Modeling can help drug developers ace a clinical trial, and the deluge of biological data at our fingertips today points toward growing use of predictive tools such as systems biology models. Now the FDA wants to take a closer look at the utility and validity of predictive modeling as part of its broader review of science and research needs.
The FDA's Center for Drug Evaluation and Research (CDER) issued a report highlighting this and other needs last week to "guide CDER's strategic planning of internal research initiatives and contributions to the development of agency regulatory science efforts." To best use them in regulatory decision-making, the CDER called for evaluation of quantitative structure-activity models, pharmacometric models (such as physiologically based pharmacokinetic models) and systems biology models.
"Given the high societal and economic cost of late stage drug failures because of efficacy or safety concerns, it is important to thoroughly assess the added value of predictive modeling to regulatory decision making during drug development," the report says. "More human data and trained modelers are needed to increase the strength of the predictive models."
The agency has begun a 60-day review and public comment period on the report, which talks about 7 main science and research needs. Other priorities in the report that might be of interest to FierceBiotech IT readers include the evaluation of electronic patient information for use in drug safety monitoring as well as enhancing clinical trials design (think adaptive approaches) and analysis.
- download the CDER's report here
- see the FDA's release