(Q)SAR Modeling and Safety Assessment in Regulatory Review

Abstract

The ability to predict clinical safety based on chemical structures is becoming an increasingly important part of regulatory decision making. (Quantitative) structure-activity relationship ((Q)SAR) models are currently used to evaluate late-arising safety concerns and possible nonclinical effects of a drug and its related compounds when adequate safety data are absent or equivocal. Regulatory use will likely increase with the standardization of analytical approaches, more complete and reliable data collection methods, and a better understanding of toxicity mechanisms.

Identifying potential endocrine disruptors among industrial chemicals and their metabolites – development and evaluation of in silico tools

“…Typically, QSARs use statistical methods to identify correlations between molecular features (descriptors) and a response for a particular endpoint (Kruhlak et al, 2012). A range of QSAR models have been developed for predicting endocrine-related effects, such as binding to transport proteins such as transthyretin (Yang et al, 2011;Papa et al, 2013), and to various receptors including: progesterone (So et al, 2000;Chen et al, 2003;Soderholm et al, 2006;Pal et al, 2011), aryl hydrocarbon (Rayne et al, 2010;Li et al, 2011Li et al, , 2013bCao et al, 2013;Yuan et al, 2013), thyroid receptor (Liu and Gramatica, 2007;Valadares et al, 2007;Vedani et al, 2007;Du et al, 2008;Li et al, 2010Li et al, , 2012, and peroxisome proliferator-activated receptor (Devillers et al, 2006;Vedani et al, 2009).…”

(Q)SAR assessments of potentially mutagenic impurities: A regulatory perspective on the utility of expert knowledge and data submission

“…Several recent publications describe expert knowledge (e.g., Dobo et al, 2012;Kruhlak et al, 2012;Naven et al, 2012;Sutter et al, 2013); however, the definition and ultimate utility appears to be evolving. From a regulatory perspective, expert knowledge is the application of subjective judgment intended to 1) maximize confidence in a (Q)SAR prediction, 2) provide rationale to supersede a positive or negative (Q)SAR prediction, or 3) provide a basis for assessing mutagenicity in absence of a (Q)SAR prediction.…”

Virtual Screening Strategies: A State of Art to Combat with Multiple Drug Resistance Strains

“…Quantitative Structure Activity Relationship (QSAR) is one of the frequently used approach in ligand based virtual screening. Generally, QSAR is used to study the structural or physiochemical relationship of active molecules with their biological targets [53][54][55]. High quality data, diverse set compounds, appropriate descriptors, suitable mathematical algorithm and proper validation sets are required for the development of any effective and successful QSAR model.…”