Computational drug repositioning for rare diseases in the era of precision medicine

Drug Discovery Today

Volumn 23, Issue 2, 2018, Pages 382-394

  Delavan, Brian ^a,b   Roberts, Ruth ^c,d   Huang, Ruili ^e   Bao, Wenjun ^f   Tong, Weida ^a   Liu, Zhichao ^a  

^a NATIONAL CENTER FOR TOXICOLOGICAL RESEARCH   (United States)

^b UNIVERSITY OF ARKANSAS AT LITTLE ROCK   (United States)

^c APCONIX   (United Kingdom)

^d UNIVERSITY OF BIRMINGHAM   (United Kingdom)

^e NATIONAL INSTITUTES OF HEALTH   (United States)

^f SAS INSTITUTE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; DRUG REPOSITIONING; GENE EXPRESSION; GENE MUTATION; GENOME; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; PERSONALIZED MEDICINE; PHENOTYPE; RARE DISEASE; REGULATORY MECHANISM; REVIEW; ANIMAL; BIOLOGY; GENETICS; HUMAN; HUMAN GENOME; PROCEDURES;

DRUG;

ANIMALS; COMPUTATIONAL BIOLOGY; DRUG REPOSITIONING; GENOME, HUMAN; GENOMICS; HUMANS; PHARMACEUTICAL PREPARATIONS; PRECISION MEDICINE; RARE DISEASES;

EID: 85033493727     PISSN: 13596446     EISSN: 18785832     Source Type: Journal    
DOI: 10.1016/j.drudis.2017.10.009     Document Type: Review

References (124)

