Human health assessments produce quantitative toxicity values or standards by relying on epidemiological data or animal studies. Such assessments are data-, time-, and resource-intensive, and cannot be realistically expected for most environmental chemicals. The National Research Council's "Science and Decisions" report (2009) called for development of default approaches to support risk estimation for toxicants lacking chemical-specific information.
To address the challenge of risk management for data-poor chemicals, we developed quantitative structure-activity relationship (QSAR) models that use chemical properties to predict toxicity values. These models were developed based on a comprehensive database of existing toxicity values from US Federal and State agencies.
Mean model errors ranged from 0.70 to 1.11 log10 units of concentration/dose. Because a diverse set of chemicals was used to build these models, the models generally have a large applicability domain that covers >80% of environmental chemicals. For details, see Wignall et al. (2018), published in Environmental Health Perspectives.
This in silico tool can predict a toxicity value with an error of less than a factor of 10, filling a critical gap in the current risk management paradigm. It can be used to quickly assess relative hazards of environmental exposures when toxicity data or risk assessments are unavailable.
This website serves as a publicly accessible web-based portal that allows end-users to calculate predicted toxicity values for the chemicals of interest, or to retrieve the existing toxicity values used to build the QSAR models. This website is maintained by the research groups of Dr. Ivan Rusyn and Dr. Weihsueh Chiu at Texas A&M University
Suggested Citation: Wignall JA, Muratov E, Sedykh A, Guyton KZ, Tropsha A, Rusyn I, Chiu WA. Conditional Toxicity Value (CTV) Predictor: An In Silico Approach for Generating Quantitative Risk Estimates for Chemicals. Environ Health Perspect. 2018 May 29;126(5):057008. doi: 10.1289/EHP2998. PubMed PMID: 29847084; PubMed Central PMCID: PMC6071978.
Disclaimer: This work was make possible, in part, by U.S. Environmental Protection Agency (EPA) grant number STAR RD83516602 and National Institutes of Health (NIH) grant number P42 ES027704. This work is solely the responsibility of the grantee and do not necessarily reflect the official views of the U.S. EPA or NIH. The U.S. EPA and NIH do not endorse any products or services described herein. With respect to this website, its contents, and any outputs, neither the U.S. government nor any of the authors make any warranty, expressed or implied, including the fitness for a particular purpose. Use of this website is at the user's own risk, and neither the U.S. government nor any of the authors assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information herein. By clicking the "Continue" button, users acknowledge that they have read and accept these terms and conditions.