cancer. Cancer Res 2018;78:5236. [Crossref]
56. Neklesa T, Snyder LB, Willard RR, et al. ARV-110: An oral androgen receptor PROTAC degrader for prostate cancer. J Clin Oncol 2019;37:259. [Crossref]
57. Mullard A. First targeted protein degrader hits the clinic. Nat Rev Drug Discov 2019; Epub ahead of print. [Crossref] [PubMed]
58. Snyder LB, Neklesa TK, Willard RR, et al. Preclinical Evaluation of Bavdegalutamide (ARV-110), a Novel PROteolysis TArgeting Chimera Androgen Receptor Degrader. Mol Cancer Ther 2025;24:511-22. [Crossref] [PubMed]
59. Antonarakis ES, Zhang N, Saha J, et al. Prevalence and Spectrum of AR Ligand-Binding Domain Mutations Detected in Circulating-Tumor DNA Across Disease States in Men With Metastatic Castration-Resistant Prostate Cancer. JCO Precis Oncol 2024;8:e2300330. [Crossref] [PubMed]
60. Shore ND, Shen J, Devitt ME, et al. Phase 1b study of bavdegalutamide, an androgen receptor PROTAC degrader, combined with abiraterone in patients with metastatic prostate cancer. J Clin Oncol 2022;40:TPS5106. [Crossref]
61. Petrylak DP, Garmezy B, Shen J, et al. 1803P Phase I/II study of bavdegalutamide, a PROTAC androgen receptor (AR) degrader in metastatic castration-resistant prostate cancer (mCRPC): Radiographic progression-free survival (rPFS) in patients (pts) with AR ligand-binding domain (LBD) mutations. Ann Oncol 2023;34:S973-4. [Crossref]
62. Gough SM, Flanagan JJ, Teh J, et al. Oral Estrogen Receptor PROTAC Vepdegestrant (ARV-471) Is Highly Efficacious as Monotherapy and in Combination with CDK4/6 or PI3K/mTOR Pathway Inhibitors in Preclinical ER+ Breast Cancer Models. Clin Cancer Res 2024;30:3549-63. [Crossref] [PubMed]
63. Hamilton EP, Ma C, De Laurentiis M, et al. VERITAC-2: a Phase III study of vepdegestrant, a PROTAC ER degrader, versus fulvestrant in ER+/HER2- advanced breast cancer. Future Oncol 2024;20:2447-55. [Crossref] [PubMed]
64. Campone M, De Laurentiis M, Jhaveri K, et al. Vepdegestrant, a PROTAC Estrogen Receptor Degrader, in Advanced Breast Cancer. N Engl J Med 2025;393:556-68. [Crossref] [PubMed]
65. Sun Y, Zhao X, Ding N, et al. PROTAC-induced BTK degradation as a novel therapy for mutated BTK C481S induced ibrutinib-resistant B-cell malignancies. Cell Res 2018;28:779-81. [Crossref] [PubMed]
66. Dhami K, Chakraborty A, Gururaja TL, et al. Kinase-deficient BTK mutants confer ibrutinib resistance through activation of the kinase HCK. Sci Signal 2022;15:eabg5216. [Crossref] [PubMed]
67. Zhu C, Yang Z, Zhang Y, et al. PROTAC for Bruton's tyrosine kinase degradation alleviates inflammation in autoimmune diseases. Cell Discov 2024;10:82. [Crossref] [PubMed]
68. Burslem GM, Schultz AR, Bondeson DP, et al. Targeting BCR-ABL1 in Chronic Myeloid Leukemia by PROTAC-Mediated Targeted Protein Degradation. Cancer Res 2019;79:4744-4753. [Crossref] [PubMed]
69. Yang Y, Gao H, Sun X, et al. Global PROTAC Toolbox for Degrading BCR-ABL Overcomes Drug-Resistant Mutants and Adverse Effects. J Med Chem 2020;63:8567-83. [Crossref] [PubMed]
70. Ma L, Xie L, Wang Y, et al. Discovery of dCDK9-202 as a Highly Potent and Selective PROTAC CDK9 Degrader with Strong In Vivo Antitumor Activity. J Med Chem 2025;68:21172-86. [Crossref] [PubMed]
71. Wu T, Zhang Z, Gong G, et al. Discovery of novel flavonoid-based CDK9 degraders for prostate cancer treatment via a PROTAC strategy. Eur J Med Chem 2023;260:115774. [Crossref] [PubMed]
72. Qin AC, Jin H, Song Y, et al. The therapeutic effect of the BRD4-degrading PROTAC A1874 in human colon cancer cells. Cell Death Dis 2020;11:805. [Crossref] [PubMed]
73. Aguilar A, Yang J, Li Y, et al. Discovery of MD-265: A Potent MDM2 Degrader That Achieves Complete Tumor Regression and Improves Long-Term Survival of Mice with Leukemia. J Med Chem 2024;67:19503-18. [Crossref] [PubMed]
74. Wang Q, Li B, Zhang W, et al. Lethal activity of BRD4 PROTAC degrader QCA570 against bladder cancer cells. Front Chem 2023;11:1121724. [Crossref] [PubMed]
75. Nie HJ, Li BF, Sun J, et al. Discovery of a Potent, selective and orally bioavailable CDK9 degrader for targeting transcription regulation in Triple-Negative breast cancer. Bioorg Chem 2024;153:107876. [Crossref] [PubMed]
76. Marei HE, Bedair K, Hasan A, et al. Targeting CDKs in cancer therapy: advances in PROTACs and molecular glues. NPJ Precis Oncol 2025;9:204. [Crossref] [PubMed]
77. Toure MA, Motoyama K, Xiang Y, et al. Targeted degradation of CDK9 potently disrupts the MYC-regulated network. Cell Chem Biol 2025;32:542-555. [Crossref] [PubMed]
78. Buhimschi AD, Armstrong HA, Toure M, et al. Targeting the C481S Ibrutinib-Resistance Mutation in Bruton's Tyrosine Kinase Using PROTAC-Mediated Degradation. Biochemistry 2018;57:3564-75. [Crossref] [PubMed]
79. Raina K, Lu J, Qian Y, et al. PROTAC-induced BET protein degradation as a therapy for castration-resistant prostate cancer. Proc Natl Acad Sci U S A 2016;113:7124-9. [Crossref] [PubMed]
80. Shi Y, Liao Y, Liu Q, et al. BRD4-targeting PROTAC as a unique tool to study biomolecular condensates. Cell Discov 2023;9:47. [Crossref] [PubMed]
81. Zhang L, Riley-Gillis B, Vijay P, et al. Acquired Resistance to BET-PROTACs (Proteolysis-Targeting Chimeras) Caused by Genomic Alterations in Core Components of E3 Ligase Complexes. Mol Cancer Ther 2019;18:1302-11. [Crossref] [PubMed]
82. Kelm JM, Pandey DS, Malin E, et al. PROTAC'ing oncoproteins: targeted protein degradation for cancer therapy. Mol Cancer 2023;22:62. [Crossref] [PubMed]
83. Bjorklund CC, Kang J, Amatangelo M, et al. Iberdomide (CC-220) is a potent cereblon E3 ligase modulator with antitumor and immunostimulatory activities in lenalidomide- and pomalidomide-resistant multiple myeloma cells with dysregulated CRBN. Leukemia 2020;34:1197-201. [Crossref] [PubMed]
84. Lonial S, Popat R, Hulin C, et al. Iberdomide plus dexamethasone in heavily pretreated late-line relapsed or refractory multiple myeloma (CC-220-MM-001): a multicentre, multicohort, open-label, phase 1/2 trial. Lancet Haematol 2022;9:e822-32. [Crossref] [PubMed]
85. Hansen JD, Correa M, Nagy MA, et al. Discovery of CRBN E3 Ligase Modulator CC-92480 for the Treatment of Relapsed and Refractory Multiple Myeloma. J Med Chem 2020;63:6648-76. [Crossref] [PubMed]
86. Richardson PG, Trudel S, Popat R, et al. Mezigdomide plus Dexamethasone in Relapsed and Refractory Multiple Myeloma. N Engl J Med 2023;389:1009-22. [Crossref] [PubMed]
87. Matyskiela ME, Lu G, Ito T, et al. A novel cereblon modulator recruits GSPT1 to the CRL4(CRBN) ubiquitin ligase. Nature 2016;535:252-7. [Crossref] [PubMed]
88. Matyskiela ME, Zhang W, Man HW, et al. A Cereblon Modulator (CC-220) with Improved Degradation of Ikaros and Aiolos. J Med Chem 2018;61:535-42. [Crossref] [PubMed]
89. Du X, Volkov OA, Czerwinski RM, et al. Structural Basis and Kinetic Pathway of RBM39 Recruitment to DCAF15 by a Sulfonamide Molecular Glue E7820. Structure 2019;27:1625-1633. [Crossref] [PubMed]
90. Singh S, Quarni W, Goralski M, et al. Targeting the spliceosome through RBM39 degradation results in exceptional responses in high-risk neuroblastoma models. Sci Adv 2021;7:eabj5405. [Crossref] [PubMed]
91. Nijhuis A, Sikka A, Yogev O, et al. Indisulam targets RNA splicing and metabolism to serve as a therapeutic strategy for high-risk neuroblastoma. Nat Commun 2022;13:1380. [Crossref] [PubMed]
92. Xu Y, Spear S, Ma Y, et al. Pharmacological depletion of RNA splicing factor RBM39 by indisulam synergizes with PARP inhibitors in high-grade serous ovarian carcinoma. Cell Rep 2023;42:113307. [Crossref] [PubMed]
93. Dittrich C, Zandvliet AS, Gneist M, et al. A phase I and pharmacokinetic study of indisulam in combination with carboplatin. Br J Cancer 2007;96:559-66. [Crossref] [PubMed]
94. Talbot DC, von Pawel J, Cattell E, et al. A randomized phase II pharmacokinetic and pharmacodynamic study of indisulam as second-line therapy in patients with advanced non-small cell lung cancer. Clin Cancer Res 2007;13:1816-22. [Crossref] [PubMed]
95. Assi R, Kantarjian HM, Kadia TM, et al. Final results of a phase 2, open-label study of indisulam, idarubicin, and cytarabine in patients with relapsed or refractory acute myeloid leukemia and high-risk myelodysplastic syndrome. Cancer 2018;124:2758-65. [Crossref] [PubMed]
96. Hsiehchen D, Goralski M, Kim J, et al. Biomarkers for RBM39 degradation in acute myeloid leukemia. Leukemia 2020;34:1924-8. [Crossref] [PubMed]
97. Campagne S, Jutzi D, Malard F, et al. Molecular basis of RNA-binding and autoregulation by the cancer-associated splicing factor RBM39. Nat Commun 2023;14:5366. [Crossref] [PubMed]
98. Wang E, Lu SX, Pastore A, et al. Targeting an RNA-Binding Protein Network in Acute Myeloid Leukemia. Cancer Cell 2019;35:369-384. [Crossref] [PubMed]
99. Singh S, Fang J, Jin H, et al. RBM39 degrader invigorates innate immunity to eradicate neuroblastoma despite cancer cell plasticity. Nat Commun 2025;16:8287. [Crossref] [PubMed]
100. Ji T, Yang Y, Yu J, et al. Targeting RBM39 through indisulam induced mis-splicing of mRNA to exert anti-cancer effects in T-cell acute lymphoblastic leukemia. J Exp Clin Cancer Res 2024;43:205. [Crossref] [PubMed]
