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
Biased agonism at µ-opioid receptor
The Quest for Safer Analgesics
Fabian August Line
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)
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
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.
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
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
is widely known that crushing of the tablets obviates the slow-release properties of OxyContin, enabling the rapid high opioid-addicts often seek. Due to false marketing one of Purdue Pharma´s affiliate companies has been fined $ 634 million (Van Zee, 2009), and litigation is still ongoing.
There are several suggested solutions on how to achieve a strong analgesic without the adverse effects of classical opioids, some approaches are opioid receptor related and some targeting other receptors. Among the approaches that target the opioid receptor subfamily are: engaging more than one receptor in the subfamily with the same agonist (Cunningham, Elballa, & Vold, 2019); creating an agonist that
specifically target receptor dimers is suggested (Painter, 2018); making a peripherally acting opioid receptor agonists, possibly one that is only activated in an area of
inflammation (Rodriguez-Gaztelumendi, Spahn, Labuz, Machelska, & Stein, 2018); µ- opioid receptor agonists that passes the blood brain barrier more slowly than the conventional opioids (Miyazaki et al., 2017) and biased µ-opioid receptor agonists, which is what this is going look more closely into. To be able to do that, a brief introduction about G-protein coupled receptors and biased agonist follows.
1.2 G-protein coupled receptors
G-protein coupled receptors (GPCRs) is a large group of receptors that transmit a wide array of signals across the cell membrane, including photons of light, ions (e.g. H+, Ca2+), odorants, gustatory substances, neurotransmitters, hormones,
autocrine/paracrine factors, intermediary metabolites, among many others. They are characterized by a common architecture with an extracellular N-terminus, an
intracellular C-terminus, and 7-transmembrane domains linked by three intra- and three extracellular loops. The term 7-transmembrane receptor is therefore also a common synonym used for GPCR (Alexander et al., 2017). The principal mode of transmembrane signal transmission by GPCR is through heterotrimeric G proteins. G proteins subsequently activate one of many different effector proteins, e.g. adenylyl cyclases, phospholipases, or ion channels.
Heterotrimeric G proteins is a family of membrane transducer proteins that are activated by GPCRs. They consist of a G-α subunit, a G-b, and a G-γ subunit, the latter two subunits are tightly bound together and often denominated Gβγ. When a GPCR acquires the active state, it confers a conformational change of its cognate G protein opening the guanine nucleotide binding pocket, allowing subsequently exchange of GDP with GTP. Thus, GPCRs can be considered guanine nucleotide exchange factors (GEFs).
In the GTP-bound state, the affinity of Gα for Gβγ is reduced, and Gα dissociates from Gβγ and activates the effector protein. Gα subunits are classified into four groups, Gai, Gas, Gaq and Ga12, which are again classified into subgroups encoded by different genes.
The Gas family typically stimulates cAMP production by activating adenylyl cyclase (AC), Gai subunitsinhibit cAMP production by inhibiting AC, whereas Gaq and Gα12
typically leads to production of the second messengers phosphoinositol trisphosphate (IP3) and diacylglycerol (DAG) by activating phospholipase C(Simon, Strathmann, &
Gautam, 1991). However, current understanding of G proteins is substantially more complex. Gα may activate several other effectors than AC. For example, both Gαs and Gαi
may activate various ion channels depending on cellular context. Furthermore, the Gβγ- subunit may also be a signal transducer in its own right. (Wettschureck & Offermanns, 2005)
The signaling efficacy of GPCRs is also regulated by a family of serine/threonine kinases called G-protein-coupled receptor kinases (GRKs). Agonist-occupied GPCRs are substrates of GRKs, and following phosphorylation catalyzed by GRKs, the affinity of GPCRs for b-arrestin increase, leading to recruitment b-arrestin to the GPCRs, resulting in what is known as desensitization and termination of G protein signaling. More recently, it has been shown that b-arrestins act as scaffolding proteins for other signaling mediators. Thus, b-arrestins can also be considered signal effectors in their own right (Smith & Rajagopal, 2016).
There is a lot of research on GPCRs, evidenced by the fact that 34 % of all FDA approved drugs have a GPCR as its target (Hauser et al., 2018). The human genome contains genes encoding about 800 different GPCRs, of which about 450 have sensory functions (~400 odorant receptors, 33 taste receptors, 10 receptors for light
perception, and 5 pheromone receptors), and about 350 have other non-sensory functions. Among the 350 GPCRs with non-sensory functions, 121 GPCR genes are currently classified as orphan receptors, i.e. they have no known endogenous ligand (Alexander et al., 2017). Only a few of the remaining 230 GPCRs have been extensively studied. The most extensively studied GPCRs include the b2-adrenergic receptor, the AT1 angiotensin II receptor, the µ-opioid receptor (MOR), and the light receptor
rhodopsin. Therefore, the current understanding of how GPCRs convey the signal upon ligand binding is very much relies on research on these model receptors.
