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Signature-based approaches for informed drug repurposing: targeting CNS disorders

Abstract

CNS disorders, and in particular psychiatric illnesses, lack definitive disease-altering therapeutics. The limited understanding of the mechanisms driving these illnesses with the slow pace and high cost of drug development exacerbates this issue. For these reasons, drug repurposing – both a less expensive and time-efficient practice compared to de novo drug development – has been a promising strategy to overcome the paucity of treatments available for these debilitating disorders. While empirical drug-repurposing has been a routine practice in clinical psychiatry, innovative, informed, and cost-effective repurposing efforts using big data (“omics”) have been designed to characterize drugs by structural and transcriptomic signatures. These strategies, in conjunction with ontological integration, provide an important opportunity to address knowledge-based challenges associated with drug development for CNS disorders. In this review, we discuss various signature-based in silico approaches to drug repurposing, its integration with multiple omics platforms, and how this data can be used for clinically relevant, evidence-based drug repurposing. These tools provide an exciting translational avenue to merge omics-based drug discovery platforms with patient-specific disease signatures, ultimately facilitating the identification of new therapies for numerous psychiatric disorders.

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Fig. 1: Chronology of drug repurposing approaches.

References

  1. 1.

    Pankevich DE, Altevogt BM, Dunlop J, Gage FH, Hyman SE. Improving and accelerating drug development for nervous system disorders. Neuron. 2014;84:546–53.

    CAS  Google Scholar 

  2. 2.

    US Food and Drug Administration. New Molecular Entity (NME) Drug and New Biologic Approvals. US Food and Drug Administration; 2014.

  3. 3.

    Lee HM, Kim Y. Drug repurposing is a new opportunity for developing drugs against neuropsychiatric disorders. Schizophr Res Treat. 2016;2016:6378137.

    Google Scholar 

  4. 4.

    Fava M. The promise and challenges of drug repurposing in psychiatry. World Psych. 2018;17:28–29.

    Google Scholar 

  5. 5.

    Hemphill CS, Sampat BN. Evergreening, patent challenges, and effective market life in pharmaceuticals. J Health Econ. 2012. https://doi.org/10.1016/j.jhealeco.2012.01.004.

  6. 6.

    Smith R. Repositioned drugs: integrating intellectual property and regulatory strategies. Drug Discov Today Ther Strat. 2011;8:131–7.

    Google Scholar 

  7. 7.

    Naylor S, Kauppi DM, Schonfeld JM. Therapeutic drug repurposing, repositioning and rescue: Part II: Business review. Drug Discov World. 2015;16:57–72.

    Google Scholar 

  8. 8.

    Naylor S, Schonfeld JM. Therapeutic drug repurposing, repositioning and rescue - Part I: Overview. Drug Discov World. 2014;16:49–62.

    Google Scholar 

  9. 9.

    Naylor S, Kauppi DM, Schonfeld JM. Therapeutic drug repurposing, repositioning and rescue: Part III: market exclusivity using Intellectual Property and regulatory pathways. Drug Discov World. 2015;16:62–9.

    Google Scholar 

  10. 10.

    Hernandez JJ, Pryszlak M, Smith L, Yanchus C, Kurji N, Shahani VM, et al. Giving drugs a second chance: overcoming regulatory and financial hurdles in repurposing approved drugs as cancer therapeutics. Front Oncol. 2017;7:273.

    Google Scholar 

  11. 11.

    Pantziarka P. Scientific advice-is drug repurposing missing a trick? Nat Rev Clin Oncol. 2017;14:455–6.

    Google Scholar 

  12. 12.

    Allison M. NCATS launches drug repurposing program. Nat Biotechnol. 2012;30:571–2.

    CAS  Google Scholar 

  13. 13.

    Caban A, Pisarczyk K, Kopacz K, Kapuśniak A, Toumi M, Rémuzat C, et al. Filling the gap in CNS drug development: evaluation of the role of drug repurposing. J Mark Access Heal Policy. 2017. https://doi.org/10.1080/20016689.2017.1299833.

  14. 14.

    Swainson J, Thomas RK, Archer S, Chrenek C, MacKay MA, Baker G, et al. Esketamine for treatment resistant depression. Expert Rev Neurother. 2019. https://doi.org/10.1080/14737175.2019.1640604.

  15. 15.

    Daly EJ, Singh JB, Fedgchin M, Cooper K, Lim P, Shelton RC, et al. Efficacy and safety of intranasal esketamine adjunctive to oral antidepressant therapy in treatment-resistant depression: a randomized clinical trial. JAMA Psychiatry. 2018. https://doi.org/10.1001/jamapsychiatry.2017.3739.

  16. 16.

    Leo RJ, Narendran R. Anticonvulsant use in the treatment of bipolar disorder: a primer for primary care physicians. Prim Care Companion J Clin Psychiatry. 1999. https://doi.org/10.4088/pcc.v01n0304.

  17. 17.

    López-Muñoz F, Shen WW, D’ocon P, Romero A, Álamo C. A history of the pharmacological treatment of bipolar disorder. Int J Mol Sci. 2018;19:2143.

    Google Scholar 

  18. 18.

    Bowden C. The effectiveness of divalproate in all forms of mania and the broader bipolar spectrum: Many questions, few answers. J Affect Disord. 2004. https://doi.org/10.1016/j.jad.2004.01.003.

  19. 19.

    Geddes JR, Miklowitz DJ. Treatment of bipolar disorder. Lancet. 2013;381:1672–82.

    CAS  Google Scholar 

  20. 20.

    Garland EJ, Behr R. Hormonal effects of valproic acid? J Am Acad Child Adolesc Psychiatry. 1996;35:1424–5.

