Analgesic Interactions of Opioids and Cannabinoids

States (30% of the population), costing an estimated $600 billion dollars annually1. The most effective pain medications are the opioid analgesics. Opioids are prescribed at high rates across various types of treatment settings (emergency room, pain treatment clinics) due to their analgesic efficacy; however, these medications also have high abuse liability and can produce dangerous side effects (e.g., respiratory depression). Several classes of non-opioid medications are used as alternatives to opioids, including NSAIDs, tricyclic and SSRI-type antidepressants, NMDA antagonists, and anticonvulsants. Although these are useful for a subset of pain conditions, they are typically not as effective as opioids for moderate-to-severe pain relief. Thus, there is a need to develop adjuvant medications that enhance the analgesic efficacy of opioids, reduce opioid doses, provide effective pain relief, and produce minimal side effects or toxicity. Currently, the only widely prescribed opioid adjuvants are the NSAIDs (e.g., acetaminophen, ibuprofen, aspirin), which are marketed in combination products with opioids (e.g., Vicodin® and Percocet®). Other opioid adjuncts are prescribed in clinical practice (e.g., gabapentin, amitriptyline); however, few well-controlled studies have assessed their safety, tolerability, and synergistic efficacy with opioids. Given the high rates of opioid abuse in Kentucky and the U.S., there is a great public health benefit to develop medications that would allow for decreased opioid use and prescribing while providing effective pain relief. The current study is designed to assess the analgesic efficacy and safety of a candidate opioid adjuvant medication. Specifically, an ideal opioid adjuvant medication could function in one or all of the following ways: 1) increase the efficacy of an opioid, 2) decrease the development of opioid tolerance, and/or 3) reduce the required opioid dose. Dronabinol (Marinol®) is currently FDA-approved for the treatment of chemotherapy-related nausea/vomiting and as an appetite stimulant for cancer and AIDS patients, but is prescribed off-label for pain relief. This candidate medication will be examined alone and in combination with an opioid analgesic on an array of laboratory-based experimental pain models. These models, when examined in healthy, pain-free participants, are thought to yield valid translational data on analgesic efficacy, specific pain neurobiological pathways, and the target clinical population (e.g., patients with neuropathic pain). The primary aim of the research component of this KL2 application is to enroll healthy adults without histories of chronic pain as outpatients in a study to assess the following aims: Aim 1: To examine the analgesic effects of dronabinol, alone and in combination with oxycodone, a prototypic opioid, using laboratory models of pain that are predictive of the clinical pain response. Secondary aims will examine the safety of these drug combinations by collecting an array of physiological, subjective, cognitive and psychomotor performance effects. Hypothesis 1.1: Dronabinol will enhance the analgesic efficacy of oxycodone when either sub-therapeutic or therapeutic doses of dronabinol and oxycodone are combined. Hypothesis 1.2: All of the dose combinations will be safely tolerated and will produce minimal aversive or impairing effects. Aim 2: To incorporate two new laboratory models of algesia, the capsaicin test and the menthol test, which model two distinct types of neuropathic pain and determine their sensitivity to opioid adjuvants. A secondary aim is to add these models to two others already available (cold pressor test and pressure algometer), to 1) create a sensitive battery of in vivo pain assays useful for assessing multiple pain modalities simultaneously, and 2) explore differential sensitivity to opioids or cannabinoids amongst the models. Hypothesis 2.1: The new pain models will display sensitivity to dronabinol, oxycodone and their combination. Hypothesis 2.2: Differential sensitivity to the test agents will be observed across the array of pain models, as each measure models a unique pain pathway, providing information about which types of pain may be responsive to cannabinoids or a cannabinoid-opioid combination.