Following the initial report of the crystal structure of the b-2-adrenergic receptor (Cherezov et al., 2007), again followed by many reports on the crystal structures of several other GPCR bound to both various agonists, antagonists and interacting proteins, our understanding of how GPCRs function and interact with drug molecules has greatly expanded. The µ-opioid receptor has not been spared from this structural revolution, thus we now know what the µ-opioid receptor looks like, as can be seen in the example in Fig. 1, bound to an irreversible antagonist. Such a crystal structure, and in particular in complex with a drug molecule, can be very useful for increasing the understanding of why some drugs result in some effects while other drugs targeting the same receptor results in other effects.
Figure 1: Crystalline structure of µ-opioid receptor with irreversible antagonist β-Funaltrexamine bound (Darcq
& Kieffer, 2018).
1.3 Biased agonism
Signaling bias, also known as functional selectivity, is a relative term used to describe how a ligand might activate a receptor and direct transmission of the signal along one particular signaling pathway more than another. Functional selectivity refers to the realization that a ligand may not be either agonist or antagonist, but rather may have both agonist and antagonist properties depending on the effector pathways coupled to the receptor. This understanding of GPCR signaling is closely tied to the realization that GPCR may engage in several different conformations, not only on or off.
Yet another term of signaling bias is biased agonism. Biased agonism refers to signaling bias solely related to the conformational change that the ligand imposes on the receptor (Violin, Crombie, Soergel, & Lark, 2014). For receptors coupling to two different
signaling effector molecules, a particular receptor conformation might stabilize the binding of one signaling molecule more than the other, and thereby activate one pathway rather than the other.
Most of the research on signaling bias is done on GPCRs, and relates to the ability of a ligand to activate G protein-dependent versus G protein-independent signaling, i.e.
to recruit β-arrestin- dependent signaling. However, biased agonists have also been described for receptors belonging to other receptor-families, like insulin-like growth factor 1 receptor, a receptor tyrosine kinase (Bareja, Patel, Hodgkinson, Payne, & Dzau, 2018). β-arrestins, a group of proteins that binds the agonist-occupied G-protein coupled receptor following phosphorylation of the receptor by a specific family of receptor kinases (G protein-coupled receptor kinases (GRKs)), were initially thought of as effectors of desensitization (Attramadal et al., 1992; Lohse, Benovic, Codina, Caron, &
Lefkowitz, 1990) and later receptor internalization (Ferguson et al., 1996). It is now clear that β-arrestins may also play an important role in signaling by acting as a scaffolding protein for a diverse range of signaling molecules (Peterson & Luttrell, 2017).
For the µ-opioid receptor (MOR), there has been a special interest for biased signaling favoring Gai over β-arrestin 2. This interest arose following the discovery that
Raehal, Walker, & Bohn, 2005), indicating that β-arrestin 2 recruitment or signaling mediated by β-arrestin 2 is responsible for both short term desensitization and respiratory depression. This discovery implied that if one was able to generate a MOR agonist with the ability to activate Gai without the concomitant recruitment of β-
arrestin 2, one would be able to develop a safer opioid analgesic with a prolonged effect and a wider therapeutic window.
The pharmaceutical and biotech industry have initiated several drug
development projects based on the hypothesis that a µ-opioid agonist biased towards G- protein signaling will represent a superior analgesic with less adverse effects than the classical opioids, which will be discussed later in this project thesis.
1.4 Quantification of signaling bias
One key element in the research on signaling bias has been the development of a method for quantifying the degree of bias. This is to ensure that the bias observed is in fact agonist bias, and not an observational bias due to differences in sensitivity of the assays or a system bias resulting from differences in the systems (mainly cell lines) compared. This is dealt with by comparing the supposedly biased agonist with an
internal reference agonist in the same system (cell-line), and the bias factor is calculated as relative to the reference agonist in the system studied. It is therefore not normally possible to compare bias factors between studies, unless they are calculated from experiments done in the same system under the same experimental conditions with the same reference agonist. There is no standard system or standard reference agonist, normally the endogenous ligand is selected as reference, but for e.g. the µ opioid receptor, the natural ligands are many, so morphine is generally chosen as reference because it is the most widely used drug targeting µ-opioid receptor. As the activity of a ligand is both dependent of the efficacy and the potency, both factors should be included in the bias calculation unless all compared agonists are full agonists. (T. Kenakin &
Christopoulos, 2013).
How exactly to calculate the bias factor is still under debate, and a lot of useful methods exist(Michel & Charlton, 2018). The gold standard has been a method based on the Black-Leff operational model of agonism(Black & Leff, 1983). Approaches based on Black and Leff´s model requires refitting of underlying concentration-response data to a more complex model, making it less suitable for the analysis of large numbers of compounds than simpler models which are often preferred in screening. (Winpenny, Clark, & Cawkill, 2016)
Fig.2 a and b show the concentration-response curves of several agonists of in the two compared assays “assay A” and “assay B”. Fig. 2c is a graphical way o
representing bias. Agonist 1 is the internal reference agonist, and it´s bias curve in fig. 2 c represents the system and observational bias. Agonist 2 is unbiased compared to agonist 1, while agonist 3 is biased towards response B and agonist 4 is biased towards
Figure 2: Quantification of bias with two assays A and B, and a bias plot showing different degrees of bias and bias towards both signaling pathways.(T. Kenakin &
Christopoulos, 2013)
1.5 µ-opioid receptor
The µ-opioid receptor is a receptor in the opioid subfamily of group A GPCRs together with ∂-opioid receptor, κ -opioid receptor and the nociceptin receptor, which have all had their crystal structures solved (Fig. 3). The opioid system has a wide range of physiological roles like regulating the sensation of pain, fear and emotional learning (Winters et al., 2017), but is far from fully understood. Because of its role as the major mediator of analgesic effects of morphine and other opiates, the µ-opioid receptor (MOR) is the most thoroughly investigated receptor of the opioid receptor
subfamily(Stein, 2016).
The µ-opioid receptor is a Gai-coupled GPCR and the endogenous ligand with highest potency is β-endorphin (Schoffelmeer, Warden, Hogenboom, & Mulder, 1991).
Among its physiological roles are modulation of nociception and psychological
experience of pain (Zubieta et al., 2001). In mice where the MOR receptor gene has been knocked out, the analgesic, rewarding and dependence-inducing effects of morphine were simultaneously eliminated (Matthes et al., 1996), suggesting that all three features are linked to MOR rather than the other opioid receptors. The effects of MOR agonists can be differentiated into central vs. peripheral effects. Respiratory depression and dependence are mainly central effects(Darcq & Kieffer, 2018; Pattinson, 2008), whereas analgesia is thought to be a combined central and peripheral effect (Shannon & Lutz, 2002), and constipation is linked mainly to peripheral MORs in the gut (Holzer, 2009)
1.6 Side effects of opioids
Opioids are the most effective pain-relieving medication we have, but its use is limited by side effects (figure 4). The side effects of opioids are mainly related to on target effects of µ-opioid receptor stimulation, and among them are adverse side effects like addiction, cognitive dysfunction, constipation and respiratory depression (Els et al., 2017). Because of these side effects, we tend to limit the use of opioids to situations with intense pain of expected limited duration, like in the post-operative setting where patients are suffering a lot of pain. The problem is that even though post-operative patient are in need of adequate pain relief, they are also more susceptible to adverse effects such as constipation, nausea, vomiting and respiratory depression. Pneumonia due to respiratory depression, or ileus due to constipation can have fatal consequences, and many post-operative patients are at a higher risk (Simpson, Bao, & Agarwala, 2019).
Another major problem with opioids is the “epidemic” of opioid addiction. In the US, it is commonly called “the opioid
crisis” and has been estimated by the US government to cause 47,600 overdose deaths every year (2018).
Still the death rates of opioid
overdoses continue to increase. The main cause of death from an opioid overdose is respiratory depression, and even though illicit drugs like heroin is a great contributor,
prescription opioids are involved in more than 35% of cases. (Scholl et al., 2018)
The main goal of opioid
analgesia research is therefore to find a safer opioid drug that diminishes side effects and does not cause
addiction. One approach to find such a drug has been to search for a biased agonist of
Figure 4: Side effects of opioids. © 2016 Ming-Chih Kao, PhD, MD.
2 Methods
In advance of performing the literature search, a protocol was created to guide the search.
The protocol included the aim of the study, the sources of information to be assessed and what data to include in the scoping review. Any changes made to the protocol is reported in the review.
2.1 Protocol
Aim: The aim of this project thesis was to survey the current literature on biased agonists of the µ-opioid receptor using the scoping review method.
The first step was to search Pubmed.com for
1. Mesh term «opioid receptor, mu”/”mu opioid receptor”
2. AND
3. Text word “biased agonist”
All relevant research papers and reviews will be included, whether clinical research or basic research.
From the included basic research or clinical trial papers the following data can be extracted:
- Data comparing novel compounds that acts as biased agonists on my opioid receptors to current opioids.
- Pharmacological data on novel compounds.
- Clinical data from any clinical trial comparing the novel compound to a present opioid or placebo.
- Discussion on clinical implication of basic studies.
These data will be presented as tables, figures or text as relevant.
From reviews, any data that broaden the understanding of the concept of biased
further research and clinical implications will be presented in this review as a short summary. Any figures or tables that broaden the understanding of the topic might also be included, and the reference lists of reviews will be scanned for relevant papers.
A search in the database clinicaltrials.gov, a registry of human clinical trials, for all reported novel compounds that act as biased agonists at the µ-opioid receptor, will be performed. Any company names and name of researchers that has discovered novel compounds will also be searched for in clinicaltrials.gov. The company names and compound names will also be searched for on Google to find any relevant press release from companies or other relevant “gray data”.
At last, I performed a search for “biased mu opioid agonists” in the database in Google Patents (www.google.com/advanced.patent.search) to see if I can find additional information and compounds.
2.1.1 Changes made to the protocol
Because biased agonism don´t have a mesh term and functional selectivity is frequently used as a synonym to biased agonism I included a search:
1. Mesh term «opioid receptor, mu”/”mu opioid receptor”
2. AND
3. Text word “functional selectivity”.
“Functional selectivity” was kept in brackets to avoid selective µ-opioid agonist without bias.
3 Results
From the PubMed searches, 147 articles were retrieved. After removing double hits, and reading through abstracts, 74 unique articles were included. 3 of these were excluded, 2 because the whole article was unavailable and 1 because it was in French.
After reading the articles another 8 were excluded, either because they were of little relevance to the topic or because the functional selectivity was caused by another mechanism than ligand bias, 4 more articles were included from the reference lists of reviews. This review is therefore based on the key points of the resulting 66 articles and reviews, as well as data from the clinicaltrials.gov, data from Google patents and
information from the webpages of Trevena Inc. and Mebias Discovery.
The 66 articles were in 5 main groups with some overlap: reviews; discovery of new biased ligands; screening for and characterizing biased ligands; physiology and/or signaling pathways of biased ligands; and clinical trials.
From clinicaltrials.gov I found 10 studies on TRV130/Oliceridine, 9 completed and 1 terminated, 2 completed studies on TRV734 and 1 study on the effects of Ketum (Mitragyna speciosa) extract recruiting.
The search for “biased mu opioid agonists” on Google Patents yielded 1790 results (18.03.19). Both published and unpublished compounds, as well as a lot of unrelated hits, but still gave some information about possible future drug candidates.
From a google search of the compounds, I found the web pages of Trevena Inc.
and Mebias Discovery, both containing information of novel compounds and posters of elsewhere unpublished data. Press releases and posters about the compounds and the development process published on the companies´ websites have also given valuable information.
3.1 Biased µ-opioid ligands
From the articles retrieved from the PubMed searches, 12 biased ligands at the µ-opioid receptor were found to be reported. There is also a company, Mebias
Discovery, that has announced that they are developing biased ligands, but with no publications except from a poster. Trevena has also got an agonist in phase 1 clinical trials where data has only been published as a poster. A summary of the compounds and their reported properties are provided in Table 1.
Table 1, G-protein biased MOR ligands.
Ligand(s) Comment Phase Reference
Morphine analogue, NAP β-arrestin recruitment antagonist, substrate for P-glycoprotein, so no central effects expected
In vitro (Zhang et al.,
2016)
Cyclopeptide F81 µ and κ agonist In vitro (Gach-Janczak et
al., 2018) 5-Substituted
Tetrahydronaphthalen-2yl) methyl with N-phenyl-N- (piperidin-4-yl) propionamide derivatives
Good in vitro binding affinity, moderate in vitro activity and low in vivo activity of lead compound
Pre-clinical in vivo studies, showed little activity
(Deekonda et al., 2015)
Endomorohine-2 analogue Does not show bias, but suggests that bias can explain properties, must be given
intracerebroventricularly.
Pre-clinical in vivo studies
(Muranyi et al., 2013)
MEL-N16-series of
endomorphine 1 and 2 analogues
Crosses blood brain barrier so can be given i.v.
Pre-clinical in vivo studies
(Liu et al., 2017) Several agonists based on
piperidine benzimidazolone backbone
Shows how 51 different alterations of the backbone changes bias factor.