101. Bewersdorf JP, Stahl M, Taylor J, et al. E7820, an anti-cancer sulfonamide, degrades RBM39 in patients with splicing factor mutant myeloid malignancies: a phase II clinical trial. Leukemia 2023;37:2512-6. [Crossref] [PubMed]
102. Surka C, Jin L, Mbong N, et al. CC-90009, a novel cereblon E3 ligase modulator, targets acute myeloid leukemia blasts and leukemia stem cells. Blood 2021;137:661-77. [Crossref] [PubMed]
103. Sellar RS, Sperling AS, Słabicki M, et al. Degradation of GSPT1 causes TP53-independent cell death in leukemia while sparing normal hematopoietic stem cells. J Clin Invest 2022;132:e153514. [Crossref] [PubMed]
104. Vick EJ, Hassan A, Choi K, et al. Targeting of IRAK4 and GSPT1 enhances therapeutic efficacy in AML via c-Myc destabilization. Leukemia 2025;39:2163-73. [Crossref] [PubMed]
105. Weisberg E, Chowdhury B, Meng C, et al. BRD9 degraders as chemosensitizers in acute leukemia and multiple myeloma. Blood Cancer J 2022;12:110. [Crossref] [PubMed]
106. Kurata K, Samur MK, Liow P, et al. BRD9 Degradation Disrupts Ribosome Biogenesis in Multiple Myeloma. Clin Cancer Res 2023;29:1807-21. [Crossref] [PubMed]
107. Hughes SJ, Stec WJ, Davies CTR, et al. Mode of action of a DCAF16-recruiting targeted glue that can selectively degrade BRD9. Nat Commun 2025;16:8516. [Crossref] [PubMed]
108. Popova T, Manié E, Boeva V, et al. Ovarian Cancers Harboring Inactivating Mutations in CDK12 Display a Distinct Genomic Instability Pattern Characterized by Large Tandem Duplications. Cancer Res 2016;76:1882-91. [Crossref] [PubMed]
109. Dubbury SJ, Boutz PL, Sharp PA. CDK12 regulates DNA repair genes by suppressing intronic polyadenylation. Nature 2018;564:141-5. [Crossref] [PubMed]
110. Słabicki M, Kozicka Z, Petzold G, et al. The CDK inhibitor CR8 acts as a molecular glue degrader that depletes cyclin K. Nature 2020;585:293-7. [Crossref] [PubMed]
111. Kozicka Z, Suchyta DJ, Focht V, et al. Design principles for cyclin K molecular glue degraders. Nat Chem Biol 2024;20:93-102. [Crossref] [PubMed]
112. Ting PY, Borikar S, Kerrigan JR, et al. A molecular glue degrader of the WIZ transcription factor for fetal hemoglobin induction. Science 2024;385:91-9. [Crossref] [PubMed]
113. Słabicki M, Park J, Nowak RP, et al. Expanding the druggable zinc-finger proteome defines properties of drug-induced degradation. Mol Cell 2025;85:3184-3201. [Crossref] [PubMed]
114. Zhuang Z, Byun WS, Kozicka Z, et al. Rational Design of CDK12/13 and BRD4 Molecular Glue Degraders. Angew Chem Int Ed Engl 2025;64:e202508427. [Crossref] [PubMed]
115. Petrylak DP, Gao X, Vogelzang NJ, et al. First-in-human phase I study of ARV-110, an androgen receptor (AR) PROTAC degrader in patients (pts) with metastatic castration-resistant prostate cancer (mCRPC) following enzalutamide (ENZ) and/or abiraterone (ABI). J Clin Oncol 2020;38:3500. [Crossref]
116. Snyder LL, Han S, Neklesa TK, et al. Abstract ND03: Discovery of ARV-766, an androgen receptor degrading PROTAC for the treatment of men with metastatic castration-resistant prostate cancer. Cancer Res 2023;83:ND03. [Crossref]
117. Petrylak DP, McKean M, Lang JM, et al. ARV-766, a proteolysis targeting chimera (PROTAC) androgen receptor (AR) degrader, in metastatic castration-resistant prostate cancer (mCRPC): Initial results of a phase 1/2 study. J Clin Oncol 2024;42:5011. [Crossref]
118. Azad AA, Gurney H, Underhill C, et al. Phase 1 study of HP518, a PROTAC AR degrader in patients with mCRPC: results on safety, pharmacokinetics, and anti-tumor activity. Invest New Drugs 2025;43:435-45. [Crossref] [PubMed]
119. Nayak S, Norris JD, Ammirante M, et al. Discovery of BMS-986365, a First-in-Class Dual Androgen Receptor Ligand-Directed Degrader and Antagonist, for the Treatment of Advanced Prostate Cancer. Clin Cancer Res 2026;32:224-41. [Crossref] [PubMed]
120. Rathkopf DE, Patel MR, Choudhury AD, et al. Safety and clinical activity of BMS-986365 (CC-94676), a dual androgen receptor ligand-directed degrader and antagonist, in heavily pretreated patients with metastatic castration-resistant prostate cancer. Ann Oncol 2025;36:76-88. [Crossref] [PubMed]
121. Hamilton EP, Laurentiis MD, Jhaveri KL, et al. Vepdegestrant, a PROTAC estrogen receptor (ER) degrader, vs fulvestrant in ER-positive/human epidermal growth factor receptor 2 (HER2)–negative advanced breast cancer: Results of the global, randomized, phase 3 VERITAC-2 study. J Clin Oncol 2025;43:LBA1000. [Crossref]
122. Lv D, Pal P, Liu X, et al. Development of a BCL-xL and BCL-2 dual degrader with improved anti-leukemic activity. Nat Commun 2021;12:6896. [Crossref] [PubMed]
123. Khan S, Wiegand J, Zhang P, et al. BCL-X(L) PROTAC degrader DT2216 synergizes with sotorasib in preclinical models of KRAS(G12C)-mutated cancers. J Hematol Oncol 2022;15:23. [Crossref] [PubMed]
124. Mahadevan D, Barve M, Mahalingam D, et al. First in human phase 1 study of DT2216, a selective BCL-xL degrader, in patients with relapsed/refractory solid malignancies. J Hematol Oncol 2025;18:98. [Crossref] [PubMed]
125. Robbins DW, Noviski MA, Tan YS, et al. Discovery and Preclinical Pharmacology of NX-2127, an Orally Bioavailable Degrader of Bruton's Tyrosine Kinase with Immunomodulatory Activity for the Treatment of Patients with B Cell Malignancies. J Med Chem 2024;67:2321-36. [Crossref] [PubMed]
126. Danilov A, Tees MT, Patel K, et al. A First-in-Human Phase 1 Trial of NX-2127, a First-in-Class Bruton's Tyrosine Kinase (BTK) Dual-Targeted Protein Degrader with Immunomodulatory Activity, in Patients with Relapsed/Refractory B Cell Malignancies. Blood 2023;142:4463. [Crossref]
127. Robbins DW, Noviski M, Rountree R, et al. NX-5948, a Selective Degrader of BTK with Activity in Preclinical Models of Hematologic and Brain Malignancies. Blood 2021;138:2251. [Crossref]
128. Searle E, Forconi F, Linton K, et al. Initial Findings from a First-in-Human Phase 1a/b Trial of NX-5948, a Selective Bruton's Tyrosine Kinase (BTK) Degrader, in Patients with Relapsed/Refractory B Cell Malignancies. Blood 2023;142:4473. [Crossref]
129. Wu Y, Meibohm B, Zhang T, et al. Translational modelling to predict human pharmacokinetics and pharmacodynamics of a Bruton's tyrosine kinase-targeted protein degrader BGB-16673. Br J Pharmacol 2024;181:4973-87. [Crossref] [PubMed]
130. Seymour JF, Cheah CY, Parrondo R, et al. First Results from a Phase 1, First-in-Human Study of the Bruton's Tyrosine Kinase (BTK) Degrader BGB-16673 in Patients with Relapsed or Refractory (R/R) B-Cell Malignancies (BGB-16673-101). Blood 2023;142:4401. [Crossref]
131. Lonial S, Richard S, Matous JV, et al. Abstract CT186: Pharmacokinetic (PK) profile of a novel IKZF1/3 degrader, CFT7455, enables significant potency advantage over other IKZF1/3 degraders in models of multiple myeloma (MM) and the results of the initial treatment cohort from a first-in-human (FIH) phase 1/2 study of CFT7455 in MM. Cancer Res 2022;82:CT186. [Crossref]
132. Horwitz S, Jain S, Soumerai JD, et al. Initial Results of a Phase 1 First-in-Human Study of Cemsidomide (CFT7455), a Novel MonoDACTM Degrader, in Patients with Non-Hodgkin's Lymphoma. Blood 2024;144:467. [Crossref]
133. Jackson KL, Yin N, Agafonov RV, et al. Discovery of CFT8634, a Potent, Selective, and Orally Bioavailable Heterobifunctional Degrader of BRD9. J Med Chem 2025;68:24848-68. [Crossref] [PubMed]
134. Poling LL, Cocozziello D, He M, et al. CFT8634, a Clinical Stage BRD9 Bi DAC™ Degrader, Is Active in a Subset of Multiple Myeloma Cell Line Models and Synergistic When Combined with Pomalidomide. Blood 2023;142:6594. [Crossref]
135. Rubio-Pérez J, Daher S, Arbour K, et al. Abstract A049: Impact of Actionable Genomic Alterations (AGA) on the Efficacy of Durvalumab in Patients with Locally Advanced NSCLC Treated with Concurrent Chemoradiotherapy. Mol Cancer Ther 2025;24:A049. [Crossref]
136. Livingston JA, Blay JY, Trent J, et al. A Phase I Study of FHD-609, a Heterobifunctional Degrader of Bromodomain-Containing Protein 9, in Patients with Advanced Synovial Sarcoma or SMARCB1-Deficient Tumors. Clin Cancer Res 2025;31:628-38. [Crossref] [PubMed]
137. Chakraborty S, Morganti C, Zaldana K, et al. A STAT3 degrader demonstrates efficacy in venetoclax resistant acute myeloid leukemia. Leukemia 2026;40:717-29. [Crossref] [PubMed]
138. Smith SD, Starodub A, Stevens DA, et al. A Phase 1 Study of KT-333, a Targeted Protein Degrader of STAT3, in Patients with Relapsed or Refractory Lymphomas, Large Granular Lymphocytic Leukemia, and Solid Tumors. Blood 2022;140:12024-12025. [Crossref]
139. Weiss MM, Zheng X, Ji N, et al. Discovery of KT-413, a Targeted Protein Degrader of IRAK4 and IMiD Substrates Targeting MYD88 Mutant Diffuse Large B-Cell Lymphoma. J Med Chem 2024;67:10548-66. [Crossref] [PubMed]
140. Stevens DA, Ewesuedo R, McDonald A, et al. Phase 1 study of KT-413, a targeted protein degrader, in adult patients with relapsed or refractory B-cell non-Hodgkin lymphoma. J Clin Oncol 2022;40:TPS3170. [Crossref]
141. Chutake YK, Mayo MF, Dumont N, et al. KT-253, a Novel MDM2 Degrader and p53 Stabilizer, Has Superior Potency and Efficacy than MDM2 Small-Molecule Inhibitors. Mol Cancer Ther 2025;24:497-510. [Crossref] [PubMed]
