Glenn J. Myatt , Arianna Bassan , Dave Bower , Kevin M. Crofton , Kevin P. Cross , Jessica C. Graham , Catrin Hasselgren , Robert A. Jolly , Scott Miller , Manuela Pavan , Raymond R Tice , Craig Zwickl , Candice Johnson
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引用次数: 8
Abstract
In silico toxicology protocols are currently needed to support the acceptance and deployment of computational toxicology methods as alternative methods for health hazard identification. Such protocols combine relevant in silico results with available experimental data to derive an assessment of major toxicological endpoints supported by a confidence score reflecting the uncertainty in the assessment. The protocols also identify relevant effects and/or mechanisms which can be used to guide the assessment of a toxicological endpoint. In addition, sufficient documentation of procedures and methods used to support an assessment is essential for both internal and external decision-making. The combination of relevant data, confidence scoring, and reporting provides a hazard assessment framework intended to increase the acceptance of in silico results in a toxicologic assessment. This article describes key principles and components of such protocols, including the hazard assessment framework and recommendations demonstrating how evaluating relevance, completeness, and confidence can be performed and documented. Also discussed are criteria used to develop an in silico protocol based on the state of the science and the importance of developing position papers to outline roadmaps for future in silico protocols used to guide assessments of more complex toxicological endpoints, such as cancer or neurotoxicity. The current status of providing such protocols is summarized for specific in silico protocols that are already published, in development, or planned.
期刊介绍:
Computational Toxicology is an international journal publishing computational approaches that assist in the toxicological evaluation of new and existing chemical substances assisting in their safety assessment. -All effects relating to human health and environmental toxicity and fate -Prediction of toxicity, metabolism, fate and physico-chemical properties -The development of models from read-across, (Q)SARs, PBPK, QIVIVE, Multi-Scale Models -Big Data in toxicology: integration, management, analysis -Implementation of models through AOPs, IATA, TTC -Regulatory acceptance of models: evaluation, verification and validation -From metals, to small organic molecules to nanoparticles -Pharmaceuticals, pesticides, foods, cosmetics, fine chemicals -Bringing together the views of industry, regulators, academia, NGOs