Adagrasib in the treatment of KRAS^G12C-mutated non-small cell lung cancer: a cost-effectiveness analysis

Introduction

Lung cancer remains the most lethal cancer type worldwide, with non-small cell lung cancer (NSCLC) comprising approximately 85% of all diagnosed cases (1,2). In the United States (U.S.) alone, NSCLC is estimated to result in 226,650 new cases and 124,730 deaths annually (1). Among NSCLC patients, a critical molecular subset involves tumors harboring the KRAS^G12C mutation, which occurs in ~14% of lung adenocarcinomas and 0.5–4% of squamous cell NSCLC cases (3). This mutation significantly complicates treatment, as patients frequently develop resistance to both chemotherapy and targeted therapies, leading to poor clinical outcomes (4). Continuous KRAS^G12C suppression is therefore essential due to the presence of a feedback loop and the ~24-hour protein resynthesis half-life (5).

Adagrasib is an orally bioavailable small-molecule therapeutic agent that selectively targets the KRAS^G12C mutation through irreversible covalent binding to the mutant cysteine residue. This interaction stabilizes the protein in an inactive guanosine diphosphate (GDP)-bound conformation, thereby effectively abrogating constitutive signaling pathways while preserving the normal function of wild-type KRAS (6).

Cohort A from the phase II KRYSTAL-1 clinical trial was used to evaluate adagrasib in patients with advanced NSCLC harboring the KRAS^G12C mutation, all of whom had progressed following prior platinum-based chemotherapy and/or anti-programmed death-1 or programmed death-ligand 1 (anti-PD-1/PD-L1) immunotherapy. Clinical outcomes revealed an objective response rate (ORR) of 42.9%, with a median progression-free survival (mPFS) of 6.5 months and a median overall survival (mOS) of 12.6 months (7). Based on these findings, the U.S. Food and Drug Administration granted accelerated approval to adagrasib in December 2022 for adult patients with locally advanced or metastatic NSCLC harboring the KRAS^G12C mutation, following disease progression after one or more prior lines of systemic therapy (8).

The escalating costs of oncology care highlight the need to critically assess the value proposition of emerging therapies. Adagrasib, while demonstrating clinical efficacy in heavily pretreated patients, carries a substantial monthly cost of $23,596 (U.S. dollar). This high pricing necessitates a cost-effectiveness evaluation to inform clinical and reimbursement decision-making, as this analysis is critical to demonstrate that the drug’s clinical benefits outweigh its financial burden in resource-constrained healthcare systems.

Here, we evaluated the cost-effectiveness of adagrasib versus chemotherapy in adult patients with KRAS^G12C-mutated NSCLC who had progressed after one or more lines of prior systemic therapy, from the perspective of U.S. payers. Key cost parameters comprised drug acquisition costs, administration costs, and costs associated with adverse event management, alongside utility weights obtained from health-related quality of life (HRQoL) measures. Sensitivity and scenario analyses would evaluate the independent impacts of adagrasib price reductions and dose adjustment strategies on cost-effectiveness. We present this article in accordance with the CHEERS reporting checklist (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-742/rc).

Methods

Model construction, participants and interventions

A partitioned survival model was constructed using TreeAge Pro 2022 software to analyze the economic and clinical impacts of adagrasib. Subsequent statistical analyses were conducted with R 4.2.1 software. This model structure incorporated three mutually exclusive health states: progression-free survival (PFS), progressive disease, and death (Figure S1). To align with chemotherapy administration cycles, a 3-week cycle length was defined. Patient demographics—specifically, a median age of 64 years and prior treatment history—were aligned with those from the KRYSTAL-1 trial and the docetaxel arm in TROPION-Lung01 trials (9). A 20-year time horizon was adopted to capture long-term outcomes (10). Primary endpoints included total costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). All calculations applied a half-cycle correction and a 3% annual discount rate to both costs and health outcomes, adjusting for time preference.

