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Using chemical and biological data to predict drug toxicity
Anika Liu1, Srijit Seal2, Hongbin Yang2
1Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Road, CB2 1EW, Cambridge, United Kingdom; Milner Therapeutics Institute, University of Cambridge, Puddicombe Way, CB2 0AW, Cambridge, United Kingdom.
SLAS Discovery : Advancing Life Sciences R & D
|January 13, 2023
Summary
Harnessing diverse biological data, like gene expression, enhances the prediction and mechanistic understanding of compound safety. This approach aids in advancing predictive toxicology and drug discovery initiatives.
Area of Science:
- Toxicology and Pharmacology
- Computational Biology
- Drug Discovery
Background:
- Predicting compound activity and safety is crucial in drug development.
- Traditional methods rely on chemical and in vivo data.
- Biological data offers deeper mechanistic insights into adverse effects.
Purpose of the Study:
- To review various data sources for predicting compound activity and safety.
- To explore the utility of chemical and biological descriptors in safety prediction.
- To highlight how biological data aids in understanding adverse effects mechanistically.
Main Methods:
- Review of chemical, in vitro, and in vivo data types.
- Analysis of compound descriptors derived from chemical structures.
- Exploration of biological perturbation responses for safety endpoint prediction.
Main Results:
- Biological data, including gene expression and cell morphology, can predict safety endpoints.
- Compound descriptors based on biological responses offer mechanistic understanding of adverse effects.
- Large-scale biological information enhances anticipation of compound biological effects.
Conclusions:
- Integrating diverse biological information offers new opportunities in predictive toxicology.
- Biological data analysis supports more informed drug discovery projects.
- Mechanistic understanding of adverse effects is improved through biological data insights.
Keywords:
Cell paintingChemical structureChemoinformaticsGene expressionMachine learningToxicity predictionMore Related Videos
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