Harnessing Human Microphysiology Systems as Key Experimental Models for Quantitative Systems Pharmacology

  • D. Lansing TaylorEmail author
  • Albert Gough
  • Mark E. Schurdak
  • Lawrence Vernetti
  • Chakra S. Chennubhotla
  • Daniel Lefever
  • Fen Pei
  • James R. Faeder
  • Timothy R. Lezon
  • Andrew M. Stern
  • Ivet Bahar
Part of the Handbook of Experimental Pharmacology book series (HEP, volume 260)


Two technologies that have emerged in the last decade offer a new paradigm for modern pharmacology, as well as drug discovery and development. Quantitative systems pharmacology (QSP) is a complementary approach to traditional, target-centric pharmacology and drug discovery and is based on an iterative application of computational and systems biology methods with multiscale experimental methods, both of which include models of ADME-Tox and disease. QSP has emerged as a new approach due to the low efficiency of success in developing therapeutics based on the existing target-centric paradigm. Likewise, human microphysiology systems (MPS) are experimental models complementary to existing animal models and are based on the use of human primary cells, adult stem cells, and/or induced pluripotent stem cells (iPSCs) to mimic human tissues and organ functions/structures involved in disease and ADME-Tox. Human MPS experimental models have been developed to address the relatively low concordance of human disease and ADME-Tox with engineered, experimental animal models of disease. The integration of the QSP paradigm with the use of human MPS has the potential to enhance the process of drug discovery and development.


Computational models of ADME-Tox Computational models of disease DILI Drug development Drug discovery Drug repurposing Induced pluripotent stem cells Microphysiology systems Omics analyses PBPK Personalized medicine Quantitative systems pharmacology Toxicology 



Support from the National Institutes of Health awards UG3DK119973 (DLT), R01DK0017781 (DLT), 1UO1 TR002383 (DLT), U24TR002632 (AG/MS), SBIR HHSN271201800008C UO1CA204826 (CC), DA035778 (IB), and P41 GM103712 (IB) is gratefully acknowledged. We also thank the members of the University of Pittsburgh Drug Discovery Institute, the Department of Computational and Systems Biology, and other collaborators at the University of Pittsburgh and beyond for critical discussions.


  1. Ahlquist RP (1948) A study of the adrenotropic receptors. Am J Phys 153:586–600. CrossRefGoogle Scholar
  2. Alex A, Harris CJ, Keighley WW, Smith DA (2015) Compound Attrition in Phase II/III. In: Attrition in the pharmaceutical industry. CrossRefGoogle Scholar
  3. Al-Hadiya BM, Bakheit AH, Abd-Elgalil AA (2014) Imatinib mesylate. Profiles Drug Subst Excip Relat Methodol 39:265–297. CrossRefPubMedGoogle Scholar
  4. Allarakhia M (2013) Open-source approaches for the repurposing of existing or failed candidate drugs: learning from and applying the lessons across diseases. Drug Des Devel Ther 7:753–766. CrossRefPubMedPubMedCentralGoogle Scholar
  5. Allen GD (1990) MODFIT: a pharmacokinetics computer program. Biopharm Drug Dispos 11:477–498CrossRefGoogle Scholar
  6. Anstee QM, Day CP (2013) The genetics of NAFLD. Nat Rev Gastroenterol Hepatol 10:645–655. CrossRefPubMedGoogle Scholar
  7. Arrowsmith J, Miller P (2013) Trial watch: phase II and phase III attrition rates 2011-2012. Nat Rev Drug Discov 12:569. CrossRefPubMedGoogle Scholar
  8. Ashburn TT, Thor KB (2004) Drug repositioning: identifying and developing new uses for existing drugs. Nat Rev Drug Discov 3:673–683. CrossRefGoogle Scholar
  9. Ashworth WB, Davies NA, Bogle ID (2016) A computational model of hepatic energy metabolism: understanding zonated damage and steatosis in NAFLD. PLoS Comput Biol 12:e1005105. CrossRefPubMedPubMedCentralGoogle Scholar
  10. Atchison L, Zhang H, Cao K, Truskey GA (2017) A tissue engineered blood vessel model of hutchinson-gilford progeria syndrome using human iPSC-derived smooth muscle cells. Sci Rep 7:8168. CrossRefPubMedPubMedCentralGoogle Scholar
  11. Auner A, Tasneem KM, Markov DA, McCawley LJ, Hutson MS (2019) Chemical-PDMS binding kinetics and implications for bioavailability in microfluidic devices. Lab Chip. CrossRefGoogle Scholar
  12. Bailey T et al (2013) Practical guidelines for the comprehensive analysis of ChIP-seq data. PLoS Comput Biol 9:e1003326. CrossRefPubMedPubMedCentralGoogle Scholar
  13. Bakan A, Nevins N, Lakdawala AS, Bahar I (2012) Druggability assessment of allosteric proteins by dynamics simulations in the presence of probe molecules. J Chem Theory Comput 8:2435–2447. CrossRefPubMedPubMedCentralGoogle Scholar
  14. Bakan A, Kapralov AA, Bayir H, Hu F, Kagan VE, Bahar I (2015) Inhibition of peroxidase activity of cytochrome c: de novo compound discovery and validation. Mol Pharmacol 88:421–427. CrossRefPubMedPubMedCentralGoogle Scholar
  15. Barry C et al (2017) Uniform neural tissue models produced on synthetic hydrogels using standard culture techniques. Exp Biol Med 242:1679–1689. CrossRefGoogle Scholar
  16. Bartol TM, Bromer C, Kinney J, Chirillo MA, Bourne JN, Harris KM, Sejnowski TJ (2015) Nanoconnectomic upper bound on the variability of synaptic plasticity. elife 4:e10778. CrossRefPubMedPubMedCentralGoogle Scholar
  17. Beckwitt CH, Clark AM, Wheeler S, Taylor DL, Stolz DB, Griffith L, Wells A (2018) Liver ‘organ on a chip’. Exp Cell Res 363:15–25. CrossRefPubMedGoogle Scholar
  18. Benam KH et al (2015) Engineered in vitro disease models. Annu Rev Pathol 10:195–262. CrossRefPubMedGoogle Scholar
  19. Besser RR, Ishahak M, Mayo V, Carbonero D, Claure I, Agarwal A (2018) Engineered microenvironments for maturation of stem cell derived cardiac myocytes. Theranostics 8:124–140. CrossRefPubMedPubMedCentralGoogle Scholar
  20. Bhatia SN, Ingber DE (2014) Microfluidic organs-on-chips. Nat Biotechnol 32:760–772. CrossRefPubMedGoogle Scholar
  21. Bhushan A et al (2013) Towards a three-dimensional microfluidic liver platform for predicting drug efficacy and toxicity in humans. Stem Cell Res Ther 4(Suppl 1):S16. CrossRefPubMedPubMedCentralGoogle Scholar
