1932

Abstract

Traditionally, chemical toxicity is determined by in vivo animal studies, which are low throughput, expensive, and sometimes fail to predict compound toxicity in humans. Due to the increasing number of chemicals in use and the high rate of drug candidate failure due to toxicity, it is imperative to develop in vitro, high-throughput screening methods to determine toxicity. The Tox21 program, a unique research consortium of federal public health agencies, was established to address and identify toxicity concerns in a high-throughput, concentration-responsive manner using a battery of in vitro assays. In this article, we review the advancements in high-throughput robotic screening methodology and informatics processes to enable the generation of toxicological data, and their impact on the field; further, we discuss the future of assessing environmental toxicity utilizing efficient and scalable methods that better represent the corresponding biological and toxicodynamic processes in humans.

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2024-01-23
2024-06-13
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Literature Cited

  1. 1.
    Quevedo C, Behl M, Ryan K, Paules RS, Alday A et al. 2019. Detection and prioritization of developmentally neurotoxic and/or neurotoxic compounds using zebrafish. Toxicol. Sci. 168:225–40
    [Google Scholar]
  2. 2.
    Hsu C-W, Huang R, Attene-Ramos M, Austin C, Simeonov A, Xia M. 2017. Advances in high-throughput screening technology for toxicology. Int. J. Risk Assess. Manag. 20:109–35
    [Google Scholar]
  3. 3.
    Xia M, Huang R, Witt KL, Southall N, Fostel J et al. 2008. Compound cytotoxicity profiling using quantitative high-throughput screening. Environ. Health Perspect. 116:284–91
    [Google Scholar]
  4. 4.
    Attene-Ramos MS, Huang R, Sakamuru S, Witt KL, Beeson GC et al. 2013. Systematic study of mitochondrial toxicity of environmental chemicals using quantitative high throughput screening. Chem. Res. Toxicol. 26:1323–32
    [Google Scholar]
  5. 5.
    Collins FS, Gray GM, Bucher JR. 2008. Transforming environmental health protection. Science 319:906–7
    [Google Scholar]
  6. 6.
    Attene-Ramos MS, Miller N, Huang R, Michael S, Itkin M et al. 2013. The Tox21 robotic platform for the assessment of environmental chemicals—from vision to reality. Drug Discov. Today 18:716–23
    [Google Scholar]
  7. 7.
    Shukla SJ, Huang R, Austin CP, Xia M. 2010. The future of toxicity testing: a focus on in vitro methods using a quantitative high-throughput screening platform. Drug Discov. Today 15:997–1007
    [Google Scholar]
  8. 8.
    Richard AM, Huang R, Waidyanatha S, Shinn P, Collins BJ et al. 2021. The Tox21 10K compound library: collaborative chemistry advancing toxicology. Chem. Res. Toxicol. 34:189–216
    [Google Scholar]
  9. 9.
    Thomas RS, Paules RS, Simeonov A, Fitzpatrick SC, Crofton KM et al. 2018. The US federal Tox21 program: a strategic and operational plan for continued leadership. ALTEX 35:163–68
    [Google Scholar]
  10. 10.
    Inglese J, Auld DS, Jadhav A, Johnson RL, Simeonov A et al. 2006. Quantitative high-throughput screening: a titration-based approach that efficiently identifies biological activities in large chemical libraries. PNAS 103:11473–78
    [Google Scholar]
  11. 11.
    Li Y, McGreal S, Zhao J, Huang R, Zhou Y et al. 2016. A cell-based quantitative high-throughput image screening identified novel autophagy modulators. Pharmacol. Res. 110:35–49
    [Google Scholar]
  12. 12.
    Li S, Zhang L, Huang R, Xu T, Parham F et al. 2021. Evaluation of chemical compounds that inhibit neurite outgrowth using GFP-labeled iPSC-derived human neurons. Neurotoxicology 83:137–45
    [Google Scholar]
  13. 13.
    Zhang L, Li S, Xia M. 2022. Correction: High-throughput neurite outgrowth assay using GFP-labeled iPSC-derived neurons. Curr. Protoc. 2:e581
    [Google Scholar]
  14. 14.
    Huang R, Xu M, Zhu H, Chen CZ, Zhu W et al. 2021. Biological activity-based modeling identifies antiviral leads against SARS-CoV-2. Nat. Biotechnol. 39:747–53
    [Google Scholar]
  15. 15.
