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The AAPS Journal

, Volume 14, Issue 3, pp 473–480 | Cite as

Paradigm Shift in Toxicity Testing and Modeling

  • Hongmao SunEmail author
  • Menghang Xia
  • Christopher P. Austin
  • Ruili HuangEmail author
Review Article Theme: New Paradigms in Pharmaceutical Sciences: In Silico Drug Discovery

Abstract

The limitations of traditional toxicity testing characterized by high-cost animal models with low-throughput readouts, inconsistent responses, ethical issues, and extrapolability to humans call for alternative strategies for chemical risk assessment. A new strategy using in vitro human cell-based assays has been designed to identify key toxicity pathways and molecular mechanisms leading to the prediction of an in vivo response. The emergence of quantitative high-throughput screening (qHTS) technology has proved to be an efficient way to decompose complex toxicological end points to specific pathways of targeted organs. In addition, qHTS has made a significant impact on computational toxicology in two aspects. First, the ease of mechanism of action identification brought about by in vitro assays has enhanced the simplicity and effectiveness of machine learning, and second, the high-throughput nature and high reproducibility of qHTS have greatly improved the data quality and increased the quantity of training datasets available for predictive model construction. In this review, the benefits of qHTS routinely used in the US Tox21 program will be highlighted. Quantitative structure–activity relationships models built on traditional in vivo data and new qHTS data will be compared and analyzed. In conjunction with the transition from the pilot phase to the production phase of the Tox21 program, more qHTS data will be made available that will enrich the data pool for predictive toxicology. It is perceivable that new in silico toxicity models based on high-quality qHTS data will achieve unprecedented reliability and robustness, thus becoming a valuable tool for risk assessment and drug discovery.

KEY WORDS

computational toxicology qHTS risk assessment Tox21 

Notes

ACKNOWLEDGMENTS

This work was supported by the Intramural Research Programs (Interagency agreement #Y2-ES-7020-01) of the National Toxicology Program, National Institute of Environmental Health Sciences (NIEHS), and the National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH). The statements, opinions, or conclusions contained therein do not necessarily represent the statements, opinions, or conclusions of NIEHS, or NCATS, NIH, or the US government. We thank in particular Anna Rossoshek for helpful comments and suggestions during the preparation of this manuscript.

