Landscape Analysis of Generic Availability for Oncologic Drugs

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.

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.

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.

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.