Some of the compounds has been tested in vivo by Schmid et al.
(Kennedy et al., 2018)
TRV130, Oliceridine Must be given i.v. Rejected by FDA after phase 3 studies
(Chen et al., 2013)
PZM21 Found by structure-based docking
and structure guided synthetic optimization.
Pre-clinical in vivo studies. Uncertain analgesic effect. The company Epiodyne is developing it further.
(Manglik et al., 2016)
PZM21 analogues Varying analgesic effects, and bias.
No compound was both potent, biased and showed analgesic properties.
Pre-clinical in vivo studies.
(Ma et al., 2019)
Mitragyna alkaloids Alkaloids extracted from the plant Ketum/kratom (Mitragyna speciosa)
Clinical trials using herbal extracts from Kratom recruiting
(Kruegel et al., 2016) Neoclerodane Diterpenes, Extracted from the plant Salvia
Divinorum In vivo studies (Harding et al.,
2005) MEB-1166, MEB-1170 Reports from poster presented at a
meeting of the Academic Drug Discovery Consortium (2017)
Pre-clinical in vivo studies, pre
development candidates
(Barrett, 2017)
TRV734 No published reports, but poster
available from Trevena’s webpage.
Orally bioavailable.
Phase 1 clinical trials. (F. J.
Skobieranda, I.
E.; Fossler, M. J.;
Soergel, D.)
TRV0109101 Do supposedly reverse opioid
induced hyperalgesia
Pre-clinical in in vivo studies.
(Koblish et al., 2017)
3.2 Discovering biased agonists
Screening for biased agonists have their own specific challenges in addition to the complexities of traditional drug screening. It has typically been performed by screening for hits in an assay measuring the effect you want, for instance G protein activation (e.g. GTPgS binding assay), and subsequently followed by performing concentration-effect experiments in the same assay and another assay measuring the unwanted signal, e.g. β-arrestin recruitment assay. The results of the assays are compared by calculating a bias factor. The gold standard of bias calculations has been a method based on Black and Leff´s operational model of agonism (Black & Leff, 1983). It has later been shown that more simplified models are sufficient, also for the µ-Opioid receptor specifically (Winpenny et al., 2016).
Today, as the knowledge about the conformational changes and the ligand- receptor interactions that causes signaling bias increases (Cheng et al., 2018; Okude et al., 2015), new methods to discover biased ligands based on just one assay or
computational methods have been developed. One approach that is based on structural knowledge of the receptor and the receptor-ligand binding is structure-based computer simulation, as was used by Manglik et al. to find PZM21 (Manglik et al., 2016), another method is 19F protein NMR, as was used by Mebias Discovery when discovering their pre development candidates MEB-1166 and MEB-1170 (Barrett, 2017). Dynamic mass redistribution assays is a third approach that seem to be able to predict bias from the morphological change in the cell upon receptor stimulation (Morse, Tran, Sun,
Levenson, & Fang, 2011).
The comparison of two assays might be more resource demanding than these modern one assay approaches, but Trevena used this way of screening when developing oliceridine and TRV734 (Chen et al., 2013).
3.3 Link between mu receptor bias and side effects
What side effects are linked to β-arrestin 2 activation and what side effects are not is currently under debate. The first studies on β-arrestin 2 ko mice showed that they suffered from less respiratory depression, prolonged analgesia, and less constipation from morphine treatment than WT mice (Bohn et al., 1999; Raehal et al., 2005). Almost all studies on supposedly biased agonists show less respiratory depression, and it has been shown that the degree of G-protein bias correlates to a wider therapeutic window measured as ED50 respiratory depression/ ED50 anti-nociception in mice (Schmid et al., 2017). The correlation of biased agonism and other side-effects, for example constipation, addiction, opioid-induced hyperalgesia and tolerance, supposedly all linked to beta-arrestin recruitment, is on the other hand more divergent.
To determine constipation effect of a drug, one can use different assays.
Accumulated fecal boli is the simplest assay. This is done by measuring the weight of fecal boli over a given time period and comparing the intervention with a control group.
Other ways of doing it is measuring intestinal transit time, where you give the test animal charcoal and see how far down the small intestine it has come during a given time period. A third approach is measuring the propulsion of the colon, either in vivo by inserting a glass bead up the test animals’ colon, or ex vivo inserting a glass bead into a buffer perfused colon.
