Drug Repurposing for Opioid Use Disorders: Integration of Computational Prediction, Clinical Corroboration, and Mechanism of Action Analyses

Molecular Psychiatry https://doi.org/10.1038/s41380-020-01011-y ARTICLE Drug repurposing for opioid use disorders: integration of computational prediction, clinical corroboration, and mechanism of action analyses 1,2 1 1 3,4,5 6 1 Mengshi Zhou ● QuanQiu Wang ● Chunlei Zheng ● A. John Rush ● Nora D. Volkow ● Rong Xu Received: 10 September 2020 / Revised: 11 December 2020 / Accepted: 17 December 2020 © The Author(s), under exclusive licence to Springer Nature Limited 2021 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, 1234567890();,: 1234567890();,: 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. Introduction diseases of the brain that in its most severe manifestation result in an escalating and an uncontrollable use of the drug Substance use disorders (SUD) when manifested in their despite its adverse consequences to the individual [1–3]. moderate or severe presentation are considered chronic SUD are estimated to affect 10.8% of the adult population in the US [4] and account for 1.5% of global disease burden [5]. In the US overdose death associated with opioids were five times higher in 2016 than 1999, leading the US These authors contributed equally: Mengshi Zhou, QuanQiu Wang government to declare the opioid crisis a public health Supplementary information The online version of this article (https:// emergency [6]. As of now opioid overdose fatalities as well doi.org/10.1038/s41380-020-01011-y) contains supplementary as drug fatalities in general have continue to rise in the US material, which is available to authorized users. [7]. There are few medications for SUD and while effective they are limited by limited utilization and high relapse rates * Rong Xu [email protected] [8]. There are no approved medications to treat cocaine, marihuana, methamphetamine, benzodiazepine, or inhalant 1 Center for Artificial Intelligence in Drug Discovery, Case Western use disorders. The traditional drug discovery process for Reserve University, Cleveland, OH, USA medication development is lengthy and costly [8, 9]. In 2 Department of Mathematics & Statistics, Saint Cloud State addition, the very modest investment from the pharmaceutical University, Saint Cloud, MN, USA sector in SUDs has limited the discovery of new medications 3 Duke University School of Medicine, Durham, NC, USA to a greater extent than for other neuro-psychiatric disorders. 4 Duke-National University of Singapore, Singapore, Singapore Thus, novel strategies to evaluate the potential for repurposing 5 Texas-Tech Health Sciences Center, Permian Basin, Odessa, TX, existing drugs to treat SUD could accelerate access to addi- USA tional medications [8, 10]. 6 National Institute on Drug Abuse, National Institutes of Health, Drug repurposing is a strategy that can help identify Bethesda, MD, USA potential new therapies for complex diseases, including M. Zhou et al. Fig. 1 Flowcharts of the integrate SUD repurposing system. a Computational drug prediction. b EHR-based clinical corroboration. c Mechanism of action analysis. SUD [11]. For example, lofexidine was approved for the the phenome-driven drug discovery system identifies drug treatment of hypertension and was recently approved for the candidates that share similar drug phenotypes (i.e., side treatment of acute opioid withdrawal [12]. With the accu- effects) and/or common targets with drugs causing or mulation of relevant data in machine-actionable formats, treating SUD. Second, we then performed retrospective data-driven computational approaches have been developed case-control studies to evaluate the clinical efficacy of to automate the drug repurposing process [13–18]. promising repositioned candidate drugs using EHRs of 72.9 However, clinically validating a promising repurposed million patients (20% of the US population). Finally, we candidate drug generated by computational algorithms examined potential mechanisms of action of promising remains a challenge. repurposed candidate drugs in targeting SUD by developing We propose an integrated drug repurposing strategy that data-driven informatics approaches. The knowledge and combines computational-based drug prediction, patient data generated by our study (i.e., promising candidate drugs Electronic Health Records (EHRs)-based clinical corro- with both supporting clinical evidence and potential boration and mechanisms of action analysis. First, we mechanism of actions) can set the foundation of experi- developed phenome-driven network-based drug discovery mental testing in animal models for SUD or for pilot testing system that prioritized repurposed anti-SUD candidate in clinical trials. drugs. The phenotypic and genetic relationships among drugs, drug phenotypes (side effects or SEs), and genes were modeled using the novel context-sensitive network Material and methods (CSN)-based modeling techniques that we previously developed [16, 19, 20, 22, 23]. Then phenome-driven net- Our study entailed three steps (Fig. 1): (1) We constructed a work-based prioritization approaches, which we recently drug side effect-gene (DSEG) computational drug prediction developed both for understanding disease mechanisms and system to prioritize drugs to treat SUD. (2) We performed for drug discovery [14, 16, 18, 19, 21–23], were used to retrospectives case-control studies to evaluate top candidate prioritized repurposed candidate drugs based on their phe- drugs using patient EHR data. (3) We performed genetic and notypic and genetic relevance to the input disease (i.e., pathway enrichment analysis of top candidate drugs to SUD). In our study, we significantly leveraged the context- understand their potential mechanisms of action. sensitive drug side effect network that we constructed based on known drug side effects. Side effects are observable Computational drug prediction phenotypes of drugs manifested at the level of the whole- body system and are mediated by a drug interacting with its Constructing drug side effect-gene prediction system on- or off-targets through a cascade of downstream pathway perturbations. While mechanisms of action (on- and off- We constructed a DSEG drug prediction system that models targets) of many drugs and the underlying molecular the interconnections among drugs, side effects, and genes mechanisms of diseases remain largely unknown, we can using the CSN-based modeling techniques that we pre- infer novel connections between drugs and diseases (drug viously developed [19–23]. The DSEG system included two repurposing) based on the observed drug and disease phe- networks (Fig. 1a): (1) a drug phenotypic (drug side effects) notypes as well as known drug-targets and protein–protein network (DPN) and (2) a protein–protein interaction net- connections [22]. Instead of directly identifying drugs that work (PPIN). DPN was constructed using drug-SE pairs target SUD’s mechanisms, which remain largely unknown, from the Side Effect Resource (SIDER) database [24] and Drug repurposing for opioid use disorders: integration of computational prediction, clinical. consisted of 1430 drug nodes, 4251 SE nodes, and 145,321 using de-identified population-level EHR data collected by drug-SE edges. PPIN was directly constructed from the IBM Watson Health from 360 hospitals and 317,000 protein–protein interactions in STRING [25] and consisted providers across 50 states from 1999 up to August, 2020, of 17,906 gene nodes and 2,091,571 gene–gene edges. representing 20% of the US population [30]. The EHRs are Drug nodes (899 drugs) on DPN were connected to gene de-identified according to the Health Insurance Portability and nodes (1021 genes) on PPIN using drug-target associations Accountability Act and the Health Information Technology from the DrugBank database [26]. for Economic and Clinical Health Act standards. After the de- identification process, the curation process normalizes the data Prioritize anti-SUD drug candidates by mapping key elements to widely-accepted biomedical terminologies [31]. Specifically, disease terms are coded using We prioritized candidate drugs using the network-based the Systematized Nomenclature of Medicine-Clinical Terms ranking algorithms that we previously used for drug (SNOMED-CT), a global standard for health terms that pro- repurposing, gene discovery, and gut microbial metabolite vides the core general terminology for EHRs [32]. We have discovery for the disease [16–23, 27–29]. In brief, given an recently

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