A physiome interoperability roadmap for personalized drug development

Interface Focus

Volumn 6, Issue 2, 2016, Pages

  Thomas, Simon ^a   Wolstencroft, Katherine ^b   Bono, Bernard De ^c,d   Hunter, Peter J ^d  

^a CYPROTEX DISCOVERY LTD   (United Kingdom)

^b LEIDEN UNIVERSITY   (Netherlands)

^c UNIVERSITY COLLEGE LONDON   (United Kingdom)

^d UNIVERSITY OF AUCKLAND   (New Zealand)

Author keywords

Data and model resources; Human physiome; Interoperability; Personalized medicine; Pharmacogenomics; Physiologically based pharmacokinetic modelling

Indexed keywords

EID: 84958819001     PISSN: 20428898     EISSN: 20428901     Source Type: Journal    
DOI: 10.1098/rsfs.2015.0094     Document Type: Article

References (64)

1. Lam YW. 2013 Scientific challenges and implementation barriers to translation of pharmacogenomics in clinical practice. ISRN Pharmacol. 2013, 641089. (doi:10.1155/2013/641089)
2. Lee JW, Aminkeng F, Bhavsar AP, Shaw K, Carleton BC, Hayden MR, Ross CJ. 2014 The emerging era of pharmacogenomics: current successes, future potential, and challenges. Clin. Genet. 86, 21-28. (doi:10.1111/cge.12392)
3. Scott SA, Sangkuhl K, Shuldiner AR, Hulot JS, Thorn CF, Altman RB, Klein TE. 2012 PharmGKB summary: very important pharmacogene information for cytochrome P450, family 2, subfamily C, polypeptide 19. Pharmacogenet. Genomics 22, 159-165. (doi:10.1097/FPC.0b013e32834d4962)
4. Van Booven D, Marsh S, McLeod H, Carrillo MW, Sangkuhl K, Klein TE, Altman RB. 2010 Cytochrome P450 2C9-CYP2C9. Pharmacogenet. Genomics 20, 277-281. (doi:10.1097/FPC.0b013e3283349e84)
5. Owen RP, Sangkuhl K, Klein TE, Altman RB. 2009 Cytochrome P450 2D6. Pharmacogenet. Genomics 19, 559-562. (doi:10.1097/FPC.0b013e32832e0e97)
6. Ingelman-Sundberg M, Sim SC, Gomez A, Rodriguez-Antona C. 2007 Influence of cytochrome P450 polymorphisms on drug therapies: pharmacogenetic, pharmacoepigenetic and clinical aspects. Pharmacol. Ther. 116, 496-526. (doi:10.1016/j.pharmthera.2007.09.004)
7. Wang XD et al. 2009 Impact of the haplotypes of the human pregnane X receptor gene on the basal and St John’s wort-induced activity of cytochrome P450 3A4 enzyme. Br. J. Clin. Pharmacol. 67, 255-261. (doi:10.1111/j.1365-2125.2008.03344.x)
8. Tompkins LM, Wallace AD. 2007 Mechanisms of cytochrome P450 induction. J. Biochem. Mol. Toxicol. 21, 176-181. (doi:10.1002/jbt.20180)
9. Barbarino JM, Haidar CE, Klein TE, Altman RB. 2014 PharmGKB summary: very important pharmacogene information for UGT1A1. Pharmacogenet. Genomics 24, 177-183. (doi:10.1097/FPC.0000000000000024)
10. Li H, Wang H. 2010 Activation of xenobiotic receptors: driving into the nucleus. Expert Opin. Drug Metab. Toxicol. 6, 409-426. (doi:10.1517/17425251003598886)
11. Hanley MJ, Abernethy DR, Greenblatt DJ. 2010 Effect of obesity on the pharmacokinetics of drugs in humans. Clin. Pharmacokinet. 49, 71-87. (doi:10.2165/11318100-000000000-00000)
12. Franconi F, Campesi I. 2014 Pharmacogenomics, pharmacokinetics and pharmacodynamics: interaction with biological differences between men and women. Br. J. Pharmacol. 171, 580-594. (doi:10.1111/bph.12362)
13. Pavek P, Dvorak Z. 2008 Xenobiotic-induced transcriptional regulation of xenobiotic metabolizing enzymes of the cytochrome P450 superfamily in human extrahepatic tissues. Curr. Drug Metab. 9, 129-143. (doi:10.2174/138920008783571774)
14. Kang D, Verotta D, Krecic-Shepard ME, Modi NB, Gupta SK, Schwartz JB. 2003 Population analyses of sustained-release verapamil in patients: effects of sex, race, and smoking. Clin. Pharmacol. Ther. 73, 31-40. (doi:10.1067/mcp.2003.21)
15. Hernandez JP, Mota LC, Baldwin WS. 2009 Activation of CAR and PXR by dietary, environmental and occupational chemicals alters drug metabolism, intermediary metabolism, andcell proliferation. Curr. Pharmacogenomics Person. Med. 7, 81-105. (doi:10.2174/187569209788654005)
16. Peters SA. 2012 Physiologically-based pharmacokinetic (PBPK) modeling and simulations: principles, methods, and applications in the pharmaceutical industry. New York, NY: John Wiley and Sons.
17. Dickschen K, Willmann S, Thelen K, Lippert J, Hempel G, Eissing T. 2012 Physiologically based pharmacokinetic modeling of tamoxifen and its metabolites in women of different CYP2D6 phenotypes provides new insight into the tamoxifen mass balance. Front. Pharmacol. 3, 92. (doi:10.3389/fphar.2012.00092)
18. Eissing T, Lippert J, Willmann S. 2012 Pharmacogenomics of codeine, morphine, and morphine-6-glucuronide: model-based analysis of the influence of CYP2D6 activity, UGT2B7 activity, renal impairment, and CYP3A4 inhibition. Mol. Diagn. Ther. 16, 43-53. (doi:10.2165/11597930-000000000-00000)
19. Poulin P et al. 2011 PHRMA CPCDC initiative on predictive models of human pharmacokinetics, part 5. Prediction of plasma concentration-time profiles in human by using the physiologically-based pharmacokinetic modeling approach. J. Pharm. Sci. 100, 4127-4157. (doi:10.1002/jps.22550)
20. Bouzom F, Ball K, Perdaems N, Walther B. 2012 Physiologically based pharmacokinetic (PBPK)modelling tools: how to fit with our needs? Biopharm. Drug Dispos. 33, 55-71. (doi:10.1002/bdd.1767)
21. Chetty M, Li L, Rose R, Machavaram K, Jamei M, Rostami-Hodjegan A, Gardner I. 2014 Prediction of the pharmacokinetics, pharmacodynamics, and efficacy of a monoclonal antibody, using a physiologically based pharmacokinetic FcRn model. Front. Immunol. 5, 670. (doi:10.3389/fimmu.2014.00670)
22. Wittig U et al. 2012 SABIO-RK—database for biochemical reaction kinetics. Nucleic Acids Res. 40, D790-D796. (doi:10.1093/nar/gkr1046)
23. Thiele I et al. 2013 A community-driven global reconstruction of human metabolism. Nat. Biotechnol. 31, 419-425. (doi:10.1038/nbt.2488)
24. Uhlen M et al. 2015 Proteomics. Tissue-based map of the human proteome. Science 347, 1260419. (doi:10.1126/science.1260419)
25. Parkinson H et al. 2011 ArrayExpress update—an archive of microarray and high-throughput sequencing-based functional genomics experiments. Nucleic Acids Res. 39, D1002-D1004. (doi:10.1093/nar/gkq1040)
26. Sahoo S, Haraldsdottir HS, Fleming RM, Thiele I. 2014 Modeling the effects of commonly used drugs on human metabolism. FEBS J. 282, 297-317. (doi:10.1111/febs.13128)
27. Hucka M et al. 2003 The systems biology markup language (SBML): a medium for representation and exchange of biochemical network models. Bioinformatics 19, 524-531. (doi:10.1093/bioinformatics/btg015)
28. Li C et al. 2010 BioModels database: an enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst. Biol. 4, 92 (doi:10.1186/1752-0509-4-92)
29. Le Novere N et al. 2005 Minimum information requested in the annotation of biochemical models (MIRIAM). Nat. Biotechnol. 23, 1509-1515. (doi:10.1038/nbt1156)