Several of these assays has been done comparing TRV130 and other biased agonists to morphine, and the results from different studies are diverging. Trevena published that in vivo colonic propulsion was less affected by treatment with oliceridine than with morphine (DeWire et al., 2013). Others have found that oliceridine reduces fecal output similar to morphine, and that both oliceridine (100 nM) and morphine (1µM) almost abolished all colonic movements ex vivo(Altarifi et al., 2017). As the results are inconsistent, the question of whether Oliceridine has a lower tendency to induce constipation remains without a definite answer. In the published clinical trials (18.03.19), no direct measurement of constipation is reported.
Another field where results are diverging is the susceptibility for addiction. Even though some studies have failed to see potential addictive behavior of the biased MOR
these studies are debated and several experimental studies (table 2) and
reviews(Negus & Freeman, 2018) of the field suggests that the abuse potential is quite similar to that of conventional opioids. In a recent study by Schwienteck et al., they quantify the efficacy and potency of abuse liability and antinociceptive effect of opioids relative to fentanyl. They find that the two biased compounds oliceridine and SR-14968, show a higher degree of anti-nociception and a lower degree of abuse liability than expected relative to fentanyl, while the conventional opioids behaved as expected from comparison to fentanyl (Schwienteck et al., 2019). See figure 5. In clinical trials with oliceridine, no significant difference in self-reported “likeability” was detected between morphine and oliceridine (Soergel et al., 2014).
Another side effect that has been linked to β-arrestin 2 signaling is opioid- induced hyperalgesia (OIH), but the literature contains incongruent reports. Whereas one study found that Oliceridine and TMZ21 both cause OIH (Araldi, Ferrari, & Levine, 2018), Trevena Inc. reported that TRV109101, another biased µ-opioid receptor agonist not only fails to induce OIH, but is also able to counter morphine-induced hyperalgesia (Koblish et al., 2017). Whether this effect is related to G-protein bias or not is unclear.
The effect of biased signaling on tolerance is also discussed. From the first β- arrestin 2 ko studies it was reported that tolerance was delayed in the hot plate test (Bohn et al., 1999), but later it was not found to be delayed in the warm water tail flick test (Bohn, Lefkowitz, & Caron, 2002). The researchers themselves suggested that the reason was that the hot plate test is related primarily to nociception regulated by neurons in the brain, while the warm water tail flick test is also regulated by neurons in the spinal cord, and that the regulation of tolerance in the brain and the spinal cord is different because of different expression of PKC. They showed that pretreatment with chelerythrine, a PKC inhibitor could reverse the antinociceptive tolerance to morphine.
For TRV130, one study found less tolerance development in the warm water tail withdrawal test (Altarifi et al., 2017), and another study even suggested that
combination therapy with TRV130 and fentanyl increases the anti-nociceptive effect and reduces the tolerance development (Mori et al., 2017), but the amount of research is limited and so is our understanding of tolerance development (Williams et al., 2013).
It is therefore difficult to conclude.
Table 2: Abuse related effects of biased µ-opioid agonists. Table adapted from (Negus & Freeman, 2018).
Figure 5: Empirically determined maximum antinociceptive and discriminative stimulus effects of conventional opioids (left) and the G-protein biased ligands TRV130 and SR-14968 (right) in rats. Both G-protein biased µ- opioid receptor agonists produced greater antinociceptive (square) and less discriminative stimulus (circle) effects than predicted from fentanyl/naltrexone mixtures (anti-nociception: solid line; discrimination: dashed
Abuse potential assessment procedure
Species Drug Abuse related effect? Reference
Subjective effect Human Oliceridine (1.5-4.5 mg, i.v.) Yes (Soergel et al.,
2014)
ICSS Rat Oliceridine (0.1-3.2 mg/kg, s.c.) Yes (Altarifi et al.,
2017)) Drug self-
administration Rat Oliceridine (N.A) Yes (Siuda, Carr,
Rominger, &
Violin, 2017) Drug self-
administration Rat Oliceridine (0.01-3.2 mg/kg, i.v) Yes (Austin
Zamarripa et al., 2018)
Place conditioning Mouse Oliceridine (1.25-10 mg/kg, i.p.) Yes (Liang, Li,
Nwaneshiudu, Irvine, & Clark, 2018)
Drug discrimination Rat Oliceridine (0,1-1 mg/kg, s.c.) Yes (Schwienteck et
al., 2019)
Drug discrimination Rat SR-14968 (0,1-1 mg/kg, s.c.) Yes (Schwienteck et
al., 2019)
Place conditioning Mouse Oliceridine (1.2mg/kg, s.c) No (Manglik et al.,
2016)
Place conditioning Mouse PZM21 (20 mg/kg, s.c) No (Manglik et al.,
2016) Place conditioning Mouse Mitragynine pseudoindoxyl (1.3, 3 mg/kg,
i.p.) No (Váradi et al.,
2016)
3.4 Bias factors of current µ-opioid receptor agonists
One major finding that supports the idea that G-protein biased µ-opioid receptor agonists are safer than conventional agonists, was the correlation between G-protein bias and therapeutic window. It was shown by Schmid et al. that the higher degree of G- protein bias the opioid substance demonstrates (as measured by a GTPgS or cAMP production assay and a β-arrestin 2 recruitment assay), the wider its therapeutic window. Therapeutic window is measured as ED50 of arterial O2 saturation over ED50
antinociceptive effect measured by hot plate test. See figure 6A.
