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In vitro transcriptomic prediction of hepatotoxicity for early drug discovery. (English) Zbl 1397.92319
Summary: Liver toxicity (hepatotoxicity) is a critical issue in drug discovery and development. Standard preclinical evaluation of drug hepatotoxicity is generally performed using in vivo animal systems. However, only a small number of preselected compounds can be examined in vivo due to high experimental costs. A more efficient yet accurate screening technique that can identify potentially hepatotoxic compounds in the early stages of drug development would thus be valuable. Here, we develop and apply a novel genomic prediction technique for screening hepatotoxic compounds based on in vivo human liver cell tests. Using a training set of in vivo rodent experiments for drug hepatotoxicity evaluation, we discovered common biomarkers of drug-induced liver toxicity among six heterogeneous compounds. This gene set was further triaged to a subset of 32 genes that can be used as a multi-gene expression signature to predict hepatotoxicity. This multi-gene predictor was independently validated and showed consistently high prediction performance on five test sets of in vivo human liver cell and in vivo animal toxicity experiments. The predictor also demonstrated utility in evaluating different degrees of toxicity in response to drug concentrations, which may be useful not only for discerning a compound’s general hepatotoxicity but also for determining its toxic concentration.
92C50 Medical applications (general)
92C40 Biochemistry, molecular biology
Full Text: DOI
[1] Bandara, L.R.; Kennedy, S., Toxicoproteomics—a new preclinical tool, Drug discov. today, 7, 411-418, (2002)
[2] Bushel, P.R.; Heinloth, A.N.; Li, J.; Huang, L.; Chou, J.W.; Boorman, G.A.; Malarkey, D.E.; Houle, C.D.; Ward, S.M.; Wilson, R.E.; Fannin, R.D.; Russo, M.W.; Watkins, P.B.; Tennant, R.W.; Paules, R.S., Blood gene expression signatures predict exposure levels, Proc. natl. acad. sci. USA, 104, 18211-18216, (2007)
[3] Chen, L.; Feng, K.Y.; Cai, Y.D.; Chou, K.C.; Li, H.P., Predicting the network of substrate−enzyme−product triads by combining compound similarity and functional domain composition, BMC bioinf., 11, 293, (2010)
[4] Chou, J.W.; Bushel, P.R., Discernment of possible mechanisms of hepatotoxicity via biological processes over-represented by co-expressed genes, BMC genom., 10, 272, (2009)
[5] Chou, K.C., Review: prediction of HIV protease cleavage sites in proteins, Anal. biochem., 233, 1-14, (1996)
[6] Chou, K.C., Prediction of protein cellular attributes using pseudo-amino acid composition, Proteins, 43, 246-255, (2001)
[7] Chou, K.C., Structural bioinformatics and its impact to biomedical science, Curr. med. chem., 11, 2105-2134, (2004)
[8] Chou, K.C., Some remarks on protein attribute prediction and pseudo amino acid composition, J. theor. biol., 273, 236-247, (2011) · Zbl 1405.92212
[9] Chou, K.C.; Zhou, G.P., Role of the protein outside active site on the diffusion-controlled reaction of enzyme, J. am. chem. soc., 104, 1409-1413, (1982)
[10] Chou, K.C.; Zhang, C.T., Prediction of protein structural classes, Crit. rev. biochem. mol. biol., 30, 275-349, (1995)
[11] Chou, K.C.; Shen, H.B., Review: recent progresses in protein subcellular location prediction, Anal. biochem., 370, (2007)
[12] Chou, K.C.; Shen, H.B., Signal-CF: a subsite-coupled and window-fusing approach for predicting signal peptides, Biochem. biophys. res. commun., 357, 633-640, (2007)
[13] Chou, K.C.; Shen, H.B., Cell-ploc: a package of web servers for predicting subcellular localization of proteins in various organisms, Nat. protoc., 3, 153-162, (2008)
[14] Chou, K.C.; Shen, H.B., Review: recent advances in developing web-servers for predicting protein attributes, Nat. sci., 2, 63-92, (2009)
[15] Chou, K.C.; Shen, H.B., Cell-ploc 2.0: an improved package of web-servers for predicting subcellular localization of proteins in various organisms, Nat. sci., 2, 1090-1103, (2010)
[16] Chou, K.C.; Wei, D.Q.; Zhong, W.Z., Binding mechanism of coronavirus main proteinase with ligands and its implication to drug design against SARS, Biochem. biophys. res. commun., 308, 148-151, (2003)
[17] Estabrook, R.W., A passion for P450s (rememberances of the early history of research on cytochrome P450), Drug. metab. dispos., 31, 1461-1473, (2003)
[18] Fielden, M.R.; Brennan, R.; Gollub, J., A gene expression biomarker provides early prediction and mechanistic assessment of hepatic tumor induction by nongenotoxic chemicals, Toxicol. sci., 99, 90-100, (2007)
[19] Gu, Q.; Ding, Y.S.; Zhang, T.L., Prediction of G-protein-coupled receptor classes in low homology using Chou’s pseudo amino acid composition with approximate entropy and hydrophobicity patterns, Protein pept. lett., 17, 559-567, (2010)
