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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 莊曜宇 | |
| dc.contributor.author | Pei-Han Liao | en |
| dc.contributor.author | 廖珮函 | zh_TW |
| dc.date.accessioned | 2021-06-08T03:52:42Z | - |
| dc.date.copyright | 2018-08-21 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-17 | |
| dc.identifier.citation | 1. Grabowski H, Vernon J, DiMasi JA: Returns on research and development for 1990s new drug introductions. Pharmacoeconomics 2002, 20 Suppl 3:11-29.
2. Morgan S, Grootendorst P, Lexchin J, Cunningham C, Greyson D: The cost of drug development: a systematic review. Health Policy 2011, 100(1):4-17. 3. DiMasi JA, Grabowski HG, Hansen RW: Innovation in the pharmaceutical industry: New estimates of R&D costs. Journal of Health Economics 2016, 47:20-33. 4. DiMasi JA: The value of improving the productivity of the drug development process: faster times and better decisions. Pharmacoeconomics 2002, 20 Suppl 3:1-10. 5. Moffat JG, Rudolph J, Bailey D: Phenotypic screening in cancer drug discovery—past, present and future. Nature reviews Drug discovery 2014, 13(8):588. 6. Swinney D: Phenotypic vs. target‐based drug discovery for first‐in‐class medicines. Clinical Pharmacology & Therapeutics 2013, 93(4):299-301. 7. Swinney DC, Anthony J: How were new medicines discovered? Nature Reviews Drug Discovery 2011, 10(7):507-519. 8. Newman DJ, Cragg GM: Natural products as sources of new drugs over the 30 years from 1981 to 2010. Journal of Natural Products 2012, 75(3):311-335. 9. Zheng XS, Chan TF, Zhou HH: Genetic and genomic approaches to identify and study the targets of bioactive small molecules. Chemistry & Biology 2004, 11(5):609-618. 10. Gupta SC, Sung B, Prasad S, Webb LJ, Aggarwal BB: Cancer drug discovery by repurposing: teaching new tricks to old dogs. Trends in pharmacological sciences 2013, 34(9):508-517. 11. Ashburn TT, Thor KB: Drug repositioning: identifying and developing new uses for existing drugs. Nature reviews Drug discovery 2004, 3(8):673. 12. Flossmann E, Rothwell PM, British Doctors Aspirin T, the UKTIAAT: Effect of aspirin on long-term risk of colorectal cancer: consistent evidence from randomised and observational studies. Lancet 2007, 369(9573):1603-1613. 13. Goldstein I, Lue TF, Padma-Nathan H, Rosen RC, Steers WD, Wicker PA: Oral sildenafil in the treatment of erectile dysfunction. Sildenafil Study Group. The New England Journal of Medicine 1998, 338(20):1397-1404. 14. Sturm A, Holtmann G, Goebell H, Gerken G: Prokinetics in patients with gastroparesis: a systematic analysis. Digestion 1999, 60(5):422-427. 15. Palumbo A, Facon T, Sonneveld P, Blade J, Offidani M, Gay F, Moreau P, Waage A, Spencer A, Ludwig H et al: Thalidomide for treatment of multiple myeloma: 10 years later. Blood 2008, 111(8):3968-3977. 16. Morris GM, Huey R, Lindstrom W, Sanner MF, Belew RK, Goodsell DS, Olson AJ: AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. Journal of computational chemistry 2009, 30(16):2785-2791. 17. Li H, Gao Z, Kang L, Zhang H, Yang K, Yu K, Luo X, Zhu W, Chen K, Shen J: TarFisDock: a web server for identifying drug targets with docking approach. Nucleic acids research 2006, 34(suppl_2):W219-W224. 18. Haupt VJ, Schroeder M: Old friends in new guise: repositioning of known drugs with structural bioinformatics. Briefings in Bioinformatics 2011, 12(4):312-326. 19. Koutsoukas A, Simms B, Kirchmair J, Bond PJ, Whitmore AV, Zimmer S, Young MP, Jenkins JL, Glick M, Glen RC et al: From in silico target prediction to multi-target drug design: current databases, methods and applications. Journal of Proteomics 2011, 74(12):2554-2574. 20. Armstrong MS, Finn PW, Morris GM, Richards WG: Improving the accuracy of ultrafast ligand-based screening: incorporating lipophilicity into ElectroShape as an extra dimension. Journal of Computer-Aided Molecular Design 2011, 25(8):785-790. 21. Keiser MJ, Roth BL, Armbruster BN, Ernsberger P, Irwin JJ, Shoichet BK: Relating protein pharmacology by ligand chemistry. Nature Biotechnology 2007, 25(2):197-206. 22. Huang T, Mi H, Lin CY, Zhao L, Zhong LL, Liu FB, Zhang G, Lu AP, Bian ZX, for MG: MOST: most-similar ligand based approach to target prediction. BMC Bioinformatics 2017, 18(1):165. 23. Ozturk H, Ozkirimli E, Ozgur A: A comparative study of SMILES-based compound similarity functions for drug-target interaction prediction. BMC Bioinformatics 2016, 17:128. 24. Wang L, Ma C, Wipf P, Liu H, Su W, Xie XQ: TargetHunter: an in silico target identification tool for predicting therapeutic potential of small organic molecules based on chemogenomic database. The AAPS Journal 2013, 15(2):395-406. 25. Seiler KP, George GA, Happ MP, Bodycombe NE, Carrinski HA, Norton S, Brudz S, Sullivan JP, Muhlich J, Serrano M: ChemBank: a small-molecule screening and cheminformatics resource database. Nucleic acids research 2007, 36(suppl_1):D351-D359. 26. Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, Lerner J, Brunet J-P, Subramanian A, Ross KN: The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 2006, 313(5795):1929-1935. 27. Wang Y, Xiao J, Suzek TO, Zhang J, Wang J, Bryant SH: PubChem: a public information system for analyzing bioactivities of small molecules. Nucleic acids research 2009, 37(suppl_2):W623-W633. 28. Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, Murino L, Tagliaferri R, Brunetti-Pierri N, Isacchi A et al: Discovery of drug mode of action and drug repositioning from transcriptional responses. Proceedings of the National Academy of Sciences 2010, 107(33):14621-14626. 29. Iwata M, Sawada R, Iwata H, Kotera M, Yamanishi Y: Elucidating the modes of action for bioactive compounds in a cell-specific manner by large-scale chemically-induced transcriptomics. Scientific reports 2017, 7:40164. 30. Cheng T, Li Q, Wang Y, Bryant SH: Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. Journal of chemical information and modeling 2011, 51(9):2440-2448. 31. Wolpaw AJ, Shimada K, Skouta R, Welsch ME, Akavia UD, Pe'er D, Shaik F, Bulinski JC, Stockwell BR: Modulatory profiling identifies mechanisms of small molecule-induced cell death. Proceedings of the National Academy of Sciences 2011:201106149. 32. Plouffe D, Brinker A, McNamara C, Henson K, Kato N, Kuhen K, Nagle A, Adrián F, Matzen JT, Anderson P: In silico activity profiling reveals the mechanism of action of antimalarials discovered in a high-throughput screen. Proceedings of the National Academy of Sciences 2008, 105(26):9059-9064. 33. Hopkins AL, Groom CR: The druggable genome. Nature Reviews Drug Discovery 2002, 1(9):727-730. 34. Overington JP, Al-Lazikani B, Hopkins AL: How many drug targets are there? Nature Reviews Drug Discovery 2006, 5(12):993-996. 35. Corsello SM, Bittker JA, Liu Z, Gould J, McCarren P, Hirschman JE, Johnston SE, Vrcic A, Wong B, Khan M et al: The Drug Repurposing Hub: a next-generation drug library and information resource. Nature Medicine 2017, 23(4):405-408. 36. Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, Gould J, Davis JF, Tubelli AA, Asiedu JK et al: A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell 2017, 171(6):1437-1452 e1417. 37. Keenan AB, Jenkins SL, Jagodnik KM, Koplev S, He E, Torre D, Wang Z, Dohlman AB, Silverstein MC, Lachmann A et al: The Library of Integrated Network-Based Cellular Signatures NIH Program: System-Level Cataloging of Human Cells Response to Perturbations. Cell Systems 2018, 6(1):13-24. 38. Smith I, Greenside PG, Natoli T, Lahr DL, Wadden D, Tirosh I, Narayan R, Root DE, Golub TR, Subramanian A et al: Evaluation of RNAi and CRISPR technologies by large-scale gene expression profiling in the Connectivity Map. PLOS Biology 2017, 15(11):e2003213. 39. Paddison PJ, Caudy AA, Bernstein E, Hannon GJ, Conklin DS: Short hairpin RNAs (shRNAs) induce sequence-specific silencing in mammalian cells. Genes & development 2002, 16(8):948-958. 40. Burke JM, Kincaid RP, Aloisio F, Welch N, Sullivan CS: Expression of short hairpin RNAs using the compact architecture of retroviral microRNA genes. Nucleic Acids Research 2017, 45(17):e154. 41. Lares MR, Rossi JJ, Ouellet DL: RNAi and small interfering RNAs in human disease therapeutic applications. Trends in biotechnology 2010, 28(11):570-579. 42. Liu C, Su J, Yang F, Wei K, Ma J, Zhou X: Compound signature detection on LINCS L1000 big data. Molecular BioSystems 2015, 11(3):714-722. 43. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES et al: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences 2005, 102(43):15545-15550. 44. Kuleshov MV, Jones MR, Rouillard AD, Fernandez NF, Duan Q, Wang Z, Koplev S, Jenkins SL, Jagodnik KM, Lachmann A: Enrichr: a comprehensive gene set enrichment analysis web server 2016 update. Nucleic acids research 2016, 44(W1):W90-W97. 45. Huang DW, Sherman BT, Tan Q, Collins JR, Alvord WG, Roayaei J, Stephens R, Baseler MW, Lane HC, Lempicki RA: The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists. Genome biology 2007, 8(9):R183. 46. Edgar R, Domrachev M, Lash AE: Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Research 2002, 30(1):207-210. 47. Liberzon A, Birger C, Thorvaldsdottir H, Ghandi M, Mesirov JP, Tamayo P: The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Systems 2015, 1(6):417-425. 48. Swets JA: Indices of discrimination or diagnostic accuracy: their ROCs and implied models. Psychological Bulletin 1986, 99(1):100-117. 49. Metz CE: Basic principles of ROC analysis. Seminars in Nuclear Medicine 1978, 8(4):283-298. 50. Hajian-Tilaki K: Receiver operating characteristic (ROC) curve analysis for medical diagnostic test evaluation. Caspian journal of internal medicine 2013, 4(2):627. 51. Hosmer Jr DW, Lemeshow S, Sturdivant RX: Applied logistic regression, vol. 398: John Wiley & Sons; 2013. 52. Terstappen GC, Schlüpen C, Raggiaschi R, Gaviraghi G: Target deconvolution strategies in drug discovery. Nature Reviews Drug Discovery 2007, 6(11):891. 53. di Bernardo D, Thompson MJ, Gardner TS, Chobot SE, Eastwood EL, Wojtovich AP, Elliott SJ, Schaus SE, Collins JJ: Chemogenomic profiling on a genome-wide scale using reverse-engineered gene networks. Nature biotechnology 2005, 23(3):377. 54. Ambesi-Impiombato A, Bernardo D: Computational biology and drug discovery: From single-target to network drugs. Current Bioinformatics 2006, 1(1):3-13. 55. Schwartz GK, Shah MA: Targeting the cell cycle: a new approach to cancer therapy. Journal of clinical oncology 2005, 23(36):9408-9421. 56. Morice M-C, Serruys PW, Sousa JE, Fajadet J, Ban Hayashi E, Perin M, Colombo A, Schuler G, Barragan P, Guagliumi G: A randomized comparison of a sirolimus-eluting stent with a standard stent for coronary revascularization. New England Journal of Medicine 2002, 346(23):1773-1780. 