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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 陳沛隆(Pei-Lung Chen) | |
dc.contributor.author | Yu-Shi Liu | en |
dc.contributor.author | 劉昱希 | zh_TW |
dc.date.accessioned | 2021-06-17T02:39:29Z | - |
dc.date.available | 2020-07-31 | |
dc.date.copyright | 2017-09-13 | |
dc.date.issued | 2017 | |
dc.date.submitted | 2017-08-17 | |
dc.identifier.citation | 1. Pirmohamed, M., Pharmacogenetics and pharmacogenomics. British Journal of Clinical Pharmacology, 2001. 52(4): p. 345-347.
2. Brandt, O., et al., Peas, beans, and the Pythagorean theorem - the relevance of glucose-6-phosphate dehydrogenase deficiency in dermatology. J Dtsch Dermatol Ges, 2008. 6(7): p. 534-9. 3. Motulsky, A.G., Drug reactions enzymes, and biochemical genetics. J Am Med Assoc, 1957. 165(7): p. 835-7. 4. Scott, S.A., Personalizing medicine with clinical pharmacogenetics. Genetics in Medicine, 2011. 13(12): p. 987-995. 5. Motulsky, A., From pharmacogenetics and ecogenetics to pharmacogenomics. Med Secoli, 2002. 14(3): p. 683-705. 6. Giacomini, K.M., et al., Pharmacogenomics and patient care: one size does not fit all. Sci Transl Med, 2012. 4(153): p. 153ps18. 7. Zhang, G. and D.W. Nebert, Personalized medicine: Genetic risk prediction of drug response. Pharmacol Ther, 2017. 175: p. 75-90. 8. Abul-Husn, N.S., et al., Implementation and utilization of genetic testing in personalized medicine. Pharmgenomics Pers Med, 2014. 7: p. 227-40. 9. Meletiadis, J., S. Chanock, and T.J. Walsh, Human pharmacogenomic variations and their implications for antifungal efficacy. Clin Microbiol Rev, 2006. 19(4): p. 763-87. 10. Lewitter, F., et al., Chapter 7: Pharmacogenomics. PLoS Computational Biology, 2012. 8(12): p. e1002817. 11. Bertilsson, L., et al., Molecular genetics of CYP2D6: clinical relevance with focus on psychotropic drugs. Br J Clin Pharmacol, 2002. 53(2): p. 111-22. 12. Meijerman, I., et al., Pharmacogenetic screening of the gene deletion and duplications of CYP2D6. Drug Metab Rev, 2007. 39(1): p. 45-60. 13. Kenakin, T., Principles: receptor theory in pharmacology. Trends Pharmacol Sci, 2004. 25(4): p. 186-92. 14. Rosenbaum, D.M., S.G. Rasmussen, and B.K. Kobilka, The structure and function of G-protein-coupled receptors. Nature, 2009. 459(7245): p. 356-63. 15. Gouaux, E. and R. Mackinnon, Principles of selective ion transport in channels and pumps. Science, 2005. 310(5753): p. 1461-5. 16. Roden, D.M. and A.L. George, Jr., The genetic basis of variability in drug responses. Nat Rev Drug Discov, 2002. 1(1): p. 37-44. 17. Roden, D.M., et al., Pharmacogenomics: the genetics of variable drug responses. Circulation, 2011. 123(15): p. 1661-70. 18. Wilke, R.A. and M.E. Dolan, Genetics and variable drug response. JAMA, 2011. 306(3): p. 306-7. 19. Gong, I.Y., et al., Clinical and genetic determinants of warfarin pharmacokinetics and pharmacodynamics during treatment initiation. PLoS One, 2011. 6(11): p. e27808. 20. Kimmel, S.E., Warfarin therapy: in need of improvement after all these years. Expert Opin Pharmacother, 2008. 9(5): p. 677-86. 21. King, A.E., D.K. Szarlej, and F. Rincon, Dabigatran-Associated Intracranial Hemorrhage: Literature Review and Institutional Experience. Neurohospitalist, 2015. 5(4): p. 234-44. 22. Mueck, W., et al., Clinical pharmacokinetic and pharmacodynamic profile of rivaroxaban. Clin Pharmacokinet, 2014. 53(1): p. 1-16. 23. Turpie, A.G., Oral, direct factor Xa inhibitors in development for the prevention and treatment of thromboembolic diseases. Arterioscler Thromb Vasc Biol, 2007. 27(6): p. 1238-47. 24. Alcindor, T. and N. Beauger, Oxaliplatin: a review in the era of molecularly targeted therapy. Curr Oncol, 2011. 18(1): p. 18-25. 25. Parel, M., et al., Hypersensitivity to oxaliplatin: clinical features and risk factors. BMC Pharmacol Toxicol, 2014. 