    CAS  Google Scholar 

  21. 21.

    Isojarvi J, Laatikainen TJ, Pakarinen AJ, Myllyla VV. Polycystic ovaries and hyperandrogenism in women taking valproate for epilepsy. N Engl J Med. 1993. https://doi.org/10.1056/NEJM199311043291904.

  22. 22.

    Romoli M, Mazzocchetti P, D’Alonzo R, Siliquini S, Rinaldi VE, Verrotti A, et al. Valproic acid and epilepsy: from molecular mechanisms to clinical evidences. Curr Neuropharmacol. 2018. https://doi.org/10.2174/1570159x17666181227165722.

  23. 23.

    Serafini G, Howland R, Rovedi F, Girardi P, Amore M. The role of ketamine in treatment-resistant depression: a systematic review. Curr Neuropharmacol. 2014. https://doi.org/10.2174/1570159x12666140619204251.

  24. 24.

    Daly EJ, Trivedi MH, Janik A, Li H, Zhang Y, Li X, et al. Efficacy of esketamine nasal spray plus oral antidepressant treatment for relapse prevention in patients with treatment-resistant depression: a randomized clinical trial. JAMA Psychiatry. 2019. https://doi.org/10.1001/jamapsychiatry.2019.1189.

  25. 25.

    Serafini G, Pompili M, Innamorati M, Dwivedi Y, Brahmachari G, Girardi P. Pharmacological properties of glutamatergic drugs targeting NMDA receptors and their application in major depression. Curr Pharm Des. 2013. https://doi.org/10.2174/13816128113199990293.

  26. 26.

    Fava M, Rush AJ, Wisniewski SR, Nierenberg AA, Alpert JE, McGrath PJ, et al. A comparison of mirtazapine and nortriptyline following two consecutive failed medication treatments for depressed outpatients: a STAR*D report. Am J Psychiatry. 2006. https://doi.org/10.1176/ajp.2006.163.7.1161.

  27. 27.

    Petersen T, Gordon JA, Kant A, Fava M, Rosenbaum JF, Nierenberg AA. Treatment resistant depression and Axis I co-morbidity. Psychol Med. 2001. https://doi.org/10.1017/S0033291701004305.

  28. 28.

    aan het Rot M, Mathew SJ, Charney DS. Neurobiological mechanisms in major depressive disorder. CMAJ. 2009;180:305–13.

    Google Scholar 

  29. 29.

    Hillhouse TM, Porter JH. A brief history of the development of antidepressant drugs: From monoamines to glutamate. Exp Clin Psychopharmacol. 2015. https://doi.org/10.1037/a0038550.

  30. 30.

    Williams NR, Heifets BD, Blasey C, Sudheimer K, Pannu J, Pankow H, et al. Attenuation of antidepressant effects of ketamine by opioid receptor antagonism. Am J Psychiatry. 2018. https://doi.org/10.1176/appi.ajp.2018.18020138.

  31. 31.

    Williams NR, Heifets BD, Bentzley BS, Blasey C, Sudheimer KD, Hawkins J, et al. Attenuation of antidepressant and antisuicidal effects of ketamine by opioid receptor antagonism. Mol Psychiatry. 2019. https://doi.org/10.1038/s41380-019-0503-4.

  32. 32.

    Sullivan CR, Koene RH, Hasselfeld K, O’Donovan SM, Ramsey A, McCullumsmith RE. Neuron-specific deficits of bioenergetic processes in the dorsolateral prefrontal cortex in schizophrenia. Mol Psychiatry. 2019. https://doi.org/10.1038/s41380-018-0035-3.

  33. 33.

    Powell TR, Murphy T, Lee SH, Price J, Thuret S, Breen G. Transcriptomic profiling of human hippocampal progenitor cells treated with antidepressants and its application in drug repositioning. J Psychopharmacol. 2017. https://doi.org/10.1177/0269881117691467.

  34. 34.

    Williams G, Gatt A, Clarke E, Corcoran J, Doherty P, Chambers D, et al. Drug repurposing for Alzheimer’s disease based on transcriptional profiling of human iPSC-derived cortical neurons. Transl Psychiatry. 2019. https://doi.org/10.1038/s41398-019-0555-x.

  35. 35.

    Boldrini M, Underwood MD, Hen R, Rosoklija GB, Dwork AJ, John Mann J, et al. Antidepressants increase neural progenitor cells in the human hippocampus. Neuropsychopharmacology. 2009. https://doi.org/10.1038/npp.2009.75.

  36. 36.

    Malberg JE, Eisch AJ, Nestler EJ, Duman RS. Chronic antidepressant treatment increases neurogenesis in adult rat hippocampus. J Neurosci. 2000. https://doi.org/10.1523/jneurosci.20-24-09104.2000.

  37. 37.

    Moreira PI, Carvalho C, Zhu X, Smith MA, Perry G. Mitochondrial dysfunction is a trigger of Alzheimer's disease pathophysiology. Biochim Biophys Acta. 2010;1802:2–10.

    CAS  Google Scholar 

  38. 38.

    Swerdlow RH, Khan SM. A ‘mitochondrial cascade hypothesis’ for sporadic Alzheimer’s disease. Med Hypotheses. 2004. https://doi.org/10.1016/j.mehy.2003.12.045.

  39. 39.

    Lionta E, Spyrou G, Vassilatis D, Cournia Z. Structure-based virtual screening for drug discovery: principles, applications and recent advances. Curr Top Med Chem. 2014. https://doi.org/10.2174/1568026614666140929124445.

  40. 40.

    Iorio F, Rittman T, Ge H, Menden M, Saez-Rodriguez J. Transcriptional data: a new gateway to drug repositioning? Drug Discov Today. 2013;18:350–7.

    CAS  Google Scholar 

  41. 41.

    Batool M, Ahmad B, Choi S. A structure-based drug discovery paradigm. Int J Mol Sci. 2019;20:2783.

    CAS  Google Scholar 

  42. 42.

    Lamb J. The connectivity map: a new tool for biomedical research. Nat Rev Cancer. 2007;7:54–60.

    CAS  Google Scholar 

  43. 43.

    Berman H, Henrick K, Nakamura H. Announcing the worldwide Protein Data Bank. Nat Struct Biol. 2003;10:980.

    CAS  Google Scholar 

  44. 44.

    Pieper U MODBASE: a database of annotated comparative protein structure models and associated resources. Nucleic Acids Res. 2006. https://doi.org/10.1093/nar/gkj059.

  45. 45.

    Kitchen DB, Decornez H, Furr JR, Bajorath J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat Rev Drug Discov. 2004;3:935–49.

    CAS  Google Scholar 

  46. 46.

    Holbeck SL. Update on NCI in vitro drug screen utilities. Eur J Cancer. 2004. https://doi.org/10.1016/j.ejca.2003.11.022.

  47. 47.

    Koleti A, Terryn R, Stathias V, Chung C, Cooper DJ, Turner JP, et al. Data portal for the library of integrated network-based cellular signatures (LINCS) program: integrated access to diverse large-scale cellular perturbation response data. Nucleic Acids Res. 2018. https://doi.org/10.1093/nar/gkx1063.

  48. 48.

    Irwin JJ, Shoichet BK. ZINC - a free database of commercially available compounds for virtual screening. J Chem Inf Model. 2005. https://doi.org/10.1021/ci049714+.

  49. 49.

    Huang SY, Zou X. Advances and challenges in protein-ligand docking. Int J Mol Sci. 2010;11:3016–34.

    CAS  Google Scholar 

  50. 50.

    Ripphausen P, Nisius B, Bajorath J. State-of-the-art in ligand-based virtual screening. Drug Discov Today. 2011;16:372–6.

    CAS  Google Scholar 

  51. 51.

    Morris GM, Lim-Wilby M. Molecular docking. Methods Mol Biol. 2008. https://doi.org/10.1007/978-1-59745-177-2_19.

  52. 52.

    Kellenberger E, Rodrigo J, Muller P, Rognan D. Comparative evaluation of eight docking tools for docking and virtual screening accuracy. Proteins Struct Funct Genet. 2004. https://doi.org/10.1002/prot.20149.

  53. 53.

    Rajamani R, Good AC. Ranking poses in structure-based lead discovery and optimization: current trends in scoring function development. Curr Opin Drug Discov Dev. 2007.

  54. 54.

    Warren GL, Andrews CW, Capelli AM, Clarke B, LaLonde J, Lambert MH, et al. A critical assessment of docking programs and scoring functions. J Med Chem. 2006. https://doi.org/10.1021/jm050362n.

  55. 55.

    Goodsell DS, Morris GM, Olson AJ. Automated docking of flexible ligands: applications of AutoDock. J Mol Recognit. 1996. https://doi.org/10.1002/(SICI)1099-1352(199601)9:1<1::AID-JMR241>3.0.CO;2-6.

  56. 56.

    Lang PT, Brozell SR, Mukherjee S, Pettersen EF, Meng EC, Thomas V, et al. DOCK 6: combining techniques to model RNA-small molecule complexes. RNA. 2009. https://doi.org/10.1261/rna.1563609.

  57. 57.

    Friesner RA, Banks JL, Murphy RB, Halgren TA, Klicic JJ, Mainz DT, et al. Glide: a new approach for rapid, accurate docking and scoring. 1. Method and assessment of docking accuracy. J Med Chem. 2004. https://doi.org/10.1021/jm0306430.

  58. 58.

    Davis IW, Baker D. RosettaLigand docking with full ligand and receptor flexibility. J Mol Biol. 2009. https://doi.org/10.1016/j.jmb.2008.11.010.

  59. 59.

    Yang JM, Chen YF, Shen TW, Kristal BS, Hsu DF. Consensus scoring criteria for improving enrichment in virtual screening. J Chem Inf Model. 2005. https://doi.org/10.1021/ci050034w.

  60. 60.

    Kim R, Skolnick J. Assessment of programs for ligand binding affinity prediction. J Comput Chem. 2008. https://doi.org/10.1002/jcc.20893.

  61. 61.

    Nikolic K, Mavridis L, Djikic T, Vucicevic J, Agbaba D, Yelekci K, et al. Drug design for CNS diseases: polypharmacological profiling of compounds using cheminformatic, 3D-QSAR and virtual screening methodologies. Front Neurosci. 2016;10:265.

    Google Scholar 

  62. 62.

    Clarke CE, Deane KH. Ropinirole versus bromocriptine for levodopa-induced complications in Parkinson's disease. Cochrane Database Syst Rev. 2001;1:CD001517.

    Google Scholar 

  63. 63.

    Adler CH, Sethi KD, Hauser RA, Davis TL, Hammerstad JP, Bertoni J, et al. Ropinirole for the treatment of early Parkinson’s disease. Neurology. 1997. https://doi.org/10.1212/WNL.49.2.393.

  64. 64.

    Singh A, Das DK, Kelley ME. Mecamylamine (Targacept). IDrugs. 2006;9:205–17.

    CAS  Google Scholar 

  65. 65.

