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Biased Agonism at Μ-Opioid Receptor

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Biased Agonism at Μ-Opioid Receptor Biased Agonism at µ-Opioid Receptor The Quest for Safer Analgesics Fabian August Line Project Thesis at the Faculty of Medicine UNIVERSITY OF OSLO 24.03.19 I II Biased agonism at µ-opioid receptor The Quest for Safer Analgesics Analgesic effect of µ-Opioid receptor stimulation is related to the G-protein, while the respiratory suppression is related to β-arrestin recruitment.(Schmid et al., 2017) Fabian August Line III IV Copyright Fabian A. Line 2019 Biased Agonism at µ-Opioid Receptor – The Quest for Safer Analgesics Fabian A. Line http://www.duo.uio.no Trykk: Reprosentralen, Universitetet i Oslo V VI Abstract Opioids are our most potent antinociceptive drugs, but their use is limited by adverse effects. Among the adverse effects are addiction, constipation and respiratory depression. In this thesis I review the current literature on biased µ-opioid receptor agonists and their potential for providing safer analgesia. Biased agonists activate one signaling effector over another. Among G-protein-coupled receptors, biased agonism is often used to denote an agonist that activates either G-protein-dependent signaling or β-arrestin-dependent signaling (G-protein-independent signaling). For the μ-opioid receptor, an agonist that activate the G-protein without recruiting β-arrestin-2 has been proposed to cause less respiratory depression, less addiction, less tolerance, less constipation and less opioid induced hyperalgesia. From the available literature I found substantial evidence for less respiratory depression by biased µ-opioid receptor agonists relative to classical opioids, while the data regarding constipation, addiction, tolerance and opioid induced hyperalgesia was less clear. Nevertheless, respiratory depression is the cause of death from opioid overdoses, hence a potent analgesic drug without this effect would be a great advancement. This far, no G-protein biased µ-opioid receptor agonist has reached the market, but a few is in development. The one that has come furthest is oliceridine by Trevena, which was recently reJected by the FDA. Trevena is still developing oliceridine, and they have another compound, TRV734, that possibly enter phase 2 studies soon. Also, Mebias Discovery has announced that they are developing two compounds that has shown a higher degree of bias and less respiratory depression than oliceridine in preclinical experiments. VII Table of Contents Abstract .......................................................................................................................................................................... VII 1 Background ............................................................................................................................................................ 1 1.1 Introduction ............................................................................................................................................................................ 1 1.2 G-protein coupled receptors ............................................................................................................................................. 3 1.3 Biased agonism ...................................................................................................................................................................... 6 1.4 Quantification of signaling bias ...................................................................................................................................... 8 1.5 µ-opioid receptor ............................................................................................................................................................... 10 1.6 Side effects of opioids ....................................................................................................................................................... 11 2 Methods ................................................................................................................................................................. 12 2.1 Protocol .................................................................................................................................................................................. 12 2.1.1 Changes made to the protocol .......................................................................................................................... 14 3 Results ................................................................................................................................................................... 15 3.1 Biased µ-opioid ligands ................................................................................................................................................... 16 3.2 Discovering biased agonists .......................................................................................................................................... 17 3.3 Link between mu receptor bias and side effects ................................................................................................... 18 3.4 Bias factors of current µ-opioid receptor agonists ............................................................................................. 21 3.5 Oliceridine, why did FDA reject it and what are the alternatives. ................................................................ 22 3.6 Main oppositions ................................................................................................................................................................ 25 4 Conclusion/discussion ..................................................................................................................................... 26 Reference list ................................................................................................................................................................. 27 Appendices ..................................................................................................................................................................... 33 Appendix 1 ............................................................................................................................................................................................... 33 Appendix 2 ............................................................................................................................................................................................... 34 Appendix 3 ............................................................................................................................................................................................... 35 Appendix 4 ............................................................................................................................................................................................... 36 Appendix 5 ............................................................................................................................................................................................... 37 VIII IX 1 Background 1.1 Introduction The need for effective pain relief without all the serious adverse effects related to opioids is evident from the amount of deaths caused by opioids every year. In the US only, opioid over-doses are estimated to inflict 47 600 deaths every year (2017) (Scholl, Seth, Kariisa, Wilson, & Baldwin, 2018). The dangers of opium and other opioids have been known for centuries, and the quest for finding a safer yet equally effective painkiller as morphine has been ongoing for more than a hundred years. There have been several attempts, but none of them really successful. Although some agents have been developed for specific types of pain, e.g. pregabalin for neuropathic pain (Derry et al., 2019)and other are under development, e.g. anti-NGF antibodies for osteoarthritis (Miller, Malfait, & Block, 2017), opioids still remain the only option for severe pain irrespective of cause. Some of the compounds first presented as safer than morphine have even turned out to be more dangerous, like heroin, or lead to a dramatic increase in the number of opioid-addict, like OxyContin. When heroin first hit the market in 1898, it was marketed by Bayer as a safe and non-addictive alternative to morphine (Tyers, 2018), something that seems absurd with the knowledge we have about heroin today, as heroin alone causes more than 30% of all opioid overdose deaths in the USA (Scholl et al., 2018). Another opiate that initially was promoted as safe is the extended release formula for oxycodone, marketed as OxyContin by Purdue Pharma from 1996. It was heavily advertised towards both physicians and patients, especially targeting patients with chronic non-malignant pain. This is a large patient group, and physicians had been reluctant to prescribe opioids to them, as these patients often will require long-term pain relief and therefore has a high risk of addiction. Purdue pharma claimed that the risk for OxyContin addiction was less than one percent, far less than any other opioid, and that it therefore was safe to prescribe for non-malignant chronic pain patients. Later it has been shown that Oxycontin is no safer than ordinary rapid onset oxycodone given 4 times a day, and that the risk for addiction is somewhere between 0%-50%, depending on criteria for addiction and what subpopulation is studied. Furthermore, it 1 is widely known that crushing of the tablets obviates the slow-release properties of OxyContin, enabling the rapid high opioid-addicts
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