Atypical Opioid Interactions – Development of Selective Mu-Delta Heterodimer Antagonists, Clinical Opioids at Non-Mu Pain Targets and Endogenous Biased Signaling
AuthorOlson, Keith Mathew
Delta Opioid Receptor
Mu Opioid Receptor
AdvisorHruby, Victor J.
Streicher, John M.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractMost clinical opioids produce analgesia through the Mu Opioid Receptor (MOR) providing the only effective treatment for chronic pain patients. These studies explore three pre-clinical strategies to improve MOR analgesia and minimize side effects: 1) compounds that target G-protein Coupled Receptors (GPCRs) heterodimers, such as heterodimerization between the Delta Opioid Receptor (DOR) and MOR (MDOR); 2) multi-functional compounds that target multiple receptor systems for synergistic effects, such as a MOR agonist and a the serotonin reuptake transporter (SERT) inhibitor; or 3) biased agonists that preferentially activate one signaling pathway associated with analgesia over another associated with side effects at the same receptor. First, several indirect lines of evidence indicate the MOR-DOR heterodimer (MDOR) can regulate MOR opioid tolerance and withdrawal. However, studying MDOR remains difficult because no selective MDOR antagonists are available. To address this need, we created a novel series of bivalent MDOR antagonists by connecting a low affinity MOR antagonist (H-Tyr-Pro-Phe-D1Nal-NH2) to a moderate affinity DOR (H- Tyr-Tic-OH) antagonist with variable length polyamide spacers (15-41 atoms). In vitro radioligand binding and [35S]-GTPγS coupling assays in MOR, DOR, and MDOR expressing cell lines show bivalent ligands produce a clear length dependence in MDOR but not MOR or DOR cell lines. The lead compound – D24M with a 24-atom spacer – displayed high potency (IC50MDOR = 0.84 nM) with 91-fold selectivity for MDOR:DOR and 1,000-fold MDOR:MOR selectivity. Second, clinicians have long appreciated subtle but distinct differences in analgesia and side effects of MOR opioids. A variety of non-MOR targets including DOR, Kappa Opioid Receptor (KOR), the Cannabinoid Receptor-1 (CB1), the Sigma-1 Receptor (σ1R), the Dopamine- (DAT), Serotonin- (SERT) and Norepinephrine- Reuptake Transporters (NET) induce analgesia and/or modulate MOR mediated side effects. To determine if different opioid profiles arise from non-MOR interactions, we evaluated the binding and function of nine clinical analgesics at the nine aforementioned targets revealing several clinical opioids contain previously unidentified affinity’s or activity’s. Hydrocodone displayed low affinity at the MOR (KI = 1800 nM) and only ~2 fold less affinity at the σ1R (KI = 4000 nM). Second buprenorphine promoted monoamine influx at DAT, SERT and NET with EC50 > 1,000 nM. These novel interactions suggest the nuanced differences of clinical opioids may arise from previously unappreciated off-target effects. Future studies will assess whether these in vitro results predict hydrocodone and buprenorphine activity in vivo. Finally, the unique function of the numerous endogenous opioid peptides at a given receptor remains unclear. How endogenous ligands interact with ORs produces obvious drug design consequences. These studies show two endogenous Dynorphin analogues – Dynorphin A and Dynorphin B – differentially regulate two ubiquitous signaling modules – βarrestin2 and Gαi/o– at the DOR. Dynorphin A and Dynorphin B swap potency rank orders for β-arrestin2 recruitment and [35S]-GTPγS signaling, indicating two distinct signaling platforms are formed. Dynorphin A but not Dynorphin B treatment simulated AC super activation, while Dynoprhin B internalized DOR better than Dynorphin A. These in vitro assays suggest endogenous Dynorphin analogues differentially regulate signals at the DOR in vitro. Future work includes further characterizing signaling differences in vitro and testing these changes in vivo.
Degree ProgramGraduate College
Degree GrantorUniversity of Arizona
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A Novel Bifunctional Opioid That Lacks Traditional Opioid Side EffectsBrookshire, Stephen William (The University of Arizona., 2012-05)Prescription opioids, such as morphine, remain an important part in the management of pain. However, their clinical utility can be limited by side effects such as constipation, nausea, and high risk of addiction. Additionally, abuse of prescription opioids has been on the rise in recent years. Therefore, it is necessary to develop effective analgesics that lack the rewarding properties of currently used opioids. The neurokinin-1 receptor (NK-1) and its endogenous ligand, Substance P (SP), have been implicated in the control of nausea and vomiting, as well as mediating the rewarding effects of opioids. Here we have characterized the side effects of a novel efficacious opioid agonist/NK-1 antagonist, TY027. TY027 fails to elicit conditioned place preference, retching or vomiting, and does not inhibit gastric motility. These findings suggest that TY027 has a superior side effect profile when compared to currently used opioids, and most importantly, it does not produce rewarding effects that may lead to addiction.