142. Khawaja MRR-u-H. Safety, pharmacokinetics (PK), pharmacodynamics (PD) and efficacy of KT-253, a targeted protein degrader of MDM2, in patients with relapsed/refractory (R/R) solid tumors, lymphoma, high grade myeloid malignancies and acute lymphoblastic leukemia (ALL). J Clin Oncol 2024;42:3084. [Crossref]
143. Rajkumar SV, Blood E, Vesole D, et al. Phase III clinical trial of thalidomide plus dexamethasone compared with dexamethasone alone in newly diagnosed multiple myeloma: a clinical trial coordinated by the Eastern Cooperative Oncology Group. J Clin Oncol 2006;24:431-6. [Crossref] [PubMed]
144. Dimopoulos M, Spencer A, Attal M, et al. Lenalidomide plus dexamethasone for relapsed or refractory multiple myeloma. N Engl J Med 2007;357:2123-32. [Crossref] [PubMed]
145. Miguel JS, Weisel K, Moreau P, et al. Pomalidomide plus low-dose dexamethasone versus high-dose dexamethasone alone for patients with relapsed and refractory multiple myeloma (MM-003): a randomised, open-label, phase 3 trial. Lancet Oncol 2013;14:1055-66. [Crossref] [PubMed]
146. Bjorklund CC, Kang J, Lu L, et al. CC-122 Is a Cereblon Modulating Agent That Is Active in Lenalidomide-Resistant and Lenalidomide/Dexamethasone-Double-Resistant Multiple Myeloma Pre-Clinical Models. Blood 2016;128:1592. [Crossref]
147. Michot JM, Bouabdallah R, Vitolo U, et al. Avadomide plus obinutuzumab in patients with relapsed or refractory B-cell non-Hodgkin lymphoma (CC-122-NHL-001): a multicentre, dose escalation and expansion phase 1 study. Lancet Haematol 2020;7:e649-59. [Crossref] [PubMed]
148. Carpio C, Bouabdallah R, Ysebaert L, et al. Avadomide monotherapy in relapsed/refractory DLBCL: safety, efficacy, and a predictive gene classifier. Blood 2020;135:996-1007. [Crossref] [PubMed]
149. Nastoupil LJ, Kuruvilla J, Chavez JC, et al. Phase Ib study of avadomide (CC-122) in combination with rituximab in patients with relapsed/refractory diffuse large B-cell lymphoma and follicular lymphoma. EJHaem 2022;3:394-405. [Crossref] [PubMed]
150. Hatake K, Chou T, Doi T, et al. Phase I, multicenter, dose-escalation study of avadomide in adult Japanese patients with advanced malignancies. Cancer Sci 2021;112:331-8. [Crossref] [PubMed]
151. Uy GL, Minden MD, Montesinos P, et al. Clinical Activity of CC-90009, a Cereblon E3 Ligase Modulator and First-in-Class GSPT1 Degrader, As a Single Agent in Patients with Relapsed or Refractory Acute Myeloid Leukemia (R/R AML): First Results from a Phase I Dose-Finding Study. Blood 2019;134:232. [Crossref]
152. Chourasia AH, Majeski H, Pasis A, et al. BTX-1188, a first-in-class dual degrader of GSPT1 and IKZF1/3, for treatment of acute myeloid leukemia (AML) and solid tumors. J Clin Oncol 2022;40:7025. [Crossref]
153. Kirkwood JM, Gonzalez R, Reintgen D, et al. A phase 2 study of tasisulam sodium (LY573636 sodium) as second-line treatment for patients with unresectable or metastatic melanoma. Cancer 2011;117:4732-9. [Crossref] [PubMed]
154. Hamid O, Ilaria R Jr, Garbe C, et al. A randomized, open-label clinical trial of tasisulam sodium versus paclitaxel as second-line treatment in patients with metastatic melanoma. Cancer 2014;120:2016-24. [Crossref] [PubMed]
155. Stilgenbauer S, Eichhorst B, Schetelig J, et al. Venetoclax in relapsed or refractory chronic lymphocytic leukaemia with 17p deletion: a multicentre, open-label, phase 2 study. Lancet Oncol 2016;17:768-78. [Crossref] [PubMed]
156. DiNardo CD, Jonas BA, Pullarkat V, et al. Azacitidine and Venetoclax in Previously Untreated Acute Myeloid Leukemia. N Engl J Med 2020;383:617-629. [Crossref] [PubMed]
157. Wilson WH, O'Connor OA, Czuczman MS, et al. Navitoclax, a targeted high-affinity inhibitor of BCL-2, in lymphoid malignancies: a phase 1 dose-escalation study of safety, pharmacokinetics, pharmacodynamics, and antitumour activity. Lancet Oncol 2010;11:1149-59. [Crossref] [PubMed]
158. de Vos S, Leonard JP, Friedberg JW, et al. Safety and efficacy of navitoclax, a BCL-2 and BCL-X(L) inhibitor, in patients with relapsed or refractory lymphoid malignancies: results from a phase 2a study. Leuk Lymphoma 2021;62:810-8. [Crossref] [PubMed]
159. Parikh SA, Kantarjian H, Schimmer A, et al. Phase II study of obatoclax mesylate (GX15-070), a small-molecule BCL-2 family antagonist, for patients with myelofibrosis. Clin Lymphoma Myeloma Leuk 2010;10:285-9. [Crossref] [PubMed]
160. Gounder MM, Bauer TM, Schwartz GK, et al. A First-in-Human Phase I Study of Milademetan, an MDM2 Inhibitor, in Patients With Advanced Liposarcoma, Solid Tumors, or Lymphomas. J Clin Oncol 2023;41:1714-24. [Crossref] [PubMed]
161. Saleh MN, Patel MR, Bauer TM, et al. Phase 1 Trial of ALRN-6924, a Dual Inhibitor of MDMX and MDM2, in Patients with Solid Tumors and Lymphomas Bearing Wild-type TP53. Clin Cancer Res 2021;27:5236-47. [Crossref] [PubMed]
162. Krivtsov AV, Evans K, Gadrey JY, et al. A Menin-MLL Inhibitor Induces Specific Chromatin Changes and Eradicates Disease in Models of MLL-Rearranged Leukemia. Cancer Cell 2019;36:660-673. [Crossref] [PubMed]
163. Issa GC, Aldoss I, DiPersio J, et al. The menin inhibitor revumenib in KMT2A-rearranged or NPM1-mutant leukaemia. Nature 2023;615:920-4. [Crossref] [PubMed]
164. Kwon MC, Thuring JW, Querolle O, et al. Preclinical efficacy of the potent, selective menin-KMT2A inhibitor JNJ-75276617 (bleximenib) in KMT2A- and NPM1-altered leukemias. Blood 2024;144:1206-20. [Crossref] [PubMed]
165. Runckel K, Barth MJ, Mavis C, et al. The SMAC mimetic LCL-161 displays antitumor activity in preclinical models of rituximab-resistant B-cell lymphoma. Blood Adv 2018;2:3516-25. [Crossref] [PubMed]
166. Pemmaraju N, Carter BZ, Bose P, et al. Final results of a phase 2 clinical trial of LCL161, an oral SMAC mimetic for patients with myelofibrosis. Blood Adv 2021;5:3163-73. [Crossref] [PubMed]
167. Ferris RL, Harrington K, Schoenfeld JD, et al. Inhibiting the inhibitors: Development of the IAP inhibitor xevinapant for the treatment of locally advanced squamous cell carcinoma of the head and neck. Cancer Treat Rev 2023;113:102492. [Crossref] [PubMed]
168. Sun XS, Tao Y, Le Tourneau C, et al. Debio 1143 and high-dose cisplatin chemoradiotherapy in high-risk locoregionally advanced squamous cell carcinoma of the head and neck: a double-blind, multicentre, randomised, phase 2 study. Lancet Oncol 2020;21:1173-87. [Crossref] [PubMed]
169. Ward GA, Lewis EJ, Ahn JS, et al. ASTX660, a Novel Non-peptidomimetic Antagonist of cIAP1/2 and XIAP, Potently Induces TNFalpha-Dependent Apoptosis in Cancer Cell Lines and Inhibits Tumor Growth. Mol Cancer Ther 2018;17:1381-1391. [Crossref] [PubMed]
170. Pawson T, Nash P. Protein-protein interactions define specificity in signal transduction. Genes Dev 2000;14:1027-47. [Crossref] [PubMed]
171. Pawson T, Nash P. Assembly of cell regulatory systems through protein interaction domains. Science 2003;300:445-52. [Crossref] [PubMed]
172. Lee MJ, Yaffe MB. Protein Regulation in Signal Transduction. Cold Spring Harb Perspect Biol 2016;8:a005918. [Crossref] [PubMed]
173. Jones S, Thornton JM. Principles of protein-protein interactions. Proc Natl Acad Sci U S A 1996;93:13-20. [Crossref] [PubMed]
174. Bogan AA, Thorn KS. Anatomy of hot spots in protein interfaces. J Mol Biol 1998;280:1-9. [Crossref] [PubMed]
175. Arkin MR, Wells JA. Small-molecule inhibitors of protein-protein interactions: progressing towards the dream. Nat Rev Drug Discov 2004;3:301-17. [Crossref] [PubMed]
176. Wells JA, McClendon CL. Reaching for high-hanging fruit in drug discovery at protein-protein interfaces. Nature 2007;450:1001-9. [Crossref] [PubMed]
177. Clackson T, Wells JA. A hot spot of binding energy in a hormone-receptor interface. Science 1995;267:383-6. [Crossref] [PubMed]
178. Keskin O, Gursoy A, Ma B, et al. Principles of protein-protein interactions: what are the preferred ways for proteins to interact?. Chem Rev 2008;108:1225-44. [Crossref] [PubMed]
179. Villoutreix BO, Labbé CM, Lagorce D, et al. A leap into the chemical space of protein-protein interaction inhibitors. Curr Pharm Des 2012;18:4648-67. [Crossref] [PubMed]
180. Li Z, Ivanov AA, Su R, et al. The OncoPPi network of cancer-focused protein-protein interactions to inform biological insights and therapeutic strategies. Nat Commun 2017;8:14356. [Crossref] [PubMed]
181. Ivanov AA, Revennaugh B, Rusnak L, et al. The OncoPPi Portal: an integrative resource to explore and prioritize protein-protein interactions for cancer target discovery. Bioinformatics 2018;34:1183-91. [Crossref] [PubMed]
182. Oltersdorf T, Elmore SW, Shoemaker AR, et al. An inhibitor of Bcl-2 family proteins induces regression of solid tumours. Nature 2005;435:677-81. [Crossref] [PubMed]
183. Vassilev LT, Vu BT, Graves B, et al. In vivo activation of the p53 pathway by small-molecule antagonists of MDM2. Science 2004;303:844-8. [Crossref] [PubMed]
184. Andreeff M, Kelly KR, Yee K, et al. Results of the Phase I Trial of RG7112, a Small-Molecule MDM2 Antagonist in Leukemia. Clin Cancer Res 2016;22:868-76. [Crossref] [PubMed]
185. Roberts AW, Davids MS, Pagel JM, et al. Targeting BCL2 with Venetoclax in Relapsed Chronic Lymphocytic Leukemia. N Engl J Med 2016;374:311-22. [Crossref] [PubMed]