We extracted core clinical data from Cohort A of the KRYSTAL-1 study (evaluating oral adagrasib at 600 mg twice daily) and from the control arm (docetaxel group) of the TROPION-Lung01 trial (administered intravenously at 75 mg/m² q3w for up to 6 cycles). Hypothetical models of subsequent treatment strategies were constructed based on National Comprehensive Cancer Network (NCCN) guidelines: after disease progression, 50% of patients in the adagrasib group were assumed to receive docetaxel treatment as part of additional anticancer therapies, while the remaining 50% transitioned to best supportive care; conversely, all patients in the docetaxel group were projected to receive best supportive care following disease progression (Figure 1).

Figure 1 The treatment sequences used in the partitioned survival models were as follows: (A) the treatment sequence for the adagrasib group; (B) the treatment sequence for the chemotherapy group.

Costs estimates

Cost assessments were conducted through the perspective of U.S. third-party public healthcare payers, with a focus on payer-relevant resource consumption and direct medical expenditures. We extracted drug pricing data from Drugs.com (11). Docetaxel dosage was determined based on the average body surface area of 1.82 m² (12). To contextualize treatment costs, expenses related to medication administration, best supportive care, end of life palliative care, and disease management (encompassing hospitalization, computed tomography (CT), laboratory examinations, and adverse event mitigation costs) were sourced from previously published databases (Table 1) (12-15,19-22). Based on the KRYSTAL-1 study and clinical processes, CT scans, lab tests, and physician visits were systematically documented. For adagrasib, costs were tracked every 6 weeks in the first year, then every 12 weeks. For docetaxel, costs were recorded during each of the first 6 cycles, followed by every 6 weeks in the first year and every 12 weeks thereafter.

Following disease progression, the costs for treatment administration, physician consultations, and laboratory monitoring were calculated during each cycle for patients receiving chemotherapy and best supportive care. CT imaging costs were recorded every other treatment cycle. To ensure cost data comparability and adjusted to 2025 U.S. dollars (U.S. $), inflation adjustments were systematically applied using the American consumer price index (CPI) methodology, with calculations performed using Tom’s inflation calculator (23). Economic evaluation utilized a willingness-to-pay (WTP) threshold of $150,000 per QALY as the cost-effectiveness benchmark (24-26).

Survival and progression transition estimates

Transition probabilities were derived from Kaplan-Meier curves of PFS and overall survival (OS) in the KRYSTAL-1 and TROPION-Lung01 trials using GetData Graph Digitizer v2.22 software. The extraction process involved: (I) calibrating the digitizer’s coordinate system to match the x-axis (time) and y-axis (survival probability) scales of the original Kaplan-Meier plots; and (II) systematically recording temporal data points along each curve to enable probability calculations. Simulated patient data were generated via the algorithm developed by Hoyle et al. (27). To identify the optimal survival model, curve data were fitted to parametric distributions—including log-logistic, exponential, log-normal, generalized gamma, gamma, Gompertz, and Weibull—using the Akaike and Bayesian information criteria (Figure S2 and Table S1). Using Microsoft Excel, we computed time-dependent transition probabilities for both patient groups by integrating survival data from the trials. Age-specific background mortality rates, estimated from U.S. life tables, served as the lower bound constraint for modeled survival probabilities to ensure the biological plausibility of long-term extrapolations (Table S2) (28).