  22. Bian YM, He XB, Jing YK, Wang LR, Wang JM, Xie XQ (2019) Computational systems pharmacology analysis of cannabidiol: a combination of chemogenomics-knowledgebase network analysis and integrated in silico modeling and simulation. Acta Pharmacol Sin 40:374–386. CrossRefPubMedGoogle Scholar
  23. Black JR, Clark SJ (2016) Age-related macular degeneration: genome-wide association studies to translation. Genet Med 18:283–289. CrossRefPubMedGoogle Scholar
  24. Black JW, Duncan WA, Durant CJ, Ganellin CR, Parsons EM (1972) Definition and antagonism of histamine H2-receptors. Nature 236:385–390CrossRefGoogle Scholar
  25. Bleakley K, Yamanishi Y (2009) Supervised prediction of drug-target interactions using bipartite local models. Bioinformatics 25:2397–2403. CrossRefPubMedPubMedCentralGoogle Scholar
  26. Blutt SE et al (2017) Gastrointestinal microphysiological systems. Exp Biol Med 242:1633–1642. CrossRefGoogle Scholar
  27. Bredel M, Jacoby E (2004) Chemogenomics: an emerging strategy for rapid target and drug discovery. Nat Rev Genet 5:262–275. CrossRefPubMedGoogle Scholar
  28. Brennan RJ, Nikolskya T, Bureeva S (2009) Network and pathway analysis of compound-protein interactions. Methods Mol Biol (Clifton, NJ) 575:225–247. CrossRefGoogle Scholar
  29. Buhule OD et al (2014) Stratified randomization controls better for batch effects in 450K methylation analysis: a cautionary tale. Front Genet 5:354. CrossRefPubMedPubMedCentralGoogle Scholar
  30. Byvatov E, Fechner U, Sadowski J, Schneider G (2003) Comparison of support vector machine and artificial neural network systems for drug/nondrug classification. J Chem Inf Comput Sci 43:1882–1889. CrossRefPubMedGoogle Scholar
  31. Cao DS, Zhang LX, Tan GS, Xiang Z, Zeng WB, Xu QS, Chen AF (2014) Computational prediction of drugtarget interactions using chemical, biological, and network features. Mol Inf 33:669–681. CrossRefGoogle Scholar
  32. Chen M, Bisgin H, Tong L, Hong H, Fang H, Borlak J, Tong W (2014) Toward predictive models for drug-induced liver injury in humans: are we there yet? Biomark Med 8:201–213. CrossRefPubMedGoogle Scholar
  33. Chen X, Yan CC, Zhang X, Zhang X, Dai F, Yin J, Zhang Y (2016) Drug-target interaction prediction: databases, web servers and computational models. Brief Bioinform 17:696–712. CrossRefPubMedGoogle Scholar
  34. Chen R, Liu X, Jin S, Lin J, Liu J (2018) machine learning for drug-target interaction prediction. Molecules 23. CrossRefGoogle Scholar
  35. Cirit M, Stokes CL (2018) Maximizing the impact of microphysiological systems with in vitro-in vivo translation. Lab Chip 18:1831–1837. CrossRefPubMedPubMedCentralGoogle Scholar
  36. Clark AM, Ma B, Taylor DL, Griffith L, Wells A (2016) Liver metastases: microenvironments and ex-vivo models. Exp Biol Med 241:1639–1652. CrossRefGoogle Scholar
  37. Clark AM et al (2018) A model of dormant-emergent metastatic breast cancer progression enabling exploration of biomarker signatures. Mol Cell Proteomics 17:619–630. CrossRefPubMedPubMedCentralGoogle Scholar
  38. Cobanoglu MC, Liu C, Hu F, Oltvai ZN, Bahar I (2013) Predicting drug-target interactions using probabilistic matrix factorization. J Chem Inf Model 53:3399–3409. CrossRefPubMedPubMedCentralGoogle Scholar
  39. Cobanoglu MC, Oltvai ZN, Taylor DL, Bahar I (2015) BalestraWeb: efficient online evaluation of drug-target interactions. Bioinformatics (Oxford, England) 31:131–133. CrossRefGoogle Scholar
  40. Conesa A et al (2016) A survey of best practices for RNA-seq data analysis. Genome Biol 17:13. CrossRefPubMedPubMedCentralGoogle Scholar
  41. Costello JC et al (2014) A community effort to assess and improve drug sensitivity prediction algorithms. Nat Biotechnol 32:1202 EP. CrossRefGoogle Scholar
  42. De Vivo M, Masetti M, Bottegoni G, Cavalli A (2016) Role of molecular dynamics and related methods in drug discovery. J Med Chem 59:4035–4061. CrossRefPubMedGoogle Scholar
  43. Digles D et al (2016) Open PHACTS computational protocols for in silico target validation of cellular phenotypic screens: knowing the knowns. Med Chem Commun 7:1237–1244. CrossRefGoogle Scholar
  44. Dudley JT et al (2011) Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci Transl Med 3:96ra76CrossRefGoogle Scholar
  45. Dutta D, Heo I, Clevers H (2017) Disease modeling in stem cell-derived 3D organoid systems. Trends Mol Med 23:393–410. CrossRefPubMedGoogle Scholar
  46. Dutta-Moscato J, Solovyev A, Mi Q, Nishikawa T, Soto-Gutierrez A, Fox IJ, Vodovotz Y (2014) A multiscale agent-based in silico model of liver fibrosis progression. Front Bioeng Biotechnol 2:18. CrossRefPubMedPubMedCentralGoogle Scholar
  47. Dziuba J et al (2014) Modeling effects of SGLT-2 inhibitor dapagliflozin treatment versus standard diabetes therapy on cardiovascular and microvascular outcomes. Diabetes Obes Metab 16:628–635. CrossRefPubMedGoogle Scholar
  48. Edington CD et al (2018) Interconnected microphysiological systems for quantitative biology and pharmacology studies. Sci Rep 8:4530. CrossRefPubMedPubMedCentralGoogle Scholar
  49. Erdem C et al (2016) Proteomic screening and lasso regression reveal differential signaling in insulin and insulin-like growth factor I (IGF1) pathways. Mol Cell Proteomics 15:3045–3057. CrossRefPubMedPubMedCentralGoogle Scholar
  50. Esch EW, Bahinski A, Huh D (2015) Organs-on-chips at the frontiers of drug discovery. Nat Rev Drug Discov 14:248–260. CrossRefPubMedPubMedCentralGoogle Scholar
  51. Ewart L et al (2017) Navigating tissue chips from development to dissemination: a pharmaceutical industry perspective. Exp Biol Med 242:1579–1585. CrossRefGoogle Scholar
  52. Ewart L et al (2018) Application of microphysiological systems to enhance safety assessment in drug discovery. Annu Rev Pharmacol Toxicol 58:65–82. CrossRefPubMedGoogle Scholar
  53. Fang Y, Eglen RM (2017) Three-dimensional cell cultures in drug discovery and development. SLAS Discov Adv Life Sci R & D 22:456–472. CrossRefGoogle Scholar
  54. Fatehullah A, Tan SH, Barker N (2016) Organoids as an in vitro model of human development and disease. Nat Cell Biol 18:246–254. CrossRefPubMedGoogle Scholar
