Abstract
Accurate prediction of drug- and chemical-induced hepatotoxicity remains to be a problem for pharmaceutical companies as well as other industries and regulators. The goal of the current study was to develop an in vitro/in silico method for the rapid and accurate prediction of drug- and chemical-induced hepatocyte injury in humans. HepaRG cells were employed for high-throughput imaging in combination with phenotypic profiling. A reference set of 69 drugs and chemicals was screened at a range of 7 concentrations, and the cellular response values were used for training a supervised classifier and for determining assay performance by using tenfold cross-validation. The results showed that the best performing phenotypic features were related to nuclear translocation of RELA (RELA proto-oncogene, NF-kB subunit; also known as NF-kappa B p65), DNA organization, and the F-actin cytoskeleton. Using a subset of 30 phenotypic features, direct hepatocyte toxicity in humans could be predicted with a test sensitivity, specificity and balanced accuracy of 73%, 92%, and 83%, respectively. The method was applied to another set of 26 drugs and chemicals with unclear annotation and their hepatocyte toxicity in humans was predicted. The results also revealed that the identified discriminative phenotypic changes were related to cell death and cellular senescence. Whereas cell death-related endpoints are widely applied in in vitro toxicology, cellular senescence-related endpoints are not, although cellular senescence can be induced by various drugs and other small molecule compounds and plays an important role in liver injury and disease. These findings show how phenotypic profiling can reveal unexpected chemical-induced mechanisms in toxicology.
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References
Abd El-Kader SM, El-Den Ashmawy EMS (2015) Non-alcoholic fatty liver disease: the diagnosis and management. World J Hepatol 7(6):846–858
Aird KM, Zhang R (2013) Detection of senescence-associated heterochromatin foci (SAHF). Methods Mol Biol 965:185–196. https://doi.org/10.1007/978-1-62703-239-1_12
Akaike H (1974) A new look at statistical model identification. IEEE Trans Autom Control 19:716–723. https://doi.org/10.1109/TAC.1974.1100705
Albrecht W, Kappenberg F, Brecklinghaus T et al (2019) Prediction of human drug-induced liver injury (DILI) in relation to oral doses and blood concentrations. Arch Toxicol 93(6):1609–1637. https://doi.org/10.1007/s00204-019-02492-9
Andersson TB, Kanebratt KP, Kenna JG (2012) The HepaRG cell line: a unique in vitro tool for understanding drug metabolism and toxicology in human. Expert Opin Drug Metab Toxicol 8(7):909–920. https://doi.org/10.1517/17425255.2012.685159
Aninat C, Piton A, Glaise D et al (2006) Expression of cytochromes P450, conjugating enzymes and nuclear receptors in human hepatoma HepaRG cells. Drug Metab Dispos Biol Fate Chem 34(1):75–83. https://doi.org/10.1124/dmd.105.006759
Antherieu S, Chesne C, Li R et al (2010) Stable expression, activity, and inducibility of cytochromes P450 in differentiated HepaRG cells. Drug Metab Dispos Biol Fate Chem 38(3):516–525. https://doi.org/10.1124/dmd.109.030197
Antherieu S, Rogue A, Fromenty B, Guillouzo A, Robin MA (2011) Induction of vesicular steatosis by amiodarone and tetracycline is associated with up-regulation of lipogenic genes in HepaRG cells. Hepatology 53(6):1895–1905. https://doi.org/10.1002/hep.24290
Aravinthan AD, Alexander GJM (2016) Senescence in chronic liver disease: is the future in aging? J Hepatol 65(4):825–834. https://doi.org/10.1016/j.jhep.2016.05.030
Ben-Hur A, Ong CS, Sonnenburg S, Scholkopf B, Ratsch G (2008) Support vector machines and kernels for computational biology. PLoS Comput Biol 4(10):e1000173. https://doi.org/10.1371/journal.pcbi.1000173
Buckalew VM Jr (1996) Habitual use of acetaminophen as a risk factor for chronic renal failure: a comparison with phenacetin. Am J Kidney Dis 28(1 Suppl 1):S7–13
