Assessing the translatability of In vivo cardiotoxicity mechanisms to In vitro models using causal reasoning

BMC Clinical Pharmacology

Mathew T Pletcher¹  Sheila Kantesaria²  James E Fischer²  Daniel Ziemek⁴  Dinesh Puppala²  Ahmed E Enayetallah³ 

[1] Rare Disease Research Unit, Pfizer Inc., Cambridge, MA, USA;Compound Safety Prediction, Pfizer Inc., Groton, CT, USA;Drug Safety Research & Development, Pfizer Inc., Groton, CT, USA;Computational Sciences CoE, Pfizer Inc., Cambridge, MA, USA

关键词: Preclinical safety;    In vitro screening;    Translatability;    Cardiotoxicity;    Causal reasoning;   

Others  :  860544
DOI  :  10.1186/2050-6511-14-46

 received in 2013-04-18, accepted in 2013-09-03,  发布年份 2013

【 摘 要 】

Drug-induced cardiac toxicity has been implicated in 31% of drug withdrawals in the USA. The fact that the risk for cardiac-related adverse events goes undetected in preclinical studies for so many drugs underscores the need for better, more predictive in vitro safety screens to be deployed early in the drug discovery process. Unfortunately, many questions remain about the ability to accurately translate findings from simple cellular systems to the mechanisms that drive toxicity in the complex in vivo environment. In this study, we analyzed translatability of cardiotoxic effects for a diverse set of drugs from rodents to two different cell systems (rat heart tissue-derived cells (H9C2) and primary rat cardiomyocytes (RCM)) based on their transcriptional response. To unravel the altered pathway, we applied a novel computational systems biology approach, the Causal Reasoning Engine (CRE), to infer upstream molecular events causing the observed gene expression changes. By cross-referencing the cardiotoxicity annotations with the pathway analysis, we found evidence of mechanistic convergence towards common molecular mechanisms regardless of the cardiotoxic phenotype. We also experimentally verified two specific molecular hypotheses that translated well from in vivo to in vitro (Kruppel-like factor 4, KLF4 and Transforming growth factor beta 1, TGFB1) supporting the validity of the predictions of the computational pathway analysis. In conclusion, this work demonstrates the use of a novel systems biology approach to predict mechanisms of toxicity such as KLF4 and TGFB1 that translate from in vivo to in vitro. We also show that more complex in vitro models such as primary rat cardiomyocytes may not offer any advantage over simpler models such as immortalized H9C2 cells in terms of translatability to in vivo effects if we consider the right endpoints for the model. Further assessment and validation of the generated molecular hypotheses would greatly enhance our ability to design predictive in vitro cardiotoxicity assays.

