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This cross-sectional study examines trends in costs of brand-name medications for treatment of non–small cell lung cancer and within-class price differences between these medications in the US from 2015 to 2020.

PURPOSE: Precision oncology promises improved outcomes but the cost-effectiveness and accessibility of targeted therapies is debatable. We report price change patterns from 2015 to 2019 for several oral anticancer medications for common solid tumor malignancies. METHODS: We collected provider utilization and payment data from the public Medicare Part D database and extracted drug price information for commonly prescribed targeted oral anticancer agents for lung, breast, and prostate cancer. We then calculated median Pearson correlation coefficient values for various drugs (containing more than two data points) within each therapeutic class. We also calculated compound annual growth rates (CAGRs) for medication costs within each class and compared them with the consumer price index (CPI). RESULTS: Our study included six epidermal growth factor receptor inhibitors (EGFRi; one generic), five anaplastic lymphoma kinase inhibitors (ALKi), two B-Raf inhibitors (BRAFi), three hormonal agents (one generic), three cyclin-dependent kinases 4 and 6 inhibitors (CDK4/6i), two poly-ADP-ribose inhibitors (PARPi), and seven antiandrogen agents (two generic). The median (range) Pearson correlation coefficient values for cost of drugs within each therapeutic class were 0.967 (0.915-0.978) for EGFRi, 0.981 (0.966-0.989) for ALKi, 0.996 for BRAFi, 0.994 (0.992-0.999) for CDK4/6i, 0.855 for PARPi, and 0.442 (–0.522 to 0.962) for antiandrogens. Therapies with two or fewer data points (generic erlotinib, dacomitinib, abiraterone, apalutamide, and darolutamide) were excluded. The median CAGRs in costs over the 5-year period were 4.56% (EGFRi), 6.40% (ALKi), 2.58% (BRAFi), 5.48% (hormonal agents), 5.21% (CDK4/6i), 27.29% (PARPi), and 34.8% (antiandrogens). The CPI over 5 years was 2.26%/year, and the average inflation rate was 1.90%/year. CONCLUSION: The median CAGR in costs for modern oral precision-driven cancer therapeutic classes mostly outpaced CPI and the average inflation. Increase in cost within the same class should be weighed against incremental clinical benefit for the patients to ensure that rising costs do not limit access to targeted therapies.

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7 Background: Despite the promise of precision oncology, the cost-effectiveness of targeted treatments is debated. Until now, the increase in price of oral targeted anti-cancer treatments used in common malignancies has not been evaluated. Here, we report patterns in price changes from 2015-2019 for multiple oral anti-cancer medications for common solid tumor malignancies. Methods: We utilized the publicly available Medicare Part D provider utilization and payment database from 2015 to 2019. We extracted drug prices (using generic names) for commonly used targeted oral anticancer agents for lung, breast, and prostate cancer. The primary outcome was the correlation of average change in Medicare spending per dosage unit among the multiple brand-name medications within each class available. We additionally calculated compound annual growth rates (CAGRs) [i.e. mean annual growth rates over a specified period of time] for medication costs within each class, and compared it with the consumer price index (a measure of the average change over time in the prices of consumer items). Results: The study included 6 EGFR inhibitors (1 generic), 5 ALK inhibitors, 2 BRAF inhibitors, 3 hormonal agents (1 generic), 3 CDK4/6 inhibitors, 2 PARP inhibitors, and 7 anti-androgen agents (2 generic). The median (range) Pearson correlation coefficient values for drugs within each class were 0.967 (0.915-0.978) for EGFRi, 0.981 (0.966-0.989) for ALKi, 0.996 for BRAFi, 0.994 (0.992-0.999) for CDK4/6i, 0.855 for PARPi, and 0.442 (-0.522-0.962) for anti-androgens. Except for anti-androgens, all other drug classes showed strong linear association in price increase between two drugs within the same-class. A coefficient could not be calculated for therapies with 2 or fewer data points ( i.e., generic erlotinib, dacomitinib, generic abiraterone, apalutamide, and darolutamide). There was no significant correlation between expenditure for anti-estrogen agents. The median (range) CAGRs in costs over this 5-year period were: were 4.56% for EGFRi, 6.40% for ALKi, 2.58% for BRAFi, 5.48% for hormonal agents, 5.21% for CDK4/6i, 27.29% for PARPi, and 34.8% for anti-androgen agents. Conclusions: The median CAGR in costs for modern oral precision driven cancer therapeutic classes mostly outpaced CPI (2.26%/year), and the average inflation rate (1.90%/year). Increase in cost within the same class should be weighed against incremental clinical benefit for the patients. For most classes despite there being multiple agents, the rise in drug expenditures correlated closely, calling into question the true value of within class competition. There is an urgent need for drug pricing reform given the average expenditure of Medicare part D, and ultimately out of pocket costs for our patients with cancer continues to trend upwards. Increased advocacy efforts are needed to ensure precision therapeutics remains an attainable and sustainable goal.