REFERENCES

  1. 1.
    Merlot C. Computational toxicology—a tool for early safety evaluation. Drug Discov Today. 2010;15(1–2):16–22.PubMedCrossRefGoogle Scholar
  2. 2.
    Erickson BE. Modernizing toxicity tests. Chem Eng News. 2011;89:25–6.Google Scholar
  3. 3.
    Tetko IV, Sushko I, Pandey AK, Zhu H, Tropsha A, Papa E, et al. Critical assessment of QSAR models of environmental toxicity against Tetrahymena pyriformis: focusing on applicability domain and overfitting by variable selection. J Chem Inf Model. 2008;48(9):1733–46.PubMedCrossRefGoogle Scholar
  4. 4.
    Shukla SJ, Huang R, Austin CP, Xia M. The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platform. Drug Discov Today. 2010;15(23–24):997–1007.PubMedCrossRefGoogle Scholar
  5. 5.
    Schmidt CW. TOX 21: new dimensions of toxicity testing. Environ Health Perspect. 2009;117(8):A348–53.PubMedCrossRefGoogle Scholar
  6. 6.
    Kavlock RJ, Austin CP, Tice RR. Toxicity testing in the 21st century: implications for human health risk assessment. Risk Anal. 2009;29(4):485–7. discussion 92–7.PubMedCrossRefGoogle Scholar
  7. 7.
    Malo N, Hanley JA, Cerquozzi S, Pelletier J, Nadon R. Statistical practice in high-throughput screening data analysis. Nat Biotechnol. 2006;24(2):167–75.PubMedCrossRefGoogle Scholar
  8. 8.
    Johnson RL, Huang R, Jadhav A, Southall N, Wichterman J, MacArthur R, et al. A quantitative high-throughput screen for modulators of IL-6 signaling: a model for interrogating biological networks using chemical libraries. Mol Biosyst. 2009;5(9):1039–50.PubMedCrossRefGoogle Scholar
  9. 9.
    Xia M, Huang R, Witt KL, Southall N, Fostel J, Cho MH, et al. Compound cytotoxicity profiling using quantitative high-throughput screening. Environ Health Perspect. 2008;116(3):284–91.PubMedCrossRefGoogle Scholar
  10. 10.
    Houck KA, Dix DJ, Judson RS, Kavlock RJ, Yang J, Berg EL. Profiling bioactivity of the ToxCast chemical library using BioMAP primary human cell systems. J Biomol Screen. 2009;14(9):1054–66.PubMedCrossRefGoogle Scholar
  11. 11.
    Yasgar A, Shinn P, Jadhav A, Auld D, Michael S, Zheng W, et al. Compound management for quantitative high-throughput screening. JALA Charlottesv Va. 2008;13(2):79–89.PubMedGoogle Scholar
  12. 12.
    Jadhav A, Ferreira RS, Klumpp C, Mott BT, Austin CP, Inglese J, et al. Quantitative analyses of aggregation, autofluorescence, and reactivity artifacts in a screen for inhibitors of a thiol protease. J Med Chem. 2010;53(1):37–51.PubMedCrossRefGoogle Scholar
  13. 13.
    Huang R, Xia M, Cho MH, Sakamuru S, Shinn P, Houck KA, et al. Chemical genomics profiling of environmental chemical modulation of human nuclear receptors. Environ Health Perspect. 2011;119(8):1142–8.PubMedCrossRefGoogle Scholar
  14. 14.
    Cristianini N, Shawe-Taylor J. An introduction to support vector machines. Cambridge: Cambridge University Press; 2005.Google Scholar
  15. 15.
    Fox T, Kriegl JM. Machine learning techniques for in silico modeling of drug metabolism. Curr Top Med Chem. 2006;6(15):1579–91.PubMedCrossRefGoogle Scholar
  16. 16.
    Judson R, Elloumi F, Setzer RW, Li Z, Shah I. A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model. BMC Bioinforma. 2008;9:241.CrossRefGoogle Scholar
  17. 17.
    Noble WS. What is a support vector machine? Nat Biotechnol. 2006;24(12):1565–7.PubMedCrossRefGoogle Scholar
  18. 18.
    Novotarskyi S, Sushko I, Korner R, Pandey AK, Tetko IV. A comparison of different QSAR approaches to modeling CYP450 1A2 inhibition. J Chem Inf Model. 2011;51(6):1271–80.PubMedCrossRefGoogle Scholar
  19. 19.
    Schroeter T, Schwaighofer A, Mika S, Laak AT, Suelzle D, Ganzer U, et al. Machine learning models for lipophilicity and their domain of applicability. Mol Pharm. 2007;4(4):524–38.PubMedCrossRefGoogle Scholar
  20. 20.
    Kavlock RJ, Ankley G, Blancato J, Breen M, Conolly R, Dix D, et al. Computational toxicology—a state of the science mini review. Toxicol Sci. 2008;103(1):14–27.PubMedCrossRefGoogle Scholar
  21. 21.
