# Biased Mu-Opioid Receptor Analgesics to Prevent Overdose and Opioid Use Disorders

> **NIH NIH UH3** · MEBIAS DISCOVERY, INC. · 2023 · $109,995

## Abstract

Approximately 100 million people in the United States suffer from pain with 9 to 12 million individuals suffering from
chronic or persistent pain.1 With opioids remaining at the forefront of treatment, it has become clear that opioid abuse and
opioid overdose have emerged as significant and complicated public health challenges. Drug overdose from opioids is the
leading cause of accidental death in the U.S. with an estimated 100 individuals a day dying from opioid overdose due to
respiratory depression.2 Although multiple factors are unquestionably responsible for the increase in the use and abuse of
opioids, there is a pressing need for an effective opioid analgesic that also addresses the significant issues surrounding
opioid abuse liability and overdose fatalities. Advances in our understanding of the pharmacological mechanisms
associated with signaling of G-protein coupled receptors have resulted in the knowledge that activation of the mu-opioid
receptor (MOR) mediates both the therapeutic and adverse effects and does so through pharmacologically distinct
signaling pathways. The adverse effects associated with morphine and other MOR agonists have been traced to action
through the β-arrestin pathway, while analgesia is tied to the G-protein pathway. G-protein specific agonists that avoid
activation of β-arrestin signaling and its associated negative consequences provide novel strategies for the development of
pathway specific or ‘biased’ drugs designed to selectively produce analgesia while eliminating unwanted adverse effects
that include respiratory depression, abuse liability, and constipation.
Mebias Discovery, Inc. has developed a novel platform and has identified highly ‘biased’ MOR agonists that are effective
analgesics but are devoid of opioid induced adverse effects. Mebias’ preclinical studies of its IND candidate MEB-1170
has shown efficacy in 3 pain models without the known opioid adverse effects (respiratory depression, tolerance to
analgesia, sedation, constipation) shown by marketed MOR drugs. In addition, MEB-1170 shows promise in abuse
liability models (self-administration, drug discrimination, condition place preference, withdrawal) suggesting it could be a
game changer as a non-addictive analgesic to replace Scheduled II opioids in pain management.
1 Califf, Robert M., Janet Woodcock, and Stephen Ostroff." A proactive response to prescription opioid abuse." New
 England Journal of Medicine 374, no.15 (2016): 1480-1485.
2 "Opioid overdose." Centers for Disease Control and Prevention. August 30, 2017. Accessed January 12, 2018.
https://www.cdc.gov/drugoverdose/epidemic/index.html

## Key facts

- **NIH application ID:** 10749222
- **Project number:** 3UH3DA047700-05S2
- **Recipient organization:** MEBIAS DISCOVERY, INC.
- **Principal Investigator:** JAMES E. BARRETT
- **Activity code:** UH3 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2023
- **Award amount:** $109,995
- **Award type:** 3
- **Project period:** 2018-09-15 → 2023-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10749222

## Citation

> US National Institutes of Health, RePORTER application 10749222, Biased Mu-Opioid Receptor Analgesics to Prevent Overdose and Opioid Use Disorders (3UH3DA047700-05S2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10749222. Licensed CC0.

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