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Landscape Analysis of Generic Availability for Oncologic Drugs

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Abstract

Background

Improving generic drug development in oncology is a key long-term goal in providing safe, effective, and affordable care to patients with a diagnosis of cancer in the United States. There are multiple drug and non-drug related variables that may influence generic drug development. To illustrate pertinent associations relevant to generic drug competition in oncology, our study assessed variables that have potentially led to difference in generic competition as compared to drug products in other therapeutic areas, i.e., cardiovascular disease in this case.

Methods

Using a combination of FDA and publicly available data, we categorized individual drug approvals from 1950 to 2021 with either an oncology or cardiovascular indication. Descriptive statistics highlighted the timeline of approval as stratified by indications. Machine learning methodology was used to assess variables associated with abbreviated new drug application (ANDA) availabilities (i.e., generic drug availabilities). Kaplan–Meier analysis with log-rank test compared the difference in the time to approval of first ANDA among products that were off-patent at the time of analysis. A multivariable Cox proportional hazards model with forward selection was used to identify variables (e.g., regulatory recommendation issued, dosage form) that were associated with ANDA availability among products that were off-patent.

Results

434 separate reference listed drugs (RLDs) with varying strengths were identified, 212 (49%) for oncology and 222 (51%) for cardiovascular indications. Compared with cardiovascular products, a greater proportion of RLDs with an oncology indication were approved after 2000 (61% vs. 34%). Also, a smaller proportion of oncologic products had generics (49% vs. 80%). Machine learning methodology revealed RLD age, patent status, product complexity, sales/prescriptions, and regulatory recommendations as variables that were associated with generic availability. Among products off-patent at the time of analysis, the median time from RLD approval to the first ANDA approval was longer for oncologic products compared to cardiovascular products (15.4 years (95% CI 13.8, 17.9) versus 12.3 years (95% CI 10.7, 13.5), p = 0.008). Cox regression analyses identified the variables of product dosage form and regulatory recommendation of requiring patient enrollment for bioequivalence (BE) establishment as being associated with reduced likelihood of ANDA approval for oncologic drugs.

Conclusion

Oncology indications were found to have a longer time from RLD approval to first ANDA approval compared with cardiovascular drugs. Our work has identified variables that may influence time to ANDA availability, with the requirement of patient enrollment for BE assessment as one important opportunity for future stakeholder engagement and regulatory considerations.

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References

  1. Darrow JJ, Avorn J, Kesselheim AS. FDA approval and regulation of pharmaceuticals, 1983–2018. JAMA. 2020;323(2):164.

    Article  PubMed  Google Scholar 

  2. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. A Cancer J Clin. 2020;70(1):7–30.

    Article  Google Scholar 

  3. Park J, Look KA. Health care expenditure burden of cancer care in the United States. J Health Care Organ Provis Financing. 2019;56:004695801988069.

    Google Scholar 

  4. Kesselheim AS, Avorn J, Sarpatwari A. The high cost of prescription drugs in the united states: origins and prospects for reform. JAMA. 2016;316(8):858–71.

    Article  PubMed  Google Scholar 

  5. Gupta R, et al. Generic drug approvals since the 1984 Hatch-Waxman act. JAMA Intern Med. 2016;176(9):1391.

    Article  PubMed  Google Scholar 

  6. Lee C-Y, et al. Forces influencing generic drug development in the United States: a narrative review. J Pharm Policy Practice. 2016. https://doi.org/10.1186/s40545-016-0079-1.

    Article  Google Scholar 

  7. Abbreviated New Drug Application (ANDA). 2021. Available from: https://www.fda.gov/drugs/types-applications/abbreviated-new-drug-application-anda.

  8. Dusetzina SB, et al. Medicare part D plans rarely cover brand-name drugs when generics are available. Health Aff (Millwood). 2020;39(8):1326–33.

    Article  PubMed  Google Scholar 

  9. Robinson JC, Jarrion Q. Competition from biosimilars drives price reductions for biologics in the French single-payer health system. Health Aff (Millwood). 2021;40(8):1190–7.