    Nigsch F, Macaluso NJ, Mitchell JB, Zmuidinavicius D. Computational toxicology: an overview of the sources of data and of modelling methods. Expert Opin Drug Metab Toxicol. 2009;5(1):1–14.PubMedCrossRefGoogle Scholar
  22. 22.
    Muster W, Breidenbach A, Fischer H, Kirchner S, Muller L, Pahler A. Computational toxicology in drug development. Drug Discov Today. 2008;13(7–8):303–10.PubMedCrossRefGoogle Scholar
  23. 23.
    Benfenati E, Gini G. Computational predictive programs (expert systems) in toxicology. Toxicology. 1997;119(3):213–25.PubMedCrossRefGoogle Scholar
  24. 24.
    Cronin MTD, Schultz TW. Pitfalls in QSAR. J Mol Struct Theochem. 2003;622(1–2):39–51.CrossRefGoogle Scholar
  25. 25.
    Cook EF, Goldman L. Empiric comparison of multivariate analytic techniques: advantages and disadvantages of recursive partitioning analysis. J Chron Dis. 1984;37(9–10):721–31.PubMedCrossRefGoogle Scholar
  26. 26.
    Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.CrossRefGoogle Scholar
  27. 27.
    Berger JO. Statistical decision theory and Bayesian analysis. New York: Springer; 1985.Google Scholar
  28. 28.
    Wold S, Sjostrom M, Eriksson L. PLS-regression: a basic tool of chemometrics. Chemometr Intell Lab. 2001;58(2):109–30.CrossRefGoogle Scholar
  29. 29.
    Helguera AM, Combes RD, Gonzalez MP, Cordeiro MN. Applications of 2D descriptors in drug design: a DRAGON tale. Curr Top Med Chem. 2008;8(18):1628–55.PubMedCrossRefGoogle Scholar
  30. 30.
    Shen J, Cheng F, Xu Y, Li W, Tang Y. Estimation of ADME properties with substructure pattern recognition. J Chem Inf Model. 2010;50(6):1034–41.PubMedCrossRefGoogle Scholar
  31. 31.
    Crippen GM, Wildman SA. Prediction of physicochemical parameters by atomic contributions. J Chem Inf Comput Sci. 1999;39(5):868–73.CrossRefGoogle Scholar
  32. 32.
    Butina D. Unsupervised data base clustering based on Daylight’s fingerprint and Tanimoto similarity: a fast and automated way to cluster small and large data sets. J Chem Inf Comput Sci. 1999;39(4):747–50.CrossRefGoogle Scholar
  33. 33.
    Buse E, Habermann G, Osterburg I, Korte R, Weinbauer GF. Reproductive/developmental toxicity and immunotoxicity assessment in the nonhuman primate model. Toxicology. 2003;185(3):221–7.PubMedCrossRefGoogle Scholar
  34. 34.
    Farine JC. Animal models in autoimmune disease in immunotoxicity assessment. Toxicology. 1997;119(1):29–35.PubMedCrossRefGoogle Scholar
  35. 35.
    Trobridge GD, Kiem HP. Large animal models of hematopoietic stem cell gene therapy. Gene Ther. 2010;17(8):939–48.PubMedCrossRefGoogle Scholar
  36. 36.
    Penha FM, Rodrigues EB, Maia M, Dib E, Fiod Costa E, Furlani BA, et al. Retinal and ocular toxicity in ocular application of drugs and chemicals—part I: animal models and toxicity assays. Ophthalmic Res. 2010;44(2):82–104.PubMedCrossRefGoogle Scholar
  37. 37.
    Perlman I. Testing retinal toxicity of drugs in animal models using electrophysiological and morphological techniques. Doc Ophthalmol. 2009;118(1):3–28.PubMedCrossRefGoogle Scholar
  38. 38.
    Chhabra RS, Bucher JR, Wolfe M, Portier C. Toxicity characterization of environmental chemicals by the US National Toxicology Program: an overview. Int J Hyg Environ Health. 2003;206(4–5):437–45.PubMedCrossRefGoogle Scholar
  39. 39.
    Judson R, Richard A, Dix DJ, Houck K, Martin M, Kavlock R, et al. The toxicity data landscape for environmental chemicals. Environ Health Perspect. 2009;117(5):685–95.PubMedGoogle Scholar
  40. 40.
    Gottmann E, Kramer S, Pfahringer B, Helma C. Data quality in predictive toxicology: reproducibility of rodent carcinogenicity experiments. Environ Health Perspect. 2001;109(5):509–14.PubMedCrossRefGoogle Scholar
  41. 41.
    Marino DJ. Physiologically based pharmacokinetic modeling using Microsoft Excel and Visual Basic for applications. Toxicol Mech Methods. 2005;15(2):137–54.PubMedCrossRefGoogle Scholar
  42. 42.