Calcinotto A, Kohli J, Zagato E, Pellegrini L, Demaria M, Alimonti A (2019) Cellular senescence: aging, cancer, and injury. Physiol Rev 99(2):1047–1078. https://doi.org/10.1152/physrev.00020.2018
Campisi J, d'Adda di Fagagna F (2007) Cellular senescence: when bad things happen to good cells. Nat Rev Mol Cell Biol 8(9):729–740. https://doi.org/10.1038/nrm2233
Chen D, Tang Z, Luo C, Chen H, Liu Z (2012) Clinical and pathological spectrums of aristolochic acid nephropathy. Clin Nephrol 78(1):54–60
Chien Y, Scuoppo C, Wang X et al (2011) Control of the senescence-associated secretory phenotype by NF-kappaB promotes senescence and enhances chemosensitivity. Genes Dev 25(20):2125–2136. https://doi.org/10.1101/gad.17276711
Ciarimboli G (2014) Membrane transporters as mediators of cisplatin side-effects. Anticancer Res 34(1):547–550
Desouza M, Gunning PW, Stehn JR (2012) The actin cytoskeleton as a sensor and mediator of apoptosis. Bioarchitecture 2(3):75–87. https://doi.org/10.4161/bioa.20975
Dutta J, Fan Y, Gupta N, Fan G, Gelinas C (2006) Current insights into the regulation of programmed cell death by NF-kappaB. Oncogene 25(51):6800–6816. https://doi.org/10.1038/sj.onc.1209938
Emadali A, Muscatelli-Groux B, Delom F et al (2006) Proteomic analysis of ischemia-reperfusion injury upon human liver transplantation reveals the protective role of IQGAP1. Mol Cell Proteom 5(7):1300–1313. https://doi.org/10.1074/mcp.M500393-MCP200
Fan Y, Dutta J, Gupta N, Fan G, Gelinas C (2008) Regulation of programmed cell death by NF-kappaB and its role in tumorigenesis and therapy. Adv Exp Med Biol 615:223–250. https://doi.org/10.1007/978-1-4020-6554-5_11
Fraczek J, Bolleyn J, Vanhaecke T, Rogiers V, Vinken M (2013) Primary hepatocyte cultures for pharmaco-toxicological studies: at the busy crossroad of various anti-dedifferentiation strategies. Arch Toxicol 87(4):577–610. https://doi.org/10.1007/s00204-012-0983-3
Franklin-Tong VE, Gourlay CW (2008) A role for actin in regulating apoptosis/programmed cell death: evidence spanning yeast, plants and animals. Biochem J 413(3):389–404. https://doi.org/10.1042/BJ20080320
Frey N, Venturelli S, Zender L, Bitzer M (2018) Cellular senescence in gastrointestinal diseases: from pathogenesis to therapeutics. Nat Rev Gastroenterol Hepatol 15(2):81–95. https://doi.org/10.1038/nrgastro.2017.146
Garside H, Marcoe KF, Chesnut-Speelman J et al (2014) Evaluation of the use of imaging parameters for the detection of compound-induced hepatotoxicity in 384-well cultures of HepG2 cells and cryopreserved primary human hepatocytes. Toxicol In Vitro 28(2):171–181. https://doi.org/10.1016/j.tiv.2013.10.015
Gerets HH, Tilmant K, Gerin B et al (2012) Characterization of primary human hepatocytes, HepG2 cells, and HepaRG cells at the mRNA level and CYP activity in response to inducers and their predictivity for the detection of human hepatotoxins. Cell Biol Toxicol 28(2):69–87. https://doi.org/10.1007/s10565-011-9208-4
Gomez-Lechon MJ, Tolosa L, Conde I, Donato MT (2014) Competency of different cell models to predict human hepatotoxic drugs. Expert Opin Drug Metab Toxicol 10(11):1553–1568. https://doi.org/10.1517/17425255.2014.967680
Guillouzo A, Corlu A, Aninat C, Glaise D, Morel F, Guguen-Guillouzo C (2007) The human hepatoma HepaRG cells: a highly differentiated model for studies of liver metabolism and toxicity of xenobiotics. Chem Biol Interact 168(1):66–73. https://doi.org/10.1016/j.cbi.2006.12.003
Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC 3:610–621. https://doi.org/10.1109/TSMC.1973.4309314
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning, data mining, interference, and prediction, 2nd edn. Springer, New York
He S, Sharpless NE (2017) Senescence in health and disease. Cell 169(6):1000–1011. https://doi.org/10.1016/j.cell.2017.05.015
Hellmann A, Rule S, Walewski J et al (2011) Effect of cytochrome P450 3A4 inducers on the pharmacokinetic, pharmacodynamic and safety profiles of bortezomib in patients with multiple myeloma or non-Hodgkin's lymphoma. Clin Pharmacokinet 50(12):781–791. https://doi.org/10.2165/11594410-000000000-00000