  55. Ferdinandy P et al (2018) Definition of hidden drug cardiotoxicity: paradigm change in cardiac safety testing and its clinical implications. Eur Heart J. CrossRefGoogle Scholar
  56. Ferreira LLG, Andricopulo AD (2019) ADMET modeling approaches in drug discovery. Drug Discov Today. CrossRefGoogle Scholar
  57. Floris M, Olla S, Schlessinger D, Cucca F (2018) Genetic-driven druggable target identification and validation. Trends Genet 34:558–570. CrossRefPubMedPubMedCentralGoogle Scholar
  58. Gadkar K, Kirouac D, Parrott N, Ramanujan S (2016a) Quantitative systems pharmacology: a promising approach for translational pharmacology. Drug Discov Today Technol 21–22:57–65. CrossRefPubMedGoogle Scholar
  59. Gadkar K, Lu J, Sahasranaman S, Davis J, Mazer NA, Ramanujan S (2016b) Evaluation of HDL-modulating interventions for cardiovascular risk reduction using a systems pharmacology approach. J Lipid Res 57:46–55. CrossRefPubMedPubMedCentralGoogle Scholar
  60. Gaulton A et al (2017) The ChEMBL database in 2017. Nucleic Acids Res 45:D945–D954. CrossRefPubMedGoogle Scholar
  61. Ge SX, Son EW, Yao R (2018) iDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data. BMC Bioinf 19:534. CrossRefGoogle Scholar
  62. Gerdes MJ et al (2013) Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc Natl Acad Sci U S A 110:11982–11987. CrossRefPubMedPubMedCentralGoogle Scholar
  63. Gfeller D, Grosdidier A, Wirth M, Daina A, Michielin O, Zoete V (2014) SwissTargetPrediction: a web server for target prediction of bioactive small molecules. Nucleic Acids Res 42:W32–W38. CrossRefPubMedPubMedCentralGoogle Scholar
  64. Goh WWB, Wong L (2018) Dealing with confounders in omics analysis. Trends Biotechnol 36:488–498. CrossRefPubMedGoogle Scholar
  65. Goh WWB, Wang W, Wong L (2017) Why batch effects matter in omics data, and how to avoid them. Trends Biotechnol 35:498–507. CrossRefPubMedGoogle Scholar
  66. Gough A, Lezon T, Faeder J, Chennubhotla C, Murphy R, Critchley-Thorne R, Taylor DL (2014) High-content analysis with cellular and tissue systems biology: a bridge between cancer cell biology and tissue-based diagnostics. In: Mendelsohn J, Gray JW, Howley PM, Israel MA, Thompson CB (eds) The molecular basis of cancer, 4th edn. Elsevier, New York, pp 369–392.e367. CrossRefGoogle Scholar
  67. Gough A, Vernetti L, Bergenthal L, Shun TY, Taylor DL (2016) The microphysiology systems database for analyzing and modeling compound interactions with human and animal organ models. Appl In Vitro Toxicol 2:103–117. CrossRefPubMedPubMedCentralGoogle Scholar
  68. Grimes T, Potter SS, Datta S (2019) Integrating gene regulatory pathways into differential network analysis of gene expression data. Sci Rep 9:5479. CrossRefPubMedPubMedCentralGoogle Scholar
  69. Haasen D, Schopfer U, Antczak C, Guy C, Fuchs F, Selzer P (2017) How phenotypic screening influenced drug discovery: lessons from five years of practice. Assay Drug Dev Technol 15:239–246. CrossRefPubMedGoogle Scholar
  70. Hachey SJ, Hughes CCW (2018) Applications of tumor chip technology. Lab Chip 18:2893–2912. CrossRefPubMedPubMedCentralGoogle Scholar
  71. Hansen J, Iyengar R (2013) Computation as the mechanistic bridge between precision medicine and systems therapeutics. Clin Pharmacol Ther 93:117–128. CrossRefPubMedGoogle Scholar
  72. Hartung T (2009) Toxicology for the twenty-first century. Nature 460:208–212. CrossRefPubMedGoogle Scholar
  73. Hasin Y, Seldin M, Lusis A (2017) Multi-omics approaches to disease. Genome Biol 18:83. CrossRefPubMedPubMedCentralGoogle Scholar
  74. He KY, Ge D, He MM (2017) Big data analytics for genomic medicine. Int J Mol Sci 18. CrossRefGoogle Scholar
  75. Hill SM et al (2016) Inferring causal molecular networks: empirical assessment through a community-based effort. Nat Methods 13:310–318. CrossRefPubMedPubMedCentralGoogle Scholar
  76. Hodos RA, Kidd BA, Shameer K, Readhead BP, Dudley JT (2016) In silico methods for drug repurposing and pharmacology. Wiley Interdiscip Rev Syst Biol Med 8:186–210CrossRefGoogle Scholar
  77. Hopkins AL (2008) Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol 4:682–690. CrossRefPubMedGoogle Scholar
  78. Horvath P et al (2016) Screening out irrelevant cell-based models of disease. Nat Rev Drug Discov 15:751–769. CrossRefPubMedGoogle Scholar
  79. Hou T, Qiao X, Xu X (2001) Research and development of 3D molecular structure database of traditional Chinese drugs. Acta Chim Sin 59:1788–1792Google Scholar
  80. Howell BA, Siler SQ, Shoda LK, Yang Y, Woodhead JL, Watkins PB (2014) A mechanistic model of drug-induced liver injury AIDS the interpretation of elevated liver transaminase levels in a phase I clinical trial. CPT Pharmacometrics Syst Pharmacol 3:e98. CrossRefPubMedPubMedCentralGoogle Scholar
  81. Huh D, Matthews BD, Mammoto A, Montoya-Zavala M, Hsin HY, Ingber DE (2010) Reconstituting organ-level lung functions on a chip. Science 328:1662–1668. CrossRefPubMedGoogle Scholar
  82. Huntley RP, Sawford T, Mutowo-Meullenet P, Shypitsyna A, Bonilla C, Martin MJ, O’Donovan C (2015) The GOA database: gene ontology annotation updates for 2015. Nucleic Acids Res 43:D1057–D1063. CrossRefPubMedGoogle Scholar
  83. Ivetac A, McCammon JA (2012) A molecular dynamics ensemble-based approach for the mapping of druggable binding sites. Methods Mol Biol (Clifton, NJ) 819:3–12. CrossRefGoogle Scholar
  84. Iyengar R, Zhao S, Chung SW, Mager DE, Gallo JM (2012) Merging systems biology with pharmacodynamics. Sci Transl Med 4:126ps127. CrossRefGoogle Scholar
  85. Jain MR et al (2015) Saroglitazar, a novel PPAR alpha/gamma agonist with predominant PPAR alpha activity, shows lipid-lowering and insulin-sensitizing effects in preclinical models. Pharmacol Res Perspect 3:e00136. CrossRefPubMedPubMedCentralGoogle Scholar
  86. Jain A et al (2018) Primary human lung alveolus-on-a-chip model of intravascular thrombosis for assessment of therapeutics. Clin Pharmacol Ther 103:332–340. CrossRefPubMedGoogle Scholar
