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In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

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In Silico Models for Developmental Toxicity.

Marco Marzo¹, Alessandra Roncaglioni², Sunil Kulkarni³

¹Department of Environmental Health Sciences, Laboratory of Environmental Chemistry and Toxicology, Istituto di Ricerche Farmacologiche Mario Negri IRCCS, Milan, Italy. marco.marzo@marionegri.it.

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

|February 21, 2022

Summary

This summary is machine-generated.

Predicting developmental toxicity using computational methods is complex. This study combines human rules and statistical approaches to improve in silico predictions, emphasizing case-by-case evaluation and model applicability domain assessment.

Keywords:

Developmental toxicity OECD Predictive reliability QSAR Similarity

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Area of Science:

Computational toxicology
In silico modeling
Developmental toxicity assessment

Background:

Developmental toxicity endpoint modeling presents significant challenges for Quantitative Structure-Activity Relationship ((Q)SAR) model developers due to its inherent complexity.
Emerging in silico methods offer opportunities to integrate diverse modeling perspectives for evaluating developmental toxicity.
Understanding model basis, assumptions, and specific predictions is crucial for users applying these computational tools.

Purpose of the Study:

To present an approach combining human-based rules and statistical methods for supporting developmental toxicity predictions.
To illustrate the application of integrated in silico methods for assessing chemical safety.
To discuss the assumptions and uncertainties associated with the proposed methodology.

Main Methods:

Integration of existing in silico methods by leveraging various modeling perspectives.
Application of a hybrid approach combining human-based rules with statistical modeling techniques.
Case-by-case evaluation of predictions, including assessment of model applicability domain and chemical analogues.

Main Results:

Demonstration of an approach to enhance the reliability of in silico developmental toxicity predictions.
Examples illustrating the combined use of rule-based and statistical modeling strategies.
Identification of key considerations for assessing model confidence and prediction validity.

Conclusions:

Combining human-based rules and statistical methods offers a promising strategy for improving in silico developmental toxicity predictions.
Thorough evaluation, including applicability domain assessment and analogue checking, is essential for confident predictions.
The methodology provides a framework for addressing uncertainties in computational toxicology for developmental endpoints.