Hickson LJ, Langhi Prata LGP, Bobart SA et al (2019) Senolytics decrease senescent cells in humans: preliminary report from a clinical trial of Dasatinib plus Quercetin in individuals with diabetic kidney disease. EBioMedicine 47:446–456. https://doi.org/10.1016/j.ebiom.2019.08.069
Kandasamy K, Chuah JK, Su R et al (2015) Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods. Sci Rep 5:12337. https://doi.org/10.1038/srep12337
Khetani SR, Kanchagar C, Ukairo O et al (2013) Use of micropatterned cocultures to detect compounds that cause drug-induced liver injury in humans. Toxicol Sci 132(1):107–117. https://doi.org/10.1093/toxsci/kfs326
Kirkland JL, Tchkonia T (2017) Cellular senescence: a translational perspective. EBioMedicine 21:21–28. https://doi.org/10.1016/j.ebiom.2017.04.013
Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: IJCAI'95 proceedings of the 14th international joint conference on artificial intelligence, Montreal, Quebec, Canada, 1995. vol 2. Morgan Kaufmann Publishers Inc. San Francisco, CA, USA, pp1137–1143
Laksameethanasan D, Tan R, Toh G, Loo LH (2013) cellXpress: a fast and user-friendly software platform for profiling cellular phenotypes. BMC Bioinform 14(Suppl 16):S4. https://doi.org/10.1186/1471-2105-14-S16-S4
Lee JJ, Miller JA, Basu S, Kee TV, Loo LH (2018) Building predictive in vitro pulmonary toxicity assays using high-throughput imaging and artificial intelligence. Arch Toxicol 92(6):2055–2075. https://doi.org/10.1007/s00204-018-2213-0
Li R, Zhang X, Tian X et al (2017) Triptolide inhibits tumor growth by induction of cellular senescence. Oncol Rep 37(1):442–448. https://doi.org/10.3892/or.2016.5258
Li Y, Kandasamy K, Chuah JKC et al (2014) Identification of nephrotoxic compounds with embryonic stem cell-derived human renal proximal tubular-like cells. Mol Pharm 11(7):1982–1990
Li Y, Oo ZY, Chang SY et al (2013) An in vitro method for the prediction of renal proximal tubular toxicity in humans. Toxicol Res 2(5):352–362
Lin Z, Will Y (2012) Evaluation of drugs with specific organ toxicities in organ-specific cell lines. Toxicol Sci 126(1):114–127. https://doi.org/10.1093/toxsci/kfr339
Loo LH, Wu LF, Altschuler SJ (2007) Image-based multivariate profiling of drug responses from single cells. Nat Methods 4(5):445–453. https://doi.org/10.1038/nmeth1032
Loo LH, Zink D (2017) High-throughput prediction of nephrotoxicity in humans. Altern Lab Anim 45(5):241–252
Lopez-Riera M, Conde I, Tolosa L et al (2017) New microRNA biomarkers for drug-induced steatosis and their potential to predict the contribution of drugs to non-alcoholic fatty liver disease. Front Pharmacol 8:3. https://doi.org/10.3389/fphar.2017.00003
Lubberstedt M, Muller-Vieira U, Mayer M et al (2011) HepaRG human hepatic cell line utility as a surrogate for primary human hepatocytes in drug metabolism assessment in vitro. J Pharmacol Toxicol Methods 63(1):59–68. https://doi.org/10.1016/j.vascn.2010.04.013
Luedde T, Schwabe RF (2011) NF-kappaB in the liver–linking injury, fibrosis and hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 8(2):108–118. https://doi.org/10.1038/nrgastro.2010.213
Mavri-Damelin D, Eaton S, Damelin LH, Rees M, Hodgson HJ, Selden C (2007) Ornithine transcarbamylase and arginase I deficiency are responsible for diminished urea cycle function in the human hepatoblastoma cell line HepG2. Int J Biochem Cell Biol 39(3):555–564. https://doi.org/10.1016/j.biocel.2006.10.007
Miller RP, Tadagavadi RK, Ramesh G, Reeves WB (2010) Mechanisms of Cisplatin nephrotoxicity. Toxins (Basel) 2(11):2490–2518. https://doi.org/10.3390/toxins2112490
Moujaber O, Fishbein F, Omran N et al (2019) Cellular senescence is associated with reorganization of the microtubule cytoskeleton. Cell Mol Life Sci 76(6):1169–1183. https://doi.org/10.1007/s00018-018-2999-1
Munoz-Espin D, Serrano M (2014) Cellular senescence: from physiology to pathology. Nat Rev Mol Cell Biol 15(7):482–496. https://doi.org/10.1038/nrm3823