15: p. 1. 26. Gammon, D., P. Bhargava, and M.J. McCormick, Hypersensitivity reactions to oxaliplatin and the application of a desensitization protocol. Oncologist, 2004. 9(5): p. 546-9. 27. Shepherd, G.M., Hypersensitivity reactions to chemotherapeutic drugs. Clin Rev Allergy Immunol, 2003. 24(3): p. 253-62. 28. Makrilia, N., et al., Hypersensitivity reactions associated with platinum antineoplastic agents: a systematic review. Met Based Drugs, 2010. 2010. 29. Mo, M., et al., Prevention of paclitaxel-induced peripheral neuropathy by lithium pretreatment. Faseb j, 2012. 26(11): p. 4696-709. 30. Ahmed, A.A., et al., Modulating microtubule stability enhances the cytotoxic response of cancer cells to Paclitaxel. Cancer Res, 2011. 71(17): p. 5806-17. 31. Weaver, B.A., How Taxol/paclitaxel kills cancer cells. Mol Biol Cell, 2014. 25(18): p. 2677-81. 32. Waters, J.C., et al., Localization of Mad2 to kinetochores depends on microtubule attachment, not tension. J Cell Biol, 1998. 141(5): p. 1181-91. 33. Zanotti, K.M. and M. Markman, Prevention and management of antineoplastic-induced hypersensitivity reactions. Drug Saf, 2001. 24(10): p. 767-79. 34. Weiss, R.B., et al., Hypersensitivity reactions from taxol. J Clin Oncol, 1990. 8(7): p. 1263-8. 35. Kloover, J.S., et al., Fatal outcome of a hypersensitivity reaction to paclitaxel: a critical review of premedication regimens. Br J Cancer, 2004. 90(2): p. 304-5. 36. Weiss, R.B. and J.R. Baker, Jr., Hypersensitivity reactions from antineoplastic agents. Cancer Metastasis Rev, 1987. 6(3): p. 413-32. 37. Dye, D. and J. Watkins, Suspected anaphylactic reaction to Cremophor EL. Br Med J, 1980. 280(6228): p. 1353. 38. Gelderblom, H., et al., Cremophor EL: the drawbacks and advantages of vehicle selection for drug formulation. Eur J Cancer, 2001. 37(13): p. 1590-8. 39. Szebeni, J., et al., Formation of complement-activating particles in aqueous solutions of Taxol: possible role in hypersensitivity reactions. Int Immunopharmacol, 2001. 1(4): p. 721-35. 40. Del Chierico, F., et al., Choice of next-generation sequencing pipelines. Methods Mol Biol, 2015. 1231: p. 31-47. 41. Zhang, J., et al., The impact of next-generation sequencing on genomics. J Genet Genomics, 2011. 38(3): p. 95-109. 42. Behjati, S. and P.S. Tarpey, What is next generation sequencing? Arch Dis Child Educ Pract Ed, 2013. 98(6): p. 236-8. 43. Sanger, F., S. Nicklen, and A.R. Coulson, DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A, 1977. 74(12): p. 5463-7. 44. Hodkinson, B.P. and E.A. Grice, Next-Generation Sequencing: A Review of Technologies and Tools for Wound Microbiome Research. Adv Wound Care (New Rochelle), 2015. 4(1): p. 50-58. 45. Mardis, E.R., The impact of next-generation sequencing technology on genetics. Trends Genet, 2008. 24(3): p. 133-41. 46. Liu, L., et al., Comparison of next-generation sequencing systems. J Biomed Biotechnol, 2012. 2012: p. 251364. 47. Mardis, E.R., Next-generation sequencing platforms. Annu Rev Anal Chem (Palo Alto Calif), 2013. 6: p. 287-303. 48. Li, H. and R. Durbin, Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics, 2009. 25(14): p. 1754-60. 49. McKenna, A., et al., The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res, 2010. 20(9): p. 1297-303. 50. DePristo, M.A., et al., A framework for variation discovery and genotyping using next-generation DNA sequencing data. Nat Genet, 2011. 43(5): p. 491-8. 51. Adzhubei, I.A., et al., A method and server for predicting damaging missense mutations. Nat Methods, 2010. 7(4): p. 248-9. 52. Kumar, P., S. Henikoff, and P.C. Ng, Predicting the effects of coding non-synonymous variants on protein function using the SIFT algorithm. Nat Protoc, 2009. 4(7): p. 1073-81. 53. Wang, K., M. Li, and H. Hakonarson, ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Research, 2010. 38(16): p. e164-e164. 54. Thorvaldsdóttir, H., J.T. Robinson, and J.P. Mesirov, Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Briefings in Bioinformatics, 2013. 14(2): p. 178-192. 55. Robinson, J.T., et al., Integrative genomics viewer. Nat Biotech, 2011. 29(1): p. 24-26. 56. Chen, C.H., et al., Population structure of Han Chinese in the modern Taiwanese population based on 10,000 participants in the Taiwan Biobank project. Hum Mol Genet, 2016. 25(24): p. 5321-5331. 57. Johnson, J.A., et al., Clinical Pharmacogenetics Implementation Consortium (CPIC) Guideline for Pharmacogenetics-Guided Warfarin Dosing: 2017 Update. Clin Pharmacol Ther, 2017. 58. Owen, R.P., et al., VKORC1 pharmacogenomics summary. Pharmacogenet Genomics, 2010. 20(10): p. 642-4. 59. Yuan, H.Y., et al., A novel functional VKORC1 promoter polymorphism is associated with inter-individual and inter-ethnic differences in warfarin sensitivity. Hum Mol Genet, 2005. 14(13): p. 1745-51. 60. McDonald, M.G., et al., CYP4F2 is a vitamin K1 oxidase: An explanation for altered warfarin dose in carriers of the V433M variant. Mol Pharmacol, 2009. 75(6): p. 1337-46. 61. Sun, X., et al., Impact of the CYP4F2 gene polymorphisms on the warfarin maintenance dose: A systematic review and meta-analysis. Biomed Rep, 2016. 4(4): p. 498-506. 62. Caldwell, M.D., et al., CYP4F2 genetic variant alters required warfarin dose. Blood, 2008. 111(8): p. 4106-12. 63. Krishna Kumar, D., et al., Effect of CYP2C9, VKORC1, CYP4F2 and GGCX genetic variants on warfarin maintenance dose and explicating a new pharmacogenetic algorithm in South Indian population. Eur J Clin Pharmacol, 2014. 70(1): p. 47-56. 64. Limdi, N.A., et al., Warfarin pharmacogenetics: a single VKORC1 polymorphism is predictive of dose across 3 racial groups. Blood, 2010. 115(18): p. 3827-34. 65. Cho, S.M., et al., Development and Comparison of Warfarin Dosing Algorithms in Stroke Patients. Yonsei Med J, 2016. 57(3): p. 635-40. 66. An, S.H., et al., Influence of UDP-Glucuronosyltransferase Polymorphisms on Stable Warfarin Doses in Patients with Mechanical Cardiac Valves. Cardiovasc Ther, 2015. 33(6): p. 324-8. 67. Wang, L.S., et al., Influence of ORM1 polymorphisms on the maintenance stable warfarin dosage. Eur J Clin Pharmacol, 2013. 69(5): p. 1113-20. 68. Choi, J.R., et al., Proposal of pharmacogenetics-based warfarin dosing algorithm in Korean patients. J Hum Genet, 2011. 56(4): p. 290-5. 69. Pare, G., et al., Genetic determinants of dabigatran plasma levels and their relation to bleeding. Circulation, 2013. 127(13): p. 1404-12. 70. Gouin-Thibault, I., et al., Interindividual variability in dabigatran and rivaroxaban exposure: contribution of ABCB1 genetic polymorphisms and interaction with clarithromycin. J Thromb Haemost, 2017. 15(2): p. 273-283. 71. Doyle, L.A., et al., A multidrug resistance transporter from human MCF-7 breast cancer cells. Proc Natl Acad Sci U S A, 1998. 95(26): p. 15665-70. 72. Harder, S., Pharmacokinetic and pharmacodynamic evaluation of rivaroxaban: considerations for the treatment of venous thromboembolism. Thromb J, 2014. 12: p. 22. 73. Kreutz, R., Pharmacodynamic and pharmacokinetic basics of rivaroxaban. Fundam Clin Pharmacol, 2012. 26(1): p. 27-32. 74. Ing Lorenzini, K., et al., Rivaroxaban-Induced Hemorrhage Associated with ABCB1 Genetic Defect. Front Pharmacol, 2016. 7: p. 494. 75. Gong, I.Y., S.E. Mansell, and R.B. Kim, Absence of both MDR1 (ABCB1) and breast cancer resistance protein (ABCG2) transporters significantly alters rivaroxaban disposition and central nervous system entry. Basic Clin Pharmacol Toxicol, 2013. 112(3): p. 164-70. 76. Marsh, S., et al., Platinum pathway. Pharmacogenet Genomics, 2009. 19(7): p. 563-4. 