УДК: 615.31

Скачать статью

А. С. Максимов1, Ж. М. Дергачёва2

 

СТРАТЕГИЯ ПЕРЕПРОФИЛИРОВАНИЯ В ПОИСКЕ НОВЫХ ЛЕКАРСТВЕННЫХ СРЕДСТВ

 

1 ООО «Искамед», 

г. Минск, Республика Беларусь

 

2 Витебский государственный ордена Дружбы народов медицинский университет, 

г. Витебск, Республика Беларусь

 

Разработка нового лекарственного средства от оригинальной идеи до выхода на рынок является сложным процессом, который состоит из многочисленных этапов, включающих идентификацию мишени, изыскание хитов, их отбор и перевод в лиды, оптимизацию лидов, выбор кандидатов и др. Поэтому одним из актуальных направлений сегодня является перепрофилирование – использование известных действующих веществ, а иногда уже зарегистрированных и применяемых лекарственных средств, по новым медицинским показаниям, что позволяет снизить общие затраты на разработку и сократить ее сроки. В статье показаны возможности и перспективы применения стратегии перепрофилирования. В настоящее время разработаны научные подходы и существуют многочисленные примеры успешной реализации данного направления. Однако вместе с этим возникли серьезные проблемы, как технологические, так и законодательные, требующие решения, которые также нашли отражение в данном обзоре.

 

Ключевые слова:

 создание лекарственных средств, перепрофилирование, изыскание, разработка, фундаментальные исследования.

 

 

SUMMARY

  1. S. Maksimov, Zh. M. Dergacheva

REPROFILING STRATEGY IN THE SEARCH OF NEW MEDICINES

1 Minsk, The Republic of Belarus

2 Vitebsk, The Republic of Belarus

The development of a new medicine from an original idea to marketing is a complex process that consists of numerous stages including target identification, search for hits, their selection and translation into leads, lead optimization, candidate selection and etc. Therefore, one of the most relevant trends today is reprofiling – the use of already known active substances and sometimes already registered and used medicines according to new medical indications which reduces overall cost on development and its time. The article shows opportunities and prospects of reprofiling strategy implementation. Today scientific approaches have been developed and there are numerous examples of successful implementation of this direction. However, alongside there are serious problems, both technological and legislative, that need to be solved and they are also reflected in this review.

Keywords:

drug design, reprofiling, early drug discovery, development, basic research.

 

ЛИТЕРАТУРА:

  1. Drug repurposing: progress, challenges and recommendations / Sudeep Pushpakom [et al.]// Nat. Rev Drug Discov. – 2019. – Vol. 18, Is. 1. – P. 41–58.
  2. Ashburn, T.T. Drug repositioning: identifying and developing new uses for existing drugs/ T.T. Ashburn, K.B. Thor // Nat. Rev. Drug Discov. – 2004. – Vol. 3, Is.8. – P. 673–683.
  3. Pammolli, F. The productivity crisis in pharmaceutical R&D/ F. Pammolli, L. Magazzini, M. Riccaboni// Nat. Rev. Drug Discov. – 2011. – Vol. 10, Is. 6. – P. 428–438.
  4. An analysis of the attrition of drug candidates from four major pharmaceutical companies/ M.J. Waring [et al.] // Nat. Rev. Drug Discov. – 2015. – Vol. 14, Is. 7. – P. 475–486.
  5. Drug Repurposing and Repositioning: Workshop summary. Roundtable on Translating Genomic-Based Research for Health (Board on Health Sciences Policy) Institute of Medicine (eds. S. H. Beachy, S. G. Johnson, S. Olson, A. C. Berger) National Academies Press, Washington DC, 2014.– 118 p.
  6. Breckenridge, A. Overcoming the legal and regulatory barriers to drug repurposing / A.Breckenridge, R. Jacob // Nat. Rev. Drug.Discov. – 2019. – Vol. 18, Is. 1.– P. 1–2.
  7. Nosengo, N. Can you teach old drugs new tricks? / N. Nosengo // Nature. – 2016. – Vol.534, Is. 7607. – P. 314–316.
  8. Antitumor activity of thalidomide inrefractory multiple myeloma / S. Singhal [et al.] // N.Engl. J. Med.– 1999. – Vol. 341, № 21. – P.1565–1571.
  9. Predicting new molecular targets for known drugs / M.J. Keiser [et al.] // Nature. – 2009.– Vol. 462,Is. 7270. – P. 175–181.
  10. Gene expression signature-based chemical genomic prediction identifies a novel class of HSP90 pathway modulators / H. Hieronymus [et al.] // Cancer Cell. – 2006. – Vol. 10, Is. 4. – P. 321–330.
  11. Computational repositioning of theanticonvulsant topiramate for inflammatory bowel disease / J.T. Dudley [et al.] // [Electronic resource]. – 2011. – Mode of access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3479650/. – Date of access: 08.11.2020.
  12. Topiramate use does not reduce flares of inflammatory bowel disease / S.D. Crockett [et al.] // Dig. Dis. Sci.– 2014. – Vol. 59, Is. 7. – P. 1535–1543.
  13. Auteri, M. GABA and GABA receptors in the gastrointestinal tract: from motility to inflammation / M. Auteri, M. G. Zizzo, R. Serio // Pharmacol. Res. – 2015. – Vol. 93. – P. 11–21.
  14. Systems chemical biology / T.I. Oprea [et al.] // Nat. Chem. Biol. – 2007. – Vol. 3, Is. 8.– P.447–450.
  15. Relating protein pharmacology by ligand chemistry / M. J. Keiser [et al.] // Nature Biotechnol. – 2007. – Vol. 25, Is. 2. – P. 197–206.
  16. Dudley, J.T. Exploiting drug-disease relationships for computational drug repositioning / J.T. Dudley, T. Deshpande, A.J. Butte // Brief Bioinform. – 2011. – Vol. 12, Is. 4. – P. 303–311.
  17. Drug target identification using side-effect similarity / M. Campillos [et al.] // Science. – 2008. – Vol. 321, Is. 5886. – P. 263–266.
  18. Docking and scoring in virtual screening for drug discovery: methods and applications / D.B. Kitchen [et al.] // Nat. Rev. DrugDiscov. – 2004. – Vol. 3, Is. 11. – P. 935–949.
  19. Predicting new indications for approved drugs using a proteochemometric method / S.Dakshanamurthy [et al.] // J. Med. Chem.– 2012. – Vol. 55, Is. 15. – P. 6832–6848.
  20. Vailhe, B. In vitro models of vasculogenesis and angiogenesis. / B.Vailhe, D.Vittet, J.Feige // Lab. Invest. – 2001. – Vol. 81 (4). – P. 439−452.
  21. Albendazole inhibits endothelial cell migration, tube formation, vasopermeability, VEGF receptor-2 expression and suppresses retinal neovascularization in ROP model of angiogenesis / M.H. Pourgholami [et al.] // Biochem. Biophys. Res. Commun. – 2010. – Vol. 397, Is. 4. – P.729−734.
  22. Identification of 14 Known Drugs as Inhibitors of the Main Protease of SARS-CoV-2 / M. M. Ghahremanpouret [et al.] // [Electronic resource]. – 2020. – Mode of access: https://www.biorxiv.org/content/10.1101/2020.08.28.271957v1.full.pdf. – Date of access: 08.11.2020.
  23. Structures of G protein-coupled receptorsreveal new opportunities for drug discovery / R.M. Cooke[et al.] // Drug Discov. Today. – 2015. –Vol. 20 (11). – P. 1355–1364.