    Yang RS, Thomas RS, Gustafson DL, Campain J, Benjamin SA, Verhaar HJ, et al. Approaches to developing alternative and predictive toxicology based on PBPK/PD and QSAR modeling. Environ Health Perspect. 1998;106 Suppl 6:1385–93.PubMedCrossRefGoogle Scholar
  43. 43.
    Germani M, Crivori P, Rocchetti M, Burton PS, Wilson AG, Smith ME, et al. Evaluation of a basic physiologically based pharmacokinetic model for simulating the first-time-in-animal study. Eur J Pharm Sci. 2007;31(3–4):190–201.PubMedCrossRefGoogle Scholar
  44. 44.
    Macarron R. Critical review of the role of HTS in drug discovery. Drug Discov Today. 2006;11(7–8):277–9.PubMedCrossRefGoogle Scholar
  45. 45.
    Gribbon P, Sewing A. High-throughput drug discovery: what can we expect from HTS? Drug Discov Today. 2005;10(1):17–22.PubMedCrossRefGoogle Scholar
  46. 46.
    Inglese J, Auld DS, Jadhav A, Johnson RL, Simeonov A, Yasgar A, et al. Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries. Proc Natl Acad Sci USA. 2006;103(31):11473–8.PubMedCrossRefGoogle Scholar
  47. 47.
    Leister KP, Huang R, Goodwin BL, Chen A, Austin CP, Xia M. Two high throughput screen assays for measurement of TNF-alpha in THP-1 cells. Curr Chem Genomics. 2011;5:21–9.PubMedCrossRefGoogle Scholar
  48. 48.
    Shukla SJ, Sakamuru S, Huang R, Moeller TA, Shinn P, Vanleer D, et al. Identification of clinically used drugs that activate pregnane X receptors. Drug Metab Dispos. 2011;39(1):151–9.PubMedCrossRefGoogle Scholar
  49. 49.
    Xia M, Guo V, Huang R, Inglese J, Nirenberg M, Austin CP. A cell-based beta-lactamase reporter gene assay for the CREB signaling pathway. Curr Chem Genomics. 2009;3(1):7–12.PubMedCrossRefGoogle Scholar
  50. 50.
    Thomas CJ, Auld DS, Huang R, Huang W, Jadhav A, Johnson RL, et al. The pilot phase of the NIH chemical genomics center. Curr Top Med Chem. 2009;9(13):1181–93.PubMedCrossRefGoogle Scholar
  51. 51.
    Titus SA, Beacham D, Shahane SA, Southall N, Xia M, Huang R, et al. A new homogeneous high-throughput screening assay for profiling compound activity on the human ether-a-go-go-related gene channel. Anal Biochem. 2009;394(1):30–8.PubMedCrossRefGoogle Scholar
  52. 52.
    Xia M, Huang R, Guo V, Southall N, Cho MH, Inglese J, et al. Identification of compounds that potentiate CREB signaling as possible enhancers of long-term memory. Proc Natl Acad Sci USA. 2009;106(7):2412–7.PubMedCrossRefGoogle Scholar
  53. 53.
    Cho MH, Niles A, Huang R, Inglese J, Austin CP, Riss T, et al. A bioluminescent cytotoxicity assay for assessment of membrane integrity using a proteolytic biomarker. Toxicol In Vitro. 2008;22(4):1099–106.PubMedCrossRefGoogle Scholar
  54. 54.
    Huang R, Southall N, Cho MH, Xia M, Inglese J, Austin CP. Characterization of diversity in toxicity mechanism using in vitro cytotoxicity assays in quantitative high throughput screening. Chem Res Toxicol. 2008;21(3):659–67.PubMedCrossRefGoogle Scholar
  55. 55.
    Inglese J, Johnson RL, Simeonov A, Xia M, Zheng W, Austin CP, et al. High-throughput screening assays for the identification of chemical probes. Nat Chem Biol. 2007;3(8):466–79.PubMedCrossRefGoogle Scholar
  56. 56.
    Di L, Kerns EH. Application of pharmaceutical profiling assays for optimization of drug-like properties. Curr Opin Drug Discov Dev. 2005;8(4):495–504.Google Scholar
  57. 57.
    McKinney JD. The molecular basis of chemical toxicity. Environ Health Perspect. 1985;61:5–10.PubMedCrossRefGoogle Scholar
  58. 58.
    Bristol DW, Wachsman JT, Greenwell A. The NIEHS predictive-toxicology evaluation project. Environ Health Perspect. 1996;104 Suppl 5:1001–10.PubMedCrossRefGoogle Scholar
  59. 59.
    Benigni R, Andreoli C, Zito R. Prediction of rodent carcinogenicity of further 30 chemicals bioassayed by the U.S. National Toxicology Program. Environ Health Perspect. 1996;104 Suppl 5:1041–4.PubMedCrossRefGoogle Scholar
  60. 60.
    Huff J, Haseman J. Long-term chemical carcinogenesis experiments for identifying potential human cancer hazards: collective database of the National Cancer Institute and National Toxicology Program (1976–1991). Environ Health Perspect. 1991;96:23–31.PubMedCrossRefGoogle Scholar