Health state utilities

To quantify the impacts of HRQoL across different disease states, we applied utility values of 0.673, 0.473, and 0 to progression-free disease, disease progression, and death, respectively. These values are based on NSCLC-specific HRQoL assessments that have been validated in published literature (18). The assessments are derived from the socio-demographic questionnaire and the EuroQol 5-Dimensions (EQ-5D) completed by NSCLC patients to rate their own current health status. This rating takes into account the general public’s preferences and considers the overall impact of the disease progression state, including physical, emotional, social, and functional aspects. In accordance with standard health economic modeling protocols, our initial model cycle focuses on quantifying the QALY loss attributable to severe adverse events (SAEs; grade ≥3) that affect at least 5% of patients (Table 1) (13,15-17). As SAEs often necessitate hospitalization, treatment modification, or prolonged recovery, thereby significantly impacting both quality of life and healthcare resource utilization. In contrast, minor adverse events (AEs) (grade 1–2) were excluded from baseline QALY calculations due to their transient nature and minimal impact on HRQoL or treatment costs (18,29-31).

Univariate and probabilistic sensitivity analyses

In the univariate sensitivity analysis, we systematically varied clinical parameters by ±20% from their baseline values to account for plausible deviations. The corresponding variations in outcomes were visualized in the tornado diagram. For probabilistic sensitivity analysis, we conducted 1,000 Monte Carlo simulations. In this approach, all preset parameters were randomly and simultaneously varied based on predefined distributions: utility values followed beta distributions, while proportions and cost parameters followed gamma distributions (Table 1).

Statistical analysis

A partitioned survival model was constructed using TreeAge Pro 2022 to assess costs and effectiveness, incorporating a 3% discount rate for both utilities and costs. R software was used for the statistical analyses.

Results

Model validation

Our model-simulated clinical outcomes demonstrated consistency with findings from corresponding clinical trials for both PFS and OS (Figure S1). Specifically, the mPFS and mOS values generated by the model were highly congruent with those documented in trial data (Table S3). This congruence lays the foundation for the model’s predictive precision.

Base case results

In this partitioned survival model analysis, the adagrasib group incurred cumulative costs of $336,707 compared to $29,932 for the chemotherapy group, while providing 1.14762 QALYs compared to 0.81169 QALYs, respectively. Despite yielding an additional 0.33593 QALYs compared to chemotherapy, the intervention incurs $306,775 higher costs, resulting in an ICER of $913,211 per QALY gained—significantly exceeding the predefined WTP threshold of $150,000/QALY, suggesting potential lack of cost-effectiveness for adagrasib at current pricing relative to chemotherapy (Table 2).

Scenario analysis of different doses for adagrasib

For adagrasib, three dosage reduction regimens are typically implemented (600 mg twice daily, 400 mg twice daily, and 200 mg twice daily) due to adverse reactions or economic constraints. As a result, the projected expenses for the adagrasib group gradually decreased, ranging from $336,707 to $130,333. The ICER also varied, ranging from $913,211 per QALY to $298,875 per QALY. Even at the maximum dosage reduction of 200 mg twice daily, the ICER consistently exceeded the predefined WTP threshold of $150,000 per QALY (Table 3).

Sensitivity analysis

The tornado diagram highlights the significant sensitivity of the ICER to specific parameters, notably the utility values of PFS, the cost of adagrasib, the discount rate, and the utility of progressed disease (Figure 2). Variations in these key factors substantially alter the ICER, whereas other model inputs have negligible effects on the outcome. Even with all parameter variations within predefined bounds, the calculated ICER range consistently exceeds the WTP threshold, which underscores the robustness of the model’s conclusions: the cost-effectiveness of adagrasib remains unfavorable at the WTP threshold.

Figure 2 Tornado diagram from univariate sensitivity analyses. This diagram illustrates the key parameters influencing the incremental cost-effectiveness ratio. The width of each bar represents the range of potential incremental cost-effectiveness ratio values resulting from changes to that parameter; red indicates high values, and blue indicates low values. Costs are in U.S. dollars. PD, progressive disease; PFS, progression-free survival; QALY, quality-adjusted life-year.

The Monte Carlo simulation revealed that all scatter points had clustered in the first quadrant of the cost-effectiveness plane, with all simulated outcomes falling above the WTP threshold line. This indicates that the outcomes are associated with both increased costs and QALYs gained, suggesting that adagrasib may yield higher health benefits while increasing total costs. Additionally, it indicates that adagrasib’s ICER consistently exceeded the $150,000/QALY threshold across all simulations (Figure 3).