  87. Johnson CH, Ivanisevic J, Siuzdak G (2016) Metabolomics: beyond biomarkers and towards mechanisms. Nat Rev Mol Cell Biol 17:451–459. CrossRefPubMedPubMedCentralGoogle Scholar
  88. Kalli M, Stylianopoulos T (2018) Defining the role of solid stress and matrix stiffness in cancer cell proliferation and metastasis. Front Oncol 8:55–55. CrossRefPubMedPubMedCentralGoogle Scholar
  89. Kanehisa M, Furumichi M, Tanabe M, Sato Y, Morishima K (2017) KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res 45:D353–D361. CrossRefPubMedGoogle Scholar
  90. Karczewski KJ, Snyder MP (2018) Integrative omics for health and disease. Nat Rev Genet 19:299–310. CrossRefPubMedPubMedCentralGoogle Scholar
  91. Kaya C et al (2018) Heterogeneities in axonal structure and transporter distribution lower dopamine reuptake efficiency. eNeuro 5. CrossRefGoogle Scholar
  92. Keating SM et al (2018) Opportunities and challenges in implementation of multiparameter single cell analysis platforms for clinical translation. Clin Transl Sci 11:267–276. CrossRefPubMedPubMedCentralGoogle Scholar
  93. Keiser MJ et al (2009) Predicting new molecular targets for known drugs. Nature 462:175–181. CrossRefPubMedPubMedCentralGoogle Scholar
  94. Koch CM, Chiu SF, Akbarpour M, Bharat A, Ridge KM, Bartom ET, Winter DR (2018) A beginner’s guide to analysis of RNA sequencing data. Am J Respir Cell Mol Biol 59:145–157. CrossRefPubMedPubMedCentralGoogle Scholar
  95. Kola I, Landis J (2004) Can the pharmaceutical industry reduce attrition rates? Nat Rev Drug Discov 3:711–715. CrossRefPubMedGoogle Scholar
  96. Kooistra AJ, Kanev GK, van Linden OP, Leurs R, de Esch IJ, de Graaf C (2016) KLIFS: a structural kinase-ligand interaction database. Nucleic Acids Res 44:D365–D371. CrossRefPubMedGoogle Scholar
  97. Kuhn M et al (2010) STITCH 2: an interaction network database for small molecules and proteins. Nucleic Acids Res 38:D552–D556. CrossRefPubMedGoogle Scholar
  98. Kurdyukov S, Bullock M (2016) DNA methylation analysis: choosing the right method. Biology 5. CrossRefGoogle Scholar
  99. LaFramboise T (2009) Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances. Nucleic Acids Res 37:4181–4193. CrossRefPubMedPubMedCentralGoogle Scholar
  100. Lambert CG, Black LJ (2012) Learning from our GWAS mistakes: from experimental design to scientific method. Biostatistics (Oxford, England) 13:195–203. CrossRefGoogle Scholar
  101. Lands AM, Arnold A, McAuliff JP, Luduena FP, Brown TG Jr (1967) Differentiation of receptor systems activated by sympathomimetic amines. Nature 214:597–598CrossRefGoogle Scholar
  102. Larance M, Lamond AI (2015) Multidimensional proteomics for cell biology. Nat Rev Mol Cell Biol 16:269–280. CrossRefPubMedGoogle Scholar
  103. Laurie S et al (2016) From wet-lab to variations: concordance and speed of bioinformatics pipelines for whole genome and whole exome sequencing. Hum Mutat 37:1263–1271. CrossRefPubMedPubMedCentralGoogle Scholar
  104. Lave T, Caruso A, Parrott N, Walz A (2016) Translational PK/PD modeling to increase probability of success in drug discovery and early development. Drug Discov Today Technol 21–22:27–34. CrossRefPubMedGoogle Scholar
  105. Lee E, Chuang HY, Kim JW, Ideker T, Lee D (2008) Inferring pathway activity toward precise disease classification. PLoS Comput Biol 4:e1000217. CrossRefPubMedPubMedCentralGoogle Scholar
  106. Lee-Montiel FT et al (2017) Control of oxygen tension recapitulates zone-specific functions in human liver microphysiology systems. Exp Biol Med 242:1617–1632. CrossRefGoogle Scholar
  107. Lenguito G et al (2017) Resealable, optically accessible, PDMS-free fluidic platform for ex vivo interrogation of pancreatic islets. Lab Chip 17:772–781. CrossRefPubMedPubMedCentralGoogle Scholar
  108. Lexa KW, Carlson HA (2011) Full protein flexibility is essential for proper hot-spot mapping. J Am Chem Soc 133:200–202. CrossRefPubMedGoogle Scholar
  109. Lezon TR, Banavar JR, Cieplak M, Maritan A, Fedoroff NV (2006) Using the principle of entropy maximization to infer genetic interaction networks from gene expression patterns. Proc Natl Acad Sci U S A 103:19033–19038. CrossRefPubMedPubMedCentralGoogle Scholar
  110. Li Q, Bender A, Pei J, Lai L (2007) A large descriptor set and a probabilistic kernel-based classifier significantly improve druglikeness classification. J Chem Inf Model 47:1776–1786. CrossRefPubMedGoogle Scholar
  111. Li X, George SM, Vernetti L, Gough AH, Taylor DL (2018) A glass-based, continuously zonated and vascularized human liver acinus microphysiological system (vLAMPS) designed for experimental modeling of diseases and ADME/TOX. Lab Chip 18:2614–2631. CrossRefPubMedPubMedCentralGoogle Scholar
  112. Lin Z, Jaberi-Douraki M, He C, Jin S, Yang RSH, Fisher JW, Riviere JE (2017) Performance assessment and translation of physiologically based pharmacokinetic models from acslX to Berkeley Madonna, MATLAB, and R Language: oxytetracycline and gold nanoparticles as case examples. Toxicol Sci 158:23–35. CrossRefPubMedGoogle Scholar
  113. Lind JU et al (2017) Cardiac microphysiological devices with flexible thin-film sensors for higher-throughput drug screening. Lab Chip 17:3692–3703. CrossRefPubMedPubMedCentralGoogle Scholar
  114. Liu Y, Wu M, Miao C, Zhao P, Li XL (2016) Neighborhood regularized logistic matrix factorization for drug-target interaction prediction. PLoS Comput Biol 12:e1004760. CrossRefPubMedPubMedCentralGoogle Scholar
  115. Loving KA, Lin A, Cheng AC (2014) Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility. PLoS Comput Biol 10:e1003741. CrossRefPubMedPubMedCentralGoogle Scholar
  116. Low LA, Tagle DA (2017) Tissue chips – innovative tools for drug development and disease modeling. Lab Chip 17:3026–3036. CrossRefPubMedPubMedCentralGoogle Scholar
  117. Macintyre G, Van Loo P, Corcoran NM, Wedge DC, Markowetz F, Hovens CM (2017) How subclonal modeling is changing the metastatic paradigm. Clin Cancer Res 23:630–635. CrossRefPubMedGoogle Scholar
  118. Manatakis DV, Raghu VK, Benos PV (2018) piMGM: incorporating multi-source priors in mixed graphical models for learning disease networks. Bioinformatics 34:i848–i856. CrossRefPubMedPubMedCentralGoogle Scholar