  24. Kharkar, P. S. Reverse docking: a powerful tool for drug repositioning and drug rescue / P.S. Kharkar, S. Warrier, R.S. Gaud // Future Med. Chem. – 2014. – Vol. 6 (3). – P. 333–342.
  25. Reverse screening methods to search for the protein targets of chemopreventive compounds / H. Huang [et al.] // [Electronic resource]. – 2018. – Mode of access: https://www.frontiersin.org/articles/10.3389/fchem.2018.00138/full. – Date of access: 08.11.2020.
  26. A critical assessment of docking programs and scoring functions / G. L. Warren [et al.] // J. Med. Chem.–2006.– Vol. 49 (20). – P. 5912–5931.
  27. Use of genome-wide association studies for drug repositioning / P. Sanseau [et al.] // Nat. Biotechnol. – 2012. – Vol. 30 (4). – P. 317–320.
  28. The RANKL / OPG system is activated in inflammatory bowel disease and relates to the state of bone loss / A. R. Moschen [et al.] // Gut. – 2005. – Vol. 54 (4). – P. 479–487.
  29. Genome-wide meta-analysis increases to 71 the number of confirmed Crohn’s disease susceptibility loci / A. Franke [et al.] // Nat. Genet. – 2010.– Vol. 42 (12). – P. 1118–1125.
  30. The effect of denosumab, the inhibitor for receptor activator of nuclear factor kappa-B ligand (RANKL), on dinitrobenzensulfonic acid (DNBS)-induced experimental model of crohn’s disease / University of Manitoba // [Electronic resource]. – 2017. – Mode of access: https://mspace.lib.umanitoba.ca/handle/1993/32400?show=full. – Date of access: 08.11.2020.
  31. Review: a meta-analysis of GWAS and age-associated diseases / W. R. Jeck [et al.] // [Electronic resource]. – 2012. – Mode of access: https://www.ncbi.nlm.nih.gov/22888763/. – Date of access: 08.11.2020.
  32. Mahil, S.K. Genetics of psoriasis / S.K. Mahil, F.Capon, J.N. Barker // Dermatol. Clin.– 2015. – Vol. 33 (1). – P. 1–11.
  33. Identification of common biological pathways and drug targets across multiple respiratory viruses based on human host gene expression analysis / S.B. Smith [et al.] // [Electronic resource]. – 2012. – Mode of access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3303816/. – Date of access: 08.11.2020.
  34. Cavalla, D. Retrospective clinical analysis for drug rescue: for new indications or stratified patient groups / D. Cavalla, C. Singal // Drug Discov. Today. –2012.– Vol. 17 (3). – P. 104–109.
  35. Jensen, P.B. Mining electronic health records: towards better research applications and clinical care / P.B. Jensen, L.J. Jensen, S.Brunak // Nat. Rev. Genet. – 2012. – Vol. 13 (6). – P.395–405.
  36. Computational drug repositioning: from data to therapeutics / M.R. Hurle [et al.] // Clin. Pharmacol. Ther.– 2013. – Vol. 93 (4). – P.335–341.
  37. Repurpose terbutaline sulfate for amyotrophic lateral sclerosis using electronic medical records / H. Paik [et al.] // [Electronic resource]. – 2015. – Mode of access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4894399/pdf/srep08580.pdf. – Date of access: 08.11.2020.
  38. European Medicines Agency. Clinical data [Electronic resource]. – Mode of access: https://clinicaldata.ema.europa.eu/web/cdp/home. – Date of access: 08.11.2020.
  39. Huang, Y.H. Biomarker harvest from one thousand cancer cell lines / Y.H.Huang, C. R. Vakoc // Cell. –2016. – Vol. 166 (3). – P. 536–537.
  40. Weinstein, J. N. Drug discovery: cell lines battle cancer / J.N. Weinstein // Nature. – 2012.–Vol. 483 (7319). – P. 544–545.
  41. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity / J. Barretina [et al.] // Nature. – 2012. – Vol. 483 (7319) – P. 603–607.
  42. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules / A. Basu [et al.] // Cell. – 2013. – Vol. 154 (5). – P. 1151–1161.
  43. A landscape of pharmacogenomic interactions in cancer / F. Iorio [et al.] // Cell. – 2016. – Vol. 166 (3). – P. 740–754.
  44. Harnessing connectivity in a large-scale small-molecule sensitivity dataset / B. Seashore-Ludlow [et al.] // Cancer Discov. – 2015.– Vol. 5 (11). – P. 1210–1223.