1. Boycott, K.M., et al. Rare-disease genetics in the era of next-generation sequencing: discovery to translation. Nat. Rev. Genet. 14 (2013), 681–691.
2. Basch, E., The missing voice of patients in drug-safety reporting. N. Engl. J. Med. 362 (2010), 865–869.
3. Lesko, L.J., Atkinson, A.J., Use of biomarkers and surrogate endpoints in drug development and regulatory decision making: criteria, validation, strategies 1. Ann. Rev. Pharmacol. Toxicol. 41 (2001), 347–366.
4. Mullard, A., 2012 FDA drug approvals. Nat. Rev. Drug Discov. 12 (2013), 87–90.
5. Schadow, G., Assessing the impact of HL7/FDA structured product label (SPL) content for medication knowledge management. AMIA Annual Symposium Proceedings, American Medical Informatics Association, 2007 pp. 646.
6. Haffner, M.E., Adopting orphan drugs — two dozen years of treating rare diseases. N. Engl. J. Med. 354 (2006), 445–447.
7. Hamosh, A., et al. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res. 33:Suppl. 1 (2005), D514–517.
8. Rowe, S.M., Verkman, A.S., Cystic fibrosis transmembrane regulator correctors and potentiators. Cold Spring Harb. Perspect. Med., 3, 2013, a009761.
9. Rowe, S.M., et al. Cystic fibrosis. N. Engl. J. Med. 352 (2005), 1992–2001.
10. Sboner, A., Elemento, O., A primer on precision medicine informatics. Brief. Bioinform. 17 (2016), 145–153.
11. Landrum, M.J., et al. ClinVar: public archive of relationships among sequence variation and human phenotype. Nucleic Acids Res. 42 (2014), D980–985.
12. Potter, B.K., et al. Translating rare-disease therapies into improved care for patients and families: what are the right outcomes, designs, and engagement approaches in health-systems research?. Genet. Med. 18 (2016), 117–123.
13. Jamuar, S.S., Tan, E.-C., Clinical application of next-generation sequencing for Mendelian diseases. Human Genom., 9, 2015, 10.
14. Briggs, M.D., et al. New therapeutic targets in rare genetic skeletal diseases. Expert Opin. Orphan Drugs 3 (2015), 1137–1154.
15. Segalat, L., Loss-of-function genetic diseases and the concept of pharmaceutical targets. Nat. Rev. Genet., 2007, 10.1186/1750-1172-2-30.
16. Ashburn, T.T., Thor, K.B., Drug repositioning: identifying and developing new uses for existing drugs. Nat. Rev. Drug Discov. 3 (2004), 673–683.
17. Liu, Z., et al. In silico drug repositioning — what we need to know. Drug Discov. Today 18 (2013), 110–115.
18. Li, Y., Jones, S., Drug repositioning for personalized medicine. Genome Med., 4, 2012, 27.
19. Ekins, S., et al. In silico repositioning of approved drugs for rare and neglected diseases. Drug Discov. Today 16 (2011), 298–310.
20. Sardana, D., et al. Drug repositioning for orphan diseases. Brief. Bioinform. 12 (2011), 346–356.
21. Weischenfeldt, J., et al. Phenotypic impact of genomic structural variation: insights from and for human disease. Nat. Rev. Genet. 14 (2013), 125–138.
22. Schumacher, K.R., et al. Social media methods for studying rare diseases. Pediatrics 133 (2014), e1345–1353.
23. van Dijk, E.L., et al. Ten years of next-generation sequencing technology. Trends Genet. 30 (2014), 418–426.
24. Aymé S., Orphanet, an information site on rare diseases. Soins, 672, 2003, 46.
25. Wei, C.-Y., et al. Pharmacogenomics of adverse drug reactions: implementing personalized medicine. Hum. Mol. Genet. 21 (2012), R58–65.
26. Frueh, F.W., et al. Pharmacogenomic biomarkerinformation in drug labels approved by the United States Food and Drug Administration: prevalence of related drug use. Pharmacotherapy 28 (2008), 992–998.
27. Vissers, L.E.L.M., Veltman, J.A., Standardized phenotyping enhances Mendelian disease gene identification. Nat. Genet. 47 (2015), 1222–1224.
28. Köhler, S., et al. The Human Phenotype Ontology project: linking molecular biology and disease through phenotype data. Nucleic Acids Res. 42 (2014), D966–974.
29. Mungall, C.J., et al. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. Nucleic Acids Res. 45 (2017), D712–722.
30. Trujillano, D., et al. A comprehensive global genotype–phenotype database for rare diseases. Mol. Genet. Genom. Med. 5 (2017), 66–75.
31. Smith, C.L., Eppig, J.T., The Mammalian Phenotype Ontology: enabling robust annotation and comparative analysis. Wiley Interdiscip. Rev. Syst. Biol. Med. 1 (2009), 390–399.