  119. Marnett LJ (2009) The COXIB experience: a look in the rearview mirror. Annu Rev Pharmacol Toxicol 49:265–290. CrossRefPubMedGoogle Scholar
  120. Martz CA et al (2014) Systematic identification of signaling pathways with potential to confer anticancer drug resistance. Sci Signal 7:ra121. CrossRefPubMedPubMedCentralGoogle Scholar
  121. Mateus A, Maatta TA, Savitski MM (2016) Thermal proteome profiling: unbiased assessment of protein state through heat-induced stability changes. Proteome Sci 15:13. CrossRefPubMedGoogle Scholar
  122. May S, Evans S, Parry L (2017) Organoids, organs-on-chips and other systems, and microbiota. Emerg Top Life Sci 1:385–400. CrossRefGoogle Scholar
  123. McCauley HA, Wells JM (2017) Pluripotent stem cell-derived organoids: using principles of developmental biology to grow human tissues in a dish. Development 144:958–962. CrossRefPubMedPubMedCentralGoogle Scholar
  124. Miedel MT, Gavlock DC, Jia S, Gough A, Taylor DL, Stern AM (2019) Modeling the effect of the metastatic microenvironment on phenotypes conferred by estrogen receptor mutations using a human liver microphysiology system. Sci Rep (final review)Google Scholar
  125. Muller KR, Ratsch G, Sonnenburg S, Mika S, Grimm M, Heinrich N (2005) Classifying ‘drug-likeness’ with kernel-based learning methods. J Chem Inf Model 45:249–253. CrossRefPubMedGoogle Scholar
  126. Musa A et al (2017) A review of connectivity map and computational approaches in pharmacogenomics. Brief Bioinform 18:903. CrossRefPubMedPubMedCentralGoogle Scholar
  127. Mustata G et al (2009) Discovery of novel Myc-Max heterodimer disruptors with a three-dimensional pharmacophore model. J Med Chem 52:1247–1250. CrossRefPubMedPubMedCentralGoogle Scholar
  128. Newman RH, Zhang J (2014) The design and application of genetically encodable biosensors based on fluorescent proteins. Methods Mol Biol (Clifton, NJ) 1071:1–16. CrossRefGoogle Scholar
  129. Nickel J et al (2014) SuperPred: update on drug classification and target prediction. Nucleic Acids Res 42:W26–W31. CrossRefPubMedPubMedCentralGoogle Scholar
  130. Oleaga C et al (2019) Long-term electrical and mechanical function monitoring of a human-on-a-chip system. Adv Funct Mater 29:1805792. CrossRefGoogle Scholar
  131. Pacana T, Sanyal AJ (2015) Recent advances in understanding/management of non-alcoholic steatohepatitis. F1000Prime Rep 7:28. CrossRefPubMedPubMedCentralGoogle Scholar
  132. Paul DS et al (2016) Increased DNA methylation variability in type 1 diabetes across three immune effector cell types. Nat Commun 7:13555. CrossRefPubMedPubMedCentralGoogle Scholar
  133. Pei F et al (2017) Connecting neuronal cell protective pathways and drug combinations in a huntington’s disease model through the application of quantitative systems pharmacology. Sci Rep 7:17803. CrossRefPubMedPubMedCentralGoogle Scholar
  134. Pei F, Li H, Liu B, Bahar I (2019) Quantitative systems pharmacological analysis of drugs of abuse reveals the pleiotropy of their targets and the effector role of mTORC1. Front Pharmacol 10:191. CrossRefPubMedPubMedCentralGoogle Scholar
  135. Pollard TD (2010) A guide to simple and informative binding assays. Mol Biol Cell 21:4061–4067. CrossRefPubMedPubMedCentralGoogle Scholar
  136. Prathipati P, Mizuguchi K (2016) Systems biology approaches to a rational drug discovery paradigm. Curr Top Med Chem 16:1009–1025CrossRefGoogle Scholar
  137. Prestigiacomo V, Weston A, Messner S, Lampart F, Suter-Dick L (2017) Pro-fibrotic compounds induce stellate cell activation, ECM-remodelling and Nrf2 activation in a human 3D-multicellular model of liver fibrosis. PLoS One 12:e0179995. CrossRefPubMedPubMedCentralGoogle Scholar
  138. Pushpakom S et al (2018) Drug repurposing: progress, challenges and recommendations. Nat Rev Drug Discov. CrossRefGoogle Scholar
  139. Rao SS, Kondapaneni RV, Narkhede AA (2019) Bioengineered models to study tumor dormancy. J Biol Eng 13:3. CrossRefPubMedPubMedCentralGoogle Scholar
  140. Ribas J, Pawlikowska J, Rouwkema J (2018) Microphysiological systems: analysis of the current status, challenges and commercial future. Microphysiol Syst 2Google Scholar
  141. Rowland M, Peck C, Tucker G (2011) Physiologically-based pharmacokinetics in drug development and regulatory science. Annu Rev Pharmacol Toxicol 51:45–73. CrossRefPubMedGoogle Scholar
  142. Sadowski J, Kubinyi H (1998) A scoring scheme for discriminating between drugs and nondrugs. J Med Chem 41:3325–3329. CrossRefPubMedGoogle Scholar
  143. Sakolish C et al (2018) Technology transfer of the microphysiological systems: a case study of the human proximal tubule tissue chip. Sci Rep 8:14882. CrossRefPubMedPubMedCentralGoogle Scholar
  144. Sanders MP, Barbosa AJ, Zarzycka B, Nicolaes GA, Klomp JP, de Vlieg J, Del Rio A (2012) Comparative analysis of pharmacophore screening tools. J Chem Inf Model 52:1607–1620. CrossRefPubMedGoogle Scholar
  145. Satapathy SK, Sanyal AJ (2015) Epidemiology and natural history of nonalcoholic fatty liver disease. Semin Liver Dis 35:221–235. CrossRefPubMedGoogle Scholar
  146. Satoh T et al (2017) A multi-throughput multi-organ-on-a-chip system on a plate formatted pneumatic pressure-driven medium circulation platform. Lab Chip 18:115–125. CrossRefPubMedGoogle Scholar
  147. Schenone M, Dancik V, Wagner BK, Clemons PA (2013) Target identification and mechanism of action in chemical biology and drug discovery. Nat Chem Biol 9:232–240. CrossRefPubMedPubMedCentralGoogle Scholar
  148. Schoeberl B et al (2009) Therapeutically targeting ErbB3: a key node in ligand-induced activation of the ErbB receptor-PI3K axis. Sci Signal 2:ra31. CrossRefPubMedGoogle Scholar
  149. Schulze K et al (2015) Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat Genet 47:505–511. CrossRefPubMedPubMedCentralGoogle Scholar
  150. Schwartz MP et al (2015) Human pluripotent stem cell-derived neural constructs for predicting neural toxicity. Proc Natl Acad Sci U S A 112:12516–12521. CrossRefPubMedPubMedCentralGoogle Scholar
