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Drug repurposing for opioid use disorders: integration of computational prediction, clinical corroboration, and mechanism of action analyses

Molecular Psychiatry volume 26, pages 5286–5296 (2021)Cite this article

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Abstract

Morbidity and mortality from opioid use disorders (OUD) and other substance use disorders (SUD) is a major public health crisis, yet there are few medications to treat them. There is an urgency to accelerate SUD medication development. We present an integrated drug repurposing strategy that combines computational prediction, clinical corroboration using electronic health records (EHRs) of over 72.9 million patients and mechanisms of action analysis. Among top-ranked repurposed candidate drugs, tramadol, olanzapine, mirtazapine, bupropion, and atomoxetine were associated with increased odds of OUD remission (adjusted odds ratio: 1.51 [1.38–1.66], 1.90 [1.66–2.18], 1.38 [1.31–1.46], 1.37 [1.29–1.46], 1.48 [1.25–1.76], p value < 0.001, respectively). Genetic and functional analyses showed these five candidate drugs directly target multiple OUD-associated genes including BDNF, CYP2D6, OPRD1, OPRK1, OPRM1, HTR1B, POMC, SLC6A4 and OUD-associated pathways, including opioid signaling, G-protein activation, serotonin receptors, and GPCR signaling. In summary, we developed an integrated drug repurposing approach and identified five repurposed candidate drugs that might be of value for treating OUD patients, including those suffering from comorbid conditions.

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Fig. 1: Flowcharts of the integrate SUD repurposing system.
Fig. 2: Odds ratios of remission from opioid dependence and the corresponding 95% CI of 10 of top 20-ranked drugs.

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References

  1. Volkow ND, McLellan AT. Opioid abuse in chronic pain-misconceptions and mitigation strategies. N. Engl J Med. 2016;374:1253–63. https://doi.org/10.1056/NEJMra1507771.

    Article  CAS  PubMed  Google Scholar 

  2. Volkow ND, Koob GF, McLellan AT. Neurobiologic advances from the brain disease model of addiction. N. Engl J Med. 2016;374:363–371.

    Article  CAS  Google Scholar 

  3. Volkow ND, Morales M. The brain on drugs: from reward to addiction. Cell. 2015;162:712–725.

    Article  CAS  Google Scholar 

  4. National Survey on Drug Use and Health—SAMHSA. Key substance use and mental health indicators in the United States: results from the 2018 National Survey on Drug Use and Health. National Survey on Drug Use and Health. 2018. https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHNationalFindingsReport2018/NSDUHNationalFindingsReport2018.pdf.

  5. GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020;396:1204–22. https://doi.org/10.1016/S0140-6736(20)30925-9.

    Article  Google Scholar 

  6. Department of Health and Human Services. HHS acting secretary declares public health emergency to address national opioid crisis. Department of Health and Human Services; 2017. p. 2016–7. https://www.hhs.gov/about/news/2017/10/26/hhs-acting-secretary-declares-public-health-emergency-address-national-opioid-crisis.html.

  7. Centers for Disease Control and Prevention (CDC) National Center for Health Statistics. Mortality. Centers for Disease Control and Prevention. 2020. https://www.cdc.gov/nchs/data/health_policy/Provisional-Drug-Overdose-Deaths-by-Quarter-Demographic-Characteristics-Q1-2020.pdf.

  8. Volkow ND, Skolnick P. New medications for substance use disorders: challenges and opportunities. Neuropsychopharmacology. 2012;37:290–292.

    Article  CAS  Google Scholar 

  9. Mullard A. New drugs cost US $2.6 billion to develop. Nat Rev Drug Discov. 2014;13:877. https://doi.org/10.1038/nrd4507.

    Article  CAS  Google Scholar 

  10. National Academies of Sciences, Engineering, and Medicine; Health and Medicine Division; Board on Health Sciences Policy; Forum on Neuroscience and Nervous System Disorders. Advancing Therapeutic Development for Pain and Opioid Use Disorders Through Public-Private Partnerships: Proceedings of a Workshop. Washington (DC): National Academies Press (US); 2018.

  11. Ashburn TT, Thor KB. Drug repurposing: identifying and developing new uses for existing drugs. Nat Rev Drug Discov. 2004;3:673–683.