    George TP, Sacco KA, Vessicchio JC, Weinberger AH, Shytle RD. Nicotinic antagonist augmentation of selective serotonin reuptake inhibitor-refractory major depressive disorder: a preliminary study. J Clin Psychopharmacol. 2008. https://doi.org/10.1097/JCP.0b013e318172b49e.

  66. 66.

    Levin ED, Simon BB. Nicotinic acetylcholine involvement in cognitive function in animals. Psychopharmacology (Berl). 1998;138:217–30.

    CAS  Google Scholar 

  67. 67.

    Tan F, Yang R, Xu X, Chen X, Wang Y, Ma H, et al. Drug repositioning by applying ‘expression profiles’ generated by integrating chemical structure similarity and gene semantic similarity. Mol Biosyst. 2014. https://doi.org/10.1039/c3mb70554d.

  68. 68.

    Lamb J, Crawford ED, Peck D, Modell JW, Blat IC, Wrobel MJ, et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science. 2006;313:1929–35.

    CAS  Google Scholar 

  69. 69.

    Peck D, Crawford ED, Ross KN, Stegmaier K, Golub TR, Lamb J. A method for high-throughput gene expression signature analysis. Genome Biol. 2006. https://doi.org/10.1186/gb-2006-7-7-r61.

  70. 70.

    Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017. https://doi.org/10.1016/j.cell.2017.10.049.

  71. 71.

    Keenan AB, Jenkins SL, Jagodnik KM, Koplev S, He E, Torre D, et al. The library of integrated network-based cellular signatures NIH program: system-level cataloging of human cells response to perturbations. Cell Syst. 2018;6:13–24.

    CAS  Google Scholar 

  72. 72.

    Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005;102:15545–50.

    CAS  Google Scholar 

  73. 73.

    Zhang SD. A simple and robust method for connecting small-molecule drugs using gene-expression signatures. BMC Bioinformatics. 2008. https://doi.org/10.1186/1471-2105-9-258.

  74. 74.

    Cheng J, Yang L. Comparing gene expression similarity metrics for connectivity map. In: 2013 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2013;165–70.

  75. 75.

    Zhang SD, Gant TW. sscMap: an extensible Java application for connecting small-molecule drugs using gene-expression signatures. BMC Bioinformatics. 2009. https://doi.org/10.1186/1471-2105-10-236.

  76. 76.

    Lee BKB, Tiong KH, Chang JK, Liew CS, Abdul Rahman ZA, Tan AC, et al. DeSigN: Connecting gene expression with therapeutics for drug repurposing and development. BMC Genomics. 2017. https://doi.org/10.1186/s12864-016-3260-7.

  77. 77.

    Zhou X, Wang M, Katsyv I, Irie H, Zhang B. EMUDRA: ensemble of multiple drug repositioning approaches to improve prediction accuracy. Bioinformatics. 2018. https://doi.org/10.1093/bioinformatics/bty325.

  78. 78.

    Kidnapillai S, Bortolasci CC, Udawela M, Panizzutti B, Spolding B, Connor T, et al. The use of a gene expression signature and connectivity map to repurpose drugs for bipolar disorder. World J Biol Psychiatry. 2018. https://doi.org/10.1080/15622975.2018.1492734.

  79. 79.

    Vargas DM, De Bastiani MA, Zimmer ER, Klamt F. Alzheimer’s disease master regulators analysis: Search for potential molecular targets and drug repositioning candidates. Alzheimer’s Res Ther. 2018. https://doi.org/10.1186/s13195-018-0394-7.

  80. 80.

    Ferguson LB, Ozburn AR, Ponomarev I, Metten P, Reilly M, Crabbe JC, et al. Genome-wide expression profiles drive discovery of novel compounds that reduce binge drinking in Mice. Neuropsychopharmacology. 2018;43:1257–66.

    Google Scholar 

  81. 81.

    Dixit A, Parnas O, Li B, Chen J, Fulco CP, Jerby-Arnon L, et al. Perturb-seq: dissecting molecular circuits with scalable single-cell RNA profiling of pooled genetic screens. Cell. 2016. https://doi.org/10.1016/j.cell.2016.11.038.

  82. 82.

    Brookes AJ. The essence of SNPs. Gene. 1999;234:177–86.

    CAS  Google Scholar 

  83. 83.

    Griffith OL, Montgomery SB, Bernier B, Chu B, Kasaian K, Aerts S, et al. ORegAnno: an open-access community-driven resource for regulatory annotation. Nucleic Acids Res. 2008. https://doi.org/10.1093/nar/gkm967.

  84. 84.

    Reich DE, Lander ES. On the allelic spectrum of human disease. Trends Genet. 2001;17:502–10.

    CAS  Google Scholar 

  85. 85.

    Fullerton JM, Nurnberger JI. Polygenic risk scores in psychiatry: will they be useful for clinicians? F1000Res. 2019. https://doi.org/10.12688/f1000research.18491.1.

  86. 86.

    Sanseau P, et al. Use of genome-wide association studies for drug repositioning. Nat Biotechnol. 2012;30:317–20.

    CAS  Google Scholar 

  87. 87.

    Sanseau P, Agarwal P, Barnes MR, Pastinen T, Richards JB, Cardon LR, et al. Reply to rational drug repositioning by medical genetics. Nat Biotechnol. 2013;31:1082.

    CAS  Google Scholar 

  88. 88.

    Kwok MK, Lin SL, Schooling CM. Re-thinking Alzheimer’s disease therapeutic targets using gene-based tests. EBioMed. 2018. https://doi.org/10.1016/j.ebiom.2018.10.001.

  89. 89.

    Watson HJ, Yilmaz Z, Sullivan PF. The psychiatric genomics consortium: history, development, and the future. Pers Psychiatry. 2020;91–101.