186. Montesinos P, Beckermann BM, Catalani O, et al. MIRROS: a randomized, placebo-controlled, Phase III trial of cytarabine +/- idasanutlin in relapsed or refractory acute myeloid leukemia. Future Oncol 2020;16:807-815. [Crossref] [PubMed]
187. Jubb H, Blundell TL, Ascher DB. Flexibility and small pockets at protein-protein interfaces: New insights into druggability. Prog Biophys Mol Biol 2015;119:2-9. [Crossref] [PubMed]
188. Yu H, Chen JK, Feng S, et al. Structural basis for the binding of proline-rich peptides to SH3 domains. Cell 1994;76:933-45. [Crossref] [PubMed]
189. Yu H, Rosen MK, Shin TB, et al. Solution structure of the SH3 domain of Src and identification of its ligand-binding site. Science 1992;258:1665-8. [Crossref] [PubMed]
190. Chen Y, Ebright YW, Ebright RH. Identification of the target of a transcription activator protein by protein-protein photocrosslinking. Science 1994;265:90-2. [Crossref] [PubMed]
191. Nikolov DB, Chen H, Halay ED, et al. Crystal structure of a TFIIB-TBP-TATA-element ternary complex. Nature 1995;377:119-28. [Crossref] [PubMed]
192. Niu W, Kim Y, Tau G, et al. Transcription activation at class II CAP-dependent promoters: two interactions between CAP and RNA polymerase. Cell 1996;87:1123-34. [Crossref] [PubMed]
193. Walensky LD, Kung AL, Escher I, et al. Activation of apoptosis in vivo by a hydrocarbon-stapled BH3 helix. Science 2004;305:1466-70. [Crossref] [PubMed]
194. Nam Y, Sliz P, Song L, et al. Structural basis for cooperativity in recruitment of MAML coactivators to Notch transcription complexes. Cell 2006;124:973-83. [Crossref] [PubMed]
195. Block P, Weskamp N, Wolf A, et al. Strategies to search and design stabilizers of protein-protein interactions: a feasibility study. Proteins 2007;68:170-86. [Crossref] [PubMed]
196. Moellering RE, Cornejo M, Davis TN, et al. Direct inhibition of the NOTCH transcription factor complex. Nature 2009;462:182-8. [Crossref] [PubMed]
197. Nahta R, Hung MC, Esteva FJ. The HER-2-targeting antibodies trastuzumab and pertuzumab synergistically inhibit the survival of breast cancer cells. Cancer Res 2004;64:2343-6. [Crossref] [PubMed]
198. Anders C, Higuchi Y, Koschinsky K, et al. A semisynthetic fusicoccane stabilizes a protein-protein interaction and enhances the expression of K+ channels at the cell surface. Chem Biol 2013;20:583-93. [Crossref] [PubMed]
199. Milroy LG, Grossmann TN, Hennig S, et al. Modulators of protein-protein interactions. Chem Rev 2014;114:4695-748. [Crossref] [PubMed]
200. Schneider JA, Craven TW, Kasper AC, et al. Design of Peptoid-peptide Macrocycles to Inhibit the β-catenin TCF Interaction in Prostate Cancer. Nat Commun 2018;9:4396. [Crossref] [PubMed]
201. Lu H, Zhou Q, He J, et al. Recent advances in the development of protein-protein interactions modulators: mechanisms and clinical trials. Signal Transduct Target Ther 2020;5:213. [Crossref] [PubMed]
202. Buchwald P. Small-molecule protein-protein interaction inhibitors: therapeutic potential in light of molecular size, chemical space, and ligand binding efficiency considerations. IUBMB Life 2010;62:724-31. [Crossref] [PubMed]
203. Smith MC, Gestwicki JE. Features of protein-protein interactions that translate into potent inhibitors: topology, surface area and affinity. Expert Rev Mol Med 2012;14:e16. [Crossref] [PubMed]
204. Ran X, Gestwicki JE. Inhibitors of protein-protein interactions (PPIs): an analysis of scaffold choices and buried surface area. Curr Opin Chem Biol 2018;44:75-86. [Crossref] [PubMed]
205. Koes DR, Camacho CJ. PocketQuery: protein-protein interaction inhibitor starting points from protein-protein interaction structure. Nucleic Acids Res 2012;40:W387-92. [Crossref] [PubMed]
206. Sable R, Jois S. Surfing the Protein-Protein Interaction Surface Using Docking Methods: Application to the Design of PPI Inhibitors. Molecules 2015;20:11569-603. [Crossref] [PubMed]
207. Zhang Z, Lu C. Assessing influences of climate change on highland barley productivity in the Qinghai-Tibet Plateau during 1978-2017. Sci Rep 2022;12:7625. [Crossref] [PubMed]
208. Konopleva M, Contractor R, Tsao T, et al. Mechanisms of apoptosis sensitivity and resistance to the BH3 mimetic ABT-737 in acute myeloid leukemia. Cancer Cell 2006;10:375-88. [Crossref] [PubMed]
209. Del Gaizo Moore V, Brown JR, Certo M, et al. Chronic lymphocytic leukemia requires BCL2 to sequester prodeath BIM, explaining sensitivity to BCL2 antagonist ABT-737. J Clin Invest 2007;117:112-21. [Crossref] [PubMed]
210. Fuller JC, Burgoyne NJ, Jackson RM. Predicting druggable binding sites at the protein-protein interface. Drug Discov Today 2009;14:155-61. [Crossref] [PubMed]
211. Penner JE, Xu L, Wang M. Satellite methods underestimate indirect climate forcing by aerosols. Proc Natl Acad Sci U S A 2011;108:13404-8. [Crossref] [PubMed]
212. Gao M, Skolnick J. The distribution of ligand-binding pockets around protein-protein interfaces suggests a general mechanism for pocket formation. Proc Natl Acad Sci U S A 2012;109:3784-9. [Crossref] [PubMed]
213. Alzyoud L, Bryce RA, Al Sorkhy M, et al. Structure-based assessment and druggability classification of protein-protein interaction sites. Sci Rep 2022;12:7975. [Crossref] [PubMed]
214. Kozakov D, Hall DR, Chuang GY, et al. Structural conservation of druggable hot spots in protein-protein interfaces. Proc Natl Acad Sci U S A 2011;108:13528-33. [Crossref] [PubMed]
215. Pihan E, Delgadillo RF, Tonkin ML, et al. Computational and biophysical approaches to protein-protein interaction inhibition of Plasmodium falciparum AMA1/RON2 complex. J Comput Aided Mol Des 2015;29:525-39. [Crossref] [PubMed]
216. Meireles LM, Dömling AS, Camacho CJ. ANCHOR: a web server and database for analysis of protein-protein interaction binding pockets for drug discovery. Nucleic Acids Res 2010;38:W407-11. [Crossref] [PubMed]
217. Konc J, Janezic D. ProBiS-2012: web server and web services for detection of structurally similar binding sites in proteins. Nucleic Acids Res 2012;40:W214-21. [Crossref] [PubMed]
218. Baspinar A, Cukuroglu E, Nussinov R, et al. PRISM: a web server and repository for prediction of protein-protein interactions and modeling their 3D complexes. Nucleic Acids Res 2014;42:W285-9. [Crossref] [PubMed]
219. Grossmann TN, Yeh JT, Bowman BR, et al. Inhibition of oncogenic Wnt signaling through direct targeting of beta-catenin. Proc Natl Acad Sci U S A 2012;109:17942-7. [Crossref] [PubMed]
220. Anananuchatkul T, Tsutsumi H, Miki T, et al. Bioorg. Med Chem Lett 2020;30:127605. [Crossref] [PubMed]
221. Fasan R, Dias RL, Moehle K, et al. Using a beta-hairpin to mimic an alpha-helix: cyclic peptidomimetic inhibitors of the p53-HDM2 protein-protein interaction. Angew Chem Int Ed Engl 2004;43:2109-12. [Crossref] [PubMed]
222. Walensky LD, Pitter K, Morash J, et al. A stapled BID BH3 helix directly binds and activates BAX. Mol Cell 2006;24:199-210. [Crossref] [PubMed]
223. Bird GH, Madani N, Perry AF, et al. Hydrocarbon double-stapling remedies the proteolytic instability of a lengthy peptide therapeutic. Proc Natl Acad Sci U S A 2010;107:14093-8. [Crossref] [PubMed]
224. Chang YS, Graves B, Guerlavais V, et al. Stapled alpha-helical peptide drug development: a potent dual inhibitor of MDM2 and MDMX for p53-dependent cancer therapy. Proc Natl Acad Sci U S A 2013;110:E3445-54. [Crossref] [PubMed]
225. Choi J, Yun JS, Song H, et al. Exploring the chemical space of protein-protein interaction inhibitors through machine learning. Sci Rep 2021;11:13369. [Crossref] [PubMed]
226. Bucholtz EC, Brown RL, Tropsha A, et al. Synthesis, evaluation, and comparative molecular field analysis of 1-phenyl-3-amino-1,2,3,4-tetrahydronaphthalenes as ligands for histamine H(1) receptors. J Med Chem 1999;42:3041-54. [Crossref] [PubMed]
227. Nabors LB, Mikkelsen T, Rosenfeld SS, et al. Phase I and correlative biology study of cilengitide in patients with recurrent malignant glioma. J Clin Oncol 2007;25:1651-7. [Crossref] [PubMed]
228. Carvajal LA, Neriah DB, Senecal A, et al. Dual inhibition of MDMX and MDM2 as a therapeutic strategy in leukemia. Sci Transl Med 2018;10:eaao3003. [Crossref] [PubMed]
229. Guerlavais V, Sawyer TK, Carvajal L, et al. Discovery of Sulanemadlin (ALRN-6924), the First Cell-Permeating, Stabilized alpha-Helical Peptide in Clinical Development. J Med Chem 2023;66:9401-9417. [Crossref] [PubMed]
230. Lao BB, Drew K, Guarracino DA, et al. Rational design of topographical helix mimics as potent inhibitors of protein-protein interactions. J Am Chem Soc 2014;136:7877-88. [Crossref] [PubMed]
231. Huhn AJ, Guerra RM, Harvey EP, et al. Selective Covalent Targeting of Anti-Apoptotic BFL-1 by Cysteine-Reactive Stapled Peptide Inhibitors. Cell Chem Biol 2016;23:1123-34. [Crossref] [PubMed]
232. de Araujo AD, Lim J, Good AC, et al. Electrophilic Helical Peptides That Bond Covalently, Irreversibly, and Selectively in a Protein-Protein Interaction Site. ACS Med Chem Lett 2017;8:22-6. [Crossref] [PubMed]
233. Lee Y, Im H, Das S, et al. Bridged alpha-helix mimetic small molecules. Chem Commun (Camb) 2019;55:13311-4. [Crossref] [PubMed]
234. Gambini L, Udompholkul P, Baggio C, et al. Design, Synthesis, and Structural Characterization of Lysine Covalent BH3 Peptides Targeting Mcl-1. J Med Chem 2021;64:4903-12. [Crossref] [PubMed]
235. Ye X, Zhang P, Tao J, et al. Discovery of reactive peptide inhibitors of human papillomavirus oncoprotein E6. Chem Sci 2023;14:12484-97. [Crossref] [PubMed]
236. Cole CC, Pogostin BH, Cahue KA, et al. Covalent Stabilization of Collagen Mimetic Triple Helices and Assemblies by Dopa Crosslinking. Chembiochem 2025;26:e202500268. [Crossref] [PubMed]
237. Tse C, Shoemaker AR, Adickes J, et al. ABT-263: a potent and orally bioavailable Bcl-2 family inhibitor. Cancer Res 2008;68:3421-8. [Crossref] [PubMed]
238. Oakes SR, Vaillant F, Lim E, et al. Sensitization of BCL-2-expressing breast tumors to chemotherapy by the BH3 mimetic ABT-737. Proc Natl Acad Sci U S A 2012;109:2766-71. [Crossref] [PubMed]
239. Souers AJ, Leverson JD, Boghaert ER, et al. ABT-199, a potent and selective BCL-2 inhibitor, achieves antitumor activity while sparing platelets. Nat Med 2013;19:202-8. [Crossref] [PubMed]
240. Vaillant F, Merino D, Lee L, et al. Targeting BCL-2 with the BH3 mimetic ABT-199 in estrogen receptor-positive breast cancer. Cancer Cell 2013;24:120-9. [Crossref] [PubMed]
241. van Delft MF, Wei AH, Mason KD, et al. The BH3 mimetic ABT-737 targets selective Bcl-2 proteins and efficiently induces apoptosis via Bak/Bax if Mcl-1 is neutralized. Cancer Cell 2006;10:389-99. [Crossref] [PubMed]
242. Gandhi L, Camidge DR, Ribeiro de Oliveira M, et al. Phase I study of Navitoclax (ABT-263), a novel Bcl-2 family inhibitor, in patients with small-cell lung cancer and other solid tumors. J Clin Oncol 2011;29:909-16. [Crossref] [PubMed]
243. Roberts AW, Seymour JF, Brown JR, et al. Substantial susceptibility of chronic lymphocytic leukemia to BCL2 inhibition: results of a phase I study of navitoclax in patients with relapsed or refractory disease. J Clin Oncol 2012;30:488-96. [Crossref] [PubMed]
244. Leverson JD, Phillips DC, Mitten MJ, et al. Exploiting selective BCL-2 family inhibitors to dissect cell survival dependencies and define improved strategies for cancer therapy. Sci Transl Med 2015;7:279ra40. [Crossref] [PubMed]
245. Roberts AW, Stilgenbauer S, Seymour JF, et al. Venetoclax in Patients with Previously Treated Chronic Lymphocytic Leukemia. Clin Cancer Res 2017;23:4527-33. [Crossref] [PubMed]
246. Davids MS, Roberts AW, Seymour JF, et al. Phase I First-in-Human Study of Venetoclax in Patients With Relapsed or Refractory Non-Hodgkin Lymphoma. J Clin Oncol 2017;35:826-33. [Crossref] [PubMed]
247. Seymour JF, Kipps TJ, Eichhorst B, et al. Venetoclax-Rituximab in Relapsed or Refractory Chronic Lymphocytic Leukemia. N Engl J Med 2018;378:1107-20. [Crossref] [PubMed]
248. Kater AP, Seymour JF, Hillmen P, et al. Fixed Duration of Venetoclax-Rituximab in Relapsed/Refractory Chronic Lymphocytic Leukemia Eradicates Minimal Residual Disease and Prolongs Survival: Post-Treatment Follow-Up of the MURANO Phase III Study. J Clin Oncol 2019;37:269-77. [Crossref] [PubMed]
249. Fischer K, Al-Sawaf O, Bahlo J, et al. Venetoclax and Obinutuzumab in Patients with CLL and Coexisting Conditions. N Engl J Med 2019;380:2225-36. [Crossref] [PubMed]
250. Jain N, Keating M, Thompson P, et al. Ibrutinib Plus Venetoclax for First-line Treatment of Chronic Lymphocytic Leukemia: A Nonrandomized Phase 2 Trial. JAMA Oncol 2021;7:1213-9. [Crossref] [PubMed]
251. Al-Sawaf O, Robrecht S, Zhang C, et al. Venetoclax-obinutuzumab for previously untreated chronic lymphocytic leukemia: 6-year results of the randomized phase 3 CLL14 study. Blood 2024;144:1924-35. [Crossref] [PubMed]
252. Nwosu GO, Ross DM, Powell JA, et al. Venetoclax therapy and emerging resistance mechanisms in acute myeloid leukaemia. Cell Death Dis 2024;15:413. [Crossref] [PubMed]
253. Lagadinou ED, Sach A, Callahan K, et al. BCL-2 inhibition targets oxidative phosphorylation and selectively eradicates quiescent human leukemia stem cells. Cell Stem Cell 2013;12:329-41. [Crossref] [PubMed]
254. Pan R, Hogdal LJ, Benito JM, et al. Selective BCL-2 inhibition by ABT-199 causes on-target cell death in acute myeloid leukemia. Cancer Discov 2014;4:362-75. [Crossref] [PubMed]
255. Chan SM, Thomas D, Corces-Zimmerman MR, et al. Isocitrate dehydrogenase 1 and 2 mutations induce BCL-2 dependence in acute myeloid leukemia. Nat Med 2015;21:178-84. [Crossref] [PubMed]
256. Pollyea DA, DiNardo CD, Arellano ML, et al. Impact of Venetoclax and Azacitidine in Treatment-Naive Patients with Acute Myeloid Leukemia and IDH1/2 Mutations. Clin Cancer Res 2022;28:2753-2761. [Crossref] [PubMed]
257. Venugopal S, Maiti A, DiNardo CD, et al. Decitabine and venetoclax for IDH1/2-mutated acute myeloid leukemia. Am J Hematol 2021;96:E154-7. [Crossref] [PubMed]
258. Bhatt S, Pioso MS, Olesinski EA, et al. Reduced Mitochondrial Apoptotic Priming Drives Resistance to BH3 Mimetics in Acute Myeloid Leukemia. Cancer Cell 2020;38:872-890. [Crossref] [PubMed]
259. DiNardo CD, Pratz K, Pullarkat V, et al. Venetoclax combined with decitabine or azacitidine in treatment-naive, elderly patients with acute myeloid leukemia. Blood 2019;133:7-17. [Crossref] [PubMed]
260. Angotzi F, Lessi F, Leoncin M, et al. Efficacy and safety of venetoclax plus hypomethylating agents in relapsed/refractory acute myeloid leukemia: a multicenter real-life experience. Front Oncol 2024;14:1370405. [Crossref] [PubMed]
261. You L, Liu Y, Mai W, et al. Venetoclax plus cytarabine and azacitidine in relapsed/refractory AML: An open-label, single-arm, phase 2 study. Eur J Cancer 2024;202:113979. [Crossref] [PubMed]
262. Xu X, Liu R, He A, et al. Real-world results of venetoclax combined with hypomethylating agents in young adults with relapsed/refractory acute myeloid leukemia. Hematology 2023;28:2265206. [Crossref] [PubMed]
263. Venditti A, Hou JZ, Fenaux P, et al. Outcomes of patients treated with venetoclax plus azacitidine versus azacitidine alone stratified by advanced age and acute myeloid leukemia composite model. Leukemia 2025;39:2697-707. [Crossref] [PubMed]
264. Solana-Altabella A, Rodríguez-Veiga R, Martínez-Cuadrón D, et al. A systematic review of venetoclax for the treatment of unfit AML patients in real-world: is all that glitters gold?. Ann Hematol 2025;104:913-35. [Crossref] [PubMed]
265. Kojima K, Konopleva M, McQueen T, et al. Mdm2 inhibitor Nutlin-3a induces p53-mediated apoptosis by transcription-dependent and transcription-independent mechanisms and may overcome Atm-mediated resistance to fludarabine in chronic lymphocytic leukemia. Blood 2006;108:993-1000. [Crossref] [PubMed]
266. Chen L, Rousseau RF, Middleton SA, et al. Pre-clinical evaluation of the MDM2-p53 antagonist RG7388 alone and in combination with chemotherapy in neuroblastoma. Oncotarget 2015;6:10207-21. [Crossref] [PubMed]
267. Fang DD, Tang Q, Kong Y, et al. MDM2 inhibitor APG-115 exerts potent antitumor activity and synergizes with standard-of-care agents in preclinical acute myeloid leukemia models. Cell Death Discov 2021;7:90. [Crossref] [PubMed]
268. Ciardullo C, Aptullahoglu E, Woodhouse L, et al. Non-genotoxic MDM2 inhibition selectively induces a pro-apoptotic p53 gene signature in chronic lymphocytic leukemia cells. Haematologica 2019;104:2429-42. [Crossref] [PubMed]
269. Wei Y, Zheng H, Lockyer PP, et al. MDM2 antagonist improves therapeutic activity of azacitidine in myelodysplastic syndromes and chronic myelomonocytic leukemia. Leuk Lymphoma 2022;63:3154-64. [Crossref] [PubMed]
270. Reis B, Jukofsky L, Chen G, et al. Acute myeloid leukemia patients' clinical response to idasanutlin (RG7388) is associated with pre-treatment MDM2 protein expression in leukemic blasts. Haematologica 2016;101:e185-8. [Crossref] [PubMed]
271. Yee K, Papayannidis C, Vey N, et al. Murine double minute 2 inhibition alone or with cytarabine in acute myeloid leukemia: Results from an idasanutlin phase 1/1b study. Leuk Res 2021;100:106489. [Crossref] [PubMed]
272. Konopleva MY, Röllig C, Cavenagh J, et al. Idasanutlin plus cytarabine in relapsed or refractory acute myeloid leukemia: results of the MIRROS trial. Blood Adv 2022;6:4147-56. [Crossref] [PubMed]
273. Reardon DA, Nabors LB, Stupp R, et al. Cilengitide: an integrin-targeting arginine-glycine-aspartic acid peptide with promising activity for glioblastoma multiforme. Expert Opin Investig Drugs 2008;17:1225-35. [Crossref] [PubMed]
274. Tchernychev B, Ren Y, Sachdev P, et al. Discovery of a CXCR4 agonist pepducin that mobilizes bone marrow hematopoietic cells. Proc Natl Acad Sci U S A 2010;107:22255-9. [Crossref] [PubMed]
275. Cabri W, Cantelmi P, Corbisiero D, et al. Therapeutic Peptides Targeting PPI in Clinical Development: Overview, Mechanism of Action and Perspectives. Front Mol Biosci 2021;8:697586. [Crossref] [PubMed]
276. Hu B, Gilkes DM, Farooqi B, et al. MDMX overexpression prevents p53 activation by the MDM2 inhibitor Nutlin. J Biol Chem 2006;281:33030-5. [Crossref] [PubMed]
277. Lee JJ, Powderly JD, Patel MR, et al. Phase 1 trial of CA-170, a novel oral small molecule dual inhibitor of immune checkpoints PD-1 and VISTA, in patients (pts) with advanced solid tumor or lymphomas. J Clin Oncol 2017;35:TPS3099. [Crossref]