    Article  CAS  Google Scholar 

  12. Srivastava AB, Mariani JJ, Levin FR. New directions in the treatment of opioid withdrawal. Lancet. 2020;395:1938–48. https://doi.org/10.1016/S0140-6736(20)30852-7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Li J, Zheng S, Chen B, Butte AJ, Swamidass SJ, Lu Z. A survey of current trends in computational drug repurposing. Brief Bioinform. 2016;17:2–12.

    Article  Google Scholar 

  14. Wang Q, Xu R. Disease comorbidity-guided drug repurposing: a case study in schizophrenia. AMIA Annu Symp Proc. 2018;2018:1300–9.

    PubMed  PubMed Central  Google Scholar 

  15. Nagaraj AB, Wang Q, Joseph P, Zheng CL, Chen Y, Kovalenko O, et al. Using a novel computational drug repurposing approach (DrugPredict) to rapidly identify potent drug candidates for cancer treatment. Oncogene. 2017. https://doi.org/10.1038/onc.2017.328.

  16. Chen Y, Xu R. Context-sensitive network-based disease genetics prediction and its implications in drug discovery. Bioinformatics 2017;33:1031–9. https://doi.org/10.1093/bioinformatics/btw737.

    Article  CAS  PubMed  Google Scholar 

  17. Xu R, Wang Q. A genomics-based systems approach towards drug repurposing for rheumatoid arthritis. BMC Genom. 2016;17:518 https://doi.org/10.1186/s12864-016-2910-0.

    Article  CAS  Google Scholar 

  18. Xu R, Wang Q. PhenoPredict: a disease phenome-wide drug repurposing approach towards schizophrenia drug discovery. J Biomed Inf. 2015;56:348–55. https://doi.org/10.1016/j.jbi.2015.06.027.

    Article  Google Scholar 

  19. Chen Y, Xu R. Context-sensitive network-based disease genetics prediction and its implications in drug discovery. Bioinformatics. 2017;33:1031–9. https://doi.org/10.1093/bioinformatics/btw737.

    Article  CAS  PubMed  Google Scholar 

  20. Chen Y, Xu R. Context-sensitive network analysis identifies food metabolites associated with Alzheimer’s disease: an exploratory study. BMC Med Genom. 2019;12:17 https://doi.org/10.1186/s12920-018-0459-2.

    Article  Google Scholar 

  21. Zheng C, Xu R. Large-scale mining disease comorbidity relationships from post-market drug adverse events surveillance data. BMC Bioinforma. 2018;19:500.

    Article  Google Scholar 

  22. Zhou M, Chen Y, Xu R. A drug-side effect context-sensitive network approach for drug target prediction. Bioinformatics. 2019;35:2100–7. https://doi.org/10.1093/bioinformatics/bty906.

    Article  CAS  PubMed  Google Scholar 

  23. Zhou M, Zheng C, Xu R. Combining phenome-driven drug-target interaction prediction with patients’ electronic health records-based clinical corroboration toward drug discovery. Bioinformatics. 2020;36:i436–i444. https://doi.org/10.1093/bioinformatics/btaa451.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Kuhn M, Letunic I, Jensen LJ, Bork P. The SIDER database of drugs and side effects. Nucleic Acids Res. 2016;44:D1075–9. https://doi.org/10.1093/nar/gkv1075.

    Article  CAS  PubMed  Google Scholar 

  25. Franceschini A, Szklarczyk D, Frankild S, Kuhn M, Simonovic M, Roth A, et al. STRING v9.1: protein-protein interaction networks, with increased coverage and integration. Nucleic Acids Res. 2013;41:808–15. https://doi.org/10.1093/nar/gks1094.

    Article  CAS  Google Scholar 

  26. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018;46:D1074–82. https://doi.org/10.1093/nar/gkx1037.

    Article  CAS  PubMed  Google Scholar 

  27. Xu R, Wang Q, Li L. A genome-wide systems analysis reveals strong link between colorectal cancer and trimethylamine N-oxide (TMAO), a gut microbial metabolite of dietary meat and fat. BMC Genom. 2015;16:S4 https://doi.org/10.1186/1471-2164-16-S7-S4.

