The international regulatory environment for drug approvals is diverse, but generally considers the use of computational simulations and predictions to be of value to drug makers throughout the discovery and development lifecycle .
This resource informs the structure and standardization of computational analysis. The FDA writes, “This guidance outlines the recommended format and content for a sponsor or applicant to submit physiologically based pharmacokinetic (PBPK) analyses to the FDA to support applications including, but not limited to, investigational new drug applications (INDs), new drug applications (NDAs), biologics license applications (BLAs), or abbreviated new drug applications (ANDAs).” Learn more about how BIOiSIM’s reporting was built to be in compliance with this guideline.
This document, referred to as the “goal letter,” “represents the product of FDA’s discussions with the regulated industry and public stakeholders, as mandated by Congress. The performance and procedural goals and other commitments specified in this letter apply to aspects of the human drug review program that are important for facilitating timely access to safe, effective, and innovative new medicines for patients.”
This paper was released by the FDA in order to address the developing landscape of AI in MIDD and to request input from industry experts. VeriSIM Life responded to this paper with guidance that we provided based on the principles we used to create our computational drug development platform, BIOiSIM.
Research from VeriSIM Life founder & CEO Dr. Jo Varshney, and other VeriSIM Life scientists is cited throughout this article from the AAPS Journal.
This review details the potential of the use of AI in model-informed drug development and encourages organizations to embrace/adopt AI as a way of evolving traditional methods.
This legislation was critical in acknowledging the benefit of, and approving the usage of AI in model-informed drug development. Passed at the end of 2022, it authorizes the FDA to accept safety and effecacy research derived from technologies such as in silico modeling as a suitable alternative to animal testing. Read our response to this legislation here.
This is a list of examples of model-informed drug development approaches that support recent regulatory action.
FDA will build on the success of the “model-informed drug development” (MIDD) approaches by continuing to advance and integrate the development and application of exposure-based, biological, and statistical models derived from preclinical and clinical data sources in drug development and regulatory review.