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Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

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Latest advances in computational genotoxicity prediction.

Russell T Naven¹, Nigel Greene, Richard V Williams

¹Compound Safety Prediction Group, Worldwide Medicinal Chemistry, Pfizer Worldwide Research and Development, Eastern Point Road, Groton, CT 06340, USA.

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Summary

Computational methods for predicting genotoxicity are established but face challenges. Key issues include defining chemical similarity thresholds and ensuring the reliability of in silico predictions for genotoxic impurities.

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Area of Science:

Pharmacology and Toxicology
Computational Chemistry
Drug Development

Background:

Computational genotoxicity prediction methods have been developed over the past 20 years.
Various methodologies have been employed and evaluated in published studies.

Purpose of the Study:

To address unresolved aspects in the application of in silico predictive systems for genotoxicity.
To improve the reliability and applicability of computational toxicology in drug safety assessment.

Main Methods:

Review and analysis of existing computational approaches for genotoxicity assessment.
Discussion of challenges in defining chemical similarity for biological outcome inference.
Exploration of criteria for determining the reliability of in silico model predictions.

Main Results:

In silico genotoxicity prediction is integral to screening strategies for genotoxic impurities in pharmaceuticals.
Challenges persist in using chemical similarity to predict mutagenic potential.
Establishing the reliability of in silico predictions for novel or under-tested compounds remains difficult.

Conclusions:

In silico genotoxicity prediction is a vital component of drug product safety evaluation.
The scientific community faces challenges in quantifying chemical similarity for mutagenicity assessment.
Defining the reliability of in silico predictions requires further research, especially for compounds with data gaps.