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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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Integrating in silico models to enhance predictivity for developmental toxicity.

Marco Marzo¹, Sunil Kulkarni², Alberto Manganaro¹

¹Laboratory of Environmental Chemistry and Toxicology, Department of Environmental Health Sciences, IRCCS-Istituto di Ricerche Farmacologiche Mario Negri, Via La Masa 19, 20156 Milano, Italy.

|October 4, 2016

Summary

Integrating multiple in silico models for developmental toxicity prediction improved performance and expanded chemical applicability. This consensus approach reduces uncertainty in computational toxicology assessments.

Keywords:

Developmental toxicity In silico In vitro and alternatives QSAR VEGA

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Area of Science:

Computational toxicology
In silico modeling
Drug development

Background:

In silico models for developmental toxicity prediction show limited success individually.
Accurate prediction of complex developmental toxicity endpoints remains a challenge.

Purpose of the Study:

To evaluate the increased predictive performance by integrating public domain and commercial in silico models.
To assess if model integration expands the applicable chemical and biological spaces for developmental toxicity prediction.

Main Methods:

Integration of selected public domain models (CAESAR, SARpy, P&G) with commercial suites (Multicase, Leadscope, Derek Nexus).
Performance assessment using various datasets.
Evaluation of applicable chemical and biological spaces.
Utilizing applicability domain tools for prediction interpretation.

Main Results:

Model integration showed varied but improved predictive performance compared to individual models across different datasets.
Integration expanded the applicable chemical and biological spaces due to diverse model specificities.
Applicability domain assessment enhanced the interpretation of in silico predictions.

Conclusions:

Integrating multiple in silico models can reduce uncertainty in developmental toxicity predictions through consensus.
The approach enhances the reliability and scope of computational toxicology assessments.
Tools for assessing applicability domains are crucial for robust in silico predictions.