  61. 61.
    Ashby J. The NIEHS predictive-toxicology evaluation project: the need to distinguish informed uncertainty from ignorant equivocation. Environ Health Perspect. 1997;105(7):688.PubMedCrossRefGoogle Scholar
  62. 62.
    Ashby J, Tennant RW. Prediction of rodent carcinogenicity for 44 chemicals: results. Mutagenesis. 1994;9(1):7–15.PubMedCrossRefGoogle Scholar
  63. 63.
    Tennant RW, Spalding J, Stasiewicz S, Ashby J. Prediction of the outcome of rodent carcinogenicity bioassays currently being conducted on 44 chemicals by the National Toxicology Program. Mutagenesis. 1990;5(1):3–14.PubMedCrossRefGoogle Scholar
  64. 64.
    Ashby J, Tennant RW. Definitive relationships among chemical structure, carcinogenicity and mutagenicity for 301 chemicals tested by the U.S. NTP. Mutat Res. 1991;257(3):229–306.PubMedCrossRefGoogle Scholar
  65. 65.
    Lee Y, Buchanan BG, Rosenkranz HS. Carcinogenicity predictions for a group of 30 chemicals undergoing rodent cancer bioassays based on rules derived from subchronic organ toxicities. Environ Health Perspect. 1996;104 Suppl 5:1059–63.PubMedCrossRefGoogle Scholar
  66. 66.
    Parry JM. Detecting and predicting the activity of rodent carcinogens. Mutagenesis. 1994;9(1):3–5.PubMedCrossRefGoogle Scholar
  67. 67.
    King RD, Srinivasan A. Prediction of rodent carcinogenicity bioassays from molecular structure using inductive logic programming. Environ Health Perspect. 1996;104 Suppl 5:1031–40.PubMedCrossRefGoogle Scholar
  68. 68.
    Sams-Dodd F. Target-based drug discovery: is something wrong? Drug Discov Today. 2005;10(2):139–47.PubMedCrossRefGoogle Scholar
  69. 69.
    Roy K, Roy PP, Leonard JT. Exploring the impact of size of training sets for the development of predictive QSAR models. Chemometr Intell Lab. 2008;90(1):31–42.CrossRefGoogle Scholar
  70. 70.
    Benigni R, Bossa C. Mechanisms of chemical carcinogenicity and mutagenicity: a review with implications for predictive toxicology. Chem Rev. 2011;111(4):2507–36.PubMedCrossRefGoogle Scholar
  71. 71.
    Schwarz T, Schwarz A. DNA repair and cytokine responses. J Investig Dermatol Symp Proc. 2009;14(1):63–6.PubMedCrossRefGoogle Scholar
  72. 72.
    Iyer RR, Pluciennik A, Burdett V, Modrich PL. DNA mismatch repair: functions and mechanisms. Chem Rev. 2006;106(2):302–23.PubMedCrossRefGoogle Scholar
  73. 73.
    Saxowsky TT, Doetsch PW. RNA polymerase encounters with DNA damage: transcription-coupled repair or transcriptional mutagenesis? Chem Rev. 2006;106(2):474–88.PubMedCrossRefGoogle Scholar
  74. 74.
    Gonzalez FJ, Peters JM, Cattley RC. Mechanism of action of the nongenotoxic peroxisome proliferators: role of the peroxisome proliferator-activator receptor alpha. J Natl Cancer Inst. 1998;90(22):1702–9.PubMedCrossRefGoogle Scholar
  75. 75.
    Jaworska J, Nikolova-Jeliazkova N, Aldenberg T. QSAR applicability domain estimation by projection of the training set descriptor space: a review. Altern Lab Anim. 2005;33(5):445–59.PubMedGoogle Scholar
  76. 76.
    Sushko I, Novotarskyi S, Korner R, Pandey AK, Cherkasov A, Li J, et al. Applicability domains for classification problems: benchmarking of distance to models for Ames mutagenicity set. J Chem Inf Model. 2010;50(12):2094–111.PubMedCrossRefGoogle Scholar
  77. 77.
    Weaver S, Gleeson MP. The importance of the domain of applicability in QSAR modeling. J Mol Graph Model. 2008;26(8):1315–26.PubMedCrossRefGoogle Scholar
  78. 78.
    Wassermann AM, Peltason L, Bajorath J. Computational analysis of multi-target structure–activity relationships to derive preference orders for chemical modifications toward target selectivity. ChemMedChem. 2010;5(6):847–58.PubMedCrossRefGoogle Scholar
  79. 79.
    Andersen ME, Krewski D. Toxicity testing in the 21st century: bringing the vision to life. Toxicol Sci. 2009;107(2):324–30.PubMedCrossRefGoogle Scholar
  80. 80.