32. Kibbe, W.A., et al. Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data. Nucleic Acids Res. 43 (2015), D1071–1078.
33. Brookes, A.J., Robinson, P.N., Human genotype-phenotype databases: aims, challenges and opportunities. Nat. Rev. Genet., 16, 2015, 702.
34. Köhler, S., et al. The human phenotype ontology in 2017. Nucleic Acids Res. 45 (2017), D865–876.
35. Groza, T., et al. The Human Phenotype Ontology: semantic unification of common and rare disease. Am. J. Hum. Genet. 97 (2015), 111–124.
36. Akawi, N., et al. Discovery of four recessive developmental disorders using probabilistic genotype and phenotype matching among 4,125 families. Nat. Genet. 47 (2015), 1363–1369.
37. Zhou, X.Z., et al. Human symptoms-disease network. Nat. Commun., 5, 2014, 10.
38. Melamed, R.D., et al. Genetic similarity between cancers and comorbid Mendelian diseases identifies candidate driver genes. Nat. Commun., 6, 2015, 10.
39. Hoehndorf, R., et al. Analysis of the human diseasome using phenotype similarity between common, genetic, and infectious diseases. Sci. Rep., 5, 2015, 10888.
40. Jegga, A.G., Candidate gene discovery and prioritization in rare diseases. Trent, R., (eds.) Clinical Bioinformatics, 2014, Springer, New York, 295–312.
41. Amyere, M., et al. Common somatic alterations identified in Maffucci syndrome by molecular karyotyping. Mol. Syndromol. 5 (2014), 259–267.
42. Kerem, B., et al. Identification of the cystic fibrosis gene: genetic analysis. Science 245 (1989), 1073–1080.
43. Lander, E., Botstein, D., Homozygosity mapping: a way to map human recessive traits with the DNA of inbred children. Science 236 (1987), 1567–1570.
44. Blauw, H.M., et al. Copy-number variation in sporadic amyotrophic lateral sclerosis: a genome-wide screen. Lancet Neurol. 7 (2008), 319–326.
45. Ng, S.B., et al. Exome sequencing identifies the cause of a mendelian disorder. Nat. Genet. 42 (2010), 30–35.
46. Ng, S.B., et al. Targeted capture and massively parallel sequencing of 12 human exomes. Nature 461 (2009), 272–276.
47. Hoischen, A., et al. De novo mutations of SETBP1 cause Schinzel–Giedion syndrome. Nat. Genet. 42 (2010), 483–485.
48. Belkadi, A., et al. Whole-genome sequencing is more powerful than whole-exome sequencing for detecting exome variants. Proc. Natl. Acad. Sci. U. S. A. 112 (2015), 5473–5478.
49. Mercer, T.R., et al. Targeted RNA sequencing reveals the deep complexity of the human transcriptome. Nat. Biotechnol. 30 (2012), 99–104.
50. Stessman, H.A.F., et al. Targeted sequencing identifies 91 neurodevelopmental-disorder risk genes with autism and developmental-disability biases. Nat. Genet. 49 (2017), 515–526.
51. Pabinger, S., et al. A survey of tools for variant analysis of next-generation genome sequencing data. Brief. Bioinform. 15 (2014), 256–278.
52. Zook, J.M., et al. Integrating human sequence data sets provides a resource of benchmark SNP and indel genotype calls. Nat. Biotechnol. 32 (2014), 246–251.
53. Xu, H., et al. Comparison of somatic mutation calling methods in amplicon and whole exome sequence data. BMC Genom., 15, 2014, 244.
54. Cornish, A., Guda, C., A comparison of variant calling pipelines using Genome in a Bottle as a reference. Biomed. Res. Int., 2015, 2015, 456479.
55. Hwang, S., et al. Systematic comparison of variant calling pipelines using gold standard personal exome variants. Sci. Rep., 5, 2015, 17875.
56. Hindorff, L.A., et al. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc. Natl. Acad. Sci. U. S. A. 106 (2009), 9362–9367.
57. Plenge, R.M., et al. Validating therapeutic targets through human genetics. Nat. Rev. Drug Discov. 12 (2013), 581–594.
58. Wang, Z.-Y., Zhang, H.-Y., Rational drug repositioning by medical genetics. Nat. Biotechnol. 31 (2013), 1080–1082.
59. Nelson, M.R., et al. The support of human genetic evidence for approved drug indications. Nat. Genet. 47 (2015), 856–860.
60. Sanseau, P., et al. Use of genome-wide association studies for drug repositioning. Nat. Biotechnol. 30 (2012), 317–320.
61. Hurle, M.R., et al. Computational drug repositioning: from data to therapeutics. Clin. Pharmacol. Ther. 93 (2013), 335–341.
62. Korte, A., Farlow, A., The advantages and limitations of trait analysis with GWAS: a review. Plant Methods 9 (2013), 1–9.