57. McCormack FX, Inoue Y, Moss J, Singer LG, Strange C, Nakata K, Barker AF, Chapman JT, Brantly ML, Stocks JM: Efficacy and safety of sirolimus in lymphangioleiomyomatosis. New England Journal of Medicine 2011, 364(17):1595-1606. 58. Lanasa MC, Andritsos L, Brown JR, Gabrilove J, Caligaris-Cappio F, Ghia P, Larson RA, Kipps TJ, Leblond V, Milligan DW: Final results of EFC6663: a multicenter, international, phase 2 study of alvocidib for patients with fludarabine-refractory chronic lymphocytic leukemia. Leukemia research 2015, 39(5):495-500. 59. Bible KC, Peethambaram PP, Oberg AL, Maples W, Groteluschen DL, Boente M, Burton JK, Dahl LCG, Tibodeau JD, Isham CR: A phase 2 trial of flavopiridol (Alvocidib) and cisplatin in platin-resistant ovarian and primary peritoneal carcinoma: MC0261. Gynecologic oncology 2012, 127(1):55-62. 60. Holkova B, Perkins EB, Ramakrishnan V, Tombes MB, Shrader E, Talreja N, Wellons MD, Hogan KT, Roodman GD, Coppola D: Phase I trial of bortezomib (PS-341; NSC 681239) and alvocidib (flavopiridol; NSC 649890) in patients with recurrent or refractory B-cell neoplasms. Clinical cancer research 2011. 61. Beutler B: Inferences, questions and possibilities in Toll-like receptor signalling. Nature 2004, 430(6996):257-263. 62. Akira S, Takeda K, Kaisho T: Toll-like receptors: critical proteins linking innate and acquired immunity. Nature Immunology 2001, 2(8):675-680. 63. Takeda K, Akira S: Toll-like receptors in innate immunity. International Immunology 2005, 17(1):1-14. 64. Hennessy EJ, Parker AE, O'Neill LA: Targeting Toll-like receptors: emerging therapeutics? Nature Reviews Drug Discovery 2010, 9(4):293-307. 65. Zhang C, Richon V, Ni X, Talpur R, Duvic M: Selective induction of apoptosis by histone deacetylase inhibitor SAHA in cutaneous T-cell lymphoma cells: relevance to mechanism of therapeutic action. Journal of Investigative Dermatology 2005, 125(5):1045-1052. 66. Grenman R, Laine K-M, Klemi P, Grenman S, Hayashida D, Joensuu H: Effects of the antiestrogen toremifene on growth of the human mammary carcinoma cell line MCF-7. Journal of cancer research and clinical oncology 1991, 117(3):223-226. 67. Ling Y-H, El-Naggar AK, Priebe W, Perez-Soler R: Cell cycle-dependent cytotoxicity, G2/M phase arrest, and disruption of p34cdc2/cyclin B1 activity induced by doxorubicin in synchronized P388 cells. Molecular pharmacology 1996, 49(5):832-841. 68. Jordan MA, Toso RJ, Thrower D, Wilson L: Mechanism of mitotic block and inhibition of cell proliferation by taxol at low concentrations. Proceedings of the National Academy of Sciences 1993, 90(20):9552-9556. 69. Khandia R, Munjal AK, Iqbal HMN, Dhama K: Heat Shock Proteins: Therapeutic Perspectives in Inflammatory Disorders. Recent Patents on Inflammation & Allergy Drug Discovery 2017, 10(2):94-104. 70. Sugita T, Tanaka S, Murakami T, Miyoshi H, Ohnuki T: Immunosuppressive effects of the heat shock protein 90-binding antibiotic geldanamycin. Biochemistry and molecular biology international 1999, 47(4):587-595. 71. Wax S, Piecyk M, Maritim B, Anderson P: Geldanamycin inhibits the production of inflammatory cytokines in activated macrophages by reducing the stability and translation of cytokine transcripts. Arthritis & Rheumatology 2003, 48(2):541-550. 72. Camelo S, Iglesias AH, Hwang D, Due B, Ryu H, Smith K, Gray SG, Imitola J, Duran G, Assaf B et al: Transcriptional therapy with the histone deacetylase inhibitor trichostatin A ameliorates experimental autoimmune encephalomyelitis. Journal of Neuroimmunology 2005, 164(1-2):10-21. 