Predicting Drug-Induced Hepatotoxicity Using QSAR and Toxicogenomics Approaches

Laboratory for Molecular Modeling, University of North Carolina , Chapel Hill, North Carolina 27599, United States.
Chemical Research in Toxicology (Impact Factor: 3.53). 06/2011; 24(8):1251-62. DOI: 10.1021/tx200148a
Source: PubMed

ABSTRACT Quantitative structure-activity relationship (QSAR) modeling and toxicogenomics are typically used independently as predictive tools in toxicology. In this study, we evaluated the power of several statistical models for predicting drug hepatotoxicity in rats using different descriptors of drug molecules, namely, their chemical descriptors and toxicogenomics profiles. The records were taken from the Toxicogenomics Project rat liver microarray database containing information on 127 drugs ( ). The model end point was hepatotoxicity in the rat following 28 days of continuous exposure, established by liver histopathology and serum chemistry. First, we developed multiple conventional QSAR classification models using a comprehensive set of chemical descriptors and several classification methods (k nearest neighbor, support vector machines, random forests, and distance weighted discrimination). With chemical descriptors alone, external predictivity (correct classification rate, CCR) from 5-fold external cross-validation was 61%. Next, the same classification methods were employed to build models using only toxicogenomics data (24 h after a single exposure) treated as biological descriptors. The optimized models used only 85 selected toxicogenomics descriptors and had CCR as high as 76%. Finally, hybrid models combining both chemical descriptors and transcripts were developed; their CCRs were between 68 and 77%. Although the accuracy of hybrid models did not exceed that of the models based on toxicogenomics data alone, the use of both chemical and biological descriptors enriched the interpretation of the models. In addition to finding 85 transcripts that were predictive and highly relevant to the mechanisms of drug-induced liver injury, chemical structural alerts for hepatotoxicity were identified. These results suggest that concurrent exploration of the chemical features and acute treatment-induced changes in transcript levels will both enrich the mechanistic understanding of subchronic liver injury and afford models capable of accurate prediction of hepatotoxicity from chemical structure and short-term assay results.

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    • "Cheng et al. (2010) examined similarities between chemical structures and molecular targets of 37 drugs that were clustered based on their bioactivity profiles. Low et al. (2011) classified 127 rat liver samples to toxic versus non-toxic responses, based on combined drug-induced expression profiles and chemical descriptors, and identified chemical substructures and genes that were responsible for liver toxicity. In a broader setting, when the goal is to find dependencies between two data sources (chemical structures and genomic responses), correlation-type approaches match the goal directly, and have the additional advantage that a predefined classification is not required. "
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    ABSTRACT: Motivation: Analysis of relationships of drug structure to biological response is key to understanding off-target and unexpected drug effects, and for developing hypotheses on how to tailor drug therapies. New methods are required for integrated analyses of a large number of chemical features of drugs against the corresponding genome-wide responses of multiple cell models.Results: In this article, we present the first comprehensive multi-set analysis on how the chemical structure of drugs impacts on genome-wide gene expression across several cancer cell lines [Connectivity Map (CMap) database]. The task is formulated as searching for drug response components across multiple cancers to reveal shared effects of drugs and the chemical features that may be responsible. The components can be computed with an extension of a recent approach called Group Factor Analysis. We identify 11 components that link the structural descriptors of drugs with specific gene expression responses observed in the three cell lines and identify structural groups that may be responsible for the responses. Our method quantitatively outperforms the limited earlier methods on CMap and identifies both the previously reported associations and several interesting novel findings, by taking into account multiple cell lines and advanced 3D structural descriptors. The novel observations include: previously unknown similarities in the effects induced by 15-delta prostaglandin J2 and HSP90 inhibitors, which are linked to the 3D descriptors of the drugs; and the induction by simvastatin of leukemia-specific response, resembling the effects of corticosteroids.Availability and implementation: Source Code implementing the method is available at: or samuel.kaski@aalto.fiSupplementary Information: Supplementary data are available at Bioinformatics online.
    Bioinformatics 12/2013; 30(17). DOI:10.1093/bioinformatics/btu456 · 4.98 Impact Factor
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    • "The authors used gene expression profiles of liver tissue obtained from rats treated with 62 chemicals at different time points (1, 3, and 5 days) to predict rat liver carcinogenicity and concluded that the toxicogenomics data–based models outperformed QSAR. Low et al. (2011) reported a similar outcome when gene expression data–based models (24-h rat liver toxicogenomics profiles of 127 compounds) were compared with conventional QSAR in modeling 28-day hepatotoxicity in the rat. However, the latter study also attempted to combine toxicogenomics data and chemical descriptors for a hybrid approach. "
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    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.
    Toxicological Sciences 03/2012; 127(1):1-9. DOI:10.1093/toxsci/kfs095 · 3.85 Impact Factor
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    ABSTRACT: The subject of drug-induced liver injury (DILI) has been evolving for decades. While various guidance and other documents have been produced to help identify and manage DILI in the clinical trial setting, as well as the clinic, there are still many aspects of the process that remain incomplete. I have selected those aspects where guidance documents either do not cover all possible scenarios or where other recommendations are open to interpretation or where controversies still exist. The following discussion includes a number of these topics, including: when is it acceptable to continue development of a drug where hepatotoxicity is observed in animal models or other preclinical assessments? Should patients with underlying liver disease be routinely included in clinical trials? Are the current clinical and biochemical stopping rules for suspected DILI appropriate for all situations? Should we still be using fold elevations based on upper limits of normal or a subject’s own baseline values to assess the level of alanine aminotransferase or other liver-associated enzyme elevations? How can we best integrate the expanding fields of toxicogenomics, pharmacogenomics, metobolomics, proteomics and other new drug and host profiling into predicting DILI? Where do we stand with respect to a DILI biomarker to replace traditional liver associated enzymes? How do we improve upon the voluntary reporting system for adverse drug reactions? What are the most useful causality assessment methodologies to diagnose DILI and is it ever possible to exclude the drug in question? And how do we best determine and manage the competing benefits and risks of an agent causing DILI?
    Pharmaceutical Medicine 06/2013; 27(3). DOI:10.1007/s40290-013-0015-5
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