63. Bush, W.S., et al. Unravelling the human genome-phenome relationship using phenome-wide association studies. Nat. Rev. Genet. 17 (2016), 129–145.
64. Roden, D.M., Denny, J.C., Integrating electronic health record genotype and phenotype datasets to transform patient care. Clin. Pharmacol. Ther. 99 (2016), 298–305.
65. Denny, J.C., et al. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nat. Biotechnol. 31 (2013), 1102–1111.
66. Rastegar-Mojarad, M., et al. Opportunities for drug repositioning from phenome-wide association studies. Nat. Biotechnol. 33 (2015), 342–345.
67. Law, V., et al. DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res 42 (2014), D1091–1097.
68. Sarntivijai, S., et al. Linking rare and common disease: mapping clinical disease-phenotypes to ontologies in therapeutic target validation. J. Biomed. Seman., 7, 2016, 8.
69. Jin, G., Wong, S.T., Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov. Today 19 (2014), 637–644.
70. Dhillon, A.S., et al. MAP kinase signalling pathways in cancer. Oncogene 26 (2007), 3279–3290.
71. Kandoth, C., et al. Mutational landscape and significance across 12 major cancer types. Nature 502 (2013), 333–339.
72. Marin, T.M., et al. Rapamycin reverses hypertrophic cardiomyopathy in a mouse model of LEOPARD syndrome–associated PTPN11 mutation. J. Clin. Investig. 121 (2011), 1026–1043.
73. Goh, K.-I., et al. The human disease network. Proc. Natl. Acad. Sci. U. S. A. 104 (2007), 8685–8690.
74. Li, Y., Agarwal, P., A pathway-based view of human diseases and disease relationships. PLoS One, 4, 2009, e0004346.
75. Kiel, C., Serrano, L., Structure-energy-based predictions and network modelling of RASopathy and cancer missense mutations. Mol. Syst. Biol., 10, 2014, 727.
76. Rockman, M.V., Kruglyak, L., Genetics of global gene expression. Nat. Rev. Genet. 7 (2006), 862–872.
77. Montgomery, S.B., Dermitzakis, E.T., The resolution of the genetics of gene expression. Hum. Mol. Genet. 18 (2009), R211–215.
78. Natsoulis, G., et al. The liver pharmacological and xenobiotic gene response repertoire. Mol. Syst. Biol., 4, 2008, 12.
79. Igarashi, Y., et al. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res. 43 (2015), D921–927.
80. Lamb, J., et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313 (2006), 1929–1935.
81. Masica, D.L., Karchin, R., Correlation of somatic mutation and expression identifies genes important in human glioblastoma progression and survival. Cancer Res. 71 (2011), 4550–4561.
82. Bertrand, D., et al. Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles. Nucleic Acids Res., 43, 2015, e44.
83. Ping, Y., et al. Identifying core gene modules in glioblastoma based on multilayer factor-mediated dysfunctional regulatory networks through integrating multi-dimensional genomic data. Nucleic Acids Res. 43 (2015), 1997–2007.
84. Ding, J., et al. Systematic analysis of somatic mutations impacting gene expression in 12 tumour types. Nat. Commun., 6, 2015, 8554.
85. Gerstung, M., et al. Combining gene mutation with gene expression data improves outcome prediction in myelodysplastic syndromes. Nat. Commun., 6, 2015, 5901.
86. Soifer, H.S., et al. MicroRNAs in disease and potential therapeutic applications. Mol. Ther. 15 (2007), 2070–2079.
87. Amato, F., et al. Gene mutation in microRNA target sites of CFTR gene: a novel pathogenetic mechanism in cystic fibrosis?. PLoS One, 8, 2013, e60448.
88. Liu, Z., et al. Deciphering miRNA transcription factor feed-forward loops to identify drug repurposing candidates for cystic fibrosis. Genome Med., 6, 2014, 94.
89. Qu, X.A., Rajpal, D.K., Applications of Connectivity Map in drug discovery and development. Drug Discov. Today 17 (2012), 1289–1298.
90. Iorio, F., et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc. Natl. Acad. Sci. U. S. A. 107 (2010), 14621–14626.
91. Dudley, J.T., et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med., 3, 2011, 6.
92. Barrett, T., et al. NCBI GEO: mining tens of millions of expression profiles — database and tools update. Nucleic Acids Res. 35 (2007), D760–765.
93. Iskar, M., et al. Characterization of drug-induced transcriptional modules: towards drug repositioning and functional understanding. Mol. Syst. Biol., 9, 2013, 13.
