Data Sheet

In Silico modeling and artificial intelligence :  FDA regulatory navigation


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 .

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In the United States, the FDA has acknowledged that model-informed drug development (MIDD) approaches can be used to support decisions on whether, when, and how to conduct certain pharmacology studies, and to support dosing recommendations in product labeling, among other applications. The use of Artificial Intelligence and Machine Learning techniques has also been recognized as increasingly playing a key role in drug discovery and development. Data and analysis derived from these approaches are acceptable within submissions for new drug and biologics applications. The presence of this data has supported hundreds of applications, as well as clinical trial design and specific regulatory approval decisions.

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FDA Policy and Guidance on use of MIDD and AI in Regulatory Submissions

August 2018
Physiologically Based Pharmacokinetic Analyses — Format and Content Guidance for Industry

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.

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June 2024
50 shades of AI in regulatory science

This document, co-authored by VeriSIM Life CSO Dr. Szczepan Baran and Dr. Weida Tong, Director of Division of Bioinformatics and Biostatistics for FDA, emphasizes the need for tailored regulatory measures to accommodate AI’s diverse roles, ensuring AI enhances rather than complicates regulatory processes.

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2023 Through 2027
PDUFA Reauthorization Performance Goals and Procedures Fiscal Years

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.”

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May 2023
Discussion Paper: Using Artificial Intelligence & Machine Learning in the Development of Drug & Biological Products

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.

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July 2023
Survey of AI-based Approaches to Generating MIDD Assets Across the Drug Development Continuum

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.

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FDA Modernization
Act 2.0

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.

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May 2022
Select Examples of MIDD Approaches to Support Regulatory Decision-making

This is a list of examples of model-informed drug development approaches that support recent regulatory action.

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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.

- PDUFA Reauthorization Performance Goals and Procedures Fiscals Years 2023 Through 2027