[20] He, Z.; Zhang, J.; Shi, X.H.; Hu, L.L.; Kong, X.; Cai, Y.D.; Chou, K.C., Predicting drug-target interaction networks based on functional groups and biological features, Plos one, 5, e9603, (2010)
[21] Jaeschke, H.; Gores, G.J.; Cederbaum, A.I.; Hinson, J.A.; Pessayre, D.; Lemasters, J.J., Mechanisms of hepatotoxicity, Toxicol. sci., 65, 166-176, (2002)
[22] Kandaswamy, K.K.; Chou, K.C.; Martinetz, T.; Moller, S.; Suganthan, P.N.; Sridharan, S.; Pugalenthi, G., AFP-pred: A random forest approach for predicting antifreeze proteins from sequence-derived properties, J. theor. biol., 270, 56-62, (2010)
[23] Kassie, F.; Knasmuller, S., Genotoxic effects of allyl isothiocyanate (AITC) and phenethyl isothiocyanate (PEITC), Chem. biol. interact., 127, 163-180, (2000)
[24] Kawata, K.; Yokoo, H.; Shimazaki, R.; Okabe, S., Classification of heavy-metal toxicity by human DNA microarray analysis, Environ. sci. technol., 41, 3769-3774, (2007)
[25] Lee, J.K.; Havaleshko, D.M.; Cho, H.; Weinstein, J.N.; Kaldjian, E.P.; Karpovich, J.; Grimshaw, A.; Theodorescu, D., A strategy for predicting the chemosensitivity of human cancers and its application to drug discovery, Proc. natl. acad. sci. USA, 104, 13086-13091, (2007)
[26] Lian, P.; Wei, D.Q.; Wang, J.F.; Chou, K.C., An allosteric mechanism inferred from molecular dynamics simulations on phospholamban pentamer in lipid membranes, Plos one, 6, e18587, (2011)
[27] Masso, M.; Vaisman, I.I., Knowledge-based computational mutagenesis for predicting the disease potential of human non-synonymous single nucleotide polymorphisms, J. theor. biol., 266, 560-568, (2010) · Zbl 1407.92082
[28] Mohabatkar, H., Prediction of cyclin proteins using Chou’s pseudo amino acid composition, Protein pept. lett., 17, 1207-1214, (2010)
[29] Spicker, J.S.; Brunak, S.; Frederiksen, K.S.; Toft, H., Integration of clinical chemistry, expression, and metabolite data leads to better toxicological class separation, Toxicol. sci., 102, 444-454, (2008)
[30] Stine, E.R.; Gunawardhana, L.; Sipes, I.G., The acute hepatotoxicity of the isomers of dichlorobenzene in fischer-344 and Sprague-dawley rats: isomer-specific and strain-specific differential toxicity, Toxicol. appl. pharmacol., 109, 472-481, (1991)
[31] Umemura, T.; Tokumo, K.; Williams, G.M., Cell proliferation induced in the kidneys and livers of rats and mice by short term exposure to the carcinogen p-dichlorobenzene, Arch. toxicol., 66, 503-507, (1992)
[32] Wang, J.F.; Chou, K.C., Insights from modeling the 3D structure of New Delhi metallo-beta-lactamse and its binding interactions with antibiotic drugs, Plos one, 6, e18414, (2011)
[33] Wang, J.F.; Gong, K.; Wei, D.Q.; Li, Y.X.; Chou, K.C., Molecular dynamics studies on the interactions of PTP1B with inhibitors: from the first phosphate-binding site to the second one, Protein eng. des. sel., 22, 349-355, (2009)
[34] Wang, P.; Hu, L.; Liu, G.; Jiang, N.; Chen, X.; Xu, J.; Zheng, W.; Li, L.; Tan, M.; Chen, Z.; Song, H.; Cai, Y.D.; Chou, K.C., Prediction of antimicrobial peptides based on sequence alignment and feature selection methods, Plos one, 6, e18476, (2011)
[35] Williams, G.M.; Iatropoulos, M.J., Alteration of liver cell function and proliferation: differentiation between adaptation and toxicity, Toxicol. pathol., 30, 41-53, (2002)
[36] Xia, F.; Lee, C.W.; Altieri, D.C., Tumor cell dependence on ran-GTP-directed mitosis, Cancer res., 68, 1826-1833, (2008)
[37] Xiao, X.; Wang, P.; Chou, K.C., Quat-2L: a web-server for predicting protein quaternary structural attributes, Mol. divers, 15, 149-155, (2011)
[38] Yang, Y.; Blomme, E.A.; Waring, J.F., Toxicogenomics in drug discovery: from preclinical studies to clinical trials, Chem. biol. interact., 150, 71-85, (2004)
[39] Youden, W.J., Index for rating diagnostic tests, Cancer, 3, 32-35, (1950)
[40] Zakeri, P.; Moshiri, B.; Sadeghi, M., Prediction of protein submitochondria locations based on data fusion of various features of sequences, J. theor. biol., 269, 208-216, (2010) · Zbl 1307.92094
[41] Zeng, Y.H.; Guo, Y.Z.; Xiao, R.Q.; Yang, L.; Yu, L.Z.; Li, M.L., Using the augmented Chou’s pseudo amino acid composition for predicting protein submitochondria locations based on auto covariance approach, J. theor. biol., 259, 366-372, (2009) · Zbl 1402.92193
[42] Zhou, H.Z.; Ma, X.; Gray, M.O.; Zhu, B.Q.; Nguyen, A.P.; Baker, A.J.; Simonis, U.; Cecchini, G.; Lovett, D.H.; Karliner, J.S., Transgenic MMP-2 expression induces latent cardiac mitochondrial dysfunction, Biochem. biophys. res. commun., 358, 189-195, (2007)
[43] Zhou, X.B.; Chen, C.; Li, Z.C.; Zou, X.Y., Using Chou’s amphiphilic pseudo-amino acid composition and support vector machine for prediction of enzyme subfamily classes, J. theor. biol., 248, 546-551, (2007)
[44] Zidek, N.; Hellmann, J.; Kramer, P.J.; Hewitt, P.G., Acute hepatotoxicity: a predictive model based on focused illumina microarrays, Toxicol. sci., 99, 289-302, (2007)
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