  90. 90.

    So H-CC, Chau CK-LL, Chiu W-TT, Ho K-SS, Lo C-PP, Yim SH-YY, et al. Analysis of genome-wide association data highlights candidates for drug repositioning in psychiatry. Nat Neurosci. 2017;20:1342–9.

    CAS  Google Scholar 

  91. 91.

    Rodriguez-López J, Arrojo M, Paz E, Páramo M, Costas J. Identification of relevant hub genes for early intervention at gene coexpression modules with altered predicted expression in schizophrenia. Prog Neuro-Psychopharmacology Biol Psychiatry. 2020. https://doi.org/10.1016/j.pnpbp.2019.109815.

  92. 92.

    McClellan J, King MC. Genetic heterogeneity in human disease. Cell. 2010;141:210–7.

    CAS  Google Scholar 

  93. 93.

    Boyle EA, Li YI, Pritchard JK. An expanded view of complex traits: from polygenic to omnigenic. Cell. 2017;169:1177–86.

    CAS  Google Scholar 

  94. 94.

    Li YR, Keating BJ. Trans-ethnic genome-wide association studies: advantages and challenges of mapping in diverse populations. Genome Med. 2014;6:91.

    Google Scholar 

  95. 95.

    McCarroll SA. Extending genome-wide association studies to copy-number variation. Hum Mol Genet. 2008. https://doi.org/10.1093/hmg/ddn282.

  96. 96.

    Damerval C, Maurice A, Josse JM, de Vienne D. Quantitative trait loci underlying gene product variation: a novel perspective for analyzing regulation of genome expression. Genetics. 1994;137:289–301.

    CAS  Google Scholar 

  97. 97.

    Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Genet. 2016. https://doi.org/10.1038/ng.3506.

  98. 98.

    Tak YG, Farnham PJ. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics Chromatin. 2015;8:57.

    Google Scholar 

  99. 99.

    Breen G, Li Q, Roth BL, O’Donnell P, Didriksen M, Dolmetsch R, et al. Translating genome-wide association findings into new therapeutics for psychiatry. Nat Neurosci. 2016;19:1392–6.

    CAS  Google Scholar 

  100. 100.

    Wu Z, Wang Y, Chen L. Network-based drug repositioning. Mol Biosyst. 2013;9:1268–81.

    CAS  Google Scholar 

  101. 101.

    Lotfi Shahreza M, Ghadiri N, Mousavi SR, Varshosaz J, Green JR. A review of network-based approaches to drug repositioning. Brief Bioinform. 2018;19:878–92.

    Google Scholar 

  102. 102.

    Gillis J, Pavlidis P. ‘Guilt by association’ is the exception rather than the rule in gene networks. PLoS Comput Biol. 2012. https://doi.org/10.1371/journal.pcbi.1002444.

  103. 103.

    Mejía-Pedroza RA, Espinal-Enríquez J, Hernández-Lemus E. Pathway-based drug repositioning for breast cancer molecular subtypes. Front Pharmacol. 2018. https://doi.org/10.3389/fphar.2018.00905.

  104. 104.

    Martínez V, Navarro C, Cano C, Fajardo W, Blanco A. DrugNet: network-based drug-disease prioritization by integrating heterogeneous data. Artif Intell Med. 2015. https://doi.org/10.1016/j.artmed.2014.11.003.

  105. 105.

    Li J, Lu Z. Pathway-based drug repositioning using causal inference. BMC Bioinform. 2013. https://doi.org/10.1186/1471-2105-14-S16-S3.

  106. 106.

    Wang W, Yang S, Zhang X, Li J. Drug repositioning by integrating target information through a heterogeneous network model. Bioinformatics. 2014. https://doi.org/10.1093/bioinformatics/btu403.

  107. 107.

    Luo H, Wang J, Li M, Luo J, Peng X, Wu FX, et al. Drug repositioning based on comprehensive similarity measures and Bi-Random walk algorithm. Bioinformatics. 2016;32:2664–71.

    CAS  Google Scholar 

  108. 108.

    Karunakaran KB, Chaparala S, Ganapathiraju MK. Potentially repurposable drugs for schizophrenia identified from its interactome. Sci Rep. 2019. https://doi.org/10.1038/s41598-019-48307-w.

  109. 109.

    Cheng T, Li Q, Wang Y, Bryant SH. Identifying compound-target associations by combining bioactivity profile similarity search and public databases mining. J Chem Inf Model. 2011. https://doi.org/10.1021/ci200192v.

  110. 110.

    Bellera CL, Balcazar DE, Vanrell MC, Casassa AF, Palestro PH, Gavernet L, et al. Computer-guided drug repurposing: Identification of trypanocidal activity of clofazimine, benidipine and saquinavir. Eur J Med Chem. 2015. https://doi.org/10.1016/j.ejmech.2015.01.065.

  111. 111.

    Maynard RL. The Merck Index: 12th edition 1996. Occup Environ Med. 19977. https://doi.org/10.1136/oem.54.4.288.

  112. 112.

    Tari LB, Patel JH. Systematic drug repurposing through text mining. Methods Mol Biol. 2014. https://doi.org/10.1007/978-1-4939-0709-0_14.

  113. 113.

    Krallinger M, Erhardt RA-A, Valencia A. Text-mining approaches in molecular biology and biomedicine. Drug Disco Today. 2005;10:439–45.

    CAS  Google Scholar 

  114. 114.

    Zheng S, Dharssi S, Wu M, Li J, Lu Z. Text Mining for Drug Discovery. Methods Mol Biol. 2019;1939:231–52.

    CAS  Google Scholar 

  115. 115.