【 授权许可】

   
2013 Enayetallah et al.; licensee BioMed Central Ltd.

【 图 表 】

【 参考文献 】

• [1]Wilke RA, Lin DW, Roden DM, Watkins PB, Flockhart D, Zineh I, Giacomini KM, Krauss RM: Identifying genetic risk factors for serious adverse drug reactions: current progress and challenges. Nature reviews Drug discovery 2007, 6(11):904-916.
• [2]Topol EJ: Failing the public health–rofecoxib, Merck, and the FDA. N Engl J Med 2004, 351(17):1707-1709.
• [3]Andersen ME, Krewski D: Toxicity testing in the 21st century: bringing the vision to life. Toxicol Sci 2009, 107(2):324-330.
• [4]Bhavani S, Nagargadde A, Thawani A, Sridhar V, Chandra N: Substructure-based support vector machine classifiers for prediction of adverse effects in diverse classes of drugs. J Chem Inf Model 2006, 46(6):2478-2486.
• [5]Gepp MM, Hutter MC: Determination of hERG channel blockers using a decision tree. Bioorg Med Chem 2006, 14(15):5325-5332.
• [6]Bidault Y: A flexible approach for optimising in silico ADME/Tox characterisation of lead candidates. Expert Opin Drug Metab Toxicol 2006, 2(1):157-168.
• [7]Demel MA, Schwaha R, Kramer O, Ettmayer P, Haaksma EE, Ecker GF: In silico prediction of substrate properties for ABC-multidrug transporters. Expert Opin Drug Metab Toxicol 2008, 4(9):1167-1180.
• [8]Hammann F, Gutmann H, Baumann U, Helma C, Drewe J: Classification of cytochrome p(450) activities using machine learning methods. Mol Pharm 2009, 6(6):1920-1926.
• [9]Octavia Y, Tocchetti CG, Gabrielson KL, Janssens S, Crijns HJ, Moens AL: Doxorubicin-induced cardiomyopathy: from molecular mechanisms to therapeutic strategies. J Mol Cell Cardiol 2012, 52(6):1213-1225.
• [10]Greineder CF, Kohnstamm S, Ky B: Heart failure associated with sunitinib: lessons learned from animal models. Curr Hypertens Rep 2011, 13(6):436-441.
• [11]Force T, Kolaja KL: Cardiotoxicity of kinase inhibitors: the prediction and translation of preclinical models to clinical outcomes. Nature reviews Drug discovery 2011, 10(2):111-126.
• [12]Braam SR, Tertoolen L, van de Stolpe A, Meyer T, Passier R, Mummery CL: Prediction of drug-induced cardiotoxicity using human embryonic stem cell-derived cardiomyocytes. Stem Cell Res 2010, 4(2):107-116.
• [13]Braam SR, Passier R, Mummery CL: Cardiomyocytes from human pluripotent stem cells in regenerative medicine and drug discovery. Trends Pharmacol Sci 2009, 30(10):536-545.
• [14]Fielden MR, Halbert DN: Iconix Biosciences, Inc. Pharmacogenomics 2007, 8(4):401-405.
• [15]Ganter B, Snyder RD, Halbert DN, Lee MD: Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics 2006, 7(7):1025-1044.
• [16]Chindelevitch L, Ziemek D, Enayetallah A, Randhawa R, Sidders B, Brockel C, Huang ES: Causal reasoning on biological networks: interpreting transcriptional changes. Bioinformatics 2012, 28(8):1114-1121.
• [17]Enayetallah AE, Ziemek D, Leininger MT, Randhawa R, Yang J, Manion TB, Mather DE, Zavadoski WJ, Kuhn M, Treadway JL, et al.: Modeling the mechanism of action of a DGAT1 inhibitor using a causal reasoning platform. PloS one 2011, 6(11):e27009.
• [18]Galindo CL, Skinner MA, Errami M, Olson LD, Watson DA, Li J, McCormick JF, McIver LJ, Kumar NM, Pham TQ, et al.: Transcriptional profile of isoproterenol-induced cardiomyopathy and comparison to exercise-induced cardiac hypertrophy and human cardiac failure. BMC Physiol 2009, 9:23. BioMed Central Full Text
• [19]Archer KJ, Dumur CI, Joel SE, Ramakrishnan V: Assessing quality of hybridized RNA in Affymetrix GeneChip experiments using mixed-effects models. Biostatistics 2006, 7(2):198-212.
• [20]Sammon JW: A nonlinear mapping for data structure analysis. IEEE Trans Comput 1969, 18(5):401-409.
• [21]Smyth GK: Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat Appl Genet Mol Biol 2004, 3:Article3.
• [22]Benjamini Y, Hochberg Y: Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B (Methodological) 1995, 57(1):289-300.
• [23]Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res 2003, 13(11):2498-2504.
• [24]Jain AK, Dubes RC: Algorithms for clustering data. Upper Saddle River: Prentice-Hall, Inc.; 1988.
