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In Silico Methods for Carcinogenicity Assessment.

Azadi Golbamaki¹, Emilio Benfenati¹, Alessandra Roncaglioni²

¹Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy.

Methods in Molecular Biology (Clifton, N.J.)

|February 21, 2022

Summary

This summary is machine-generated.

Predictive models, including quantitative structure-activity relationships (QSAR), are crucial for identifying potential carcinogens and reducing animal testing. This study evaluates QSAR models for predicting chemical carcinogenicity, particularly in pharmaceutical applications.

Keywords:

Applicability domain index Carcinogenicity Genotoxicity In silico Nongenotoxicity QSAR SARpy Structural alerts Toxtree

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Area of Science:

Computational toxicology and cheminformatics
Environmental health and cancer prevention

Background:

Identifying chemical carcinogenicity is vital for preventing environmentally induced cancers.
Various predictive models, from biological assays to theoretical approaches, have been developed.
Quantitative Structure-Activity Relationship ((Q)SAR) models offer a promising alternative to animal testing by replacing, reducing, and refining their use.

Purpose of the Study:

To review and describe prominent (Q)SAR models for predicting chemical carcinogenicity.
To evaluate the performance of selected (Q)SAR models.
To interpret the results by applying these models to pharmaceutical molecules.

Main Methods:

Review and description of established (Q)SAR models based on expert knowledge and statistical methods.
Performance evaluation of selected predictive models.
Application and interpretation of model predictions on pharmaceutical compounds.

Main Results:

Detailed description of various (Q)SAR models for carcinogenicity prediction.
Evaluation of the performance metrics for the selected models.
Demonstration of model applicability to pharmaceutical molecules.

Conclusions:

(Q)SAR models are valuable tools for predicting chemical carcinogenicity.
These models contribute to the replacement, reduction, and refinement of animal testing in toxicology.
The evaluated models show potential for application in pharmaceutical safety assessment.