Computational Toxicologist US Food and Drug Administration Silver Spring, Maryland
(Quantitative) structure-activity relationship ((Q)SAR) computational models can make a prediction of toxicity based solely on a chemical’s structure and are used by the US Food and Drug Administration (FDA) Center for Drug Evaluation and Research (CDER) to provide predictions for chemical substances under review when adequate experimental data are unavailable. For example, (Q)SAR models are routinely used to predict the mutagenicity and carcinogenicity of drug impurities as an alternative to traditional testing in accordance with international regulatory guidance. This presentation will provide an overview of the (Q)SAR modeling methodology and will include typical use cases that support regulatory decision-making. The value of (Q)SAR modeling as an alternative method to standard toxicology testing—consistent with FDA’s commitment to reduce, refine, and replace animal testing—will also be highlighted.
Learning Objectives:
Understand, in general terms, how (Q)SAR models are developed and applied
Be familiar with pharmaceutical regulatory guidance that recommends the use of (Q)SAR models as an alternative assessment approach
Understand from case studies how model predictions and other output data are interpreted and are used to support a regulatory decision
Recognize how (Q)SAR modeling aligns with FDA’s commitment to reduce, refine, and replace animal testing