• [25]Teekakirikul P, Eminaga S, Toka O, Alcalai R, Wang L, Wakimoto H, Nayor M, Konno T, Gorham JM, Wolf CM, et al.: Cardiac fibrosis in mice with hypertrophic cardiomyopathy is mediated by non-myocyte proliferation and requires Tgf-beta. J Clin Invest 2010, 120(10):3520-3529.
• [26]Van der Heiden K, Cuhlmann S, le Luong A, Zakkar M, Evans PC: Role of nuclear factor kappaB in cardiovascular health and disease. Clin Sci 2010, 118(10):593-605.
• [27]Aragno M, Mastrocola R, Alloatti G, Vercellinatto I, Bardini P, Geuna S, Catalano MG, Danni O, Boccuzzi G: Oxidative stress triggers cardiac fibrosis in the heart of diabetic rats. Endocrinology 2008, 149(1):380-388.
• [28]Wolkart G, Kaber G, Kojda G, Brunner F: Role of endogenous hydrogen peroxide in cardiovascular ischaemia/reperfusion function: studies in mouse hearts with catalase-overexpression in the vascular endothelium. Pharmacol Res 2006, 54(1):50-56.
• [29]Rona G: Catecholamine cardiotoxicity. J Mol Cell Cardiol 1985, 17(4):291-306.
• [30]Kojima S, Wu ST, Wikman-Coffelt J, Parmley WW: Acute amiodarone terminates ventricular fibrillation by modifying cellular Ca++ homeostasis in isolated perfused rat hearts. J Pharmacol Exp Ther 1995, 275(1):254-262.
• [31]Haworth RA, Goknur AB, Berkoff HA: Inhibition of ATP-sensitive potassium channels of adult rat heart cells by antiarrhythmic drugs. Circulation research 1989, 65(4):1157-1160.
• [32]Goldfine ID, Maddux B, Woeber KA: Effect of amiodarone on L-triiodothyronine stimulation of [3H] thymidine incorporation into GH3 cells. J Endocrinol Invest 1982, 5(3):165-168.
• [33]Jonckheer MH, Blockx P, Broeckaert I, Cornette C, Beckers C: ‘Low T3 syndrome’ in patients chronically treated with an iodine-containing drug, amiodarone. Clin Endocrinol (Oxf) 1978, 9(1):27-35.
• [34]Morgan P, Van Der Graaf PH, Arrowsmith J, Feltner DE, Drummond KS, Wegner CD, Street SD: Can the flow of medicines be improved? Fundamental pharmacokinetic and pharmacological principles toward improving Phase II survival. Drug Discov Today 2012, 17(9–10):419-424.
• [35]Puppala D, Collis LP, Sun SZ, Bonato V, Chen X, Anson B, Pletcher M, Fermini B, Engle SJ: Comparative gene expression profiling in human-induced pluripotent stem cell–derived cardiocytes and human and cynomolgus heart tissue. Toxicol Sci 2013, 131(1):292-301.
• [36]Guo L, Abrams RM, Babiarz JE, Cohen JD, Kameoka S, Sanders MJ, Chiao E, Kolaja KL: Estimating the risk of drug-induced proarrhythmia using human induced pluripotent stem cell-derived cardiomyocytes. Toxicol Sci 2011, 123(1):281-289.
• [37]Lin Z, Will Y: Evaluation of drugs with specific organ toxicities in organ-specific cell lines. Toxicol Sci 2012, 126(1):114-127.
• [38]Benbow JW, Aubrecht J, Banker MJ, Nettleton D, Aleo MD: Predicting safety toleration of pharmaceutical chemical leads: cytotoxicity correlations to exploratory toxicity studies. Toxicol Lett 2010, 197(3):175-182.
• [39]Evans PM, Liu C: Roles of Krupel-like factor 4 in normal homeostasis, cancer and stem cells. Acta Biochim Biophys Sin (Shanghai) 2008, 40(7):554-564.
• [40]Zhang Y, Wang Y, Liu Y, Wang N, Qi Y, Du J: Kruppel-like factor 4 transcriptionally regulates TGF-beta1 and contributes to cardiac myofibroblast differentiation. PloS one 2013, 8(4):e63424.
• [41]Leenders JJ, Pinto YM, Creemers EE: Tapping the brake on cardiac growth-endogenous repressors of hypertrophic signaling. J Mol Cell Cardiol 2011, 51(2):156-167.
• [42]Al-Shabanah OA, Aleisa AM, Hafez MM, Al-Rejaie SS, Al-Yahya AA, Bakheet SA, Al-Harbi MM, Sayed-Ahmed MM: Desferrioxamine attenuates doxorubicin-induced acute cardiotoxicity through TFG-beta/Smad p53 pathway in rat model. Oxid Med Cell Longev 2012, 2012:619185.
• [43]Wang Z, Yao T, Song Z: Extracellular signal-regulated kinases 1/2 suppression aggravates transforming growth factor-beta1 hepatotoxicity: a potential mechanism for liver injury in methionine-choline deficient-diet-fed mice. Exp Biol Med 2010, 235(11):1347-1355.
• [44]Prud’homme GJ: Pathobiology of transforming growth factor beta in cancer, fibrosis and immunologic disease, and therapeutic considerations. Lab Invest 2007, 87(11):1077-1091.