30. Le Novere N et al. 2009 The systems biology graphical notation. Nat. Biotechnol. 27, 735-741. (doi:10.1038/nbt.1558)
31. Bateman A et al. 2015 UniProt: a hub for protein information. Nucleic Acids Res. 43, D204-D212. (doi:10.1093/nar/gku989)
32. Degtyarenko K et al. 2008 ChEBI: a database and ontology for chemical entities of biological interest. Nucleic Acids Res. 36, D344-D350. (doi:10.1093/nar/gkm791)
33. Ashburner M et al. 2000 Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat. Genet. 25, 25-29. (doi:10.1038/75556)
34. Courtot M et al. 2011 Controlled vocabularies and semantics in systems biology. Mol. Syst. Biol. 7, 543. (doi:10.1038/msb.2011.77)
35. Brazma A et al. 2001 Minimum information about a microarray experiment (MIAME)—toward standards for microarray data. Nat. Genet. 29, 365-371. (doi:10.1038/ng1201-365)
36. Ratnam J et al. 2014 The application of the open pharmacological concepts triple store (open PHACTS) to support drug discovery research. PLoS ONE 9, e115460. (doi:10.1371/journal.pone.0115460)
37. ICRP. 2002 Basic anatomical and physiological data for use in radiological protection: reference values. A report of age-and gender-related differences in the anatomical and physiological characteristics of reference individuals. ICRP Publication 89. Ann. ICRP 32, 5-265. (doi:10.1016/S0146-6453(02)00021-0)
38. Safaei S. 2015 Simulating blood flow in an anatomical arterial network. PhD thesis, University of Auckland, New Zealand.
39. Hunter PJ, Pullan AJ, Smaill BH. 2003 Modeling total heart function. Annu. Rev. Biomed. Eng. 5, 147-177. (doi:10.1146/annurev.bioeng.5.040202.121537)
40. Sinclair MD, Lee J, Cookson AN, Rivolo S, Hyde ER, Smith NP 2015 Measurement and modeling of coronary blood flow. WIREs Syst. Biol. Med. 7, 335-356. (doi:10.1002/wsbm.1309)
41. LeGrice IJ, Smaill BH, Chai LZ, Edgar SG, Gavin JB, Hunter PJ. 1995 Laminar structure of the heart: ventricular myocyte arrangement and connective tissue architecture in the dog. Am. J. Physiol. 269, H571-H582.
42. Nielsen PM, Le Grice IJ, Smaill BH, Hunter PJ. 1991 Mathematical model of geometry and fibrous structure of the heart. Am. J. Physiol. 260, H1365-H1378.
43. Ho H, Bartlett A, Hunter P. 2012 Geometric modelling of patient-specific hepatic structures using cubic hermite elements. Abdom. Imaging Comput. Clin. Appl. 7029, 264-271. (doi: 10.1007/978-3-642-28557-8_33)
44. Ho H, Sorrell K, Bartlett A, Hunter P. 2013 Modeling the hepatic arterial buffer response in the liver. Med. Eng. Phys. 35, 1053-1058. (doi:10.1016/j.medengphy.2012.10.008)
45. Schwen LO, Wei W, Gremse F, Ehling J, Wang L, Dahmen U, Preusser T. 2015 Algorithmically generated rodent hepatic vascular trees in arbitrary detail. J. Theor. Biol. 365, 289-300. (doi:10.1016/j.jtbi.2014.10.026)
46. Friebel A, Neitsch J, Johann T, Hammad S, Hengstler JG, Drasdo D, Hoehme S. 2015 TiQuant: software for tissue analysis, quantification and surface reconstruction. Bioinformatics 31, 3234-3236. (doi:10.1093/bioinformatics/btv346)
47. Hoehme S et al. 2010 Prediction and validation of cell alignment along microvessels as order principle to restore tissue architecture in liver regeneration. Proc. Natl Acad. Sci. USA 107, 10371-10376. (doi:10.1073/pnas.0909374107)
48. Holzhutter HG, Drasdo D, Preusser T, Lippert J, Henney AM. 2012 The virtual liver: a multidisciplinary, multilevel challenge for systems biology. WIREs Syst. Biol. Med. 4, 221-235. (doi:10.1002/wsbm.1158)
