Predictive Modeling of Chemical Hazard by Integrating Numerical Descriptors of Chemical Structures and Short-term Toxicity Assay Data
Open Access
- 2 March 2012
- journal article
- research article
- Published by Oxford University Press (OUP) in Toxicological Sciences
- Vol. 127 (1), 1-9
- https://doi.org/10.1093/toxsci/kfs095
Abstract
Quantitative structure-activity relationship (QSAR) models are widely used for in silico prediction of in vivo toxicity of drug candidates or environmental chemicals, adding value to candidate selection in drug development or in a search for less hazardous and more sustainable alternatives for chemicals in commerce. The development of traditional QSAR models is enabled by numerical descriptors representing the inherent chemical properties that can be easily defined for any number of molecules; however, traditional QSAR models often have limited predictive power due to the lack of data and complexity of in vivo endpoints. Although it has been indeed difficult to obtain experimentally derived toxicity data on a large number of chemicals in the past, the results of quantitative in vitro screening of thousands of environmental chemicals in hundreds of experimental systems are now available and continue to accumulate. In addition, publicly accessible toxicogenomics data collected on hundreds of chemicals provide another dimension of molecular information that is potentially useful for predictive toxicity modeling. These new characteristics of molecular bioactivity arising from short-term biological assays, i.e., in vitro screening and/or in vivo toxicogenomics data can now be exploited in combination with chemical structural information to generate hybrid QSAR–like quantitative models to predict human toxicity and carcinogenicity. Using several case studies, we illustrate the benefits of a hybrid modeling approach, namely improvements in the accuracy of models, enhanced interpretation of the most predictive features, and expanded applicability domain for wider chemical space coverage.This publication has 68 references indexed in Scilit:
- Quantitative High-Throughput Screening for Chemical Toxicity in a Population-Based In Vitro ModelToxicological Sciences, 2012
- The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platformDrug Discovery Today, 2010
- In Vitro Screening for Population Variability in Chemical ToxicityToxicological Sciences, 2010
- Genomic indicators in the blood predict drug-induced liver injuryThe Pharmacogenomics Journal, 2010
- An integrated functional genomic study of acute phenobarbital exposure in the ratBMC Genomics, 2010
- A Novel Two-Step Hierarchical Quantitative Structure–Activity Relationship Modeling Work Flow for Predicting Acute Toxicity of Chemicals in RodentsEnvironmental Health Perspectives, 2009
- Profiling Chemicals Based on Chronic Toxicity Results from the U.S. EPA ToxRef DatabaseEnvironmental Health Perspectives, 2009
- Identification of compounds that potentiate CREB signaling as possible enhancers of long-term memoryProceedings of the National Academy of Sciences of the United States of America, 2009
- Transforming Environmental Health ProtectionScience, 2008
- Do Structurally Similar Molecules Have Similar Biological Activity?Journal of Medicinal Chemistry, 2002