77. Marsh, S., et al., Pharmacogenetic assessment of toxicity and outcome after platinum plus taxane chemotherapy in ovarian cancer: the Scottish Randomised Trial in Ovarian Cancer. J Clin Oncol, 2007. 25(29): p. 4528-35. 78. Ross, D. and D. Siegel, NAD(P)H:quinone oxidoreductase 1 (NQO1, DT-diaphorase), functions and pharmacogenetics. Methods Enzymol, 2004. 382: p. 115-44. 79. Siegel, D., et al., Rapid polyubiquitination and proteasomal degradation of a mutant form of NAD(P)H:quinone oxidoreductase 1. Mol Pharmacol, 2001. 59(2): p. 263-8. 80. Marsh, S., et al., Pharmacogenetic analysis of paclitaxel transport and metabolism genes in breast cancer. Pharmacogenomics J, 2007. 7(5): p. 362-5. 81. Marsh, S., Taxane pharmacogenetics. Personalized Med, 2006. 3: p. 33-43. 82. Steed, H. and M.B. Sawyer, Pharmacology, pharmacokinetics and pharmacogenomics of paclitaxel. Pharmacogenomics, 2007. 8(7): p. 803-15. 83. Oshiro, C., et al., Taxane pathway. Pharmacogenet Genomics, 2009. 19(12): p. 979-83. 84. Kliewer, S.A., B. Goodwin, and T.M. Willson, The nuclear pregnane X receptor: a key regulator of xenobiotic metabolism. Endocr Rev, 2002. 23(5): p. 687-702. 85. Rizzo, R., et al., Association of CYP1B1 with hypersensitivity induced by taxane therapy in breast cancer patients. Breast Cancer Res Treat, 2010. 124(2): p. 593-8. 86. Sissung, T.M., et al., Association of the CYP1B1*3 allele with survival in patients with prostate cancer receiving docetaxel. Mol Cancer Ther, 2008. 7(1): p. 19-26. 87. Abraham, J.E., et al., Replication of genetic polymorphisms reported to be associated with taxane-related sensory neuropathy in patients with early breast cancer treated with Paclitaxel. Clin Cancer Res, 2014. 20(9): p. 2466-75. 88. Boulanger, J., et al., Management of hypersensitivity to platinum- and taxane-based chemotherapy: cepo review and clinical recommendations. Curr Oncol, 2014. 21(4): p. e630-41. 89. Fokkema, I.F.A.C., J.T. den Dunnen, and P.E.M. Taschner, LOVD: Easy creation of a locus-specific sequence variation database using an “LSDB-in-a-box” approach. Human Mutation, 2005. 26(2): p. 63-68. 90. Fokkema, I.F., et al., LOVD v.2.0: the next generation in gene variant databases. Hum Mutat, 2011. 32(5): p. 557-63. 91. Danecek, P., et al., The variant call format and VCFtools. Bioinformatics, 2011. 27(15): p. 2156-8. 92. Wildeman, M., et al., Improving sequence variant descriptions in mutation databases and literature using the Mutalyzer sequence variation nomenclature checker. Hum Mutat, 2008. 29(1): p. 6-13. 93. den Dunnen, J.T. and S.E. Antonarakis, Mutation nomenclature extensions and suggestions to describe complex mutations: a discussion. Hum Mutat, 2000. 15(1): p. 7-12. 94. Hoogenboom, J., Importing Next Generation Sequencing Data in LOVD 3.0. 2012. 95. King, C.R., et al., Gamma-glutamyl carboxylase and its influence on warfarin dose. Thromb Haemost, 2010. 104(4): p. 750-4. 96. Rieder, M.J., A.P. Reiner, and A.E. Rettie, Gamma-glutamyl carboxylase (GGCX) tagSNPs have limited utility for predicting warfarin maintenance dose. J Thromb Haemost, 2007. 5(11): p. 2227-34. 97. Wajih, N., et al., The inhibitory effect of calumenin on the vitamin K-dependent gamma-carboxylation system. Characterization of the system in normal and warfarin-resistant rats. J Biol Chem, 2004. 279(24): p. 25276-83. 98. Kisor, D.F., et al., Pharmacogenetics, Kinetics, and Dynamics for Personalized Medicine. 2013: Jones & Bartlett Publishers. 99. Johnson, J.A., et al., Clinical Pharmacogenetics Implementation Consortium Guidelines for CYP2C9 and VKORC1 genotypes and warfarin dosing. Clin Pharmacol Ther, 2011. 90(4): p. 625-9. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/68869 | - |