    Xue H, Li J, Xie H, Wang Y. Review of drug repositioning approaches and resources. Int J Biol Sci. 2018;14:1232–44.

    CAS  Google Scholar 

  116. 116.

    Li J, Zhu X, Chen JY. Building disease-specific drug-protein connectivity maps from molecular interaction networks and PubMed abstracts. PLoS Comput Biol. 2009. https://doi.org/10.1371/journal.pcbi.1000450.

  117. 117.

    WHO. ATC - structure and principles. WHO Collaborating Centre for Drug Statistics and Methodology; 2012.

  118. 118.

    Napolitano F, Zhao Y, Moreira VM, Tagliaferri R, Kere J, D’Amato M, et al. Drug repositioning: a machine-learning approach through data integration. J Cheminform. 2013. https://doi.org/10.1186/1758-2946-5-30.

  119. 119.

    Chen L, Zeng WM, Cai YD, Feng KY, Chou KC. Predicting anatomical therapeutic chemical (ATC) classification of drugs by integrating chemical-chemical interactions and similarities. PLoS One. 2012. https://doi.org/10.1371/journal.pone.0035254.

  120. 120.

    Liu Z, Guo F, Gu J, Wang Y, Li Y, Wang D, et al. Similarity-based prediction for anatomical therapeutic chemical classification of drugs by integrating multiple data sources. Bioinformatics. 2015;31:1788–95.

    CAS  Google Scholar 

  121. 121.

    WHOCC - Structure and principles. https://www.whocc.no/atc/structure_and_principles/. Accessed 1 April 2020.

  122. 122.

    Edwards IR, Aronson JK. Adverse drug reactions: definitions, diagnosis, and management. Lancet. 2000. https://doi.org/10.1016/S0140-6736(00)02799-9.

  123. 123.

    Kant A, Bilmen J, Hopkins PM. Adverse drug reactions. Pharmacol. Physiol. Anesth. Found. Clin. Appl. 2018.

  124. 124.

    Rohilla A, Yadav S. Adverse drug reactions: an overview. Int J Pharmacol Res. 2013. https://doi.org/10.7439/IJPR.V3I1.41.

  125. 125.

    Wang Z, Clark NR, Ma’ayan A. Drug-induced adverse events prediction with the LINCS L1000 data. Bioinformatics. 2016. https://doi.org/10.1093/bioinformatics/btw168.

  126. 126.

    Kim S, Chen J, Cheng T, Gindulyte A, He J, He S, et al. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res. 2019. https://doi.org/10.1093/nar/gky1033.

  127. 127.

    Kuhn M, Letunic I, Jensen LJ, Bork P. The SIDER database of drugs and side effects. Nucleic Acids Res. 2016. https://doi.org/10.1093/nar/gkv1075.

  128. 128.

    Tatonetti NP, Ye PP, Daneshjou R, Altman RB. Data-driven prediction of drug effects and interactions. Sci Transl Med. 2012. https://doi.org/10.1126/scitranslmed.3003377.

  129. 129.

    Geurts P, Ernst D, Wehenkel L. Extremely randomized trees. Mach Learn. 2006. https://doi.org/10.1007/s10994-006-6226-1.

  130. 130.

    Mechanism matters. Nat Med. 2010;16:347. https://doi.org/10.1038/nm0410-347.

  131. 131.

    Iorio F, Bosotti R, Scacheri E, Belcastro V, Mithbaokar P, Ferriero R, et al. Discovery of drug mode of action and drug repositioning from transcriptional responses. Proc Natl Acad Sci USA. 2010. https://doi.org/10.1073/pnas.1000138107.

  132. 132.

    Madhukar NS, Khade PK, Huang L, Gayvert K, Galletti G, Stogniew M, et al. A Bayesian machine learning approach for drug target identification using diverse data types. Nat Commun. 2019. https://doi.org/10.1038/s41467-019-12928-6.

  133. 133.

    Friedman R. Drug resistance in cancer: molecular evolution and compensatory proliferation. Oncotarget. 2016. https://doi.org/10.18632/oncotarget.7459.

  134. 134.

    Berger AH, Brooks AN, Wu X, Shrestha Y, Chouinard C, Piccioni F, et al. High-throughput phenotyping of lung cancer somatic mutations. Cancer Cell. 2016. https://doi.org/10.1016/j.ccell.2016.06.022.

  135. 135.

    Pajouhesh H, Lenz GR. Medicinal chemical properties of successful central nervous system drugs. NeuroRx. 2005. https://doi.org/10.1602/neurorx.2.4.541.

  136. 136.

    Daina A, Michielin O, Zoete V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci Rep. 2017. https://doi.org/10.1038/srep42717.

  137. 137.

    Wawer MJ, Li K, Gustafsdottir SM, Ljosa V, Bodycombe NE, Marton MA, et al. Toward performance-diverse small-molecule libraries for cell-based phenotypic screening using multiplexed high-dimensional profiling. Proc Natl Acad Sci USA. 2014. https://doi.org/10.1073/pnas.1410933111.

  138. 138.

    Sullivan CR, Mielnik CA, O’Donovan SM, Funk AJ, Bentea E, DePasquale EA, et al. Connectivity analyses of bioenergetic changes in Schizophrenia: identification of novel treatments. Mol Neurobiol. 2019. https://doi.org/10.1007/s12035-018-1390-4.

  139. 139.

    Altar CA, Jurata LW, Charles V, Lemire A, Liu P, Bukhman Y, et al. Deficient hippocampal neuron expression of proteasome, ubiquitin, and mitochondrial genes in multiple schizophrenia cohorts. Biol Psychiatry. 2005. https://doi.org/10.1016/j.biopsych.2005.03.031.