    Zhang L, Zhao J, Ding WX, Xia M. 2022. GFP-LC3 high-content assay for screening autophagy modulators. Methods Mol. Biol. 2474:83–89
    [Google Scholar]
  16. 16.
    Slavov S, Stoyanova-Slavova I, Li S, Zhao J, Huang R et al. 2017. Why are most phospholipidosis inducers also hERG blockers?. Arch. Toxicol. 91:3885–95
    [Google Scholar]
  17. 17.
    Sun H, Huang R, Xia M, Shahane S, Southall N, Wang Y. 2017. Prediction of hERG liability—using SVM classification, bootstrapping and jackknifing. Mol. Inform. 36:1600126
    [Google Scholar]
  18. 18.
    Zhao J, Xia M. 2022. Cell-based hERG channel inhibition assay in high-throughput format. Methods Mol. Biol. 2474:21–28
    [Google Scholar]
  19. 19.
    Kavlock RJ, Austin CP, Tice RR. 2009. Toxicity testing in the 21st century: implications for human health risk assessment. Risk Anal. 29:485–87
    [Google Scholar]
  20. 20.
    Markossian S, Grossman A, Brimacombe K, Arkin M, Auld D et al., eds. 2004. Assay Guidance Manual Bethesda, MD: Eli Lilly & Company, Natl. Cent. Adv. Transl. Sci.
    [Google Scholar]
  21. 21.
    Southall NT, Jadhav A, Huang R, Nguyen T, Wang Y 2009. Enabling the large-scale analysis of quantitative high-throughput screening data. Handbook of Drug Screening R Seethala, L Zhang 442–63. New York: Taylor & Francis. , 2nd ed..
    [Google Scholar]
  22. 22.
    Hill AV. 1910. The possible effects of the aggregation of the molecules of haemoglobin on its dissociation curves. J. Physiol. 40:4–7
    [Google Scholar]
  23. 23.
    Wang Y, Jadhav A, Southal N, Huang R, Nguyen DT. 2010. A grid algorithm for high throughput fitting of dose-response curve data. Curr. Chem. Genom. 4:57–66
    [Google Scholar]
  24. 24.
    Huang R. 2016. A quantitative high-throughput screening data analysis pipeline for activity profiling. Methods Mol. Biol. 1473:111–22
    [Google Scholar]
  25. 25.
    Sakamuru S, Zhu H, Xia M, Simeonov A, Huang R. 2020. Profiling the Tox21 chemical library for environmental hazards: applications in prioritisation, predictive modelling, and mechanism of toxicity characterisation. Big Data in Predictive Toxicology D Neagu, A-N Richarz 242–63. London: Royal Soc. Chem.
    [Google Scholar]
  26. 26.
    Kumar GN, Surapaneni S. 2001. Role of drug metabolism in drug discovery and development. Med. Res. Rev. 21:397–411
    [Google Scholar]
  27. 27.
    Lynch C, Sakamuru S, Huang R, Niebler J, Ferguson SS, Xia M. 2021. Characterization of human pregnane X receptor activators identified from a screening of the Tox21 compound library. Biochem. Pharmacol. 184:114368
    [Google Scholar]
  28. 28.
    Mackowiak B, Hodge J, Stern S, Wang H. 2018. The roles of xenobiotic receptors: beyond chemical disposition. Drug Metab. Dispos. 46:1361–71
    [Google Scholar]
  29. 29.
    Zhou C, Verma S, Blumberg B. 2009. The steroid and xenobiotic receptor (SXR), beyond xenobiotic metabolism. Nucl. Recept. Signal. 7:e001
    [Google Scholar]
  30. 30.
    Lynch C, Sakamuru S, Xia M. 2022. Screening method for the identification of compounds that activate pregnane X receptor. Curr. Protoc. 2:e615
    [Google Scholar]
  31. 31.
    Lynch C, Mackowiak B, Huang R, Li L, Heyward S et al. 2019. Identification of modulators that activate the constitutive androstane receptor from the Tox21 10K compound library. Toxicol. Sci. 167:282–92
    [Google Scholar]
  32. 32.
    Gašperšič R, Koritnik B, Črne-Finderle N, Sketelj J. 1999. Acetylcholinesterase in the neuromuscular junction. Chem. Biol. Interact. 119–20:301–8
    [Google Scholar]
  33. 33.
