FDA Wants Bioinformatic Approach to Evaluating Generic Drugs
The FDA is working on a new bioinformatic approach to evaluating the sameness of a generic drug’s active ingredient compared with that of the reference product.
Bioinformatics combines computer science, statistics, mathematics and engineering to develop software that can interpret biological data. The FDA is working with the Massachusetts Institute of Technology and the University of Kansas on developing this approach, Sau Lee, acting associate director for research policy and implementation in the FDA’s Office of Pharmaceutical Science, tells DID.
The idea is to provide regulators with greater confidence in their data and speed FDA approval of new generic drugs, say Lee, who spoke at the FDA’s Science Forum in Silver Spring, Md. He was unable to provide more details on how a bioinformatics approach might work in practical terms, as the project is still in the proof-of-concept stage.
According to Lee, the agency has traditionally used a number of criteria to determine that a generic drug is essentially the same as its branded predecessor. The method involves breaking drugs down into smaller chemical chains and looking at their in vitro physicochemical properties and pharmacodynamics, as well as evaluating the sameness of starting materials.
The FDA used these methods on generic versions of well-known drugs like Teva's multiple sclerosis therapy Copaxone (glatiramer acetate), for which the first generic was approved earlier this year, and Sanofi's blood-thinner Lovenox (enoxaparin).
The problem, Lee says, is that these methods are cumbersome. It took the FDA eight years to approve the first generic version of Lovenox, which came out in 2010. — Lena Freund