  140. 140.

    Stone WS, Faraone SV, Su J, Tarbox SI, Van Eerdewegh P, Tsuang MT. Evidence for linkage between regulatory enzymes in glycolysis and schizophrenia in a multiplex sample. Am J Med Genet. 2004. https://doi.org/10.1002/ajmg.b.20132.

  141. 141.

    Cheng F, Desai RJ, Handy DE, Wang R, Schneeweiss S, Barabási AL, et al. Network-based approach to prediction and population-based validation of in silico drug repurposing. Nat Commun. 2018. https://doi.org/10.1038/s41467-018-05116-5.

  142. 142.

    Bang S, Jeong S, Choi N, Kim HN. Brain-on-a-chip: a history of development and future perspective. Biomicrofluidics. 2019;13:051301.

    Google Scholar 

  143. 143.

    Xu H, Jiao Y, Qin S, Zhao W, Chu Q, Wu K. Organoid technology in disease modelling, drug development, personalized treatment and regeneration medicine. Exp Hematol Oncol. 2018;7:30.

    CAS  Google Scholar 

  144. 144.

    Lopez-Munoz F, Alamo C. Monoaminergic neurotransmission: the history of the discovery of antidepressants from 1950s Until Today. Curr Pharm Des. 2009. https://doi.org/10.2174/138161209788168001.

  145. 145.

    Wong DT, Bymaster FP, Engleman EA. Prozac (fluoxetine, lilly 110140), the first selective serotonin uptake inhibitor and an antidepressant drug: Twenty years since its first publication. Life Sci. 1995;57:411–41.

    CAS  Google Scholar 

  146. 146.

    Wenthur CJ, Bennett MR, Lindsley CW. Classics in Chemical Neuroscience: Fluoxetine (Prozac). Acs Chemical Neuroscience. 2014;5:14–23.

    CAS  Google Scholar 

  147. 147.

    Sullivan CR, Koene RH, Hasselfeld K, O’Donovan SM, Ramsey A, McCullumsmith RE. Neuron-specific deficits of bioenergetic processes in the dorsolateral prefrontal cortex in schizophrenia. Mol Psychiatry. 2018. https://doi.org/10.1038/s41380-018-0035-3.

  148. 148.

    Gawel DR, Serra-Musach J, Lilja S, Aagesen J, Arenas A, Asking B, et al. A validated single-cell-based strategy to identify diagnostic and therapeutic targets in complex diseases. Genome Med. 2019. https://doi.org/10.1186/s13073-019-0657-3.

  149. 149.

    Yang X, Kui L, Tang M, Li D, Wei K, Chen W, et al. High-throughput transcriptome profiling in drug and biomarker discovery. Front Genet. 2020;11:19.

    CAS  Google Scholar 

  150. 150.

    Moret N, Clark NA, Hafner M, Wang Y, Lounkine E, Medvedovic M, et al. Cheminformatics tools for analyzing and designing optimized small-molecule collections and libraries. Cell Chem Biol. 2019. https://doi.org/10.1016/j.chembiol.2019.02.018.

  151. 151.

    Lin E, Lin CH, Lane HY. Precision psychiatry applications with pharmacogenomics: artificial intelligence and machine learning approaches. Int J Mol Sci. 2020;21:969.

    Google Scholar 

  152. 152.

    De Leon J. Pharmacogenomics: the promise of personalized medicine for CNS disorders. Neuropsychopharmacology. 2009;34:159–72.

    Google Scholar 

  153. 153.

    Zubenko GS, Sommer BR, Cohen BM. On the marketing and use of pharmacogenetic tests for psychiatric treatment. JAMA Psychiatry. 2018;75:769–70.

    Google Scholar 

  154. 154.

    Goldberg TE, Weinberger DR. Effects of neuroleptic medications on the cognition of patients with schizophrenia: a review of recent studies. J Clin Psychiatry. 1996;57(Suppl 9):62–5.

    CAS  Google Scholar 

  155. 155.

    Ising M, Lucae S, Binder EB, Bettecken T, Uhr M, Ripke S, et al. A genomewide association study points to multiple loci that predict antidepressant drug treatment outcome in depression. Arch Gen Psychiatry. 2009. https://doi.org/10.1001/archgenpsychiatry.2009.95.

  156. 156.

    Tansey KE, Guipponi M, Perroud N, Bondolfi G, Domenici E, Evans D, et al. Genetic predictors of response to serotonergic and noradrenergic antidepressants in major depressive disorder: a genome-wide analysis of individual-level data and a meta-analysis. PLoS Med. 2012. https://doi.org/10.1371/journal.pmed.1001326.

  157. 157.

    Thomson PA, Malavasi ELV, Grünewald E, Soares DC, Borkowska M, Millar JK. DISC1 genetics, biology and psychiatric illness. Front Biol (Beijing). 2013;8:1–31.

    CAS  Google Scholar 

  158. 158.

    Symmons O, Spitz F. From remote enhancers to gene regulation: charting the genome’s regulatory landscapes. Philos Trans R Soc B Biol Sci. 2013;368:20120358.

    Google Scholar 

  159. 159.

    Talevi A. Drug repositioning: current approaches and their implications in the precision medicine era. Expert Rev Precis Med Drug Dev. 2018. https://doi.org/10.1080/23808993.2018.1424535.

  160. 160.

    Menke A. Pharmacogenomics and personalized medicine dovepress precision pharmacotherapy: psychiatry’s future direction in preventing, diagnosing, and treating mental disorders. Pharmgenomics Pers Med. 2018. https://doi.org/10.2147/PGPM.S146110.