Nagano T, Nakano M, Nakashima A et al (2016) Identification of cellular senescence-specific genes by comparative transcriptomics. Sci Rep 6:31758. https://doi.org/10.1038/srep31758
Nishio K, Inoue A (2005) Senescence-associated alterations of cytoskeleton: extraordinary production of vimentin that anchors cytoplasmic p53 in senescent human fibroblasts. Histochem Cell Biol 123(3):263–273. https://doi.org/10.1007/s00418-005-0766-5
O'Brien PJ, Irwin W, Diaz D et al (2006) High concordance of drug-induced human hepatotoxicity with in vitro cytotoxicity measured in a novel cell-based model using high content screening. Arch Toxicol 80(9):580–604. https://doi.org/10.1007/s00204-006-0091-3
Oeckinghaus A, Ghosh S (2009) The NF-kappaB family of transcription factors and its regulation. Cold Spring Harb Perspect Biol 1(4):a000034. https://doi.org/10.1101/cshperspect.a000034
Ogrodnik M, Miwa S, Tchkonia T et al (2017) Cellular senescence drives age-dependent hepatic steatosis. Nat Commun 8:15691. https://doi.org/10.1038/ncomms15691
Perkins ND, Gilmore TD (2006) Good cop, bad cop: the different faces of NF-kappaB. Cell Death Differ 13(5):759–772. https://doi.org/10.1038/sj.cdd.4401838
Persson M, Loye AF, Mow T, Hornberg JJ (2013) A high content screening assay to predict human drug-induced liver injury during drug discovery. J Pharmacol Toxicol Methods 68(3):302–313. https://doi.org/10.1016/j.vascn.2013.08.001
Petrova NV, Velichko AK, Razin SV, Kantidze OL (2016) Small molecule compounds that induce cellular senescence. Aging Cell 15(6):999–1017. https://doi.org/10.1111/acel.12518
Proctor WR, Foster AJ, Vogt J et al (2017) Utility of spherical human liver microtissues for prediction of clinical drug-induced liver injury. Arch Toxicol 91(8):2849–2863. https://doi.org/10.1007/s00204-017-2002-1
Quiros Y, Vicente-Vicente L, Morales AI, Lopez-Novoa JM, Lopez-Hernandez FJ (2011) An integrative overview on the mechanisms underlying the renal tubular cytotoxicity of gentamicin. Toxicol Sci 119(2):245–256
Rai TS, Adams PD (2012) Lessons from senescence: chromatin maintenance in non-proliferating cells. Biochem Biophys Acta 1819(3–4):322–331. https://doi.org/10.1016/j.bbagrm.2011.07.014
Randhawa PS, Starzl TE, Demetris AJ (1997) Tacrolimus (FK506)-associated renal pathology. Adv Anat Pathol 4(4):265–276
Robertson JD, Orrenius S, Zhivotovsky B (2000) Review: nuclear events in apoptosis. J Struct Biol 129(2–3):346–358. https://doi.org/10.1006/jsbi.2000.4254
Rogalinska M (2002) Alterations in cell nuclei during apoptosis. Cell Mol Biol Lett 7(4):995–1018
Rovillain E, Mansfield L, Caetano C et al (2011) Activation of nuclear factor-kappa B signalling promotes cellular senescence. Oncogene 30(20):2356–2366. https://doi.org/10.1038/onc.2010.611
Shahane SA, Huang R, Gerhold D, Baxa U, Austin CP, Xia M (2014) Detection of phospholipidosis induction: a cell-based assay in high-throughput and high-content format. J Biomol Screen 19(1):66–76. https://doi.org/10.1177/1087057113502851
Sidransky H, Verney E (1982) Acute effects of selected hepatotoxic agents on polyribosomes and protein synthesis in the livers of rats fed purified diets containing hepatocarcinogens. Exp Mol Pathol 36(1):72–85
Sirenko O, Hesley J, Rusyn I, Cromwell EF (2014) High-content assays for hepatotoxicity using induced pluripotent stem cell-derived cells. Assay Drug Dev Technol 12(1):43–54. https://doi.org/10.1089/adt.2013.520
Sjogren AK, Breitholtz K, Ahlberg E et al (2018) A novel multi-parametric high content screening assay in ciPTEC-OAT1 to predict drug-induced nephrotoxicity during drug discovery. Arch Toxicol 92(10):3175–3190. https://doi.org/10.1007/s00204-018-2284-y
Sjogren AK, Hornberg JJ (2019) Compound selection and annotation to validate the predictivity of in vitro toxicity assays for use in drug discovery, in response to Commentary by Dr. Zink (Zink, D. Arch Toxicol (2018)). Arch Toxicol 93(1):225–226. https://doi.org/10.1007/s00204-018-2359-9