    Simmons SO, Fan CY, Ramabhadran R. Cellular stress response pathway system as a sentinel ensemble in toxicological screening. Toxicol Sci. 2009;111(2):202–25.PubMedCrossRefGoogle Scholar
  81. 81.
    Simmons SO, Fan CY, Yeoman K, Wakefield J, Ramabhadran R. NRF2 oxidative stress induced by heavy metals is cell type dependent. Curr Chem Genomics. 2011;5:1–12.PubMedCrossRefGoogle Scholar
  82. 82.
    Yamamoto KN, Hirota K, Kono K, Takeda S, Sakamuru S, Xia M, et al. Characterization of environmental chemicals with potential for DNA damage using isogenic DNA repair-deficient chicken DT40 cell lines. Environ Mol Mutagen. 2011;52(7):547–61.PubMedCrossRefGoogle Scholar
  83. 83.
    Miller SC, Huang R, Sakamuru S, Shukla SJ, Attene-Ramos MS, Shinn P, et al. Identification of known drugs that act as inhibitors of NF-kappaB signaling and their mechanism of action. Biochem Pharmacol. 2010;79(9):1272–80.PubMedCrossRefGoogle Scholar
  84. 84.
    Xia M, Huang R, Sun Y, Semenza GL, Aldred SF, Witt KL, et al. Identification of chemical compounds that induce HIF-1alpha activity. Toxicol Sci. 2009;112(1):153–63.PubMedCrossRefGoogle Scholar
  85. 85.
    Xia M, Shahane SA, Huang R, Titus SA, Shum E, Zhao Y, et al. Identification of quaternary ammonium compounds as potent inhibitors of hERG potassium channels. Toxicol Appl Pharmacol. 2011;252(3):250–8.PubMedCrossRefGoogle Scholar
  86. 86.
    Stouch TR, Kenyon JR, Johnson SR, Chen XQ, Doweyko A, Li Y. In silico ADME/Tox: why models fail. J Comput Aided Mol Des. 2003;17(2–4):83–92.PubMedCrossRefGoogle Scholar
  87. 87.
    Veith H, Southall N, Huang R, James T, Fayne D, Artemenko N, et al. Comprehensive characterization of cytochrome P450 isozyme selectivity across chemical libraries. Nat Biotechnol. 2009;27(11):1050–5.PubMedCrossRefGoogle Scholar
  88. 88.
    Li Q, Cheng T, Wang Y, Bryant SH. PubChem as a public resource for drug discovery. Drug Discov Today. 2010;15(23–24):1052–7.PubMedCrossRefGoogle Scholar
  89. 89.
    Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH. PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic Acids Res. 2009;37:W623–33. Web Server issue.PubMedCrossRefGoogle Scholar
  90. 90.
    Cheng F, Yu Y, Shen J, Yang L, Li W, Liu G, et al. Classification of cytochrome P450 inhibitors and noninhibitors using combined classifiers. J Chem Inf Model. 2011;51:996–1011.Google Scholar
  91. 91.
    Shen MY, Su BH, Esposito EX, Hopfinger AJ, Tseng YJ. A comprehensive support vector machine binary hERG classification model based on extensive but biased end point hERG data sets. Chem Res Toxicol. 2011;24(6):934–49.PubMedCrossRefGoogle Scholar
  92. 92.
    Sun H, Veith H, Xia M, Austin CP, Huang R. Predictive models for CYP450 isozymes based on qHTS data. J Chem Inf Model. 2011;51:2474–81.PubMedCrossRefGoogle Scholar
  93. 93.
    Bezdek JC, Pal NR. Some new indexes of cluster validity. IEEE Trans Syst Man Cybern B Cybern. 1998;28(3):301–15.PubMedCrossRefGoogle Scholar
  94. 94.
    DiMaggio Jr PA, Subramani A, Judson RS, Floudas CA. A novel framework for predicting in vivo toxicities from in vitro data using optimal methods for dense and sparse matrix reordering and logistic regression. Toxicol Sci. 2010;118(1):251–65.PubMedCrossRefGoogle Scholar
  95. 95.
    Zhu H, Rusyn I, Richard A, Tropsha A. Use of cell viability assay data improves the prediction accuracy of conventional quantitative structure–activity relationship models of animal carcinogenicity. Environ Health Perspect. 2008;116(4):506–13.PubMedGoogle Scholar
  96. 96.
    Sedykh A, Zhu H, Tang H, Zhang L, Richard A, Rusyn I, et al. Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity. Environ Health Perspect. 2011;119(3):364–70.PubMedCrossRefGoogle Scholar

Copyright information

© American Association of Pharmaceutical Scientists 2012

Authors and Affiliations

  1. 1.Department of Health and Human Services, NIH Chemical Genomics CenterNational Institutes of HealthBethesdaUSA

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