94. Ling, H., et al. MicroRNAs and other non-coding RNAs as targets for anticancer drug development. Nat. Rev. Drug Discov. 12 (2013), 847–865.
95. Li, Z., Rana, T.M., Therapeutic targeting of microRNAs: current status and future challenges. Nat. Rev. Drug Discov. 13 (2014), 622–638.
96. Liu, X.Y., et al. SM2miR: a database of the experimentally validated small molecules’ effects on microRNA expression. Bioinformatics 29 (2013), 409–411.
97. Rukov, J.L., et al. Pharmaco-miR: linking microRNAs and drug effects. Brief. Bioinform. 15 (2014), 648–659.
98. Orphadata: Free access data from Orphanet. INSERM (1997) Available at: http://www.orphadata.org.
99. Boztug, K., et al. A novel syndrome with congenital neutropenia caused by mutations in G6PC3. N. Engl. J. Med. 360 (2009), 32–43.
100. Zeidler, C., et al. Management of kostmann syndrome in the g-csf era. Br. J. Haematol. 109 (2000), 490–495.
101. Sparks, R., et al. Expanding the immunology toolbox: embracing public-data reuse and crowdsourcing. Immunity 45 (2016), 1191–1204.
102. Bhattacharya, S., et al. ImmPort: disseminating data to the public for the future of immunology. Immunol. Res. 58 (2014), 234–239.
103. Heng, T.S.P., et al. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9 (2008), 1091–1094.
104. Kidd, B.A., et al. Mapping the effects of drugs on the immune system. Nat. Biotechnol. 34 (2016), 47–54.
105. Li, J., et al. A survey of current trends in computational drug repositioning. Brief. Bioinform. 17 (2016), 2–12.
106. Jin, G., Wong, S.T.C., Toward better drug repositioning: prioritizing and integrating existing methods into efficient pipelines. Drug Discov. Today 19 (2014), 637–644.
107. Blatt, J., Corey, S.J., Drug repurposing in pediatrics and pediatric hematology oncology. Drug Discov. Today 18 (2013), 4–10.
108. Liu, Z., et al. Potential reuse of oncology drugs in the treatment of rare diseases. Trends Pharmacol. Sci. 37 (2016), 843–857.
109. Ekins, S., et al. In silico pharmacology for drug discovery: methods for virtual ligand screening and profiling. Br. J. Pharmacol. 152 (2007), 9–20.
110. Chen, Y.Z., Zhi, D.G., Ligand–protein inverse docking and its potential use in the computer search of protein targets of a small molecule. Proteins Struct. Funct. Bioinform. 43 (2001), 217–226.
111. Kolb, P., et al. Docking and chemoinformatic screens for new ligands and targets. Curr. Opin. Biotechnol. 20 (2009), 429–436.
112. Berman, H.M., et al. The Protein Data Bank. Nucleic Acids Res. 28 (2000), 235–242.
113. Andronis, C., et al. Literature mining, ontologies and information visualization for drug repurposing. Brief. Bioinform. 12 (2011), 357–368.
114. Agarwal, P., Searls, D.B., Literature mining in support of drug discovery. Brief. Bioinform. 9 (2008), 479–492.
115. Su, E.W., Sanger, T.M., Systematic drug repositioning through mining adverse event data in ClinicalTrials.gov. PeerJ, 5, 2017, e3154.
116. Yang, H.-T., et al. Literature-based discovery of new candidates for drug repurposing. Brief. Bioinform. 18 (2017), 488–497.
117. Barrett, T., et al. NCBI GEO: archive for functional genomics data sets–update. Nucleic Acids Res. 41 (2013), D991–995.
118. Parkinson, H., et al. ArrayExpress–a public repository for microarray gene expression data at the EBI. Nucleic Acids Res. 33:1 (2005), D553–555.
119. Panic, G., et al. Activity profile of an FDA-approved compound library against Schistosoma mansoni. PLoS Negl. Trop. Dis., 9, 2015, 15.
120. Campillos, M., et al. Drug target identification using side-effect similarity. Science 321 (2008), 263–266.
121. Clohessy, J.G., Pandolfi, P.P., Mouse hospital and co-clinical trial project–from bench to bedside. Nat. Rev. Clin. Oncol 12 (2015), 491–498.
122. Lynam, E.B., et al. A patient focused solution for enrolling clinical trials in rare and selective cancer indications: a landscape of haystacks and needles. Drug Inf. J. 46 (2012), 472–478.
123. Koboldt, D.C., et al. The next-generation sequencing revolution and its impact on genomics. Cell 155 (2013), 27–38.
124. Frail, D.E., et al. Pioneering government-sponsored drug repositioning collaborations: progress and learning. Nat. Rev. Drug Discov. 14 (2015), 833–841.