    Article  CAS  Google Scholar 

  28. Xu R, Wang Q. Towards understanding brain-gut-microbiome connections in Alzheimer’s disease. BMC Syst Biol 2016;10:63 https://doi.org/10.1186/s12918-016-0307-y.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wang Q, Xu R. CoMNRank: an integrated approach to extract and prioritize human microbial metabolites from MEDLINE records [published online ahead of print, 2020 Aug 11]. J Biomed Inf. 2020;109:103524 https://doi.org/10.1016/j.jbi.2020.103524.

    Article  Google Scholar 

  30. IBM Explorys. IBM Watson Health. IBM Explorys. https://www.ibm.com/watson/health/explorys/. Accessed June–August 2020. Accessed June–Aug 2020.

  31. Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res. 2004;32:D267–D270. https://doi.org/10.1093/nar/gkh061.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. SNOMED International. The systematized nomenclature of medicine—Clinical Terms (SNOMED CT). SNOMED International. 2020. http://www.snomed.org/snomed-ct/why-snomed-ct.

  33. Zhou M, Xu R, Kaelber DC, Gurney ME. Tumor necrosis factor (TNF) blocking agents are associated with lower risk for Alzheimer’s disease in patients with rheumatoid arthritis and psoriasis. PLoS ONE. 2020;15:e0229819. https://doi.org/10.1371/journal.pone.0229819.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Wang QQ, Kaelber DC, Xu R, Volkow ND. COVID-19 risk and outcomes in patients with substance use disorders: analyses from electronic health records in the United States. Mol Psychiatry. 2020:1–10. https://doi.org/10.1038/s41380-020-00880-7.

  35. Wang QQ, Xu R, Volkow ND. Increased risk of COVID-19 infection and mortality in people with mental disorders: analysis from electronic health records in the United States. World Psychiatry. 2020. https://doi.org/10.1002/wps.20806.

  36. Wang QQ, Berger NA, Xu R. Analyses of risk, racial disparity, and outcomes of cancers and coronavirus disease 2019 from electronic health 4 records in the US. JAMA Oncol. 2020. https://doi.org/10.1001/jamaoncol.2020.6178.

  37. Wang QQ, Berger NA, Xu R. When hematologic malignancies meet COVID-19 in the United States: infections, death and disparities. Blood Rev. https://doi.org/10.1016/j.blre.2020.100775.

  38. The National Library of Medicine (NLM). DailyMed. The National Library of Medicine. https://healthdata.gov/dataset/dailymed. Accessed June–Aug 2020.

  39. Kuritz SJ, Landis JR, Koch GG. A general overview of Mantel-Haenszel methods: applications and recent developments. Annu Rev Public Health. 1988;9:123–160. https://doi.org/10.1146/annurev.pu.09.050188.001011.

    Article  CAS  PubMed  Google Scholar 

  40. Wang SC, Chen YC, Lee CH, Cheng CM. Opioid addiction, genetic susceptibility, and medical treatments: a review. Int J Mol Sci 2019;20:4294 https://doi.org/10.3390/ijms20174294.

    Article  CAS  PubMed Central  Google Scholar 

  41. Darcq E, Kieffer BL. Opioid receptors: drivers to addiction? Nat Rev Neurosci. 2018;19:499–514. https://doi.org/10.1038/s41583-018-0028-x.

    Article  CAS  PubMed  Google Scholar 

  42. Crist RC, Reiner BC, Berrettini WH. A review of opioid addiction genetics. Curr Opin Psychol. 2019;27:31–35.

    Article  Google Scholar 

  43. Szklarczyk D, Santos A, von Mering C, Jensen LJ, Bork P, Kuhn M. STITCH 5: augmenting protein-chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44:D380–D384. https://doi.org/10.1093/nar/gkv1277.

    Article  CAS  PubMed  Google Scholar 

  44. Liberzon A, Subramanian A, Pinchback R, Thorvaldsdóttir H, Tamayo P, Mesirov JP. Molecular signatures database (MSigDB) 3.0. Bioinformatics. 2011;27:1739–40. https://doi.org/10.1093/bioinformatics/btr260.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Wang Q, Xu R. Data-driven multiple-level analysis of gut-microbiome-immune-joint interactions in rheumatoid arthritis. BMC Genom. 2019;20:124 https://doi.org/10.1186/s12864-019-5510-y.