278. Shustov AR, Horwitz SM, Zain J, et al. Preliminary Results of the Stapled Peptide ALRN-6924, a Dual Inhibitor of MDMX and MDM2, in Two Phase IIa Dose Expansion Cohorts in Relapsed/Refractory TP53 Wild-Type Peripheral T-Cell Lymphoma. Blood 2018;132:1623. [Crossref]
279. Sallman DA, Borate U, Cull EH, et al. Phase 1/1b Study of the Stapled Peptide ALRN-6924, a Dual Inhibitor of MDMX and MDM2, As Monotherapy or in Combination with Cytarabine for the Treatment of Relapsed/Refractory AML and Advanced MDS with TP53 Wild-Type. Blood 2018;132:4066. [Crossref]
280. Meric-Bernstam F, Somaiah N, DuBois SG, et al. 475P: A phase IIa clinical trial combining ALRN-6924 and palbociclib for the treatment of patients with tumors harboring wild-type p53 and MDM2 amplification or MDM2/CDK4 co-amplification. Ann Oncol 2019;30:v179-80. [Crossref]
281. Andric Z, Ceric T, Rancic M, et al. 1654P A phase Ib/II study of the dual MDMX/MDM2 inhibitor, ALRN-6924, for the prevention of chemotherapy-induced myelosuppression. Ann Oncol 2021;32:S1166. [Crossref]
282. Vince JE, Wong WW, Khan N, et al. IAP antagonists target cIAP1 to induce TNFalpha-dependent apoptosis. Cell 2007;131:682-93. [Crossref] [PubMed]
283. Wu H, Tschopp J, Lin SC. Smac mimetics and TNFalpha: a dangerous liaison?. Cell 2007;131:655-8. [Crossref] [PubMed]
284. Laukens B, Jennewein C, Schenk B, et al. Smac mimetic bypasses apoptosis resistance in FADD- or caspase-8-deficient cells by priming for tumor necrosis factor alpha-induced necroptosis. Neoplasia 2011;13:971-9. [Crossref] [PubMed]
285. Petersen SL, Peyton M, Minna JD, et al. Overcoming cancer cell resistance to Smac mimetic induced apoptosis by modulating cIAP-2 expression. Proc Natl Acad Sci U S A 2010;107:11936-41. [Crossref] [PubMed]
286. Sun H, Lu J, Liu L, et al. Potent and selective small-molecule inhibitors of cIAP1/2 proteins reveal that the binding of Smac mimetics to XIAP BIR3 is not required for their effective induction of cell death in tumor cells. ACS Chem Biol 2014;9:994-1002. [Crossref] [PubMed]
287. Eytan DF, Snow GE, Carlson SG, et al. Combination effects of SMAC mimetic birinapant with TNFalpha, TRAIL, and docetaxel in preclinical models of HNSCC. Laryngoscope 2015;125:E118-24. [Crossref] [PubMed]
288. Perimenis P, Galaris A, Voulgari A, et al. IAP antagonists Birinapant and AT-406 efficiently synergise with either TRAIL, BRAF, or BCL-2 inhibitors to sensitise BRAFV600E colorectal tumour cells to apoptosis. BMC Cancer 2016;16:624. [Crossref] [PubMed]
289. Najem S, Langemann D, Appl B, et al. Smac mimetic LCL161 supports neuroblastoma chemotherapy in a drug class-dependent manner and synergistically interacts with ALK inhibitor TAE684 in cells with ALK mutation F1174L. Oncotarget 2016;7:72634-53. [Crossref] [PubMed]
290. Tchoghandjian A, Soubéran A, Tabouret E, et al. Inhibitor of apoptosis protein expression in glioblastomas and their in vitro and in vivo targeting by SMAC mimetic GDC-0152. Cell Death Dis 2016;7:e2325. [Crossref] [PubMed]
291. Shekhar TM, Burvenich IJG, Harris MA, et al. Smac mimetics LCL161 and GDC-0152 inhibit osteosarcoma growth and metastasis in mice. BMC Cancer 2019;19:924. [Crossref] [PubMed]
292. Zhou L, Zhang Y, Meads MB, et al. IAP and HDAC inhibitors interact synergistically in myeloma cells through noncanonical NF-κB- and caspase-8-dependent mechanisms. Blood Adv 2021;5:3776-3788. [Crossref] [PubMed]
293. Infante JR, Dees EC, Olszanski AJ, et al. Phase I dose-escalation study of LCL161, an oral inhibitor of apoptosis proteins inhibitor, in patients with advanced solid tumors. J Clin Oncol 2014;32:3103-10. [Crossref] [PubMed]
294. Amaravadi RK, Schilder RJ, Martin LP, et al. A Phase I Study of the SMAC-Mimetic Birinapant in Adults with Refractory Solid Tumors or Lymphoma. Mol Cancer Ther 2015;14:2569-75. [Crossref] [PubMed]
295. Noonan AM, Bunch KP, Chen JQ, et al. Pharmacodynamic markers and clinical results from the phase 2 study of the SMAC mimetic birinapant in women with relapsed platinum-resistant or -refractory epithelial ovarian cancer. Cancer 2016;122:588-97. [Crossref] [PubMed]
296. Srivastava AK, Jaganathan S, Stephen L, et al. Effect of a Smac Mimetic (TL32711, Birinapant) on the Apoptotic Program and Apoptosis Biomarkers Examined with Validated Multiplex Immunoassays Fit for Clinical Use. Clin Cancer Res 2016;22:1000-10. [Crossref] [PubMed]
297. Beug ST, Beauregard CE, Healy C, et al. Smac mimetics synergize with immune checkpoint inhibitors to promote tumour immunity against glioblastoma. Nat Commun 2017;8:14278. [Crossref] [PubMed]
298. Heaton WL, Senina AV, Pomicter AD, et al. Autocrine Tnf signaling favors malignant cells in myelofibrosis in a Tnfr2-dependent fashion. Leukemia 2018;32:2399-411. [Crossref] [PubMed]
299. Craver BM, Nguyen TK, Nguyen J, et al. The SMAC mimetic LCL-161 selectively targets JAK2(V617F) mutant cells. Exp Hematol Oncol 2020;9:1. [Crossref] [PubMed]
300. Zhang T, Hatcher JM, Teng M, et al. Recent Advances in Selective and Irreversible Covalent Ligand Development and Validation. Cell Chem Biol 2019;26:1486-500. [Crossref] [PubMed]
301. Sutanto F, Konstantinidou M, Dömling A. Covalent inhibitors: a rational approach to drug discovery. RSC Med Chem 2020;11:876-84. [Crossref] [PubMed]
302. Boike L, Henning NJ, Nomura DK. Advances in covalent drug discovery. Nat Rev Drug Discov 2022;21:881-98. [Crossref] [PubMed]
303. Singh J. The Ascension of Targeted Covalent Inhibitors. J Med Chem 2022;65:5886-901. [Crossref] [PubMed]
304. Singh J, Petter RC, Baillie TA, et al. The resurgence of covalent drugs. Nat Rev Drug Discov 2011;10:307-17. [Crossref] [PubMed]
305. Ghosh AK, Samanta I, Mondal A, et al. Covalent Inhibition in Drug Discovery. ChemMedChem 2019;14:889-906. [Crossref] [PubMed]
306. Bhole RP, Joshi GO, Kapare HS, et al. Covalent drug – An emerging framework for targeted drug development. Results Chem 2024;8:101615. [Crossref]
307. Zheng L, Li Y, Wu D, et al. Development of covalent inhibitors: principle, design, and application in cancer. MedComm-Oncol 2023;2:e56. [Crossref]
308. Cheke RS, Kharkar PS. Covalent inhibitors: An ambitious approach for the discovery of newer oncotherapeutics. Drug Dev Res 2024;85:e22132. [Crossref] [PubMed]
309. Xie X, Yu T, Li X, et al. Recent advances in targeting the "undruggable" proteins: from drug discovery to clinical trials. Signal Transduct Target Ther 2023;8:335. [Crossref] [PubMed]
310. Ahronian LG, Corcoran RB. Strategies for monitoring and combating resistance to combination kinase inhibitors for cancer therapy. Genome Med 2017;9:37. [Crossref] [PubMed]
311. Schaefer D, Cheng X. Recent Advances in Covalent Drug Discovery. Pharmaceuticals (Basel) 2023;16:663. [Crossref] [PubMed]
312. Abdeldayem A, Raouf YS, Constantinescu SN, et al. Advances in covalent kinase inhibitors. Chem Soc Rev 2020;49:2617-87. [Crossref] [PubMed]
313. De Cesco S, Kurian J, Dufresne C, et al. Covalent inhibitors design and discovery. Eur J Med Chem 2017;138:96-114. [Crossref] [PubMed]
314. Lonsdale R, Ward RA. Structure-based design of targeted covalent inhibitors. Chem Soc Rev 2018;47:3816-30. [Crossref] [PubMed]
315. Longley DB, Harkin DP, Johnston PG. 5-fluorouracil: mechanisms of action and clinical strategies. Nat Rev Cancer 2003;3:330-8. [Crossref] [PubMed]
316. Mini E, Nobili S, Caciagli B, et al. Cellular pharmacology of gemcitabine. Ann Oncol 2006;17:v7-12. [Crossref] [PubMed]
317. Sogbein O, Paul P, Umar M, et al. Bortezomib in cancer therapy: Mechanisms, side effects, and future proteasome inhibitors. Life Sci 2024;358:123125. [Crossref] [PubMed]
318. Greig SL. Osimertinib: First Global Approval. Drugs 2016;76:263-73. [Crossref] [PubMed]
319. Deeks ED. Neratinib: First Global Approval. Drugs 2017;77:1695-704. [Crossref] [PubMed]
320. Shirley M. Dacomitinib: First Global Approval. Drugs 2018;78:1947-53. [Crossref] [PubMed]
321. Syed YY. Selinexor: First Global Approval. Drugs 2019;79:1485-94. [Crossref] [PubMed]
322. Syed YY. Zanubrutinib: First Approval. Drugs 2020;80:91-7. [Crossref] [PubMed]
323. Blair HA. Sotorasib: First Approval. Drugs 2021;81:1573-9. [Crossref] [PubMed]
324. Dhillon S. Adagrasib: First Approval. Drugs 2023;83:275-85. [Crossref] [PubMed]
325. Markham A. Tepotinib: First Approval. Drugs 2020;80:829-33. [Crossref] [PubMed]
326. Syed YY. Futibatinib: First Approval. Drugs 2022;82:1737-43. [Crossref] [PubMed]
327. Keam SJ. Pirtobrutinib: First Approval. Drugs 2023;83:547-53. [Crossref] [PubMed]
328. Dhillon S. Sunvozertinib: First Approval. Drugs 2023;83:1629-34. [Crossref] [PubMed]
329. Gu S, Li Q, Li C, et al. Abstract 2664: Development of covalent KRASG12C inhibitor APG-1842 for the treatment of solid tumors. Cancer Res 2022;82:2664. [Crossref]
330. Tanaka N, Lin JJ, Li C, et al. Clinical Acquired Resistance to KRAS(G12C) Inhibition through a Novel KRAS Switch-II Pocket Mutation and Polyclonal Alterations Converging on RAS-MAPK Reactivation. Cancer Discov 2021;11:1913-22. [Crossref] [PubMed]
331. Bhavar P, Surampudi U, Sarma P, et al. Abstract 4107: Discovery and preclinical evaluation of VRTX126, a novel, highly selective and potent KRASG12C inhibitor. Cancer Res 2022;82:4107. [Crossref]
332. Zhang Z, Guiley KZ, Shokat KM. Chemical acylation of an acquired serine suppresses oncogenic signaling of K-Ras(G12S). Nat Chem Biol 2022;18:1177-83. [Crossref] [PubMed]
333. Serafim RAM, Gehringer M, Borsari C. Targeted Covalent Inhibitors in Drug Discovery, Chemical Biology and Beyond. Pharmaceuticals (Basel) 2024;17:206. [Crossref] [PubMed]
334. Zhao Z, Bourne PE. Advances in reversible covalent kinase inhibitors. Med Res Rev 2025;45:629-53. [Crossref] [PubMed]
335. Gai C, Harnor SJ, Zhang S, et al. Advanced approaches of developing targeted covalent drugs. RSC Med Chem 2022;13:1460-75. [Crossref] [PubMed]
336. Ostrem JM, Peters U, Sos ML, et al. K-Ras(G12C) inhibitors allosterically control GTP affinity and effector interactions. Nature 2013;503:548-51. [Crossref] [PubMed]
337. Anscombe E, Meschini E, Mora-Vidal R, et al. Identification and Characterization of an Irreversible Inhibitor of CDK2. Chem Biol 2015;22:1159-64. [Crossref] [PubMed]
338. Dalton SE, Dittus L, Thomas DA, et al. Selectively Targeting the Kinome-Conserved Lysine of PI3Kδ as a General Approach to Covalent Kinase Inhibition. J Am Chem Soc 2018;140:932-9. [Crossref] [PubMed]
339. Ambati GG, Jachak SM. Natural Product Inhibitors of Cyclooxygenase (COX) Enzyme: A Review on Current Status and Future Perspectives. Curr Med Chem 2021;28:1877-905. [Crossref] [PubMed]
340. Borges PHO, Ferreira SB, Silva FP Jr. Recent Advances on Targeting Proteases for Antiviral Development. Viruses 2024;16:366. [Crossref] [PubMed]
341. Teng M, Ficarro SB, Yoon H, et al. Rationally Designed Covalent BCL6 Inhibitor That Targets a Tyrosine Residue in the Homodimer Interface. ACS Med Chem Lett 2020;11:1269-73. [Crossref] [PubMed]
342. Shishido Y, Tomoike F, Kimura Y, et al. A covalent G-site inhibitor for glutathione S-transferase Pi (GSTP(1-1)). Chem Commun (Camb) 2017;53:11138-41. [Crossref] [PubMed]
343. Noce B, Di Bello E, Fioravanti R, et al. LSD1 inhibitors for cancer treatment: Focus on multi-target agents and compounds in clinical trials. Front Pharmacol 2023;14:1120911. [Crossref] [PubMed]
344. Han H, Gracia AV, Røise JJ, et al. A covalent inhibitor targeting the papain-like protease from SARS-CoV-2 inhibits viral replication. RSC Adv 2023;13:10636-41. [Crossref] [PubMed]
345. Chan AM, Goodis CC, Pommier EG, et al. Recent applications of covalent chemistries in protein-protein interaction inhibitors. RSC Med Chem 2022;13:921-8. [Crossref] [PubMed]
346. Ni D, Li X, He X, et al. Drugging K-Ras(G12C) through covalent inhibitors: Mission possible? Pharmacol Ther. 2019;202:1-17. [Crossref] [PubMed]
347. Nussinov R, Tsai CJ. Allostery in disease and in drug discovery. Cell 2013;153:293-305. [Crossref] [PubMed]
348. Jumper J, Evans R, Pritzel A, et al. Highly accurate protein structure prediction with AlphaFold. Nature 2021;596:583-9. [Crossref] [PubMed]
349. Rehman AU, Zhao C, Wu Y, et al. Targeting SHP2 Cryptic Allosteric Sites for Effective Cancer Therapy. Int J Mol Sci 2024;25:6201. [Crossref] [PubMed]
350. Morstein J, Bowcut V, Fernando M, et al. Targeting Ras-, Rho-, and Rab-family GTPases via a conserved cryptic pocket. Cell 2024;187:6379-6392. [Crossref] [PubMed]
351. Liu Y, Zhang W, Jang H, et al. J Chem Inf Model. 2025;65:966-80. [Crossref] [PubMed]
352. Kim HN, Gasmi-Seabrook GMC, Uchida A, et al. Switch II Pocket Inhibitor Allosterically Freezes KRAS(G12D) Nucleotide-binding Site and Arrests the GTPase Cycle. J Mol Biol 2025;437:169162. [Crossref] [PubMed]
353. Nussinov R, Yavuz BR, Jang H. Allostery in Disease: Anticancer Drugs, Pockets, and the Tumor Heterogeneity Challenge. J Mol Biol 2025;437:169050. [Crossref] [PubMed]
354. Meller A, De Oliveira S, Davtyan A, et al. Discovery of a cryptic pocket in the AI-predicted structure of PPM1D phosphatase explains the binding site and potency of its allosteric inhibitors. Front Mol Biosci 2023;10:1171143. [Crossref] [PubMed]
355. Schoepfer J, Jahnke W, Berellini G, et al. Discovery of Asciminib (ABL001), an Allosteric Inhibitor of the Tyrosine Kinase Activity of BCR-ABL1. J Med Chem 2018;61:8120-35. [Crossref] [PubMed]
356. Hoch M, Huth F, Manley PW, et al. Clinical Pharmacology of Asciminib: A Review. Clin Pharmacokinet 2024;63:1513-28. [Crossref] [PubMed]
357. Réa D, Mauro MJ, Boquimpani C, et al. A phase 3, open-label, randomized study of asciminib, a STAMP inhibitor, vs bosutinib in CML after 2 or more prior TKIs. Blood 2021;138:2031-41. [Crossref] [PubMed]
358. Mauro MJ, Minami Y, Hochhaus A, et al. Asciminib remained superior vs bosutinib in late-line CML-CP after nearly 4 years of follow-up in ASCEMBL. Blood Adv 2025;9:4248-59. [Crossref] [PubMed]
359. Hochhaus A, Kim DW, Cortes JE, et al. Asciminib monotherapy in patients with chronic myeloid leukemia in chronic phase without BCR::ABL1(T315I) treated with at least 2 prior TKIs: Phase 1 final results. Leukemia 2025;39:1114-23. [Crossref] [PubMed]
360. Zhang Q, Chen Y, Ni D, et al. Targeting a cryptic allosteric site of SIRT6 with small-molecule inhibitors that inhibit the migration of pancreatic cancer cells. Acta Pharm Sin B 2022;12:876-89. [Crossref] [PubMed]
361. Kaufman M, Weissberg D, Bider D. Repair of recurrent inguinal hernia with Marlex mesh. Surg Gynecol Obstet 1985;160:505-6. [PubMed]
362. Robertus JD, Ladner JE, Finch JT, et al. Structure of yeast phenylalanine tRNA at 3 A resolution. Nature 1974;250:546-51. [Crossref] [PubMed]
363. Zamecnik PC, Stephenson ML. Inhibition of Rous sarcoma virus replication and cell transformation by a specific oligodeoxynucleotide. Proc Natl Acad Sci U S A 1978;75:280-4. [Crossref] [PubMed]
364. Fire A, Xu S, Montgomery MK, et al. Potent and specific genetic interference by double-stranded RNA in Caenorhabditis elegans. Nature 1998;391:806-11. [Crossref] [PubMed]
365. Kim Y. Drug Discovery Perspectives of Antisense Oligonucleotides. Biomol Ther (Seoul) 2023;31:241-52. [Crossref] [PubMed]
366. Jinek M, Chylinski K, Fonfara I, et al. A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science 2012;337:816-21. [Crossref] [PubMed]
367. Sparmann A, Vogel J. RNA-based medicine: from molecular mechanisms to therapy. EMBO J 2023;42:e114760. [Crossref] [PubMed]
368. Bartolucci D, Pession A, Hrelia P, et al. Precision Anti-Cancer Medicines by Oligonucleotide Therapeutics in Clinical Research Targeting Undruggable Proteins and Non-Coding RNAs. Pharmaceutics 2022;14:1453. [Crossref] [PubMed]
369. Wilson RC, Doudna JA. Molecular mechanisms of RNA interference. Annu Rev Biophys 2013;42:217-39. [Crossref] [PubMed]
370. Castanotto D, Rossi JJ. The promises and pitfalls of RNA-interference-based therapeutics. Nature 2009;457:426-33. [Crossref] [PubMed]
371. Friedrich M, Aigner A. Therapeutic siRNA: State-of-the-Art and Future Perspectives. BioDrugs 2022;36:549-71. [Crossref] [PubMed]
372. Davis ME, Zuckerman JE, Choi CH, et al. Evidence of RNAi in humans from systemically administered siRNA via targeted nanoparticles. Nature 2010;464:1067-70. [Crossref] [PubMed]
373. Ocker M, Neureiter D, Lueders M, et al. Variants of bcl-2 specific siRNA for silencing antiapoptotic bcl-2 in pancreatic cancer. Gut 2005;54:1298-308. [Crossref] [PubMed]
374. Okamoto K, Ocker M, Neureiter D, et al. J. Cell Mol Med 2007;11:349-61. [Crossref] [PubMed]
375. Sass G, Leukel P, Schmitz V, et al. Inhibition of heme oxygenase 1 expression by small interfering RNA decreases orthotopic tumor growth in livers of mice. Int J Cancer 2008;123:1269-77. [Crossref] [PubMed]
376. Adams D, Gonzalez-Duarte A, O'Riordan WD, et al. Patisiran, an RNAi Therapeutic, for Hereditary Transthyretin Amyloidosis. N Engl J Med 2018;379:11-21. [Crossref] [PubMed]
377. Kim YK. RNA therapy: rich history, various applications and unlimited future prospects. Exp Mol Med 2022;54:455-65. [Crossref] [PubMed]
378. Ghasemiyeh P, Mohammadi-Samani S. siRNA-based delivery systems: Technologies, carriers, applications, and approved products. Eur. J Pharmacol 2025;996:177441. [Crossref] [PubMed]
379. Hu B, Zhong L, Weng Y, et al. Therapeutic siRNA: state of the art. Signal Transduct Target Ther 2020;5:101. [Crossref] [PubMed]
380. Shukla S, Sumaria CS, Pradeepkumar PI. Exploring chemical modifications for siRNA therapeutics: a structural and functional outlook. ChemMedChem 2010;5:328-49. [Crossref] [PubMed]
381. Zhu Y, Zhu L, Wang X, et al. RNA-based therapeutics: an overview and prospectus. Cell Death Dis 2022;13:644. [Crossref] [PubMed]
382. Ali Zaidi SS, Fatima F, Ali Zaidi SA, et al. Engineering siRNA therapeutics: challenges and strategies. J Nanobiotechnology 2023;21:381. [Crossref] [PubMed]
383. Devi GR. siRNA-based approaches in cancer therapy. Cancer Gene Ther. 2006;13:819-29. [Crossref] [PubMed]
384. Vanhecke D, Janitz M. High-throughput gene silencing using cell arrays. Oncogene 2004;23:8353-8. [Crossref] [PubMed]
385. Zhang Q, Zeng SX, Lu H. Targeting p53-MDM2-MDMX loop for cancer therapy. Subcell Biochem 2014;85:281-319. [Crossref] [PubMed]
386. Jan SA, Debnath A, Singh RK, et al. Targeting Undruggable Proteins: The siRNA Revolution Beyond Small Molecules - Advances, Challenges, and Future Prospects in Therapeutic Innovation. Curr Gene Ther 2025; Epub ahead of print. [Crossref] [PubMed]