Figure 3 Incremental cost-effectiveness scatter plot diagram for adagrasib group versus the chemotherapy group. A Monte Carlo simulation with 1,000 samples revealed that all points lie within the northeast quadrant and above the willingness-to-pay line, suggesting the potential of adagrasib to achieve more QALYs, but at a higher cost. QALYs, quality-adjusted life-years; WTP, willingness-to-pay.

Consistent with these findings, the cost-effectiveness acceptability curve demonstrates that adagrasib would not be considered cost-effective for patients with a predetermined WTP threshold. Adagrasib demonstrates better cost-effectiveness than chemotherapy only if the WTP threshold exceeds $905,000 (Figure 4).

Figure 4 Cost-effectiveness acceptability curve from probabilistic sensitivity analyses. At a willingness-to-pay threshold above 905,000 U.S. $ per QALY, adagrasib is considered cost-effective compared to chemotherapy. QALY, quality-adjusted life-year.

Discussion

As far as we know, this is the first study to analyze the cost-effectiveness of adagrasib in KRAS^G12C-mutated NSCLC patients. Based on the model results, the base-case analysis revealed that, when compared to chemotherapy, adagrasib offers improved health outcomes while leading to higher costs in patients with previously treated KRAS^G12C-mutated NSCLC. Probabilistic sensitivity analysis further revealed that adagrasib is unlikely to be considered a cost-effective alternative, as its ICER consistently exceeds the predefined WTP threshold for chemotherapy.

Previous studies have analyzed the cost-effectiveness of sotorasib, another KRAS^G12C inhibitor. In the U.S., the ICER of sotorasib compared to docetaxel was calculated at $1,501,852 per QALY (32). Notably, comparable findings emerged for adagrasib in our study, with an ICER of $913,211 per QALY—both values surpass conventional WTP thresholds. These findings collectively highlight the need for pricing strategies that align more closely with cost-effectiveness benchmarks in contemporary healthcare systems.

The health utility value assigned to PFS is the key parameter influencing outcomes in our model. To ensure methodological rigor, baseline utilities were derived from estimates reported in previously published NSCLC quality-of-life studies. A sensitivity analysis was conducted by varying this parameter across its plausible range. After testing both upper and lower bounds of reported utility values, conclusions remained robust. This finding underscores the robustness of our results, demonstrating that variations in PFS utility alone do not materially alter the overall cost-effectiveness assessment.

Our model’s sensitivity analyses revealed significant sensitivity to adagrasib-related expenses. Despite varying the parameter within a specific range ($104.87–157.31 per 200 mg), even at the lower bound of this interval (equivalent to a 20% discount), ICERs still exceed $150,000/QALY, far surpassing conventional cost-effectiveness thresholds. This is partly because adagrasib’s clinical efficacy prolongs treatment duration, thereby inflating cumulative costs. While the drug’s therapeutic value is undeniable, its pricing structure may create an unsustainable economic burden, making price reduction not just beneficial but imperative to aligning clinical benefits with financial viability.

An alternative approach involves optimizing drug regimens by adjusting dosages and schedules to reduce costs while striving to maintain clinical outcomes as much as possible. This requires evaluating whether calibrated modifications—such as patient-tailored dosing algorithms informed by real-world data—can preserve these outcomes. In our scenario analysis of adagrasib, three dosage reduction regimens were simulated to explore cost-efficacy trade-offs. Despite minimizing the dosage, the regimens demonstrated a lack of cost-effectiveness, underscoring the need for balanced trade-offs between clinical benefits and affordability.

