Search

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals., Objectives: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 ( LD 50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [ LD 50 ( LD 50 ≤ 50 mg / kg )], and nontoxic chemicals ( L D 50 > 2,000 mg / kg )., Methods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches., Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results., Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

A major challenge in toxicology is the development of non-animal methods for the assessment of human health risks that might result from repeated systemic exposure. We present here a perspective that considers the opportunities that computational modelling methods may offer in addressing this challenge. Our approach takes the form of a commentary designed to inform responses to future calls for research in predictive toxicology. It is considered essential that computational model-building activities be at the centre of the initiative, driving an iterative process of development, testing and refinement. It is critical that the models provide mechanistic understanding and quantitative predictions. The aim would be to predict effects in humans; in order to help define a challenging but yet feasible initial goal the focus would be on liver mitochondrial toxicity. This will inevitably present many challenges that naturally lead to a modular approach, in which the overall problem is broken down into smaller, more self-contained sub-problems that will subsequently need to be connected and aligned to develop an overall understanding. The project would investigate multiple modelling approaches in order to encourage links between the various disciplines that hitherto have often operated in isolation. The project should build upon current activities in the wider scientific community, to avoid duplication of effort and to ensure that investment is maximised. Strong leadership will be required to ensure alignment around a set of common goals that would be derived using a problem-statement driven approach. Finally, although the focus here is on toxicology, there is a clear link to the wider challenges in systems medicine and improving human health., (Copyright © 2012. Published by Elsevier Ireland Ltd.. All rights reserved.)

There is a continuing interest in, and increasing imperatives for, the development of alternative methods for toxicological evaluations that do not require the use of animals. Although a significant investment has resulted in some achievements, progress has been patchy and there remain many challenges. Among the most significant hurdles is developing non-animal methods that would permit assessment of the potential for a chemical or drug to cause adverse health effects following repeated systemic exposure. Developing approaches to address this challenge has been one of the objectives of the European Partnership for Alternative Approaches to Animal Testing (EPAA). The EPAA is a unique partnership between the European Commission and industry that has interests in all aspects of reducing, refining and replacing the use of animals (the '3Rs'). One possible strategy that emerged from a broad scientific debate sponsored by the EPAA was the opportunity for developing entirely new paradigms for toxicity testing based upon harnessing the increasing power of computational chemistry in combination with advanced systems biology. This brief commentary summarizes a workshop organized by the EPAA in 2010, that had the ambitious title of 'Harnessing the Chemistry of Life: Revolutionizing Toxicology'. At that workshop international experts in chemistry, systems biology and toxicology sought to map out how best developments in these sciences could be exploited to design new strategies for toxicity testing using adverse effects in the liver as an initial focus of attention. Here we describe the workshop design and outputs, the primary purpose being to stimulate debate about the need to align different areas of science with toxicology if new and truly innovative approaches to toxicity testing are to be developed., (Copyright © 2011 John Wiley & Sons, Ltd.)