» Articles » PMID: 29678766

In Silico Toxicology Protocols

Overview
Publisher Elsevier
Specialties Pharmacology
Toxicology
Date 2018 Apr 22
PMID 29678766
Citations 53
Authors
Affiliations
Soon will be listed here.
Abstract

The present publication surveys several applications of in silico (i.e., computational) toxicology approaches across different industries and institutions. It highlights the need to develop standardized protocols when conducting toxicity-related predictions. This contribution articulates the information needed for protocols to support in silico predictions for major toxicological endpoints of concern (e.g., genetic toxicity, carcinogenicity, acute toxicity, reproductive toxicity, developmental toxicity) across several industries and regulatory bodies. Such novel in silico toxicology (IST) protocols, when fully developed and implemented, will ensure in silico toxicological assessments are performed and evaluated in a consistent, reproducible, and well-documented manner across industries and regulatory bodies to support wider uptake and acceptance of the approaches. The development of IST protocols is an initiative developed through a collaboration among an international consortium to reflect the state-of-the-art in in silico toxicology for hazard identification and characterization. A general outline for describing the development of such protocols is included and it is based on in silico predictions and/or available experimental data for a defined series of relevant toxicological effects or mechanisms. The publication presents a novel approach for determining the reliability of in silico predictions alongside experimental data. In addition, we discuss how to determine the level of confidence in the assessment based on the relevance and reliability of the information.

Citing Articles

Predictive Modeling of Pesticides Reproductive Toxicity in Earthworms Using Interpretable Machine-Learning Techniques on Imbalanced Data.

Kotli M, Piir G, Maran U ACS Omega. 2025; 10(5):4732-4744.

PMID: 39959051 PMC: 11822515. DOI: 10.1021/acsomega.4c09719.


QSAR Classification Modeling Using Machine Learning with a Consensus-Based Approach for Multivariate Chemical Hazard End Points.

Fuadah Y, Pramudito M, Firdaus L, Vanheusden F, Lim K ACS Omega. 2025; 9(51):50796-50808.

PMID: 39741811 PMC: 11683616. DOI: 10.1021/acsomega.4c09356.


The Role of Simulation Science in Public Health at the Agency for Toxic Substances and Disease Registry: An Overview and Analysis of the Last Decade.

Desai S, Wilson J, Ji C, Sautner J, Prussia A, Demchuk E Toxics. 2024; 12(11).

PMID: 39590991 PMC: 11598116. DOI: 10.3390/toxics12110811.


Computational Strategies for Assessing Adverse Outcome Pathways: Hepatic Steatosis as a Case Study.

Ortega-Vallbona R, Palomino-Schatzlein M, Tolosa L, Benfenati E, Ecker G, Gozalbes R Int J Mol Sci. 2024; 25(20).

PMID: 39456937 PMC: 11508863. DOI: 10.3390/ijms252011154.


Development, Use, and Validation of (Q)SARs for Predicting Genotoxicity and Carcinogenicity: Experiences from Italian National Institute of Health Activities.

Battistelli C, Bossa C Methods Mol Biol. 2024; 2834:231-247.

PMID: 39312168 DOI: 10.1007/978-1-0716-4003-6_11.


References
1.
Barber C, Amberg A, Custer L, Dobo K, Glowienke S, Van Gompel J . Establishing best practise in the application of expert review of mutagenicity under ICH M7. Regul Toxicol Pharmacol. 2015; 73(1):367-77. DOI: 10.1016/j.yrtph.2015.07.018. View

2.
Raies A, Bajic V . In silico toxicology: computational methods for the prediction of chemical toxicity. Wiley Interdiscip Rev Comput Mol Sci. 2016; 6(2):147-172. PMC: 4785608. DOI: 10.1002/wcms.1240. View

3.
Schultz T, Amcoff P, Berggren E, Gautier F, Klaric M, Knight D . A strategy for structuring and reporting a read-across prediction of toxicity. Regul Toxicol Pharmacol. 2015; 72(3):586-601. DOI: 10.1016/j.yrtph.2015.05.016. View

4.
Schilter B, Benigni R, Boobis A, Chiodini A, Cockburn A, Cronin M . Establishing the level of safety concern for chemicals in food without the need for toxicity testing. Regul Toxicol Pharmacol. 2013; 68(2):275-96. DOI: 10.1016/j.yrtph.2013.08.018. View

5.
Dobo K, Greene N, Fred C, Glowienke S, Harvey J, Hasselgren C . In silico methods combined with expert knowledge rule out mutagenic potential of pharmaceutical impurities: an industry survey. Regul Toxicol Pharmacol. 2012; 62(3):449-55. DOI: 10.1016/j.yrtph.2012.01.007. View