  45. Wei, W.Q. Extracting research-quality phenotypes from electronic health records tosupport precision medicine / W.Q.Wei, J.C.Denny // [Electronic resource]. – 2015. – Mode of access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4416392/f. – Date of access: 08.11.2020.
  46. China Kadoorie Biobank of 0,5 million people: survey methods, baseline characteristics and long-term follow-up / Z. Chen [et al.] // Int. J. Epidemiol. – 2011. – Vol. 40 (6). – P. 1652–1666.
  47. A phenome-wide association study of a lipoprotein-associated phospholipase A2 loss-of-function variant in 90 000 Chinese adults / I.Y. Millwood [et al.] // Int. J. Epidemiol. – 2016. – Vol. 45 (5). – P. 1588–1599.
  48. Effect of darapladib on major coronary events after an acute coronary syndrome: the SOLID-TIMI 52 randomized clinical trial / M. L. O’Donoghue[et al.] // JAMA. – 2014. – Vol. 312 (10). – P. 1006–1015.
  49. Darapladib for preventing ischemic events in stable coronary heart disease / H. D. White [et al.] // N. Engl. J. Med. – 2014. – Vol. 370 (18). – P. 1702–1711.
  50. Eisenstein, M. Big data: the power of petabytes / M. Eisenstein // [Electronic resource]. – 2015. – Mode of access: https://www.nature.com/articles/527S2a. – Date of access: 08.11.2020.
  51. Peplow, M. The 100,000 Genomes Project / M. Peplow // [Electronic resource]. – 2016. – Mode of access: https://www.bmj.com/content/353/bmj.i1757. – Date of access: 08.11.2020.
  52. Collins, F.S. A new initiative on precision medicine / F.S.Collins, H.Varmus // N. Engl. J. Med. – 2015. – Vol. 372 (9). – P. 793–795.
  53. Cyranoski, D. China embraces precision medicine on a massive scale / D.Cyranoski // Nature. – 2016. – Vol. 529 (7584). – P. 9–10.
  54. AstraZeneca launches integrated genomics approach to transform drug discovery and development [Electronic resource]. – Mode of access: https://www.astrazeneca.com/media-centre/press-releases/2016/AstraZeneca-launches-integrated-genomics-approach-to-transform-drug-discovery-and-development-22042016.html#. – Date of access: 08.11.2020.
  55. Phosphatidylinositol 3-kinase α-selective inhibition with alpelisib (BYL719) in PIK3CA-altered solid tumors: results from thefirst-in-human study / D.Juric [et al.] // J. Clin. Oncol. – 2018. – Vol. 36 (13). – P. 1291–1299.
  56. Targeted therapy in patients with PIK3CA-related overgrowth syndrome / Q. Venot [et al.] // Nature. – 2018. – Vol. 558 (7741). – P. 540–546.
  57. Gligorijevic, V. Integrative methods for analyzing big data in precision medicine / V.Gligorijevic, N. Malod-Dognin, N. Przulj // Proteomics. – 2016. – Vol. 16 (5). – P. 741–758.
  58. Big Data Application in Biomedical Research and Health Care: A Literature Review / J.Luo [et al] // Biomed. Inform. Insights. – 2016. – Vol. 8. – P. 1–10.
  59. Chen, Y. IBM Watson: how cognitive computing can be applied to big data challenges in life sciences research / Y. Chen, J.D.Elenee Argentinis, G. Weber // Clin. Ther. – 2016. – Vol. 38 (4). – P. 688–701.
  60. Methods of integrating data to uncover genotype-phenotype interactions / M.D.Ritchie [et al.] // Nat. Rev. Genet.– 2015. – Vol. 16 (2). – P. 85–97.
  61. A network integration approach for drug-target interaction prediction and computational drug repositioning from heterogeneous information / Y. Luo [et al.] // [Electronic resource]. – 2017. – Mode of access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5603535/. – Date of access: 08.11.2020.
  62. Drug repositioning: a machine-learning approach through data integration / F.Napolitano [et al.] // [Electronic resource]. – 2013. – Mode of access: http://www.jcheminf.com/content/5/1/30.– Date of access: 08.11.2020.
  63. Wicks, P. Accelerated clinical discovery using self-reported patient data collected online and a patient-matching algorithm / P. Wicks [et al.] // Nat. Biotechnol. – 2011. – Vol. 29 (5). – P.411–414.
  64. Cellular targets of gefitinib / D.Brehmer [et al.] // Cancer Res. – 2005. – Vol. 65 (2). – P.379–382.
  65. Monitoring drug target engagement in cells and tissues using the cellular thermal shift assay / D. Martinez Molina [et al.] // Science. – 2013. – Vol. 341 (6141). – P. 84–87.