  151. Sebastiani P et al (2010) Genetic signatures of exceptional longevity in humans. Science 2010.
  152. Sebastiani P et al (2011) Retraction. Science 333:404. CrossRefPubMedGoogle Scholar
  153. Sekar JAP, Tapia J-J, Faeder JR (2017) Automated visualization of rule-based models. PLoS Comput Biol 13:e1005857. CrossRefPubMedPubMedCentralGoogle Scholar
  154. Senutovitch N, Vernetti L, Boltz R, DeBiasio R, Gough A, Taylor DL (2015) Fluorescent protein biosensors applied to microphysiological systems. Exp Biol Med 240:795–808. CrossRefGoogle Scholar
  155. Seok J et al (2013) Genomic responses in mouse models poorly mimic human inflammatory diseases. Proc Natl Acad Sci U S A 110:3507–3512. CrossRefPubMedPubMedCentralGoogle Scholar
  156. Shamir ER, Ewald AJ (2014) Three-dimensional organotypic culture: experimental models of mammalian biology and disease. Nat Rev Mol Cell Biol 15:647–664. CrossRefPubMedPubMedCentralGoogle Scholar
  157. Sharma M, Mitnala S, Vishnubhotla RK, Mukherjee R, Reddy DN, Rao PN (2015) The riddle of nonalcoholic fatty liver disease: progression from nonalcoholic fatty liver to nonalcoholic steatohepatitis. J Clin Exp Hepatol 5:147–158. CrossRefPubMedPubMedCentralGoogle Scholar
  158. Shuler ML (2017) Organ-, body- and disease-on-a-chip systems. Lab Chip 17:2345–2346. CrossRefPubMedGoogle Scholar
  159. Simian M, Bissell MJ (2017) Organoids: a historical perspective of thinking in three dimensions. J Cell Biol 216:31–40. CrossRefPubMedPubMedCentralGoogle Scholar
  160. Sin A, Chin KC, Jamil MF, Kostov Y, Rao G, Shuler ML (2004) The design and fabrication of three-chamber microscale cell culture analog devices with integrated dissolved oxygen sensors. Biotechnol Prog 20:338–345. CrossRefPubMedGoogle Scholar
  161. Skardal A, Shupe T, Atala A (2016) Organoid-on-a-chip and body-on-a-chip systems for drug screening and disease modeling. Drug Discov Today 21:1399–1411. CrossRefPubMedGoogle Scholar
  162. Slenter DN et al (2018) WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res 46:D661–D667. CrossRefPubMedGoogle Scholar
  163. Sliz E et al (2018) NAFLD risk alleles in PNPLA3, TM6SF2, GCKR and LYPLAL1 show divergent metabolic effects. Hum Mol Genet 27:2214–2223. CrossRefPubMedPubMedCentralGoogle Scholar
  164. Smagris E et al (2015) Pnpla3I148M knockin mice accumulate PNPLA3 on lipid droplets and develop hepatic steatosis. Hepatology 61:108–118. CrossRefPubMedGoogle Scholar
  165. Smietana K, Siatkowski M, Moller M (2016) Trends in clinical success rates. Nat Rev Drug Discov 15:379–380. CrossRefPubMedGoogle Scholar
  166. Sorger P et al (2011) Quantitative and systems pharmacology in the post-genomic era: new approaches to discovering drugs and understanding therapeutic mechanisms. An NIH White Paper by the QSP Workshop Group – October, 2011Google Scholar
  167. Soto-Gutierrez A, Gough A, Vernetti LA, Taylor DL, Monga SP (2017) Pre-clinical and clinical investigations of metabolic zonation in liver diseases: the potential of microphysiology systems. Exp Biol Med 242:1605–1616. CrossRefGoogle Scholar
  168. Spagnolo DM et al (2016) Pointwise mutual information quantifies intratumor heterogeneity in tissue sections labeled with multiple fluorescent biomarkers. J Pathol Inf 7:47. CrossRefGoogle Scholar
  169. Spagnolo DM et al (2017) Platform for quantitative evaluation of spatial intratumoral heterogeneity in multiplexed fluorescence images. Cancer Res 77:e71–e74. CrossRefPubMedPubMedCentralGoogle Scholar
  170. Speliotes EK et al (2011) Genome-wide association analysis identifies variants associated with nonalcoholic fatty liver disease that have distinct effects on metabolic traits. PLoS Genet 7:e1001324. CrossRefPubMedPubMedCentralGoogle Scholar
  171. Stern AM, Schurdak ME, Bahar I, Berg JM, Taylor DL (2016) A perspective on implementing a quantitative systems pharmacology platform for drug discovery and the advancement of personalized medicine. J Biomol Screen 21:521–534. CrossRefPubMedPubMedCentralGoogle Scholar
  172. Subramanian A et al (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102:15545–15550. CrossRefPubMedPubMedCentralGoogle Scholar
  173. Sun M et al (2018) Effects of matrix stiffness on the morphology, adhesion, proliferation and osteogenic differentiation of mesenchymal stem cells. Int J Med Sci 15:257–268. CrossRefPubMedPubMedCentralGoogle Scholar
  174. Sutherland RM, Inch WR, McCredie JA, Kruuv J (1970) A multi-component radiation survival curve using an in vitro tumour model. Int J Radiat Biol Relat Stud Phys Chem Med 18:491–495CrossRefGoogle Scholar
  175. Sweeney LM, Shuler ML, Babish JG, Ghanem A (1995) A cell culture analogue of rodent physiology: application to naphthalene toxicology. Toxicol In Vitro 9:307–316CrossRefGoogle Scholar
  176. Szabo A, Merks RM (2013) Cellular potts modeling of tumor growth, tumor invasion, and tumor evolution. Front Oncol 3:87. CrossRefPubMedPubMedCentralGoogle Scholar
  177. Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M (2016) STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res 44:D380–D384. CrossRefPubMedGoogle Scholar