    Article  Google Scholar 

  46. Wang Q, Li L, Xu R. A systems biology approach to predict and characterize human gut microbial metabolites in colorectal cancer. Sci Rep. 2018;8:6225 https://doi.org/10.1038/s41598-018-24315-0.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. National Institute on Drug Abuse. Common comorbidities with substance use disorders research report. National Institute on Drug Abuse. 2018. https://www.drugabuse.gov/publications/drugfacts/comorbidity-substance-use-disorders-other-mental-illnesses.

  48. Kaye AD. Tramadol, pharmacology, side effects, and serotonin syndrome: a review. Pain Physician. 2015;18:395–400.

    Article  Google Scholar 

  49. Lofwall MR, Babalonis S, Nuzzo PA, Siegel A, Campbell C, Walsh SL. Efficacy of extended-release tramadol for treatment of prescription opioid withdrawal: a two-phase randomized controlled trial. Drug Alcohol Depend. 2013;133:188–97.

    Article  CAS  Google Scholar 

  50. Sarkar S, Varshney M, Patil V, Lal R. Maintainence treatment of opioid dependence with tramadol. J Neurosci Rural Pr. 2017;8:S98.

    Google Scholar 

  51. Bymaster FP, Rasmussen K, Calligaro DO, Nelson DL, DeLapp NW, Wong DT, et al. In vitro and in vive biochemistry of olanzapine: a novel, atypical antipsychotic drug. J Clin Psychiatry. 1997;58:28–36.

    CAS  PubMed  Google Scholar 

  52. Al-Hasani R, Bruchas MR. Molecular mechanisms of opioid receptor-dependent signaling and behavior. Anesthesiology. 2011;115:1363–81. https://doi.org/10.1097/ALN.0b013e318238bba6.

    Article  CAS  PubMed  Google Scholar 

  53. Koob GF, Volkow ND. Neurobiology of addiction: a neurocircuitry analysis. Lancet Psychiatry. 2016;3:760–773.

    Article  Google Scholar 

  54. Grond S, Sablotzki A. Clinical pharmacology of tramadol. Clin Pharmacokinet. 2004;43:879–923.

    Article  CAS  Google Scholar 

  55. Radcliff JA, Vellanki SC, Moore PS, Weisensell BJ, Cather JK. A retrospective chart review of outcomes resulting from a three-day tramadol taper for acute opioid withdrawal. J Addict Dis. 2018;37:252–8.

    Article  Google Scholar 

  56. Stahl SM, Pradko JF, Haight BR, Modell JG, Rockett CB, Learned-Coughlin S. A review of the neuropharmacology of bupropion, a dual norepinephrine and dopamine reuptake inhibitor. Prim Care Companion J Clin Psychiatry. 2004;6:159–66. https://doi.org/10.4088/pcc.v06n0403.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Hamdy MM, Elbadr MM, Barakat A. Bupropion attenuates morphine tolerance and dependence: Possible role of glutamate, norepinephrine, inflammation, and oxidative stress. Pharm Rep. 2018;70:955–62. https://doi.org/10.1016/j.pharep.2018.04.003.

    Article  CAS  Google Scholar 

  58. Brackins T, Brahm NC, Kissack JC. Treatments for methamphetamine abuse: a literature review for the clinician. J Pharm Pr. 2011;24:541–50. https://doi.org/10.1177/0897190011426557.

    Article  Google Scholar 

  59. Wong DT, Threlkeld PG, Best KL, Bymaster FP. A new inhibitor of norepinephrine uptake devoid of affinity for receptors in rat brain. J Pharm Exp Ther. 1982;222:61–5.

    CAS  Google Scholar 

  60. Garnock-Jones KP, Keating GM. Atomoxetine: a review of its use in attention-deficit hyperactivity disorder in children and adolescents. Paed Drugs. 2009;11:203–26.

    Article  Google Scholar 

  61. Carpenter LL, Milosavljevic N, Schecter JM, Tyrka AR, Price LH. Augmentation with open-label atomoxetine for partial or nonresponse to antidepressants. J Clin Psychiatry. 2005;66:1234–8.