Evaluation of Machine-Learning Algorithms for Predicting Opioid Overdose Risk Among Medicare Beneficiaries With Opioid PrescriptionsLo-Ciganic, Wei-Hsuan; Huang, James L; Zhang, Hao H; Weiss, Jeremy C; Wu, Yonghui; Kwoh, C Kent; Donohue, Julie M; Cochran, Gerald; Gordon, Adam J; Malone, Daniel C; et al. (AMER MEDICAL ASSOC, 2019-03-22)IMPORTANCE Current approaches to identifying individuals at high risk for opioid overdose target many patients who are not truly at high risk. OBJECTIVE To develop and validate a machine-learning algorithm to predict opioid overdose risk among Medicare beneficiaries with at least 1 opioid prescription. DESIGN, SETTING, AND PARTICIPANTS A prognostic study was conducted between September 1, 2017, and December 31, 2018. Participants (n = 560 057) included fee-for-service Medicare beneficiaries without cancer who filled 1 or more opioid prescriptions from January 1, 2011, to December 31, 2015. Beneficiaries were randomly and equally divided into training, testing, and validation samples. EXPOSURES Potential predictors (n = 268), including sociodemographics, health status, patterns of opioid use, and practitioner-level and regional-level factors, were measured in 3-month windows, starting 3 months before initiating opioids until loss of follow-up or the end of observation. MAIN OUTCOMES AND MEASURES Opioid overdose episodes from inpatient and emergency department claims were identified. Multivariate logistic regression (MLR), least absolute shrinkage and selection operator-type regression (LASSO), random forest (RF), gradient boosting machine (GBM), and deep neural network (DNN) were applied to predict overdose risk in the subsequent 3 months after initiation of treatment with prescription opioids. Prediction performance was assessed using the C statistic and other metrics (eg, sensitivity, specificity, and number needed to evaluate [NNE] to identify one overdose). The Youden index was used to identify the optimized threshold of predicted score that balanced sensitivity and specificity. RESULTS Beneficiaries in the training (n = 186 686), testing (n = 186 685), and validation (n = 186 686) samples had similar characteristics (mean [SD] age of 68.0 [14.5] years, and approximately 63% were female, 82% were white, 35% had disabilities, 41% were dual eligible, and 0.60% had at least 1 overdose episode). In the validation sample, the DNN (C statistic = 0.91; 95% CI, 0.88-0.93) and GBM (C statistic = 0.90; 95% CI, 0.87-0.94) algorithms outperformed the LASSO (C statistic = 0.84; 95% CI, 0.80-0.89), RF (C statistic = 0.80; 95% CI, 0.75-0.84), and MLR (C statistic = 0.75; 95% CI, 0.69-0.80) methods for predicting opioid overdose. At the optimized sensitivity and specificity, DNN had a sensitivity of 92.3%, specificity of 75.7%, NNE of 542, positive predictive value of 0.18%, and negative predictive value of 99.9%. The DNN classified patients into low-risk (76.2%[142 180] of the cohort), medium-risk (18.6%[34 579] of the cohort), and high-risk (5.2% of the cohort) subgroups, with only 1 in 10 000 in the low-risk subgroup having an overdose episode. More than 90% of overdose episodes occurred in the high-risk and medium-risk subgroups, although positive predictive values were low, given the rare overdose outcome. CONCLUSIONS AND RELEVANCE Machine-learning algorithms appear to perform well for risk prediction and stratification of opioid overdose, especially in identifying low-risk subgroups that have minimal risk of overdose.
Studies on opioid delta receptor mediated antinociception, opioid antinociceptive tolerance and physical dependencePorreca, Frank; Bilsky, Edward James, 1967- (The University of Arizona., 1997)The central hypothesis of this dissertation is that agonists and antagonists acting at the delta opioid receptor will have therapeutic applications in treating acute and chronic pain states and in the treatment of drug addiction. It is further hypothesized that delta compounds will have better therapeutic profiles than currently available opioids that act predominantly at the mu receptor. In advancing the central hypothesis, selective nonpeptidic delta compounds, that readily cross the blood brain barrier after systemic administration, were tested. BW373U86, a nonpeptidic ligand with moderate selectivity and activity at delta opioid receptors represented a lead compound. A structurally related molecule, SNC80, displayed an improved selectivity and activity profile compared to BW373U86. Importantly, SNC80 produced antinociception following systemic administration which was blocked by delta, but not mu, selective antagonists. The pharmacology of delta opioid receptors was further studied using antisense oligodeoxynucleotides that disrupted the synthesis of delta receptors in vivo and in vitro. The experiments provided further evidence for distinct delta receptor subtypes and demonstrated the utility of the antisense approache in studying neurochemical processes in vivo. Several studies addressed the phenomenon of opioid tolerance and physical dependence, two processes which compromise the clinical application of currently available opioid analgesics. The observation that NMDA receptor antagonists block the development of antinociceptive tolerance to repeated administrations of morphine was confirmed. The results were extended by demonstrating that NMDA antagonists did not block antinociceptive tolerance to more selective delta or mu agonists. These studies caution against the generalization that an effect seen with morphine is applicable to all opioid agonists. Further hypotheses regarding the mechanisms of opioid tolerance and physical dependence were tested using inhibitors of protein kinases and putative neutral and inverse opioid antagonists. These studies advanced the hypothesis that opioid receptor phosphorylation may play a critical role in the development of opioid antinociceptive tolerance and physical dependence. In summary, this dissertation has provided strong evidence that nonpeptidic delta selective opioid agonists and antagonists can be developed and that these compounds will have therapeutic applications in the treatment of pain and addictive disorders.