Despite adagrasib’s approval as a breakthrough for patients previously treated for KRAS^G12C-mutated NSCLC, third-party payers face substantial affordability and sustainability concerns. Budgetary constraints and opportunity costs force payers to prioritize lower-cost alternatives, even when clinical needs remain unmet. Evidence demonstrates that economic toxicity—manifesting as treatment discontinuation, delayed treatment, or non-adherence—directly compromises patient outcomes, particularly among economically disadvantaged groups (33). These disparities highlight a moral obligation to decouple socioeconomic status from access to essential therapies, ensuring equitable care (34). Similarly, novel cancer therapies such as adagrasib frequently lack cost-effectiveness advantages during clinical application due to their high drug prices (35-39). Meanwhile, pricing new drugs requires balancing ethical and economic priorities. One year of life can also be considered invaluable and precious, its worth far exceeding a mere annual cost of several hundred thousand dollars—a sum that, in ethical terms, cannot fully equate to the intrinsic value of extending human existence. Another critical point is that new drugs often command premium pricing during patent exclusivity periods, as pharmaceutical companies must recoup substantial research and development investments within a constrained timeframe to sustain innovation. This pricing strategy, while perceived by some as excessive, reflects the economic reality of balancing short-term cost recovery with long-term medical advancement. Ultimately, the path forward demands a collaborative reimagining of drug pricing frameworks—one that harmonizes patient-centered ethics, equitable access, and the sustainability of medical innovation, ensuring no life is priced beyond reach.

There were certain limitations in our study. First, a significant proportion of the evidence base originates from randomized controlled trials, which inherently possess methodological characteristics distinguishing them from real-world datasets. In clinical practice, patients with multiple chronic conditions face complex health challenges, as the interplay between cancer treatments and comorbidity management significantly impacts treatment cost-effectiveness. Consequently, decision-makers must account for discrepancies between trial populations and actual patient populations, as well as variations in care delivery systems. Second, our analytical framework omitted localized radiation therapy, surgical procedures, and alternative intervention strategies, which may attenuate the model’s external validity when projecting outcomes for patients transitioning into this clinical state. Third, since the survival data for adagrasib currently only come from the single-arm phase II KRYSTAL-1 trial, our study selected docetaxel arm from the TROPION-Lung01 trial as the comparator group to facilitate economic comparisons with conventional chemotherapy. While this approach may introduce selection bias, the trial populations are relatively comparable. Both cohorts consist of previously treated NSCLC patients with a median age of 64 years, and over 73% of them exhibit adenocarcinoma histology. The obvious increase in PFS, coupled with the complexity regarding OS, underscores the importance of eagerly anticipating the phase III double-arm head-to-head controlled study. This study will ultimately confirm the extent of the benefit in OS when adagrasib is directly compared with conventional treatments. Subsequently, direct cost-effectiveness comparisons can be conducted. Despite these limitations, this analysis offers the first valuable insights into the cost-effectiveness of adagrasib for previously treated patients with KRAS^G12C-mutated NSCLC. The findings underscore the necessity of comprehensive evaluation of both clinical outcomes and economic implications when formulating treatment decisions. Further research could address the aforementioned limitations to enhance the robustness of these conclusions.

Conclusions

In this model, adagrasib was not considered to be cost-effective compared to chemotherapy for previously treated KRAS^G12C-mutated NSCLC patients from the perspective of a U.S. payer. Amid rising cancer care costs and growing demand for effective therapies, evaluating new treatments’ cost-effectiveness is critical. Policymakers must weigh upfront treatment costs against long-term impacts on patient outcomes and healthcare system sustainability.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the CHEERS reporting checklist. Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-742/rc

Peer Review File: Available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-742/prf

Funding: This work was supported by the National Natural Science Foundation of China (NSFC) (No. 72474153), the Tianjin Key Medical Discipline Construction Project (No. TJYXZDXK-3-003A) and the Key Project of Science & Technology Development Fund of Tianjin Education Commission for Higher Education, China (No. 2022ZD064).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tcr.amegroups.com/article/view/10.21037/tcr-2025-742/coif). The 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/.

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