  161. 161.

    Li YY, Jones SJM. Drug repositioning for personalized medicine. Genome Med. 2012;4:27.

    CAS  Google Scholar 

  162. 162.

    Saeedi S, Israel S, Nagy C, Turecki G. The emerging role of exosomes in mental disorders. Transl Psychiatry. 2019;9:122.

    Google Scholar 

  163. 163.

    Bentea E, Depasquale EAK, O’Donovan SM, Sullivan CR, Simmons M, Meador-Woodruff JH, et al. Kinase network dysregulation in a human induced pluripotent stem cell model of DISC1 schizophrenia. Mol Omi. 2019. https://doi.org/10.1039/c8mo00173a.

  164. 164.

    Spencer T, Biederman J, Heiligenstein J, Wilens T, Faries D, Prince J, et al. An open-label, dose-ranging study of atomoxetine in children with attention deficit hyperactivity disorder. J Child Adolesc Psychopharmacol. 2001. https://doi.org/10.1089/10445460152595577.

  165. 165.

    Schwartz J, Murrough JW, Iosifescu DV. Ketamine for treatment-resistant depression: recent developments and clinical applications: Table 1. Evid Based Ment Heal. 2016;19:35–38.

    Google Scholar 

  166. 166.

    Ferry L, Johnston JA. Efficacy and safety of bupropion SR for smoking cessation: data from clinical trials and five years of postmarketing experience. Int J Clin Pract. 2003;57:224–30.

    CAS  Google Scholar 

  167. 167.

    Corbett A, Ballard C. New and emerging treatments for Alzheimer’s disease. Expert Opin Emerg Drugs. 2012;17:147–56.

    CAS  Google Scholar 

  168. 168.

    Wang J, Ho L, Chen L, Zhao Z, Zhao W, Qian X, et al. Valsartan lowers brain β-amyloid protein levels and improves spatial learning in a mouse model of Alzheimer disease. J Clin Invest. 2007. https://doi.org/10.1172/JCI31547.

  169. 169.

    Nickell JR, Grinevich VP, Siripurapu KB, Smith AM, Dwoskin LP. Potential therapeutic uses of mecamylamine and its stereoisomers. Pharm Biochem Behav. 2013;108:28–43.

    CAS  Google Scholar 

  170. 170.

    Belanoff JK, Flores BH, Kalezhan M, Sund B, Schatzberg AF. Rapid reversal of psychotic depression using mifepristone. J Clin Psychopharmacol. 2001. https://doi.org/10.1097/00004714-200110000-00009.

  171. 171.

    Saraf G, Viswanath B, Hatti S, Malyala A, Benegal V. A comparison of baclofen and topiramate with acamprosate as anticraving agents: A naturalistic follow-up in a tertiary care de-addiction unit. Alcohol Clin Exp Res. 2012. https://doi.org/10.1111/j.1530-0277.2012.01803.x.

  172. 172.

    Gorsane MA, Kebir O, Hache G, Blecha L, Aubin HJ, Reynaud M, et al. Is baclofen a revolutionary medication in alcohol addiction management? Review and recent updates. Subst Abus. 2012. https://doi.org/10.1080/08897077.2012.663326.

  173. 173.

    Hayes JF, Lundin A, Wicks S, Lewis G, Wong ICK, Osborn DPJ, et al. Association of hydroxylmethyl glutaryl coenzyme a reductase inhibitors, l-type calcium channel antagonists, and biguanides with rates of psychiatric hospitalization and self-harm in individuals with serious mental illness. JAMA Psychiatry. 2018. https://doi.org/10.1001/jamapsychiatry.2018.3907.

  174. 174.

    Zhang Yshuai, Li Jdong, Yan C. An update on vinpocetine: new discoveries and clinical implications. Eur J Pharm. 2018;819:30–34.

    CAS  Google Scholar 

  175. 175.

    Gaspar HA, Gerring Z, Hübel C, Middeldorp CM, Derks EM, Breen G. Using genetic drug-target networks to develop new drug hypotheses for major depressive disorder. Transl Psychiatry. 2019. https://doi.org/10.1038/s41398-019-0451-4.

  176. 176.

    De Jong S, Vidler LR, Mokrab Y, Collier DA, Breen G. Gene-set analysis based on the pharmacological profiles of drugs to identify repurposing opportunities in schizophrenia. J Psychopharmacol. 2016. https://doi.org/10.1177/0269881116653109.

  177. 177.

    Lencz T, Malhotra AK. Targeting the schizophrenia genome: a fast track strategy from GWAS to clinic. Mol Psychiatry. 2015. https://doi.org/10.1038/mp.2015.28.

  178. 178.

    Wang S, Meng X, Wang Y, Liu Y, Xia J. HPO-Shuffle: an associated gene prioritization strategy and its application in drug repurposing for the treatment of canine epilepsy. Biosci Rep. 2019. https://doi.org/10.1042/BSR20191247.

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RS and REM outlined and wrote the manuscript. NDH and RSA participated in writing the GWAS section. KA participated in writing the signature-based repurposing and adverse drug reaction sections. ARH participated in writing the drug permeability and drug patent-related sections. JR participated in writing the precision medicine section. HME participated in writing the text-mining section. ASI, SAM, and JC participated in writing the drug classification section. JM participated in writing the structure-based repurposing section.

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Correspondence to Rammohan Shukla.

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Shukla, R., Henkel, N.D., Alganem, K. et al. Signature-based approaches for informed drug repurposing: targeting CNS disorders. Neuropsychopharmacol. 46, 116–130 (2021). https://doi.org/10.1038/s41386-020-0752-6

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