  66. The use of cellular thermal shift assay (CETSA) to study crizotinib resistance in ALK-expressing human cancers [Electronic resource] / A. Alshareef [et al.] // Mode of access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4979501/. – Date of access: 12.11.2020.
  67. Miettinen, T. P. NQO2 is a reactive oxygen species generating off-target for acetaminophen / T. P. Miettinen, M. Bjorklund // Mol. Pharm. – 2014. – Vol. 11 (12). – P. 4395–4404.
  68. Dynamic reprogramming of the kinome in response to targeted MEK inhibition in triple-negative breast cancer / J.S.Duncan [et al.] // Cell. – 2012. – Vol. 149 (2). – P. 307–321.
  69. Chemical proteomics reveals ferrochelatase as a common off-target of kinase inhibitors / S.Klaeger [et al.] // ACS Chem. Biol. – 2016. – Vol. 11 (5). – P. 1245–1254.
  70. Crizotinib inhibits NF2-associated schwannoma through inhibition of focal adhesion kinase 1 / S. Troutman [et al.] // Oncotarget. – 2016. – Vol. 7 (34). – P. 54515–54525.
  71. Drug-resistant aurora A mutants for cellular target validation of the small molecule kinase inhibitors MLN8054 and MLN8237 / D. A. Sloane [et al.] // ACS Chem. Biol. – 2010. – Vol. 5 (6). – P. 563–576.
  72. The hVps34-SGK3 pathway alleviates sustained PI3K/Akt inhibition by stimulating mTORC1 and tumour growth / R. Bago [et al.] // EMBO J.– 2016. – Vol. 35 (17). – P. 1902–1922.
  73. Inhibition of drug-resistant mutants of ABL, KIT, and EGF receptor kinases / T.A.Carter [et al.] // Proc. Natl. Acad. Sci. USA. – 2005. – Vol. 102 (31). – P. 11011–11016.
  74. A quantitative analysis of kinase inhibitor selectivity / M. W. Karaman [et al.] // Nat. Biotechnol. – 2008. – Vol. 26 (1). – P. 127–132.
  75. Munoz, L. Non-kinase targets of protein kinase inhibitors / L. Munoz // Nat. Rev. Drug Discov. – 2017. – Vol. 16 (6). – P. 424–440.
  76. Hsieh, Y. Y. Repositioning of a cyclin-dependent kinase inhibitor GW8510 as a ribonucleotide reductase M2 inhibitor to treat human colorectal cancer / Y. Y. Hsieh [et al.] // [Electronic resource]. – 2016. – Mode of access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4979501/. – Date of access: 08.11.2020.
  77. Identification of small-molecule inhibitors of Zika virus infection and induced neural cell death via a drug repurposing screen / M. Xu [et al.] // Nat. Med. – 2016. – Vol. 22 (10). – P. 1101–1107.
  78. Rapid antimicrobial susceptibility test for identification of new therapeutics and drug combinations against multidrug-resistant bacteria / W. Sun [et al.] // [Electronic resource]. – 2016. – Mode of access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5148025/. – Date of access: 08.11.2020.
  79. Opportunities and challenges in phenotypic drug discovery: an industry perspective / J.G.Moffat [et al.] / Nat. Rev. Drug Discov. – 2017. – Vol. 16 (8). – P. 531–543.
  80. High-throughput cell-based screening of 4910 known drugs and drug-like small molecules identifies disulfiram as an inhibitor of prostate cancer cell growth / K. Iljin [et al.] // Clin. Cancer Res.– 2009. – Vol. 15 (19). – P. 6070–6078.
  81. Larval zebrafish model for FDA approved drug repositioning for tobacco dependence treatment / M.A. Cousin [et al.] // [Electronic resource]. – 2014. – Mode of access: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3962344/. – Date of access: 08.11.2020.
  82. Bezprozvanny, I. The rise and fall of Dimebon / I. Bezprozvanny // Drug News Perspect. – 2010. – Vol. 23, №8. – P. 518–523.
  83. Safety and efficacy of ceftriaxone for amyotrophic lateral sclerosis: a multistage, randomized, double-blind, placebo-controlled trial / M. Cudkowicz [et al.] // Lancet

Neurol. – 2014. – Vol.13, №11. – P. 1083–1091.

 

Адрес для корреспонденции: 

210009, Республика Беларусь,

г. Витебск, пр. Фрунзе, 27,

УО «Витебский государственный ордена

Дружбы народов медицинский университет»,

кафедра фармацевтической химии

с курсом ФПК и ПК,

тел. моб.: +375 29 711 50 83,

E-mail: Адрес электронной почты защищен от спам-ботов. Для просмотра адреса в вашем браузере должен быть включен Javascript.,

Дергачёва Ж.М.

Поступила    25.11.2020 г.