  178. Takebe T, Zhang B, Radisic M (2017) Synergistic engineering: organoids meet organs-on-a-chip. Cell Stem Cell 21:297–300. CrossRefPubMedGoogle Scholar
  179. Tan YM, Worley RR, Leonard JA, Fisher JW (2018) Challenges associated with applying physiologically based pharmacokinetic modeling for public health decision-making. Toxicol Sci 162:341–348. CrossRefPubMedPubMedCentralGoogle Scholar
  180. Taylor DL (2012) A new vision of drug discovery and development. Eur Pharm Rev 17:20–24Google Scholar
  181. Teschendorff AE (2018) Avoiding common pitfalls in machine learning omic data science. Nat Mater. CrossRefGoogle Scholar
  182. Torras N, Garcia-Diaz M, Fernandez-Majada V, Martinez E (2018) Mimicking epithelial tissues in three-dimensional cell culture models. Front Bioeng Biotechnol 6:197. CrossRefPubMedPubMedCentralGoogle Scholar
  183. Trapnell C et al (2014) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat Biotechnol 32:381–386. CrossRefPubMedPubMedCentralGoogle Scholar
  184. Trietsch SJ, Israels GD, Joore J, Hankemeier T, Vulto P (2013) Microfluidic titer plate for stratified 3D cell culture. Lab Chip 13:3548–3554. CrossRefPubMedGoogle Scholar
  185. Truskey GA et al (2013) Design considerations for an integrated microphysiological muscle tissue for drug and tissue toxicity testing. Stem Cell Res Ther 4(Suppl 1):S10. CrossRefPubMedPubMedCentralGoogle Scholar
  186. Tsamandouras N, Kostrzewski T, Stokes CL, Griffith LG, Hughes DJ, Cirit M (2017) Quantitative assessment of population variability in hepatic drug metabolism using a perfused three-dimensional human liver microphysiological system. J Pharmacol Exp Ther 360:95–105. CrossRefPubMedPubMedCentralGoogle Scholar
  187. Uttam S, Chennubhotla C, Stern AM, Taylor DL (2019) Computational and systems pathology analytics platform applied to hyperplexed fluorescence-labeled patient tissues predicts risk of colorectal cancer recurrence and infers relevant signaling networks. Nat Biotech. in reviewGoogle Scholar
  188. Vaidya TR, Ande A, Ait-Oudhia S (2019) Combining multiscale experimental and computational systems pharmacological approaches to overcome resistance to HER2-targeted therapy in breast cancer. J Pharmacol Exp Ther. CrossRefGoogle Scholar
  189. van den Berg A, Mummery CL, Passier R, van der Meer AD (2019) Personalised organs-on-chips: functional testing for precision medicine. Lab Chip 19:198–205. CrossRefPubMedGoogle Scholar
  190. van der Worp HB, Howells DW, Sena ES, Porritt MJ, Rewell S, O’Collins V, Macleod MR (2010) Can animal models of disease reliably inform human studies? PLoS Med 7:e1000245. CrossRefPubMedPubMedCentralGoogle Scholar
  191. van Laarhoven T, Nabuurs SB, Marchiori E (2011) Gaussian interaction profile kernels for predicting drug-target interaction. Bioinformatics 27:3036–3043. CrossRefPubMedGoogle Scholar
  192. Vernetti LA, Senutovitch N, Boltz R, DeBiasio R, Shun TY, Gough A, Taylor DL (2016) A human liver microphysiology platform for investigating physiology, drug safety, and disease models. Exp Biol Med 241:101–114. CrossRefGoogle Scholar
  193. Vernetti L et al (2017) Functional coupling of human microphysiology systems: intestine, liver, kidney proximal tubule, blood-brain barrier and skeletal muscle. Sci Rep 7:42296. CrossRefPubMedPubMedCentralGoogle Scholar
  194. Visscher PM, Brown MA, McCarthy MI, Yang J (2012a) Five years of GWAS discovery. Am J Hum Genet 90:7–24. CrossRefPubMedPubMedCentralGoogle Scholar
  195. Visscher PM, Goddard ME, Derks EM, Wray NR (2012b) Evidence-based psychiatric genetics, AKA the false dichotomy between common and rare variant hypotheses. Mol Psychiatry 17:474–485. CrossRefPubMedGoogle Scholar
  196. Visser SA, de Alwis DP, Kerbusch T, Stone JA, Allerheiligen SR (2014) Implementation of quantitative and systems pharmacology in large pharma. CPT Pharmacometrics Syst Pharmacol 3:e142. CrossRefPubMedPubMedCentralGoogle Scholar
  197. Volkamer A, Kuhn D, Rippmann F, Rarey M (2012) DoGSiteScorer: a web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics 28:2074–2075. CrossRefGoogle Scholar
  198. Wagener M, van Geerestein VJ (2000) Potential drugs and nondrugs: prediction and identification of important structural features. J Chem Inf Comput Sci 40:280–292CrossRefGoogle Scholar
  199. Walters WP, Murcko MA (2002) Prediction of ‘drug-likeness’. Adv Drug Deliv Rev 54:255–271CrossRefGoogle Scholar
  200. Wang T, Wu MB, Lin JP, Yang LR (2015) Quantitative structure-activity relationship: promising advances in drug discovery platforms. Expert Opin Drug Discovery 10:1283–1300. CrossRefGoogle Scholar
  201. Watson DE, Hunziker R, Wikswo JP (2017) Fitting tissue chips and microphysiological systems into the grand scheme of medicine, biology, pharmacology, and toxicology. Exp Biol Med 242:1559–1572. CrossRefGoogle Scholar
  202. Weedon MN et al (2006) A common haplotype of the glucokinase gene alters fasting glucose and birth weight: association in six studies and population-genetics analyses. Am J Hum Genet 79:991–1001. CrossRefPubMedPubMedCentralGoogle Scholar
  203. Wei Y et al (2018) GCDB: a glaucomatous chemogenomics database for in silico drug discovery. Database (Oxford) 2018. epublish.
