In silico models for genotoxicity and drug regulation. (2nd August 2020)

• Record Type: Journal Article
• Authors: Benigni, Romualdo; Bassan, Arianna; Pavan, Manuela
• Is Part Of: Expert opinion on drug metabolism and toxicology. Volume 16:Number 8(2020)
• Page Start: 651
• Page End: 662
• Publication Date: 2020-08-02
• Subjects: Alternative methods -- genotoxicity -- predictive methods -- QSAR -- structure–activity -- toxicology; Drugs -- Toxicology -- Periodicals; Drugs -- Metabolism -- Periodicals; 615.7
• DOI: 10.1080/17425255.2020.1785428
• Languages: English
• ISSNs: 1742-5255

Abstract

ABSTRACT: Introduction: Whereas in the past, (Q)SAR methods have been largely used to support the design of new drugs, in the last few decades, there has been a new interest in its applications for the assessment of drug safety. In particular, the ICH M7 guideline has introduced the concept that (Q)SAR predictions for the Ames mutagenicity of drug impurities can be used for regulatory purposes. Areas covered: This review introduces the ICH M7 conceptual framework and illustrates the most updated evaluations of the in silico approaches for the prediction of genotoxicity. The strengths and weaknesses of the state-of-the-art are presented and future perspectives are discussed. Expert opinion: Given the growing recognition of (Q)SAR approaches, more investment will be devoted to its improvement. The major areas of research should be the expansion and curation of the experimental training sets, with particular attention to the portions of chemical space which are poorly represented. New modeling methodologies (e.g. machine-learning methods) may support this effort, particularly for treating proprietary data without disclosure. Research on new integrative approaches for regulatory decisions will also be important.