Latest advances in computational genotoxicity prediction

Abstract

Introduction: Computational approaches for genotoxicity prediction have existed for over two decades. Numerous methodologies have been utilized and the results of various evaluations have published.

Areas covered: In silico methods are considered mature enough to be part of draft FDA regulatory guidelines for the assessment of genotoxic impurities. However, aspects of how best to use predictive systems remain unresolved: i) methodologies to measure how similar two compounds need to be in order to assume they have the same biological outcome; and ii) defining whether a compound is close enough to the model training set such that a model prediction can be considered reliable.

Expert opinion: In silico prediction of genotoxicity is a fundamental part of screening strategies for the assessment genotoxic impurities in drug products. However, the concept of using chemical similarity to infer mutagenic potential from one of known activity to another whose activity is unknown remains a scientific challenge. Similarly, defining when an in silico model prediction can be considered to be reliable is also difficult. Reaction mechanisms and the functional group building blocks of chemistry are pretty much constant, and so when data-gaps appear, it tends to be for compounds that have been regularly used but rarely tested.