dc.description.abstract | Medical drugs show different efficacy and/or adverse drug reactions (ADRs) on patients. This is mostly due to the variations of DNA sequence. Pharmacogenomics is the study and applications about how genetic variations in individuals influence the drug response, which is composed of both pharmacokinetics and pharmacodynamics. Pharmacogenomics plays an important role in optimal drug choice and drug dosing. There are numerous genes involved in pharmacogenomics, which imposes a big challenge because of the complexity and high cost using conventional techniques like Sanger sequencing. In the present study, we set up a genetic testing platform through capture-based target enrichment followed by next-generation sequencing (NGS). Our panel covered approximately 345 major pharmacogenomics genes, including pharmacokinetics genes (for example, ADME genes regarding to absorption, distribution, metabolism and excretion) and pharmacodynamics genes, such as ABCB1, CFTR, CYPs, DRYP, EGFR, HLAs, KRAS, NAT2, RYR1, TPMT, UGT1A1 and VKORC1. A great proportion of our genes overlapped with those listed on FDA labeled biomarkers, Pharmacogenomics Knowledgebase (PharmGKB) and PharmaADME. We applied this panel to 34 individuals, including 6 controls with whole genome sequencing data from Taiwan Biobank for technical validation and 28 patients recruited from NTUH, trying to find specific ADR gene biomarkers. Also, we used LOVD (Leiden Open Variation Database) system to build a pharmacogenomics database in a uniform table format contains information on gene, variant, and drug response according to the PharmGKB clinical annotation file of warfarin, dabigatran, rivaroxaban, oxaliplatin and paclitaxel. In conclusion, the NGS-based pharmacogenomics panel and the pharmacogenomics LOVD database could be beneficial for precision medicine in clinical applications and academic research. | en |
dc.description.provenance | Made available in DSpace on 2021-06-17T02:39:29Z (GMT). No. of bitstreams: 1 ntu-106-R04455005-1.pdf: 2876911 bytes, checksum: f82a1fb56c75b3667eaa11e99ca2b977 (MD5) Previous issue date: 2017 | en |
dc.description.tableofcontents | 謝辭 i
摘要 ii Abstract iv Table of contents vi List of figures ix List of tables xi Abbreviations xii 1. Introduction 1 1.1 Pharmacogenomics 1 1.2 Pharmacokinetics and pharmacodynamics 3 1.3 Warfarin 5 1.4 Dabigatran 6 1.5 Rivaroxaban 7 1.6 Oxaliplatin 8 1.7 Paclitaxel 9 1.8 Aim and purpose 10 2. Materials and methods 13 2.1 Design a pharmacogenomics panel based on next-generation sequencing 13 2.2 Bioinformatics analysis of next-generation sequencing data 16 2.3 Drug types and Patients recruitment 17 2.4 Samples from Taiwan Biobank 19 2.5 Analysis criteria of patient data 20 2.6 Leiden Open (source) Variation Database (LOVD) 21 3. Results 22 3.1 Taiwan Biobank samples for panel validation 22 3.2 Warfarin 22 3.3 Dabigatran 26 3.4 Rivaroxaban 28 3.5 Oxaliplatin 31 3.6 Paclitaxel 33 3.7 HLA-genotyping of oxaliplatin-related and paclitaxel-related ADR patients 35 3.8 Pharmacogenomics database on LOVD 36 4. Discussion 43 Figures 47 Tables 65 References 72 Appendix 80 | |
dc.language.iso | en | |
dc.title | 利用次世代定序技術建立藥物基因體學之基因檢測平台 | zh_TW |
dc.title | Establishment of pharmacogenomics testing platform based on next-generation sequencing | en |
dc.type | Thesis | |
dc.date.schoolyear | 105-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 沈麗娟(Li-Jiuan Shen),張以承(Yi-Cheng Chang) | |
dc.subject.keyword | 藥物基因體學,藥物動力學/藥效學,個人化醫療,次世代定序,臺灣人體生物資料庫, | zh_TW |
dc.subject.keyword | Pharmacogenomics,PK/PD,precision medicine,Next-generation sequencing (NGS),Leiden Open Variation Database (LOVD),Taiwan Biobank, | en |
dc.relation.page | 86 | |
dc.identifier.doi | 10.6342/NTU201703406 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2017-08-17 | |
dc.contributor.author-college | 醫學院 | zh_TW |
dc.contributor.author-dept | 基因體暨蛋白體醫學研究所 | zh_TW |
顯示於系所單位: | 基因體暨蛋白體醫學研究所 |
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