49. D’Alessandro LA, Hoehme S, Henney A, Drasdo D, Klingmuller U. 2015 Unraveling liver complexity from molecular to organ level: challenges and perspectives. Prog. Biophys. Mol. Biol. 117, 78-86. (doi:10.1016/j.pbiomolbio.2014.11.005)
50. Cheng LK, O’Grady G, Du P, Egbuji JU, Windsor JA, Pullan AJ. 2010 Gastrointestinal system. WIREs Syst. Biol. Med. 2, 65-79. (doi:10.1002/wsbm.19)
51. Du P, O’Grady G, Davidson JB, Cheng LK, Pullan AJ. 2010 Multiscale modeling of gastrointestinal electrophysiology and experimental validation. Crit. Rev. Biomed. Eng. 38, 225-254. (doi:10.1615/CritRevBiomedEng.v38.i3.10)
52. O’Grady G, Du P, Egbuji JU, Lammers WJ, Wahab A, Pullan AJ, Cheng LK, Windsor JA. 2009 A novel laparoscopic device for measuring gastrointestinal slow-wave activity. Surg. Endosc. 23, 2842-2848. (doi:10.1007/s00464-009-0515-2)
53. Cheng LK, Du P, O’Grady, G. 2013 Mapping and modeling gastrointestinal bioelectricity: from engineering bench to bedside. Physiology 28, 310-317. (doi:10.1152/physiol.00022.2013)
54. Pal A, Indireshkumar K, Schwizer W, Abrahamsson B, Fried M, Brasseur JG. 2004 Gastric flow and mixing studied using computer simulation. Proc. R. Soc. Lond. B 271, 2587-2594. (doi:10.1098/rspb.2004.2886)
55. Lindfors L, Jonsson M, Weibull E, Brasseur JG, Abrahamsson B. 2015 Hydrodynamic effects on drug dissolution and deaggregation in the small intestine—a study with felodipine as a model drug. J. Pharm. Sci. 104, 2969-2976. (doi:10.1002/jps.24487)
56. Howatson Tawhai M, Pullan AJ, Hunter PJ. 2000 Generation of an anatomically based threedimensional model of the conducting airways. Ann. Biomed. Eng. 28, 793-802. (doi:10.1114/1.1289457)
57. Tawhai M, Clark A, Donovan G, Burrowes K. 2011 Computational modeling of airway and pulmonary vascular structure and function: development of a ‘lung physiome’. Crit. Rev. Biomed. Eng. 39, 319-336. (doi:10.1615/CritRevBiomedEng.v39.i4.50)
58. Burrowes KS, Doel T, Brightling C. 2014 Computational modeling of the obstructive lung diseases asthma and COPD. J. Transl. Med. 12(Suppl 2), S5. (doi:10.1186/1479-5876-12-S2-S5)
59. Wu T, Martens H, Hunter P, Mithraratne K. 2014 Emulating facial biomechanics using multivariate partial least squares surrogate models. Int. J. Numer. Methods Biomed. Eng. 30, 1103-1120. (doi:10.1002/cnm.2646)
60. Harnisch L, Matthews I, Chard J, Karlsson MO. 2013 Drug and disease model resources: a consortium to create standards and tools to enhance model-based drug development. CPT Pharmacometrics Syst. Pharmacol. 2, e34. (doi:10.1038/psp.2013.10)
61. Hofmann-Apitius M, Alarcon-Riquelme ME, Chamberlain C, McHale D. 2015 Towards the taxonomy of human disease. Nat. Rev. Drug Discov. 14, 75-76. (doi:10.1038/nrd4537)
62. Chichester C, Digles D, Siebes R, Loizou A, Groth P, Harland L. 2015 Drug discovery FAQs: workflows for answering multidomain drug discovery questions. Drug Discov. Today 20, 399-405. (doi:10.1016/j.drudis.2014.11.006)
63. de Bono B, Hoehndorf R, Wimalaratne S, Gkoutos G, Grenon P. 2011 The RICORDO approach to semantic interoperability for biomedical data and models: strategy, standards and solutions. BMC Res. Notes 4, 313. (doi:10.1186/1756-0500-4-313)
64. de Bono B, Grenon P, Sammut SJ. 2012 ApiNATOMY: a novel toolkit for visualizing multiscale anatomy schematics with phenotype-related information. Hum. Mutat. 33, 837-848. (doi:10.1002/humu.22065)