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July 7, 2017
NIH Cutting Edge Science Meeting Series to End the Opioid Crisis
Executive Summary — Understanding the Neurobiological Mechanisms of Pain
As part of a government-wide effort to address the opioid crisis, NIH is initiating a public-private collaborative research initiative on (1) new and innovative medications and biologics to treat opioid addiction and for overdose prevention and reversal; (2) safe, effective, and non-addictive strategies to manage chronic pain; and (3) neurobiology of chronic pain.
To identify the scientific strategies with the greatest potential for solutions to the opioid problem, NIH brought together innovative experts from government, industry, and academia for a series of three cutting-edge science meetings. NIH seeks to identify and pursue promising opportunities for addressing the opioid crisis, with the aim of developing new safe and effective therapeutics for opioid addiction, overdose, and chronic pain in half the time it currently takes.
The first of these meetings was held on June 5th, entitled Medications Development for Opioid Use Disorders and Overdose Prevention and Reversal. The second meeting, entitled Development of Safe, Effective, and Non-Addictive Pain Treatments, was held on June 16th. The third meeting, held on July 7th and summarized below, focused on Understanding the Neurobiological Mechanisms of Pain.
- A large, coordinated initiative is needed to make rapid progress in developing new, effective analgesics without abuse liability.
- Sharing of data, resources, and expertise between industry and academic researchers could be used to help promote efficient use of limited resources.
- More neuroscientists are needed in the field of pain research; Newly available neuroscience technologies should be leveraged for pain research.
- Pain processing is complex with diffuse mechanisms throughout the brain and periphery. There is a need for multidisciplinary efforts to understand pain at the molecular, cellular, circuit, and system levels.
- Standardized, objective measures of pain and treatment response are needed for animal models with translational validity in humans.
- Extensive patient phenotyping is needed to develop objective biomarkers of pain and treatment response which will enable precision medicine approaches to more effectively treat each patient’s pain, as well as the factors that impact the patient’s experience of pain.
- Objective screens, based on the neurobiology of pain, are needed to validate targets and accelerate the drug development process.
- There are major research gaps regarding the prolonged effects of chronic pain, long term impacts of treatment (e.g. hyperalgesia due to chronic opioid use), and the transition from acute to chronic pain.
Potential Next Steps:
NIH will consider strategies to:
- Promote collaborative cross-sector data and expertise sharing to facilitate more efficient use of resources
- Develop biomarkers of pain with predictive clinical validity and predictors of treatment response
- Develop biologically based objective measures of pain
- Use existing failed and successful analgesics to evaluate and improve drug screening models and assays (human cell based models, animal models, genetic tools to modify targets in cells (CRISPR)) to develop a standardized platform for the validation of drug targets
- Develop a standardized, composite set of clinically relevant outcome measures to enhance clinical research
- Facilitate a research collaboratory to explore:
- Mechanisms of pain, including maladaptive plasticity associated with chronic pain, in the peripheral nervous system, spinal cord, and brain
- The long-term impact of chronic opioid treatment (e.g. hyperalgesia due to chronic opioid use, mechanism of tolerance)
- The transition from acute to chronic pain and factors that prevent transition and resilience factors that protect against it.
- Stratification of patients who will/will not respond to treatment, and to placebo
- The impact of co-morbidities on clinical trajectory, treatment response, and long-term outcomes
- Mechanisms underlying placebo response
This page last reviewed on November 21, 2017