Funded Projects

The UCSF Core Center for Patient-centric Mechanistic Phenotyping in Chronic Low Back Pain (UCSF REACH) is an interdisciplinary consortium of basic and clinical scientists dedicated to understanding and clarifying the biopsychosocial mechanisms of chronic low back pain (cLBP). The goal of REACH is to define cLBP phenotypes and pain mechanisms that can lead to effective, personalized treatments for patients across the population. UCSF REACH has six cores that will support a single research project that is focused on the challenge of developing validated and adoptable tools that enable comprehensive yet routine clinical assessment and treatment of cLBP patients. Overall, the object of REACH is to make optimum use of all available resources to catalyze discovery and translation of novel diagnostics and therapeutics that improve outcomes of cLBP patients.

Current diagnostics and treatments of chronic low back pain (cLBP) rely primarily on subjective metrics and do not target all the multidimensional biopsychosocial mechanisms. This multidisciplinary effort will develop and validate a digital health platform and provide meaningful data-driven metrics that enable an integrated approach to clinical evaluation and treatment of cLBP. This platform will facilitate the use of quantitative spinal motion metrics (function), patient-reported outcomes, and patient preference information to enable deep patient phenotyping and inform clinical decision making on personalized treatments in order to improve outcomes. This effort will involve software and hardware development to enable data collection, analysis, and visualization in clinical settings. The outcome of this project will be a digital health platform with data to support regulatory submission for clinical use. At the end of this effort, the researchers will have a validated tool for integration in clinical research studies supported by the BACPAC Consortium.

Chronic overlapping pain conditions represent up to half of all chronic pain cases and can be more debilitating than other forms of chronic pain. These conditions include but are not limited to the following: temporomandibular disorders, fibromyalgia, irritable bowel syndrome, vulvodynia, interstitial cystitis/painful bladder syndrome, painful endometriosis, chronic tension type headache, migraine headache, chronic low back pain, and chronic fatigue syndrome. Common neurobiological mechanisms have been suspected to account for the overlap between these conditions, but until recently it has been difficult to efficiently classify each condition within individual patients. A digital classification tool for clinicians has been developed for this purpose, but access to the tool remains limited. Here we propose converting this chronic overlapping pain conditions classification tool into a common web-based application format.

The NIH Back Pain Consortium (BACPAC) Research Program brings together leading centers with expertise in studying and treating chronic low back pain to advance understanding of the mechanisms that underlie the condition and to identify novel treatment strategies. BACPAC is undertaking a multisite precision medicine clinical trial taking into account patient-specific information to understand which patients with chronic low back pain respond best to various nonopioid, evidence-based treatments. The trial seeks to enroll a racially, ethnically, and socioeconomically diverse patient population to ensure that the results are applicable to all Americans with chronic low back pain. This project aims to develop comprehensive recruitment and retention plans for study sites that can recruit from historically underrepresented populations in clinical research (e.g., Black and Hispanic populations) and to provide dedicated financial resources to engage patients from these populations using tailored, culturally appropriate strategies.

Knee osteoarthritis is one of the leading causes of disability worldwide, particularly among older adults. Despite multiple guidelines for care, most patients do not receive adequate treatment, and about 30% are prescribed long-term opioids. This award will be used to recruit and support an early career faculty member from a group underrepresented in biomedicine. This research, part of the Pain Management Effectiveness Research Network will evaluate conservative and more aggressive treatments for knee osteoarthritis and determine which individual-level factors contribute to treatment outcomes.

The University of Michigan (UM) will lead a Mechanistic Research Center (MRC) as part of the broader BACPAC initiative that will take patients with chronic low back pain (cLBP) and use a patient-centric, SMART design study to follow these individuals longitudinally as they try several different evidence-based therapies while mechanistic studies are overlaid to draw crucial inferences about what treatments will work in what patient endotypes. