Machine learning enhances genotoxicity assessment using MultiFlow® DNA damage assay.

Area of Science:

• Pharmacology and Toxicology
• Computational Chemistry
• Biotechnology

Background:

• Genotoxicity testing is crucial for pharmaceutical safety assessment.
• Mechanistic assays like the MultiFlow® DNA damage assay (MFA) provide insights into DNA damage pathways.
• Machine learning (ML) has shown potential in enhancing the classification of genotoxicant modes of action (MoA).

Purpose of the Study:

• To develop and validate ML models for predicting the MoA of genotoxicity using MFA data.
• To improve the accuracy and reliability of genotoxicity risk assessment for pharmaceuticals.
• To create a user-friendly tool for analyzing MFA data and MoA predictions.

Main Methods:

• Application of state-of-the-art ML algorithms from the R package 'caret' to MFA data.
• Integration of molecular descriptors from in silico models to enhance model performance.
• Development of a graphical user interface using the R package 'shiny' for data visualization and analysis.
• Validation of models on training, internal test, and external test datasets.

Main Results:

• The best ML model achieved 95% accuracy on the training dataset and correctly predicted genotoxicity in 16 out of 17 cases in the test dataset.
• Incorporating molecular descriptors improved performance, particularly for complex pharmaceutical cases.
• External validation on 49 compounds demonstrated high model accuracy at 92%.
• A user-friendly graphical interface was developed to facilitate broad laboratory use.

Conclusions:

• ML models, when tailored, can significantly enhance the precision of MoA determination in genotoxicity testing.
• The developed approach offers a robust method for genotoxicity assessment, aiding in pharmaceutical safety evaluations.
• Integration of MoA predictions can serve as valuable evidence in regulatory genotoxicity assessment workflows.