Su R, Xiong S, Zink D, Loo LH (2016) High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures. Arch Toxicol 90(11):2793–2808. https://doi.org/10.1007/s00204-015-1638-y
Trask OJ (2004) Nuclear factor kappa B (NF-kappaB) translocation assay development and validation for high content screening. doi:NBK100914 [bookaccession]
Underhill GH, Khetani SR (2018) Bioengineered liver models for drug testing and cell differentiation studies. Cell Mol Gastroenterol Hepatol 5(3):426–439. https://doi.org/10.1016/j.jcmgh.2017.11.012
van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605
van Deursen JM (2014) The role of senescent cells in ageing. Nature 509(7501):439–446. https://doi.org/10.1038/nature13193
Vinken M, Hengstler JG (2018) Characterization of hepatocyte-based in vitro systems for reliable toxicity testing. Arch Toxicol 92(10):2981–2986. https://doi.org/10.1007/s00204-018-2297-6
Wang C, Chen WJ, Wu YF et al (2018) The extent of liver injury determines hepatocyte fate toward senescence or cancer. Cell Death Dis 9(5):575. https://doi.org/10.1038/s41419-018-0622-x
Ware BR, Berger DR, Khetani SR (2015) Prediction of drug-induced liver injury in micropatterned co-cultures containing iPSC-derived human hepatocytes. Toxicol Sci 145(2):252–262. https://doi.org/10.1093/toxsci/kfv048
Wisniewski JR, Vildhede A, Noren A, Artursson P (2016) In-depth quantitative analysis and comparison of the human hepatocyte and hepatoma cell line HepG2 proteomes. J Proteom 136:234–247. https://doi.org/10.1016/j.jprot.2016.01.016
Xiao M, Chen W, Wang C et al (2018) Senescence and cell death in chronic liver injury: roles and mechanisms underlying hepatocarcinogenesis. Oncotarget 9(9):8772–8784. https://doi.org/10.18632/oncotarget.23622
Xu JJ, Henstock PV, Dunn MC, Smith AR, Chabot JR, de Graaf D (2008) Cellular imaging predictions of clinical drug-induced liver injury. Toxicol Sci 105(1):97–105. https://doi.org/10.1093/toxsci/kfn109
Yang L, Su T, Li XM et al (2012) Aristolochic acid nephropathy: variation in presentation and prognosis. Nephrol Dial Transplant 27(1):292–298. https://doi.org/10.1093/ndt/gfr291
Yosef R, Pilpel N, Papismadov N et al (2017) p21 maintains senescent cell viability under persistent DNA damage response by restraining JNK and caspase signaling. EMBO J 36(15):2280–2295. https://doi.org/10.15252/embj.201695553
You L, Dong X, Ni B et al (2018) Triptolide induces apoptosis through fas death and mitochondrial pathways in HepaRG cell line. Front Pharmacol 9:813. https://doi.org/10.3389/fphar.2018.00813
Zeigerer A, Wuttke A, Marsico G, Seifert S, Kalaidzidis Y, Zerial M (2017) Functional properties of hepatocytes in vitro are correlated with cell polarity maintenance. Exp Cell Res 350(1):242–252. https://doi.org/10.1016/j.yexcr.2016.11.027
Zhang JH, Chung TD, Oldenburg KR (1999) A simple statistical parameter for use in evaluation and validation of high throughput screening assays. J Biomol Screen 4(2):67–73. https://doi.org/10.1177/108705719900400206
Zhang L, Zhao J, Gurkar A, Niedernhofer LJ, Robbins PD (2019) Methods to quantify the NF-kappaB pathway during senescence. Methods Mol Biol 1896:231–250. https://doi.org/10.1007/978-1-4939-8931-7_18
Zhang R, Chen W, Adams PD (2007) Molecular dissection of formation of senescence-associated heterochromatin foci. Mol Cell Biol 27(6):2343–2358. https://doi.org/10.1128/MCB.02019-06
Acknowledgements
We thank Jia Jun Lee (IBN, Youth Research Programme) for his contributions to the experimental work. This work was supported by the NanoBio Lab, IBN and the Bioinformatics Institute (Agency for Science, Technology and Research, Singapore).
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Hussain, F., Basu, S., Heng, J.J. . et al. Predicting direct hepatocyte toxicity in humans by combining high-throughput imaging of HepaRG cells and machine learning-based phenotypic profiling. Arch Toxicol 94, 2749–2767 (2020). https://doi.org/10.1007/s00204-020-02778-3
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DOI: https://doi.org/10.1007/s00204-020-02778-3