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.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: eLife digest Cancer is not just one disease, but a collection of disorders; as such there is no single general treatment that is effective against all cancers. Different tissues and organs—including the lungs, skin, and kidneys—can get cancer, and each need different treatments. Even two patients with the same type of cancer might respond differently to the same treatment. Being able to distinguish between different cancer types would help doctors personalize a patient's cancer therapy—which would hopefully improve the outcome of the treatment. An important step in developing such personalized treatments is to find out how each type of cancer cell behaves and to see how this behavior differs both from normal, healthy cells and other types of cancer. Countless chemical reactions take place inside living cells, and these reactions essentially dictate how a cell will grow and behave. The chemical reactions occurring inside a cancerous cell can be described as its ‘metabolic phenotype’ and will likely be different to the chemical reactions occurring in a healthy cell. Now Yizhak, Gaude et al. have used a range of data, including gene expression data, to create computer models of the metabolic phenotypes of 60 different types of human cancer cell. The same approach was also used to create metabolic models of over 200 healthy human cells that were dividing normally. Yizhak, Gaude et al. used these metabolic models to predict how quickly the different types of cancer cell would divide and how the cells would respond to drug treatments. It may be possible to reduce the spread of all types of cancer—without also affecting healthy cells—by targeting proteins that help cancerous cells to proliferate. Yizhak, Gaude et al. used all of the models to search for genes that encode such proteins. One gene that was predicted to provide such a drug target encodes an enzyme that is needed to make and break down fatty acid molecules. Experiments confirmed that inhibiting this gene slowed the proliferation of both leukemia and kidney cancer cells, but had less of an effect on the growth of healthy bone marrow or kidney cells. Finally, Yizhak, Gaude et al. generated detailed metabolic profiles of cancer cells taken from over 700 breast and lung cancer patients and were able to use the models to successfully predict the outcome of the diseases in these patients. Yizhak, Gaude et al.'s findings might help future efforts aimed at developing and delivering personalized cancer therapies. The next challenge is to use additional data—such as gene sequencing data—to generate more detailed and more accurate metabolic models for many cancer patients, to both predict their individual responses to available drugs and identify new patient-specific treatments. DOI:
    eLife Sciences 11/2014; 3. DOI:10.7554/eLife.03641 · 8.52 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: Evaluating immunomodulatory effects of xenobiotics is an important component of the toxicity studies. Herein we report on the establishment of a novel in vitro test system for the immunotoxicity screening of xenobiotics based on human lymphoblastoid cell lines (LCLs). Four immunotoxic compounds; tributyltin chloride, cyclosporine A, benzo(a) pyrene and verapamil hydrochloride, as well as three immune-inert compounds; urethane, furosemide and mannitol were selected for characterization. The treatment of LCLs with immunosuppressive compounds resulted in reduced viability. The IC50 values determined in human LCLs were in agreement with the data obtained for human peripheral mononuclear cells. Since cytokine production reflects lymphocyteś responses to external stimuli, we evaluated the functional responses of LCLs by monitoring their pro-inflammatory and immunoregulatory cytokine production. Our findings prove that LCLs allowed for reliable differentiation between immunomodulatory and immune-inert compounds. Hence, pre-treatment with immunomodulatory compounds led to a decrease in the production of pro-inflammatory TNFα, IL-6 and immunoregulatory IL-2, IL-4, IL-10 and IFNγ cytokines, when compared to untreated ionomycin/PMA stimulated cells. Moreover, testing a panel of ten LCLs derived from unrelated healthy individuals reflects inter-individual variability in response to immunomodulatory xenobiotics. In conclusion, LCLs provide a novel alternative method for the testing of the immunotoxic effects of xenobiotics. Copyright © 2014. Published by Elsevier Ireland Ltd.
    Toxicology Letters 12/2014; 233(1). DOI:10.1016/j.toxlet.2014.12.013 · 3.36 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: The advent of new testing systems and "omics"-technologies has left regulatory toxicology facing one of the biggest challenges for decades. That is the question whether and how these methods can be used for regulatory purposes. The new methods undoubtedly enable regulators to address important open questions of toxicology such as species-specific toxicity, mixture toxicity, low-dose effects, endocrine effects or nanotoxicology, while promising faster and more efficient toxicity testing with the use of less animals. Consequently, the respective assays, methods and testing strategies are subject of several research programs worldwide. On the other hand, the practical application of such tests for regulatory purposes is a matter of ongoing debate. This document summarizes key aspects of this debate in the light of the European "regulatory status quo", while elucidating new perspectives for regulatory toxicity testing.
    Archives of Toxicology 03/2015; DOI:10.1007/s00204-015-1510-0 · 5.08 Impact Factor

Full-text (2 Sources)

Available from
May 27, 2014