Further they show that newly synthesized and more G-protein biased agonists (SR-17018, SR-15099, SR-15098, SR-14968) show the same trend with broader
therapeutic window than any of the conventional opioids, while the newly synthesized β-arrestin 2 biased agonist SR-1150 have a narrower therapeutic window. SR-1150s degree of bias and therapeutic window are both similar to Fentanyl, which has the narrowest therapeutic window of all opioids tested (Figure 6). The correlation between bias and therapeutic window was for the β-arrestin biased ligands only valid when the GTPgS binding assay is used to measure G-protein signaling, and not when a cAMP assay is used (Figure 6 B). They suggest that a greater
variation in the cAMP data might be the reason (Schmid et al., 2017).
The bias factors of different morphine metabolites have also been characterized, the differences between metabolites are not very large, but the authors suggest that difference in metabolic pathways for morphine can be relevant for some of the interpatient differences in side effects of morphine (Frolich et al., 2011).
The relative bias between endogenous opioids are also different. This shows that biased agonism isn´t just related to drugs, but is also a part of normal physiology, though the
physiological consequences are still unclear(Thompson et al., 2015).
Figure 6 Correlation between bias factor and therapeutic window for µ-opioid receptor agonists. There is a correlation between degree of g-protein bias (x-axis) and therapeutic window (y-axis) for β-arrestin biased compound, this is only when the GTPgS represents G-protein signaling (right)(
3.5 Oliceridine, why did FDA reject it and what are the alternatives.
Oliceridine, also known as TRV130, was the first biased µ-opioid receptor agonist to enter clinical trials. Although the results were generally promising, the drug was rejected by the FDA in Oct. 2018 by 8 against 7 votes (Trevena, 2018b). A short summary of the clinical trials follows.
In phase 1 clinical trial, it was found that oliceridine was generally well tolerated, with a similar side effect profile as morphine. Oliceridine had a more rapid onset of analgesia than morphine, consistent with expectation from measurements of distribution time. (Soergel et al., 2014).
In phase 2 clinical trial, two different clinical pain models were used to study the effect and side-effects of oliceridine. In the bunionectomy study, representing skeletal pain, the participants have been through bunionectomy, removal of a bony overgrowth in the foot, and in the abdominoplasty study, the participants have been through a
“tummy tuck”, a removal of excess skin and fat from the abdomen. This were quite small single center studies. In the abdominoplasty study the patients had to change the
Oliceridine dosing regimen from initial loading dose of 1.5 mg and self-administered 0.1 mg doses maximum every 10 min to initial loading dose 1.5 mg and self-administered 0.35 mg max every10 min. This was done after an interim analysis, likely because a high number of patients in the oliceridine group required rescue medication. Overall the phase 2 studies showed that Oliceridine was comparable to morphine, with a tendency towards less side effects. See table 3. (Singla et al., 2017; Viscusi et al., 2016)
Table 3 Adverse events in phase 2b abdominoplasty study of Oliceridine. TEAE = treatment emergent adverse event. (Singla et al., 2017)
In the phase 3 clinical trials, the bunionectomy and abdominoplasty pain studies were repeated, but as larger multicenter studies named Apollo 1 and Apollo 2, before a more general open label safety study were performed including more than 750 patients, named Athena. Nothing has been published from these phase 3 studies except from posters on Trevena’s websites about Apollo 1 and 2, showing that Oliceridine has a similar adverse events profile as morphine, with a slight tendency towards less side effects, but without any reported confidence intervals (ER Viscusi; F Skobieranda; DG Soergel; DA Burt, 2017; F. S. Skobieranda, N; Burt, DA; Soergel, DG, 2017).Trevena has announced that Oliceridine was rejected by the FDA (Trevena, 2018b). According to Trevena, FDA rejected Oliceridine because of uncertainty around the propensity to cause delayed QT time in the dosages needed to cause anti-nociception, and they
requested additional nonclinical data and validation reports (Trevena, 2018c). Trevena has announced that they will meet the requests from the FDA and apply for approval again (Trevena, 2019), and they are working on development of the alternative compound TRV734. They have announced that they have a collaboration with the national institute of drug abuse (NIDA) about TRV734s potential use as a drug for management of opioid dependence, and are apparently still trying to develop such a potential use further (Trevena, 2018a).