    Picciotto MR, Higley MJ, Mineur YS. 2012. Acetylcholine as a neuromodulator: Cholinergic signaling shapes nervous system function and behavior. Neuron 76:116–29
    [Google Scholar]
  34. 34.
    Soreq H, Seidman S. 2001. Acetylcholinesterase—new roles for an old actor. Nat. Rev. Neurosci. 2:294–302
    [Google Scholar]
  35. 35.
    Čolović MB, Krstić DZ, Lazarević-Pašti TD, Bondžić AM, Vasić VM. 2013. Acetylcholinesterase inhibitors: pharmacology and toxicology. Curr. Neuropharmacol. 11:315–35
    [Google Scholar]
  36. 36.
    Li S, Huang R, Solomon S, Liu Y, Zhao B et al. 2017. Identification of acetylcholinesterase inhibitors using homogenous cell-based assays in quantitative high-throughput screening platforms. Biotechnol. J. 12:1600715
    [Google Scholar]
  37. 37.
    Li S, Li AJ, Zhao J, Santillo MF, Xia M. 2022. Acetylcholinesterase inhibition assays for high-throughput screening. Methods Mol. Biol. 2474:47–58
    [Google Scholar]
  38. 38.
    Li SZ, Zhao JH, Huang RL, Santillo MF, Houck KA, Xia MH. 2019. Use of high-throughput enzyme-based assay with xenobiotic metabolic capability to evaluate the inhibition of acetylcholinesterase activity by organophosphorous pesticides. Toxicol. In Vitro 56:93–100
    [Google Scholar]
  39. 39.
    Li S, Zhao J, Huang R, Travers J, Klumpp-Thomas C et al. 2021. Profiling the Tox21 chemical collection for acetylcholinesterase inhibition. Environ. Health Perspect. 129:47008
    [Google Scholar]
  40. 40.
    Santillo MF, Xia M 2021. High-throughput screening for identifying acetylcholinesterase inhibitors: insights on novel inhibitors and the use of liver microsomes. SLAS Discov. 27:65–67
    [Google Scholar]
  41. 41.
    Ooka M, Zhao J, Shah P, Travers J, Klumpp-Thomas C et al. 2022. Identification of environmental chemicals that activate p53 signaling after in vitro metabolic activation. Arch. Toxicol. 96:1975–87
    [Google Scholar]
  42. 42.
    Williams AB, Schumacher B. 2016. p53 in the DNA-damage-repair process. Cold Spring Harb. Perspect. Med. 6:a026070
    [Google Scholar]
  43. 43.
    Witt KL, Hsieh JH, Smith-Roe SL, Xia M, Huang R et al. 2017. Assessment of the DNA damaging potential of environmental chemicals using a quantitative high-throughput screening approach to measure p53 activation. Environ. Mol. Mutagen. 58:494–507
    [Google Scholar]
  44. 44.
    Taneja I, Karsauliya K, Rashid M, Sonkar AK, Rama Raju KS et al. 2018. Species differences between rat and human in vitro metabolite profile, in vivo predicted clearance, CYP450 inhibition and CYP450 isoforms that metabolize benzanthrone: implications in risk assessment. Food Chem. Toxicol. 111:94–101
    [Google Scholar]
  45. 45.
    Tollefsen KE, Scholz S, Cronin MT, Edwards SW, de Knecht J et al. 2014. Applying adverse outcome pathways (AOPs) to support integrated approaches to testing and assessment (IATA). Regul. Toxicol. Pharmacol. 70:629–40
    [Google Scholar]
  46. 46.
    Wei Z, Fang Y, Gosztyla ML, Li AJ, Huang W et al. 2021. A direct peptide reactivity assay using a high-throughput mass spectrometry screening platform for detection of skin sensitizers. Toxicol. Lett. 338:67–77
    [Google Scholar]
  47. 47.
    Gerberick GF, Troutman JA, Foertsch LM, Vassallo JD, Quijano M et al. 2009. Investigation of peptide reactivity of pro-hapten skin sensitizers using a peroxidase-peroxide oxidation system. Toxicol. Sci. 112:164–74
    [Google Scholar]
  48. 48.
    Draize JH, Woodard G, Calvery HO. 1944. Methods for the study of irritation and toxicity of substances applied topically to the skin and mucous membranes. J. Pharmacol. Exp. Ther. 82:377–90
    [Google Scholar]
  49. 49.
