FDA looks to fund AI/ML-based postmarket surveillance research approach for complex generics
The FDA today issued a funding opportunity announcement seeking to fund work that explores how real-world data sources could couple with artificial intelligence (AI)/machine learning (ML) algorithms to compare clinical outcomes between complex generic and brand-name products. The work could represent early steps toward modernizing one part of FDA’s regulation of this complicated product class.
Regulatory background: complex generics
- Unlike new drug products, generic drugs do not require extensive clinical evidence to demonstrate safety or efficacy. Sponsors of these products may rely on evidence that the reference listed drug (RLD) is safe and effective to support market access. Under the terms of the 1984 Drug Price Competition and Patent Term Restoration Act (the Hatch-Waxman Act), a generic drug must only show that it is bioequivalent to the RLD. Specifically, companies are required to show that “the rate and extent of absorption of the drug do not show a significant difference from the rate and extent of absorption of the listed drug when administered at the same molar dose of the therapeutic ingredient under similar experimental conditions.”
- Complex generics are defined as products with a complex active ingredient, formulation, delivery route, dosage form or method of administration. Some examples include polymers or large molecules like peptides, liposome formulations, and drug-device combination products. Development of these products may be significantly more difficult due to the inherent complexity of the product. For example, an inhalation product (such as an Orally Inhaled and Nasal Drug Products) requiring a metered-dose inhaler (MDI) may also be required to administer a specific amount of a product in a specific particle size to ensure proper absorption of the product. Alternately, an extremely small product (such as a nanomedicine) may be difficult to characterize, making it challenging to demonstrate sameness.
- The generic drug pathway was not set up with complex products in mind. As the FDA has previously acknowledged: “Many complex drug products did not exist” when the Hatch-Waxman Amendments was first passed into law. “As drugs have increased in complexity in recent years, the scientific and regulatory roadmaps for drug development and approval may not be as well-established for more complex generics.” In effect, the system for generic products was set up without these types of products in mind – meaning that the FDA (and industry) has had to backfill a regulatory framework for these products without an established path forward.
Regulatory context: FDA’s work to foster complex generics through the product lifecycle
- FDA has allocated extensive research funding to support generic drug regulatory science. For example, the FDA funded an initiative with the Universities of Maryland and Michigan to establish the Center for Research on Complex Generics (CRCG) in 2020, which hosts research projects, trainings and workshops with both regulators and industry to work on complex generics topics. The agency also hosts regular workshops related to generic drug regulatory science topics, such as a May 2023 workshop to discuss technical issues, policy gaps and advice for developers of drug-device combination products.
- Since its creation in 2012, the generic drug user fee program (GDUFA) has focused on regulatory program improvements for complex generics. Notably, the first reauthorization (GDUFA II) renovated the complex generic drug development and approval process. This established the pre-ANDA program for complex generics, allowing developers to get assistance and engagement with regulators in advance of ANDA submissions to help clarify regulatory expectations and the path forward for their development and submissions. Most recently, GDUFA III included enhancements like promoting the development of more product specific guidances (PSGs) to foster development of complex products. AgencyIQ has compiled a list of all the Commitments under GDUFA III and when the FDA is expected to meet them, which you can read here.
- FDA has issued guidance in recent years to expand on its expectations and advice for complex generic development. For example, the agency finalized guidance in November 2020 that describes meeting types that sponsors of complex generics can take advantage of to facilitate the efficient review and approval of their products. Titled “Formal Meetings Between FDA and ANDA Applicants of Complex Products Under GDUFA”, the agency subsequently updated the document in October 2022 to reflect changes under GDUFA III [ Read AgencyIQ analysis here.].
- Following the approval of a complex generic, the agency has outlined considerations—and concerns—regarding post-market surveillance. FDA issued a broadly scoped draft guidance document intended for agency staff in November 2019 entitled, “Best Practices in Drug and Biological Product Postmarket Safety Surveillance.” The document describes that generic drug surveillance relies on the monitoring on adverse event (AE) data, with a specific focus on AEs related to differences between the RLD and generic products. “A potential absence of therapeutic equivalence may present as a perceived increase or decrease in the therapeutic effect when a patient is switched from an innovator to a generic drug, or from one generic drug to another,” says the guidance. Challenges to this surveillance include data quality issues when AE reports do not accurately distinguish between RLDs and specific generics, “the complexity of the generic drug,” and “differences in the user interface.”