  204. Wen M, Zhang Z, Niu S, Sha H, Yang R, Yun Y, Lu H (2017) Deep-learning-based drug-target interaction prediction. J Proteome Res 16:1401–1409. CrossRefPubMedGoogle Scholar
  205. Wevers NR et al (2016) High-throughput compound evaluation on 3D networks of neurons and glia in a microfluidic platform. Sci Rep 6:38856. CrossRefPubMedPubMedCentralGoogle Scholar
  206. Wikswo JP et al (2013a) Engineering challenges for instrumenting and controlling integrated organ-on-chip systems. IEEE Trans Biomed Eng 60:682–690. CrossRefPubMedPubMedCentralGoogle Scholar
  207. Wikswo JP, Curtis EL, Eagleton ZE, Evans BC, Kole A, Hofmeister LH, Matloff WJ (2013b) Scaling and systems biology for integrating multiple organs-on-a-chip. Lab Chip 13:3496–3511. CrossRefPubMedPubMedCentralGoogle Scholar
  208. Wishart DS (2016) Emerging applications of metabolomics in drug discovery and precision medicine. Nat Rev Drug Discov 15:473–484. CrossRefPubMedPubMedCentralGoogle Scholar
  209. Wishart DS et al (2018) DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res 46:D1074–D1082. CrossRefPubMedGoogle Scholar
  210. Wood AR et al (2014) Defining the role of common variation in the genomic and biological architecture of adult human height. Nat Genet 46:1173–1186. CrossRefPubMedPubMedCentralGoogle Scholar
  211. Woodhead JL, Watkins PB, Howell BA, Siler SQ, Shoda LKM (2017) The role of quantitative systems pharmacology modeling in the prediction and explanation of idiosyncratic drug-induced liver injury. Drug Metab Pharmacokinet 32:40–45. CrossRefPubMedGoogle Scholar
  212. Workman MJ et al (2017) Engineered human pluripotent-stem-cell-derived intestinal tissues with a functional enteric nervous system. Nat Med 23:49–59. CrossRefPubMedGoogle Scholar
  213. Wray NR, Wijmenga C, Sullivan PF, Yang J, Visscher PM (2018) Common disease is more complex than implied by the core gene omnigenic model. Cell 173:1573–1580. CrossRefPubMedGoogle Scholar
  214. Wu Y, Wang G (2018) Machine learning based toxicity prediction: from chemical structural description to transcriptome analysis. Int J Mol Sci 19. CrossRefGoogle Scholar
  215. Wu M et al (2018) Conditional gene knockout and reconstitution in human iPSCs with an inducible Cas9 system. Stem Cell Res 29:6–14. CrossRefPubMedGoogle Scholar
  216. Wu N, Feng Z, He X, Kwon W, Wang J, Xie XQ (2019) Insight of captagon abuse by chemogenomics knowledgebase-guided systems pharmacology target mapping analyses. Sci Rep 9:2268. CrossRefPubMedPubMedCentralGoogle Scholar
  217. Xia Z, Wu LY, Zhou X, Wong ST (2010) Semi-supervised drug-protein interaction prediction from heterogeneous biological spaces. BMC Syst Biol 4(Suppl 2):S6. CrossRefPubMedPubMedCentralGoogle Scholar
  218. Xie N, Tang B (2016) The application of human iPSCs in neurological diseases: from bench to bedside. Stem Cells Int 2016:6484713. CrossRefPubMedPubMedCentralGoogle Scholar
  219. Xu X, Ma S, Feng Z, Hu G, Wang L, Xie XQ (2016) Chemogenomics knowledgebase and systems pharmacology for hallucinogen target identification-Salvinorin A as a case study. J Mol Graph Model 70:284–295. CrossRefPubMedPubMedCentralGoogle Scholar
  220. Yamanishi Y, Araki M, Gutteridge A, Honda W, Kanehisa M (2008) Prediction of drug-target interaction networks from the integration of chemical and genomic spaces. Bioinformatics 24:i232–i240. CrossRefPubMedPubMedCentralGoogle Scholar
  221. Yamanishi Y, Kotera M, Moriya Y, Sawada R, Kanehisa M, Goto S (2014) DINIES: drug-target interaction network inference engine based on supervised analysis. Nucleic Acids Res 42:W39–W45. CrossRefPubMedPubMedCentralGoogle Scholar
  222. Yang K, Han X (2016) Lipidomics: techniques, applications, and outcomes related to biomedical sciences. Trends Biochem Sci 41:954–969. CrossRefPubMedPubMedCentralGoogle Scholar
  223. Yin A, Yamada A, Stam WB, van Hasselt JGC, van der Graaf PH (2018) Quantitative systems pharmacology analysis of drug combination and scaling to humans: the interaction between noradrenaline and vasopressin in vasoconstriction. Br J Pharmacol 175:3394–3406. CrossRefPubMedPubMedCentralGoogle Scholar
  224. Yu J et al (2015) Quantitative systems pharmacology approaches applied to microphysiological systems (MPS): data interpretation and multi-MPS integration. CPT Pharmacometrics Syst Pharmacol 4:585–594. CrossRefPubMedPubMedCentralGoogle Scholar
  225. Zernov VV, Balakin KV, Ivaschenko AA, Savchuk NP, Pletnev IV (2003) Drug discovery using support vector machines. The case studies of drug-likeness, agrochemical-likeness, and enzyme inhibition predictions. J Chem Inf Comput Sci 43:2048–2056. CrossRefPubMedGoogle Scholar
  226. Zhang B, Radisic M (2017) Organ-on-a-chip devices advance to market. Lab Chip 17:2395–2420. CrossRefPubMedGoogle Scholar
  227. Zhao S, Iyengar R (2012) Systems pharmacology: network analysis to identify multiscale mechanisms of drug action. Annu Rev Pharmacol Toxicol 52:505–521. CrossRefPubMedPubMedCentralGoogle Scholar
  228. Zhuang X, Lu C (2016) PBPK modeling and simulation in drug research and development. Acta Pharm Sin B 6:430–440. CrossRefPubMedPubMedCentralGoogle Scholar
  229. Zong N, Kim H, Ngo V, Harismendy O (2017) Deep mining heterogeneous networks of biomedical linked data to predict novel drug-target associations. Bioinformatics 33:2337–2344. CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • D. Lansing Taylor
    • 1
    • 2
    Email author
  • Albert Gough
    • 1
    • 2
  • Mark E. Schurdak
    • 1
    • 2
  • Lawrence Vernetti
    • 1
    • 2
  • Chakra S. Chennubhotla
    • 1
    • 2
  • Daniel Lefever
    • 1
  • Fen Pei
    • 1
    • 2
  • James R. Faeder
    • 1
    • 2
  • Timothy R. Lezon
    • 1
    • 2
  • Andrew M. Stern
    • 1
    • 2
  • Ivet Bahar
    • 1
    • 2
  1. 1.University of Pittsburgh Drug Discovery InstitutePittsburghUSA
  2. 2.Department of Computational and Systems BiologyUniversity of PittsburghPittsburghUSA

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