    Organ. Econ. Coop. Dev. (OECD) 2021. Test no.439: in vitro skin irritation: reconstructed human epidermis test method OECD Guidel. OECD Paris:
    [Google Scholar]
  50. 50.
    Wei Z, Liu X, Ooka M, Zhang L, Song MJ et al. 2020. Two-dimensional cellular and three-dimensional bio-printed skin models to screen topical-use compounds for irritation potential. Front. Bioeng. Biotechnol. 8:109
    [Google Scholar]
  51. 51.
    Chance B, Williams GR. 1956. The respiratory chain and oxidative phosphorylation. Adv. Enzymol. Relat. Subj. Biochem. 17:65–134
    [Google Scholar]
  52. 52.
    Chen LB. 1988. Mitochondrial membrane potential in living cells. Annu. Rev. Cell Biol. 4:155–81
    [Google Scholar]
  53. 53.
    Lemasters JJ, Qian T, He L, Kim JS, Elmore SP et al. 2002. Role of mitochondrial inner membrane permeabilization in necrotic cell death, apoptosis, and autophagy. Antioxid. Redox Signal. 4:769–81
    [Google Scholar]
  54. 54.
    Sakamuru S, Attene-Ramos MS, Xia M. 2016. Mitochondrial membrane potential assay. Methods Mol. Biol. 1473:17–22
    [Google Scholar]
  55. 55.
    Sakamuru S, Li X, Attene-Ramos MS, Huang R, Lu J et al. 2012. Application of a homogenous membrane potential assay to assess mitochondrial function. Physiol. Genom. 44:495–503
    [Google Scholar]
  56. 56.
    Attene-Ramos MS, Huang R, Michael S, Witt KL, Richard A et al. 2015. Profiling of the Tox21 chemical collection for mitochondrial function to identify compounds that acutely decrease mitochondrial membrane potential. Environ. Health Perspect. 123:49–56
    [Google Scholar]
  57. 57.
    Xia M, Huang R, Shi Q, Boyd WA, Zhao J et al. 2018. Comprehensive analyses and prioritization of Tox21 10K chemicals affecting mitochondrial function by in-depth mechanistic studies. Environ. Health Perspect. 126:077010
    [Google Scholar]
  58. 58.
    Wei Z, Zhao J, Niebler J, Hao JJ, Merrick BA, Xia M. 2020. Quantitative proteomic profiling of mitochondrial toxicants in a human cardiomyocyte cell line. Front. Genet. 11:719
    [Google Scholar]
  59. 59.
    Mav D, Shah RR, Howard BE, Auerbach SS, Bushel PR et al. 2018. A hybrid gene selection approach to create the S1500+ targeted gene sets for use in high-throughput transcriptomics. PLOS ONE 13:e0191105
    [Google Scholar]
  60. 60.
    Merrick BA. 2019. Next generation sequencing data for use in risk assessment. Curr. Opin. Toxicol. 18:18–26
    [Google Scholar]
  61. 61.
    Rentschler T, Gries P, Behrens T, Bruelheide H, Kuhn P et al. 2019. Comparison of catchment scale 3D and 2.5D modelling of soil organic carbon stocks in Jiangxi Province, PR China. PLOS ONE 14:e0220881
    [Google Scholar]
  62. 62.
    Brandon EFA, Bulder AS, van Engelen JGM, Mahieu CM, Mennes WC et al. 2013. Does EU legislation allow the use of the Benchmark Dose (BMD) approach for risk assessment?. Regul. Toxicol. Pharmacol. 67:182–88
    [Google Scholar]
  63. 63.
    Huang R, Xia M, Nguyen D-T, Zhao T, Sakamuru S et al. 2016. Tox21Challenge to build predictive models of nuclear receptor and stress response pathways as mediated by exposure to environmental chemicals and drugs. Front. Environ. Sci. 3:85
    [Google Scholar]
  64. 64.
    Huang R, Xia M, Sakamuru S, Zhao J, Shahane SA et al. 2016. Modelling the Tox21 10K chemical profiles for in vivo toxicity prediction and mechanism characterization. Nat. Commun. 7:10425
    [Google Scholar]
  65. 65.
    Judson RS, Martin MT, Reif DM, Houck KA, Knudsen TB et al. 2010. Analysis of eight oil spill dispersants using rapid, in vitro tests for endocrine and other biological activity. Environ. Sci. Technol. 44:5979–85
    [Google Scholar]
  66. 66.