    Article  PubMed  Google Scholar 

  10. Dusetzina SB, et al. Cost sharing and adherence to tyrosine kinase inhibitors for patients with chronic Myeloid Leukemia. J Clin Oncol. 2014;32(4):306–11.

    Article  PubMed  Google Scholar 

  11. FDA. Orange Book: Approved Drug Products with Therapeutic Equivalence Evaluations. 2020. Available from: https://www.accessdata.fda.gov/scripts/cder/ob/index.cfm.

  12. FDA. Product-Specific Guidances for Generic Drug Development. 2021. Available from: https://www.accessdata.fda.gov/scripts/cder/psg/index.cfm.

  13. Database, I.: Available from: https://www.iqvia.com/ (2021).

  14. Letter, G.D.U.F.A.G.I.C.; Available from: https://www.fda.gov/media/101052/download.

  15. R Core Team R: A language and environment for statistical computing. R Foundation for Statistical Computing, V., Austria. Available from: https://cran.r-project.org/web/packages/partykit/index.html (2022).

  16. Hothorn T, Hornik K, Zeileis A. Unbiased recursive partitioning: a conditional inference framework. J Comput Graph Stat. 2006;15(3):651–74.

    Article  Google Scholar 

  17. FDA Drug Competition Action Plan. 2022. Available from: https://www.fda.gov/drugs/guidance-compliance-regulatory-information/fda-drug-competition-action-plan.

  18. Desai S, McWilliams JM. Consequences of the 340B drug pricing program. N Engl J Med. 2018;378(6):539–48.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Miller KL, et al. Drugs and biologics receiving FDA orphan drug designation: an analysis of the most frequently designated products and their repositioning strategies. Expert Opin Orphan Drugs. 2022;9(11–12):265–72.

    PubMed  PubMed Central  Google Scholar 

  20. M9 Biopharmaceutics Classification System Based Biowaivers. 2021. Available from: https://www.fda.gov/media/148472/download.

  21. Implementation of the Biologics Price Competition and Innovation Act of 2009. Available from: https://www.fda.gov/drugs/guidance-compliance-regulatory-information/implementation-biologics-price-competition-and-innovation-act-2009.

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Authors and Affiliations

  1. Division of Oncology 3, Office of Oncologic Diseases, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA

    Vaibhav Kumar

  2. Division of Quantitative Methods and Modeling, Office of Research and Standards, Office of Generic Drugs, Center for Drug Evaluation and Research, U.S. Food and Drug Administration, Silver Spring, MD, USA

    Fenggong Wang, Meng Hu & Liang Zhao

  3. Oncology Center of Excellence, Office of the Commissioner, U.S. Food and Drug Administration, Silver Spring, MD, USA

    Paul Kluetz

Authors
  1. Vaibhav Kumar
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  2. Fenggong Wang
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  3. Meng Hu
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  4. Paul Kluetz
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  5. Liang Zhao
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Contributions

VK: Substantial contribution to concept and design, drafting manuscript, final approval, and agreement for accountability for all aspects of work. FW: Substantial contribution to concept, design, analysis, drafting and revision of manuscript, final approval, and agreement for agreement for accountability for all aspects of work. MH: Substantial contribution to concept and design, manuscript revision, final approval, and agreement for accountability for all aspects of work. PK: Substantial contribution to concept and design, manuscript revision, final approval, and agreement for accountability for all aspects of work. LZ: Substantial contribution to concept and design, manuscript revision, final approval, and agreement for accountability for all aspects of work.

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Correspondence to Liang Zhao.

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Kumar, V., Wang, F., Hu, M. et al. Landscape Analysis of Generic Availability for Oncologic Drugs. Ther Innov Regul Sci 57, 1279–1286 (2023). https://doi.org/10.1007/s43441-023-00562-w

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  • DOI: https://doi.org/10.1007/s43441-023-00562-w

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