FDA Accepts In Silico DDT to Predict DILI

The U.S. Food and Drug Administration's Center for Drug Evaluation and Research accepted the first Letter of Intent for an in silico drug development tool into the Innovative Science and Technology Approaches for New Drugs Qualification Program on June 3, 2026. The AI-Driven Digital Liver Model aims to predict drug-induced liver injury for small-molecule candidates by comparing chemical structures against historical reference drugs. The acceptance marks the initial step in a three-step qualification process for the New Approach Methodology.

FDA Accepts In Silico DDT to Predict DILI Per the U.S. Food and Drug Administration, the FDA's Center for Drug Evaluation and Research CDER has accepted the first Letter of Intent LOI for an in silico drug development tool DDT into the Innovative Science and Technology Approaches for New Drugs ISTAND DDT Qualification Program, the agency announced on 2026-06-03. The tool, described by FDA as an AI-Driven Digital Liver Model , aims to help predict drug-induced liver injury DILI for small-molecule candidates by comparing chemical structures against historical reference drugs with known DILI risk, and is classified as a New Approach Methodology NAM . "New technologies are showing incredible promise in helping improve and streamline drug development," said Michael Davis, MD, PhD, Acting Director of CDER. The FDA describes the LOI acceptance as the first step in a three-step DDT qualification process. What happened Per the U.S. Food and Drug Administration, the FDA's Center for Drug Evaluation and Research CDER has accepted the first Letter of Intent LOI for an in silico drug development tool DDT into the Innovative Science and Technology Approaches for New Drugs ISTAND DDT Qualification Program. The submission concerns an AI-Driven Digital Liver Model intended to support prediction of drug-induced liver injury DILI for small-molecule new drug candidates. The FDA states this LOI acceptance is the first step in a three-step DDT qualification process. "New technologies are showing incredible promise in helping improve and streamline drug development, with the ultimate goal of enhancing patient care," said Michael Davis, MD, PhD, Acting Director of CDER. "The AI-Driven Digital Liver Model shows promise in assessing the risk of hepatotoxicity during preclinical phases of drug development," said Jeffrey Siegel, MD, Director of the Office of Drug Evaluation Sciences. Technical details Per the FDA announcement, the DDT is described as a New Approach Methodology NAM that leverages artificial intelligence to compare the chemical structures of new drug candidates against historical reference drugs with known DILI risk. The agency frames the tool as intended to complement other methods of DILI risk assessment as part of a weight-of-evidence approach. The FDA also notes that current modelling does not accurately identify DILI risk in humans and that DILI is a leading cause of clinical-trial termination and drug attrition during the Investigational New Drug process. Editorial analysis Industry observers note that regulatory qualification pathways, such as ISTAND, function as formal interfaces between computational tool developers and regulators. Such acceptance of an LOI typically signals that a tool will undergo structured evaluation against validation, reproducibility, and context-of-use criteria, which can reduce uncertainty for sponsor submissions and external validators. What to watch Observers should track progression through the ISTAND qualification steps, publication or independent benchmarking of the model's predictive performance, whether the FDA issues context-of-use language if the DDT becomes qualified, and subsequent uptake by sponsors in preclinical packages. Per the FDA, no qualification is final at the LOI stage; additional data and review steps remain. Scoring Rationale Regulatory acceptance of a first in silico DDT under ISTAND is notable for computational toxicology and drug-development practitioners. It creates a formal pathway that could hasten adoption of NAMs, but qualification is early-stage and additional validation is required. Practice interview problems based on real data 1,500+ SQL & Python problems across 15 industry datasets — the exact type of data you work with. Try 250 free problems /problems