73. Taub DD, Murphy WJ, Asai O, Fenton RG, Peltz G, Key ML, Turcovski-Corrales S, Longo DL: Induction of alloantigen-specific T cell tolerance through the treatment of human T lymphocytes with wortmannin. The Journal of Immunology 1997, 158(6):2745-2755. 74. Yanik EL, Gustafson SK, Kasiske BL, Israni AK, Snyder JJ, Hess GP, Engels EA, Segev DL: Sirolimus use and cancer incidence among US kidney transplant recipients. American Journal of Transplantation 2015, 15(1):129-136. 75. Kawamori T, Lubet R, Steele VE, Kelloff GJ, Kaskey RB, Rao CV, Reddy BS: Chemopreventive effect of curcumin, a naturally occurring anti-inflammatory agent, during the promotion/progression stages of colon cancer. Cancer research 1999, 59(3):597-601. 76. Singh AK, Sidhu GS, Deepa T, Maheshwari RK: Curcumin inhibits the proliferation and cell cycle progression of human umbilical vein endothelial cell. Cancer Letters 1996, 107(1):109-115. 77. Wilken R, Veena MS, Wang MB, Srivatsan ES: Curcumin: A review of anti-cancer properties and therapeutic activity in head and neck squamous cell carcinoma. Molecular cancer 2011, 10(1):12. 78. Lin YG, Kunnumakkara AB, Nair A, Merritt WM, Han LY, Armaiz-Pena GN, Kamat AA, Spannuth WA, Gershenson DM, Lutgendorf SK: Curcumin inhibits tumor growth and angiogenesis in ovarian carcinoma by targeting the nuclear factor-κB pathway. Clinical Cancer Research 2007, 13(11):3423-3430. 79. Choudhuri T, Pal S, Agwarwal ML, Das T, Sa G: Curcumin induces apoptosis in human breast cancer cells through p53‐dependent Bax induction. FEBS letters 2002, 512(1-3):334-340. 80. Kunnumakkara AB, Guha S, Krishnan S, Diagaradjane P, Gelovani J, Aggarwal BB: Curcumin potentiates antitumor activity of gemcitabine in an orthotopic model of pancreatic cancer through suppression of proliferation, angiogenesis, and inhibition of nuclear factor-κB–regulated gene products. Cancer research 2007, 67(8):3853-3861. 81. Pearson RD, Hewlett EL: Niclosamide therapy for tapeworm infections. Annals of internal medicine 1985, 102(4):550-551. 82. Köhler P, Bachmann R: Intestinal tubulin as possible target for the chemotherapeutic action of mebendazole in parasitic nematodes. Molecular and biochemical parasitology 1981, 4(5-6):325-336. 83. Li Y, Li P-K, Roberts MJ, Arend RC, Samant RS, Buchsbaum DJ: Multi-targeted therapy of cancer by niclosamide: A new application for an old drug. Cancer letters 2014, 349(1):8-14. 84. Pan J-X, Ding K, Wang C-Y: Niclosamide, an old antihelminthic agent, demonstrates antitumor activity by blocking multiple signaling pathways of cancer stem cells. Chinese journal of cancer 2012, 31(4):178. 85. Sack U, Walther W, Scudiero D, Selby M, Kobelt D, Lemm M, Fichtner I, Schlag PM, Shoemaker RH, Stein U: Novel effect of antihelminthic Niclosamide on S100A4-mediated metastatic progression in colon cancer. Journal of the National Cancer Institute 2011, 103(13):1018-1036. 86. Osada T, Chen M, Yang X-Y, Spasojevic I, Vandeusen JB, Hsu D, Clary BM, Clay TM, Chen W, Morse MA: Anti-helminth compound niclosamide downregulates Wnt Signaling and elicits antitumor responses in tumors with activating APC mutations. Cancer research 2011:canres. 3978.2010. 87. Mukhopadhyay T, Sasaki J-i, Ramesh R, Roth JA: Mebendazole elicits a potent antitumor effect on human cancer cell lines both in vitro and in vivo. Clinical cancer research 2002, 8(9):2963-2969. 88. Nygren P, Fryknäs M, Ågerup B, Larsson R: Repositioning of the anthelmintic drug mebendazole for the treatment for colon cancer. Journal of cancer research and clinical oncology 2013, 139(12):2133-2140. 89. Pantziarka P, Bouche G, Meheus L, Sukhatme V, Sukhatme VP: Repurposing Drugs in Oncology (ReDO)—mebendazole as an anti-cancer agent. ecancermedicalscience 2014, 8. 90. Jurenka JS: Anti-inflammatory properties of curcumin, a major constituent of Curcuma longa: a review of preclinical and clinical research. Alternative Medicine Review 2009, 14(2):141-153. 91. Ge Z, Da Y, Xue Z, Zhang K, Zhuang H, Peng M, Li Y, Li W, Simard A, Hao J et al: Vorinostat, a histone deacetylase inhibitor, suppresses dendritic cell function and ameliorates experimental autoimmune encephalomyelitis. Experimental Neurology 2013, 241:56-66. 92. Javeed A, Ashraf M, Riaz A, Ghafoor A, Afzal S, Mukhtar MM: Paclitaxel and immune system. European Journal of Pharmaceutical Sciences 2009, 38(4):283-290. 93. Toyama T, Asano Y, Akamata K, Noda S, Taniguchi T, Takahashi T, Ichimura Y, Shudo K, Sato S, Kadono T: Tamibarotene Ameliorates Bleomycin-Induced Dermal Fibrosis by Modulating Phenotypes of Fibroblasts, Endothelial Cells, and Immune Cells. Journal of Investigative Dermatology 2016, 136(2):387-398. 94. Jin Y, Wang L, Liu D, Lin X: Tamibarotene modulates the local immune response in experimental periodontitis. International Immunopharmacology 2014, 23(2):537-545. 95. Luo Y, Zhao X, Zhou J, Yang J, Zhang Y, Kuang W, Peng J, Chen L, Zeng J: A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information. Nature Communications 2017, 8(1):573. 96. Mizutani S, Pauwels E, Stoven V, Goto S, Yamanishi Y: Relating drug–protein interaction network with drug side effects. Bioinformatics 2012, 28(18):i522-i528. 97. Campillos M, Kuhn M, Gavin A-C, Jensen LJ, Bork P: Drug target identification using side-effect similarity. Science 2008, 321(5886):263-266. 98. Wang W, Yang S, Zhang X, Li J: Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics 2014, 30(20):2923-2930. 99. Feng Y, Mitchison TJ, Bender A, Young DW, Tallarico JA: Multi-parameter phenotypic profiling: using cellular effects to characterize small-molecule compounds. Nature Reviews Drug Discovery 2009, 8(7):567. 100. Lang P, Yeow K, Nichols A, Scheer A: Cellular imaging in drug discovery. Nature Reviews Drug Discovery 2006, 5(4):343. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/21911 | - |
| dc.description.abstract | 一顆藥物從成千上萬的化合物中被篩選出來到最後審查通過上市需要耗費龐大的資金及平均至少十年以上的時間，如何加速整個新藥開發的過程一直是醫學上很迫切的問題。不論是透過提升前期藥物研發效率或是針對已上市的藥物找到別的可能應用的疾病，亦即所謂老藥新用都可有效加速整體藥物發展的過程。在本篇研究中我們建立了一套預測分析流程，透過分析藥物擾動以及小髮夾核糖核酸擾動所產生基因表現資料之間的關聯去預測藥物可能的作用標的，以期能加速藥物研發時確認藥物作用標的及探討作用機制的過程。透過利用已知藥物及其作用標的的資料進行驗證，本方法在接收者操作特徵曲線下面積的指標達到0.71的結果，展現出其在預測藥物可能作用標的的潛力，此外，我們也將此方法預測出的結果透過功能分析，進一步應用到老藥新用上，並舉癌症及免疫疾病為例，對於已知藥物對此兩疾病的重要機制進行篩選，找出我們認為有可能拿來應用的候選藥物，而在預測的潛力藥物結果中，亦有許多得到實驗研究證實且已進入臨床試驗階段，另一方面展現此方法在老藥新用上的應用性。 | zh_TW |
| dc.description.abstract | The journey of a drug, from being selected in the laboratory to finally be sold on the market, is tedious, money-consuming and full of risks. It is an urgent need to shorten the process of drug discovery and development. Either accelerating the initial phase – drug discovery or repurposing existing drugs for new indications could be beneficial to achieve the goal. In this study, we have developed an analysis pipeline for predicting potential targets of drugs based on only perturbational profiles in L1000 data. Through analyzing the associations between compounds and short hairpin RNAs (shRNAs), the potential drug-target interactions could be inferred. The performance of the prediction results was evaluated by a known dataset extracted from Drug Repurposing Hub, and an average area under the receiver operating characteristic curve (AUC) of 0.71 has been achieved. Finally, we further applied our approach to explore opportunities for drug repurposing in cancer and inflammatory diseases through functional analysis. Several putative anti-cancer drugs and anti-inflammatory drugs revealed from the prediction are supported by preclinical or clinical studies, which demonstrated the efficacy of the proposed approach. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-08T03:52:42Z (GMT). No. of bitstreams: 1 ntu-107-R05945016-1.pdf: 1813055 bytes, checksum: 746324a8d4a364cfae993eb22983b373 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 中文摘要 III ABSTRACT IV CONTENTS V LIST OF FIGURES VII LIST OF TABLES VIII CHAPTER 1. INTRODUCTION 1 1.1. Motivation 1 1.1.1. Drug discovery and development 1 1.1.2. Prediction of drug-target interactions 2 1.1.3. Drug repurposing 3 1.2. Related work and challenges 3 1.2.1. Structure-based methods 4 1.2.2. Ligand-based methods 4 1.2.3. Bioactivity profiling methods 5 1.3. Specific aims of this study 6 1.4. Background 6 1.4.1. Perturbational profiles in LINCS L1000 data 6 1.4.2. Mechanisms of action of short-hairpin RNAs 8 CHAPTER 2. MATERIALS AND METHODS 9 2.1. The analysis pipeline for drug-target interaction prediction 9 2.2. Data preprocessing 10 2.3. Gene set enrichment analysis for compound-shRNA associations 12 2.3.1. Gene set enrichment analysis 12 2.3.2. Construction of self-defined gene sets and query gene lists 13 2.3.3. Enrichment scores of drug-target interactions 14 2.4. Prediction of drug-target interactions 15 2.5. Data collection 16 2.5.1. L1000 data 16 2.5.2. MSigDB gene sets 17 2.5.3. Known drug-target interactions 18 CHAPTER 3. RESULTS 19 3.1. Evaluation metric 19 3.2. Prediction of drug-target interactions 20 3.2.1. Overall performance 20 3.2.2. Preprocessing of perturbational profiles from LINCS L1000 data 21 3.3. Drug repurposing 22 3.3.1. Predicted interactions of known drugs 22 3.3.2. Functional analysis 23 CHAPTER 4. DISCUSSION 27 4.1. An overview of this study 27 4.2. Prediction for drug-target interactions 28 4.3. Drug repurposing 30 4.3.1. Efficacy for inferring mechanisms of action of drugs 30 4.3.2. Identify putative anti-cancer drugs through functional analysis 32 4.3.3. Identify putative anti-inflammatory drugs through functional analysis 33 4.4. Advantages of the proposed analysis pipeline 34 4.4.1. High accessibility of input data 34 4.4.2. Novel targets can be explored 35 4.5. Limitations and future work 36 CHAPTER 5. CONCLUSION 39 FIGURES 41 TABLES 47 REFERENCES 57 | |
| dc.language.iso | en | |
| dc.title | 利用LINCS L1000資料中藥物擾動及小髮夾RNA對細胞之影響預測藥物與標的之交互作用 | zh_TW |
| dc.title | Inferring Drug-Target Interactions Based on Perturbational Profiles in LINCS L1000 Data | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.coadvisor | 蕭自宏 | |
| dc.contributor.oralexamcommittee | 蔡孟勳,盧子彬,賴亮全 | |
| dc.subject.keyword | 藥物—作用標的交互作用,計算藥物開發,老藥新用, | zh_TW |
| dc.subject.keyword | Drug-target interaction,computational drug discovery,drug repurposing, | en |
| dc.relation.page | 66 | |
| dc.identifier.doi | 10.6342/NTU201803879 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2018-08-17 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 生醫電子與資訊學研究所 | zh_TW |
| 顯示於系所單位： | 生醫電子與資訊學研究所 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-107-1.pdf 未授權公開取用 | 1.77 MB | Adobe PDF |
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