    Sakamuru S, Huang R, Xia M. 2022. Use of Tox21 screening data to evaluate the COVID-19 drug candidates for their potential toxic effects and related pathways. Front. Pharmacol. 13:935399
    [Google Scholar]
  67. 67.
    Hubbard TD, Hsieh JH, Rider CV, Sipes NS, Sedykh A et al. 2019. Using Tox21 high-throughput screening assays for the evaluation of botanical and dietary supplements. Appl. Vitro Toxicol. 5:10–25
    [Google Scholar]
  68. 68.
    Hsieh JH, Smith-Roe SL, Huang R, Sedykh A, Shockley KR et al. 2019. Identifying compounds with genotoxicity potential using Tox21 high-throughput screening assays. Chem. Res. Toxicol. 32:1384–401
    [Google Scholar]
  69. 69.
    Ooka M, Yang S, Zhang L, Kojima K, Huang R et al. 2023. Lestaurtinib induces DNA damage that is related to estrogen receptor activation. Curr. Res. Toxicol. 4:100102
    [Google Scholar]
  70. 70.
    Kabir M, Padilha EC, Shah P, Huang R, Sakamuru S et al. 2022. Identification of selective CYP3A7 and CYP3A4 substrates and inhibitors using a high-throughput screening platform. Front. Pharmacol. 13:899536
    [Google Scholar]
  71. 71.
    Xu T, Kabir M, Sakamuru S, Shah P, Padilha EC et al. 2023. Predictive models for human cytochrome P450 3A7 selective inhibitors and substrates. J. Chem. Inf. Model. 63:846–55
    [Google Scholar]
  72. 72.
    Li Z, Lang Y, Sakamuru S, Samrat S, Trudeau N et al. 2020. Methylene blue is a potent and broad-spectrum inhibitor against Zika virus in vitro and in vivo. Emerg. Microbes Infect. 9:2404–16
    [Google Scholar]
  73. 73.
    Zhu H, Chen CZ, Sakamuru S, Zhao J, Ngan DK et al. 2021. Mining of high throughput screening database reveals AP-1 and autophagy pathways as potential targets for COVID-19 therapeutics. Sci. Rep. 11:6725
    [Google Scholar]
  74. 74.
    Bhhatarai B, Wilson DM, Bartels MJ, Chaudhuri S, Price PS, Carney EW. 2015. Acute toxicity prediction in multiple species by leveraging mechanistic ToxCast mitochondrial inhibition data and simulation of oral bioavailability. Toxicol. Sci. 147:386–96
    [Google Scholar]
  75. 75.
    Burns AP, Zhang YQ, Xu T, Wei Z, Yao Q et al. 2021. A universal and high-throughput proteomics sample preparation platform. Anal. Chem. 93:8423–31
    [Google Scholar]
  76. 76.
    Ooka M, Lynch C, Xia M. 2020. Application of in vitro metabolism activation in high-throughput screening. Int. J. Mol. Sci. 21:8182
    [Google Scholar]
  77. 77.
    Yang S, Ooka M, Margolis RJ, Xia M. 2023. Liver three-dimensional cellular models for high-throughput chemical testing. Cell Rep. Methods 3:100432
    [Google Scholar]
  78. 78.
    Krewski D, Andersen ME, Tyshenko MG, Krishnan K, Hartung T et al. 2020. Toxicity testing in the 21st century: progress in the past decade and future perspectives. Arch. Toxicol. 94:1–58
    [Google Scholar]
  79. 79.
    Proenca S, Escher BI, Fischer FC, Fisher C, Gregoire S et al. 2021. Effective exposure of chemicals in in vitro cell systems: a review of chemical distribution models. Toxicol. Vitro 73:105133
    [Google Scholar]
  80. 80.
    Judson RS, Magpantay FM, Chickarmane V, Haskell C, Tania N et al. 2015. Integrated model of chemical perturbations of a biological pathway using 18 in vitro high-throughput screening assays for the estrogen receptor. Toxicol. Sci. 148:137–54
    [Google Scholar]
  81. 81.
    Kumar D, Baligar P, Srivastav R, Narad P, Raj S et al. 2021. Stem cell based preclinical drug development and toxicity prediction. Curr. Pharm. Des. 27:2237–51
    [Google Scholar]
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