Quantitative high-throughput screening for chemical toxicity in a population-based in vitro model.

University of North Carolina, Chapel Hill, North Carolina 27599, USA.
Toxicological Sciences (Impact Factor: 4.48). 01/2012; 126(2):578-88. DOI: 10.1093/toxsci/kfs023
Source: PubMed

ABSTRACT A shift in toxicity testing from in vivo to in vitro may efficiently prioritize compounds, reveal new mechanisms, and enable predictive modeling. Quantitative high-throughput screening (qHTS) is a major source of data for computational toxicology, and our goal in this study was to aid in the development of predictive in vitro models of chemical-induced toxicity, anchored on interindividual genetic variability. Eighty-one human lymphoblast cell lines from 27 Centre d'Etude du Polymorphisme Humain trios were exposed to 240 chemical substances (12 concentrations, 0.26nM-46.0μM) and evaluated for cytotoxicity and apoptosis. qHTS screening in the genetically defined population produced robust and reproducible results, which allowed for cross-compound, cross-assay, and cross-individual comparisons. Some compounds were cytotoxic to all cell types at similar concentrations, whereas others exhibited interindividual differences in cytotoxicity. Specifically, the qHTS in a population-based human in vitro model system has several unique aspects that are of utility for toxicity testing, chemical prioritization, and high-throughput risk assessment. First, standardized and high-quality concentration-response profiling, with reproducibility confirmed by comparison with previous experiments, enables prioritization of chemicals for variability in interindividual range in cytotoxicity. Second, genome-wide association analysis of cytotoxicity phenotypes allows exploration of the potential genetic determinants of interindividual variability in toxicity. Furthermore, highly significant associations identified through the analysis of population-level correlations between basal gene expression variability and chemical-induced toxicity suggest plausible mode of action hypotheses for follow-up analyses. We conclude that as the improved resolution of genetic profiling can now be matched with high-quality in vitro screening data, the evaluation of the toxicity pathways and the effects of genetic diversity are now feasible through the use of human lymphoblast cell lines.

Download full-text


Available from: Eric Lock, Jul 29, 2015
  • Source
    • "The utility of such in vitro models to toxicol ogy, especially for exploring the extent and nature of genetic components of inter­ individual variability in PD and systems dynamics, was recently demonstrated (Lock et al. 2012; O'Shea et al. 2011 "
    [Show abstract] [Hide abstract]
    ABSTRACT: Background: Characterizing variability in the extent and nature of responses to environmental exposures is a critical aspect of human health risk assessment. Objective: Our goal was to explore how next-generation human health risk assessments may better characterize variability in the context of the conceptual framework for the source-to-outcome continuum. Methods: This review was informed by a National Research Council workshop titled “Biological Factors that Underlie Individual Susceptibility to Environmental Stressors and Their Implications for Decision-Making.” We considered current experimental and in silico approaches, and emerging data streams (such as genetically defined human cells lines, genetically diverse rodent models, human omic profiling, and genome-wide association studies) that are providing new types of information and models relevant for assessing interindividual variability for application to human health risk assessments of environmental chemicals. Discussion: One challenge for characterizing variability is the wide range of sources of inherent biological variability (e.g., genetic and epigenetic variants) among individuals. A second challenge is that each particular pair of health outcomes and chemical exposures involves combinations of these sources, which may be further compounded by extrinsic factors (e.g., diet, psychosocial stressors, other exogenous chemical exposures). A third challenge is that different decision contexts present distinct needs regarding the identification—and extent of characterization—of interindividual variability in the human population. Conclusions: Despite these inherent challenges, opportunities exist to incorporate evidence from emerging data streams for addressing interindividual variability in a range of decision-making contexts.
    Environmental Health Perspectives 10/2012; 121(1). DOI:10.1289/ehp.1205687 · 7.03 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Our work concerns interaction spherical study of three toxic products, bromobenzene, tetrachloroethylene, and 4-hydroxy-chromene-2-one; using the Leap Frog algorithm, we calculated new values of cut-off of the box through Lennard-Jones potential parameters. This model was adapted to allow the determination of the characteristics for the SP1, SP2 and SP3 state points and was applied to study the properties for the three products by molecular dynamics. This method provides an advantage to confirm the structure–activity relationship for these compounds. We calculated the thermodynamic and structural properties for both canonical NVT and isothermal–isobaric NPT ensembles of these products. Numerical system results have been compared with both experimental data and recent investigation theoretical. Our simulation model isobaric–isothermal system gives accurate results, and comparing with the canonical system, this model agrees very well with the experimental data. We aim to demonstrate that the classical approach with a low statistical uncertainty for liquid toxic leads to data in very good agreement with experiment or other types of calculations. We obtained a good prediction of the thermodynamic properties. We hope that this model with a lower threshold to 2.5σ could be an effective starting material for studying the properties of complex systems.
    Research on Chemical Intermediates 04/2013; 39(4). DOI:10.1007/s11164-012-0722-7 · 1.54 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    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 · 4.48 Impact Factor
Show more