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[Adverse Effect Predictions Based on Computational Toxicology Techniques and Large-scale Databases].

Yoshihiro Uesawa¹

¹Department of Clinical Pharmaceutics, Meiji Pharmaceutical University.

Yakugaku Zasshi : Journal of the Pharmaceutical Society of Japan

|February 2, 2018

Summary

Computational toxicology methods analyze drug adverse effect databases to predict potential toxicities. Volcano plotting visualizes drug-adverse effect relationships, aiding drug discovery and repositioning.

Keywords:

adverse effect computational toxicology database prediction model quantitative structure-activity relationship

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Area of Science:

Pharmacology
Toxicology
Computational Chemistry

Background:

Understanding chemical structure-toxicity relationships is crucial for drug safety.
Post-marketing surveillance and early drug discovery benefit from predictive toxicology.

Purpose of the Study:

To describe computational toxicology techniques for analyzing large-scale adverse effect databases.
To introduce volcano plotting as a novel visualization method for drug-adverse effect relationships.

Main Methods:

Analysis of large-scale spontaneous adverse effect report databases.
Application of computational toxicology techniques.
Utilization of volcano plotting for data visualization and analysis.

Main Results:

Identification of chemical features associated with drug adverse effects.
Demonstration of volcano plotting's utility in clarifying drug-adverse effect associations.
Generation of data applicable to drug repositioning strategies.

Conclusions:

Computational toxicology and advanced visualization methods enhance drug safety assessment.
Volcano plotting offers a powerful tool for exploring complex drug-adverse effect data.
These analyses support informed decision-making in drug discovery and repositioning.