387. Zhang L, Yang N, Mohamed-Hadley A, et al. Vector-based RNAi, a novel tool for isoform-specific knock-down of VEGF and anti-angiogenesis gene therapy of cancer. Biochem Biophys Res Commun 2003;303:1169-78. [Crossref] [PubMed]
388. Mundlia P, Singh SP, Kaushal A, et al. RNA-targeting therapeutics: drugging the undruggable. Future Med Chem 2025;17:1213-5. [Crossref] [PubMed]
389. Stirchak EP, Summerton JE, Weller DD. Uncharged stereoregular nucleic acid analogs: 2. Morpholino nucleoside oligomers with carbamate internucleoside linkages. Nucleic Acids Res 1989;17:6129-41. [Crossref] [PubMed]
390. Nielsen PE, Egholm M, Berg RH, et al. Sequence-selective recognition of DNA by strand displacement with a thymine-substituted polyamide. Science 1991;254:1497-500. [Crossref] [PubMed]
391. Obika S, Nanbu D, Hari Y, et al. Synthesis of 2′-O,4′-C-methyleneuridine and -cytidine. Novel bicyclic nucleosides having a fixed C3′-endo sugar puckering. Tetrahedron Lett 1997;38:8735-8. [Crossref]
392. Bielinska A, Shivdasani RA, Zhang LQ, et al. Regulation of gene expression with double-stranded phosphorothioate oligonucleotides. Science 1990;250:997-1000. [Crossref] [PubMed]
393. Cerritelli SM, Crouch RJ. Ribonuclease H: the enzymes in eukaryotes. FEBS J 2009;276:1494-505. [Crossref] [PubMed]
394. Hofman CR, Corey DR. Targeting RNA with synthetic oligonucleotides: Clinical success invites new challenges. Cell Chem Biol 2024;31:125-38. [Crossref] [PubMed]
395. Kurreck J. Antisense technologies. Improvement through novel chemical modifications. Eur J Biochem 2003;270:1628-44. [Crossref] [PubMed]
396. Quemener AM, Bachelot L, Forestier A, et al. The powerful world of antisense oligonucleotides: From bench to bedside. Wiley Interdiscip Rev RNA 2020;11:e1594. [Crossref] [PubMed]
397. Goyenvalle A, Griffith G, Babbs A, et al. Functional correction in mouse models of muscular dystrophy using exon-skipping tricyclo-DNA oligomers. Nat Med 2015;21:270-5. [Crossref] [PubMed]
398. Gagliardi M, Ashizawa AT. The Challenges and Strategies of Antisense Oligonucleotide Drug Delivery. Biomedicines 2021;9:433. [Crossref] [PubMed]
399. Juliano RL. Addressing cancer signal transduction pathways with antisense and siRNA oligonucleotides. NAR Cancer 2020;2:zcaa025. [Crossref] [PubMed]
400. Hagedorn PH, Hansen BR, Koch T, et al. Managing the sequence-specificity of antisense oligonucleotides in drug discovery. Nucleic Acids Res 2017;45:2262-82. [Crossref] [PubMed]
401. Suvarna V, Singh V, Murahari M. Current overview on the clinical update of Bcl-2 anti-apoptotic inhibitors for cancer therapy. Eur J Pharmacol 2019;862:172655. [Crossref] [PubMed]
402. Vickers TA, Rahdar M, Prakash TP, et al. Kinetic and subcellular analysis of PS-ASO/protein interactions with P54nrb and RNase H1. Nucleic Acids Res 2019;47:10865-80. [Crossref] [PubMed]
403. Rankin R, Pontarollo R, Ioannou X, et al. CpG motif identification for veterinary and laboratory species demonstrates that sequence recognition is highly conserved. Antisense Nucleic Acid Drug Dev 2001;11:333-40. [Crossref] [PubMed]
404. Kozovska Z, Rajcaniova S, Munteanu P, et al. CRISPR: History and perspectives to the future. Biomed Pharmacother 2021;141:111917. [Crossref] [PubMed]
405. Beane JD, Lee G, Zheng Z, et al. Clinical Scale Zinc Finger Nuclease-mediated Gene Editing of PD-1 in Tumor Infiltrating Lymphocytes for the Treatment of Metastatic Melanoma. Mol Ther 2015;23:1380-90. [Crossref] [PubMed]
406. Kanbar K, El Darzi R, Jaalouk DE. Precision oncology revolution: CRISPR-Cas9 and PROTAC technologies unleashed. Front Genet 2024;15:1434002. [Crossref] [PubMed]
407. Sánchez-Rivera FJ, Jacks T. Applications of the CRISPR-Cas9 system in cancer biology. Nat Rev Cancer 2015;15:387-95. [Crossref] [PubMed]
408. Yao S, He Z, Chen C. CRISPR/Cas9-Mediated Genome Editing of Epigenetic Factors for Cancer Therapy. Hum Gene Ther 2015;26:463-71. [Crossref] [PubMed]
409. Malina A, Mills JR, Cencic R, et al. Repurposing CRISPR/Cas9 for in situ functional assays. Genes Dev 2013;27:2602-14. [Crossref] [PubMed]
410. Chen C, Liu Y, Rappaport AR, et al. MLL3 is a haploinsufficient 7q tumor suppressor in acute myeloid leukemia. Cancer Cell 2014;25:652-65. [Crossref] [PubMed]
411. Heckl D, Kowalczyk MS, Yudovich D, et al. Generation of mouse models of myeloid malignancy with combinatorial genetic lesions using CRISPR-Cas9 genome editing. Nat Biotechnol 2014;32:941-6. [Crossref] [PubMed]
412. Lee DH, Ahn H, Sim HI, et al. A CRISPR activation screen identifies MUC-21 as critical for resistance to NK and T cell-mediated cytotoxicity. J Exp Clin Cancer Res 2023;42:272. [Crossref] [PubMed]
413. Gilbert LA, Larson MH, Morsut L, et al. CRISPR-mediated modular RNA-guided regulation of transcription in eukaryotes. Cell 2013;154:442-51. [Crossref] [PubMed]
414. Kostyusheva A, Brezgin S, Bayurova E, et al. ATM and ATR Expression Potentiates HBV Replication and Contributes to Reactivation of HBV Infection upon DNA Damage. Viruses 2019;11:997. [Crossref] [PubMed]
415. Choudhury SR, Cui Y, Lubecka K, et al. CRISPR-dCas9 mediated TET1 targeting for selective DNA demethylation at BRCA1 promoter. Oncotarget 2016;7:46545-56. [Crossref] [PubMed]
416. Moses C, Nugent F, Waryah CB, et al. Activating PTEN Tumor Suppressor Expression with the CRISPR/dCas9 System. Mol Ther Nucleic Acids 2019;14:287-300. [Crossref] [PubMed]
417. Lawhorn IE, Ferreira JP, Wang CL. Evaluation of sgRNA target sites for CRISPR-mediated repression of TP53. PLoS One 2014;9:e113232. [Crossref] [PubMed]
418. Khalil A. Editorial: Precision oncology in the era of CRISPR-Cas9 technology. Front Genet 2024;15:1506627. [Crossref] [PubMed]
419. Sun D, Shi X, Li S, et al. CAR‑T cell therapy: A breakthrough in traditional cancer treatment strategies Mol Med Rep 2024;29:47. (Review). [Crossref] [PubMed]
420. Zheng H, Liu M, Shi S, et al. MAP4K4 and WT1 mediate SOX6-induced cellular senescence by synergistically activating the ATF2-TGFβ2-Smad2/3 signaling pathway in cervical cancer. Mol Oncol 2024;18:1327-46. [Crossref] [PubMed]
421. Shi J, Wang E, Milazzo JP, et al. Discovery of cancer drug targets by CRISPR-Cas9 screening of protein domains. Nat Biotechnol 2015;33:661-7. [Crossref] [PubMed]
422. Mehta A, Merkel OM. Immunogenicity of Cas9 Protein. J Pharm Sci 2020;109:62-7. [Crossref] [PubMed]
423. Sadybekov AV, Katritch V. Computational approaches streamlining drug discovery. Nature 2023;616:673-85. [Crossref] [PubMed]
424. Kim H, Kim E, Lee I, et al. Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches. Biotechnol Bioprocess Eng 2020;25:895-930. [Crossref] [PubMed]
425. Xie S, Zhu H, Huang N. AI-Designed Molecules in Drug Discovery, Structural Novelty Evaluation, and Implications. J Chem Inf Model 2025;65:8924-33. [Crossref] [PubMed]
426. Qiu X, Li H, Ver Steeg G, et al. Advances in AI for Protein Structure Prediction: Implications for Cancer Drug Discovery and Development. Biomolecules 2024;14:339. [Crossref] [PubMed]
427. Loeffler HH, He J, Tibo A, et al. Reinvent 4: Modern AI-driven generative molecule design. J Cheminform 2024;16:20. [Crossref] [PubMed]
428. You Y, Lai X, Pan Y, et al. Artificial intelligence in cancer target identification and drug discovery. Signal Transduct Target Ther 2022;7:156. [Crossref] [PubMed]
429. Vamathevan J, Clark D, Czodrowski P, et al. Applications of machine learning in drug discovery and development. Nat Rev Drug Discov 2019;18:463-77. [Crossref] [PubMed]
430. Wu K, Jiang H, Hicks DR, et al. Design of intrinsically disordered region binding proteins. Science 2025;389:eadr8063. [Crossref] [PubMed]
431. Rashid Bhat A, Ahmed S. Artificial intelligence (AI) in drug design and discovery: A comprehensive review. In Silico Res Biomed 2025;1:100049.
432. Lazo JS, Sharlow ER. Drugging Undruggable Molecular Cancer Targets. Annu Rev Pharmacol Toxicol 2016;56:23-40. [Crossref] [PubMed]
433. López-Cortés A, Cabrera-Andrade A, Echeverría-Garcés G, et al. Unraveling druggable cancer-driving proteins and targeted drugs using artificial intelligence and multi-omics analyses. Sci Rep 2024;14:19359. [Crossref] [PubMed]
434. Pathmanathan S, Grozavu I, Lyakisheva A, et al. Drugging the undruggable proteins in cancer: A systems biology approach. Curr Opin Chem Biol 2022;66:102079. [Crossref] [PubMed]
435. Jung H, Lee Y. Targeting the Undruggable: Recent Progress in PROTAC-Induced Transcription Factor Degradation. Cancers (Basel) 2025;17:1871. [Crossref] [PubMed]
436. Verdine GL, Walensky LD. The challenge of drugging undruggable targets in cancer: lessons learned from targeting BCL-2 family members. Clin Cancer Res 2007;13:7264-70. [Crossref] [PubMed]
437. Wu I, Pan D, Tang S, et al. Targeting the “undruggable” cancer driver genes: Ras, Myc, and p53. BIOCELL 2023;47:1459-72. [Crossref]
438. Zhang C, Liu Y, Li G, et al. Targeting the undruggables-the power of protein degraders. Sci Bull (Beijing) 2024;69:1776-97. [Crossref] [PubMed]
439. Huang L, Guo Z, Wang F, et al. KRAS mutation: from undruggable to druggable in cancer. Signal Transduct Target Ther 2021;6:386. [Crossref] [PubMed]