It is not only Trevena that has announced that they are working to get new biased µ opioid receptor agonists approved as drugs. The company Mebias Discovery
has announced that they are developing new biased agonists with greater bias factors than Oliceridine. At present, they have only published a poster, presented at a meeting in the Academic Drug Discovery Consortium in October 2017. Here they claim that their compounds MEB-1170 and MEB-1166 cause no respiratory depression in dosages with equal antinociceptive effect as 4xED80 of morphine measured in the tail flick study, while Oliceridine dosed in the same way cause a drop in pO2 from about 95 mmHg to 75 mmHg (Barrett, 2017), see figure 7.
The researchers behind the article about correlation between G-protein bias and therapeutic window have patented their biased ligands, and more patents on both peptides and small molecular substances can be found in the Google Patents database (GooglePatents, 2019), which might indicate that we´ll probably see more attempts on obtaining FDA approval for biased agonists in the future.
Figure 7: Respiratory depression caused by Morphine, TRV130, and the new compounds MEB-1170, MEB-1166 developed by Mebias Discovery. (Barrett, 2017) © Mebias Discovery
3.6 Main oppositions
The searches did not reveal a great deal of opposition, but some reviews are skeptical about clinical relevance and weather bias between G-protein activation and β- arrestin recruitment is sufficient to predict physiological outcome (Thompson et al., 2015). In Schwienteck et al. (2019), they claim to show that it is, at least for respiratory depression, and they claim that their finding explains the clinical experience that fentanyl has a narrower therapeutic window than morphine. They do not have a
reference to support this claim, and actually I find references that point to the complete opposite, that fentanyl has a wider therapeutic window than morphine(Mather, 1995).
This is interesting and should be looked closer into.
One basic science article preforming mutations on phosphorylation sights on the µ-opioid receptor found an increased analgesic effect and decrease in tolerance
development for morphine. But they also found an increase in constipation and respiratory depression, and concluded that less β-arrestin recruitment did not mean reduced side effects (Kliewer et al., 2019). As they don´t show that the mutated receptors do not attract β-arrestin, and it is known from other receptors that beta arrestin recruitment can happen without phosphorylation (Jala, Shao, & Haribabu, 2005) I find their conclusion too stern. The amount of data pointing towards a beneficial profile of G-protein biased MOR-agonist is generally more convincing, at least in the non-clinical part of the literature. Nevertheless, they point to a need for greater understanding of the signaling pathways related to respiratory depression and constipation.
4 Conclusion/discussion
The development of newer and safer analgesics is a goal for the future. The rates of overdose deaths are high, so a less harmful and non-abuseable pain-killer, giving equally strong pain-relief as opioids, would mean that more patients could be adequately treated. A G-protein biased µ-opioid receptor ligand is one of the
possibilities we have, and still, even after the FDA refusal of oliceridine, it is possible that it reaches the clinic. If so is going to happen, it is important that we learn from the past, and do not promise to much without evidence, heroin and OxyContin have
certainly not provided us with less opioid addiction, as first promised.
Based on the available data, it is likely that G-protein bias can overcome some of the problems with conventional opioids, though far from all. Most promising is the diminished respiratory depression. An opioid with less liability to cause respiratory depression would be safer to use in the post-operative setting, and the liability to cause overdose deaths is likely to be reduced. If Mebias claim about their novel agonists lacking respiratory depression effect holds true, this is very promising for a new drug (figure 7).
Even though a drug not causing respiratory depression would be a great
improvement, the ultimate goal must be a safe and strong antinociceptive agent without propensity for abuse. From the data available today, it does not seem that a G-protein biased µ-opioid agonist is the solution to this problem, but a better understanding of what causes addiction is needed.
As the data is diverging for other side-effects, like tolerance, constipation and opioid induced hyperalgesia, a greater understanding of the signaling pathways downstream of µ-opioid receptors is needed. More physiological data on more biased compounds than oliceridine, like the Mebias compounds, could give us some answers, but probably a better understanding of the pathways downstream of µ-opioid receptor in general is needed. This could again make it possible to make more sophisticated biased compounds. As a GPCR can exist in “very many more” states than the previous understanding of active and inactive state (Terry Kenakin, 2017), the potential for shifting the signal in a wanted direction using biased agonists is likely far from realized,
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