Now, FDA is wants to build technical infrastructure to compare postmarket clinical outcomes of complex generics and RLD products.
- On January 15, FDA began accepting applications in response to a November 2023-issued funding opportunity titled, “Utilizing Real-World Data and Algorithmic Analyses to Assess Post-Market Clinical Outcomes in Patients Switching Amongst Therapeutically Equivalent Complex Generic Drug Products and Reference Listed Drugs.”
- The funding announcement explicitly states that FDA has concerns about limitations of its current surveillance system. As explained in the 2019 guidance, these include “potential biases in reporting and a small dataset relative to the number of dispensed prescriptions.” Per the announcement, these issues are exacerbated by a rising volume of complex generics on the market.
- To address this issue, FDA wants to fund research into a “modernized approach” that leverages “real-world data (RWD) combined with machine learning (ML) and/or artificial intelligence (AI).” The AI/ML model would supplement the current surveillance approach, explains the notice. Taken together, this “has great potential to identify and/or confirm post-market signals for complex generic drug products earlier, more accurately, and in an automated and repeatable fashion compared with current surveillance methods, which could lead to more timely and definitive regulatory action.”
- The project that FDA is seeking to fund should specifically compare clinical outcomes in patients who switch between a complex generic drug product(s) and RLD. The RWD source can include clinical or transactional databases (e.g., electronic health record, insurance claims) that allow the study of “relationships and patterns in prescription dispensation, clinical diagnoses, and healthcare encounters.” Some specific types of projects FDA is interested in supporting range from assessing the feasibility of these analyses, to exploratory analyses, to retrospective comparative studies looking at particular clinical outcomes.
- FDA wants the awardee to work closely with agency personnel and be open to data sharing. While “the final research strategy would be developed based upon the innovation and expertise of the award recipient,” the agency will have a heavy hand. FDA will provide feedback on the research strategy, as well as take part in study design, conduct, and analysis. This may not be the right fit for proprietary technology; the notice states that “the results of this research are intended to be disseminated and published, including making any tools (e.g., data analysis tools) that arise from this effort available to other researchers.” Later, the notice writes that it would be ideal to share the data itself, as well. In addition to sharing individual-level data and code with the FDA Office of Generic Drugs (OGD), “it is strongly preferred that FDA would explicitly be granted permission to make such data publicly available after a reasonable amount of time following the completion of the award period (e.g. 1 year after the end of the award period).”
- What is the budget and timeframe? In terms of logistics, the notice states that the FDA/CDER intends to commit up to $300,000 in FY 2024 to fund one (1) award. The notice lists budgets of $500,000 for a second and third year of the project.
- While the notice indicates that FDA is turning a critical eye toward clinical equivalence of some complex generics, there is a lot of technical and regulatory work to do before a new surveillance approach could be operationalized. In this way, this funding opportunity serves as a look into FDA’s long-term vision for the program.
- Complex generics continue to be a top priority of FDA leadership. At the annual Center of Excellence in Regulatory Science and Innovation (CERSI) Summit in early January 2024, CDER Director PATRIZIA CAVAZZONI emphasized the need for modernization. “Remember that the generic program was established in the 1980s, so this is a very, very old statute. And it is the single biggest problem that we have when it comes to complex generics. In this day and age where there is so much focus on the price of drugs, establishing a modern pathway for combination products would help on multiple fronts, including accelerating the development and approval of complex generics.” [ Read full AgencyIQ analysis of that event here.].
- What’s next? FDA began accepting applications on January 15, with letters of intent due February 15. The final application due date is March 31, 2024. FDA anticipates the project will commence in July 2024.
Featuring previous research by Alexander Gaffney.
To contact the author of this item, please email Amanda Conti ( [email protected]).
To contact the editor of this item, please email Alexander Gaffney ( [email protected])