    Article  CAS  Google Scholar 

  62. Lofwall MR, Walsh SL, Bigelow GE, Strain EC. Modest opioid withdrawal suppression efficacy of oral tramadol in humans. Psychopharmacology. 2007;194:381–393.

    Article  CAS  Google Scholar 

  63. Cowie MR, Blomster JI, Curtis LH, Duclaux S, Ford I, et al. Electronic health records to facilitate clinical research. Clin Res Cardiol. 2017;106:1–9. https://doi.org/10.1007/s00392-016-1025-6.

    Article  PubMed  Google Scholar 

  64. Coorevits P, Sundgren M, Klein GO, Bahr A, Claerhout B, Daniel C, et al. Electronic health records: new opportunities for clinical research. J Intern Med. 2013;274:547–560. https://doi.org/10.1111/joim.12119.

    Article  CAS  PubMed  Google Scholar 

  65. Ahmad FS, Chan C, Rosenman MB, Post WS, Fort DG, Greenland P, et al. Validity of cardiovascular data from electronic sources: the Multi-Ethnic Study of Atherosclerosis and HealthLNK. Circulation. 2017;136:1207–1216. https://doi.org/10.1161/CIRCULATIONAHA.117.027436.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Safran C, Bloomrosen M, Hammond WE, Labkoff S, Markel-Fox S, Tang PC, et al. Toward a national framework for the secondary use of health data: an American Medical Informatics Association White Paper. J Am Med Inf Assoc. 2007;14:1–9. https://doi.org/10.1197/jamia.M2273.

    Article  Google Scholar 

  67. Madden JM, Lakoma MD, Rusinak D, Lu CY, Soumerai SB. Missing clinical and behavioral health data in a large electronic health record (EHR) system. J Am Med Inform Assoc. 2016; https://doi.org/10.1093/jamia/ocw021.

  68. The Substance Abuse and Mental Health Services Administration (SAMHSA). Key substance use and mental health indicators in the United States: results from the 2018 National Survey on Drug Use and Health. Substance Abuse and Mental Health Services Administration. https://www.samhsa.gov/data/sites/default/files/cbhsq-reports/NSDUHNationalFindingsReport2018/NSDUHNationalFindingsReport2018.pdf. Accessed 12 Nov 2020.

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Acknowledgements

We acknowledge support from Eunice Kennedy Shriver National Institute of Child Health & Human Development of the National Institutes of Health under the NIH Director’s New Innovator Award number DP2HD084068, NIH National Institute on Aging R01 AG057557, R01 AG061388, R56 AG062272, National Institute on Drug Addiction UG1DA049435, American Cancer Society Research Scholar Grant RSG-16-049-01—MPC, and The Clinical and Translational Science Collaborative (CTSC) of Cleveland 1UL1TR002548-01.

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  1. These authors contributed equally: Mengshi Zhou, QuanQiu Wang

Authors and Affiliations

  1. Center for Artificial Intelligence in Drug Discovery, Case Western Reserve University, Cleveland, OH, USA

    Mengshi Zhou, QuanQiu Wang, Chunlei Zheng & Rong Xu

  2. Department of Mathematics & Statistics, Saint Cloud State University, Saint Cloud, MN, USA

    Mengshi Zhou

  3. Duke University School of Medicine, Durham, NC, USA

    A. John Rush

  4. Duke-National University of Singapore, Singapore, Singapore

    A. John Rush

  5. Texas-Tech Health Sciences Center, Permian Basin, Odessa, TX, USA

    A. John Rush

  6. National Institute on Drug Abuse, National Institutes of Health, Bethesda, MD, USA

    Nora D. Volkow

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Contributions

RX conceived, designed, and supervised the study. MZ performed drug prediction and clinical evaluation. QW performed the mechanism of action study. RX, QW, MZ, and NDV drafted the manuscript. NDV, CLZ, and AR critically contributed to data interpretation and result discussion. All authors approved the manuscript.

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Correspondence to Rong Xu.

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Zhou, M., Wang, Q., Zheng, C. et al. Drug repurposing for opioid use disorders: integration of computational prediction, clinical corroboration, and mechanism of action analyses. Mol Psychiatry 26, 5286–5296 (2021). https://doi.org/10.1038/s41380-020-01011-y

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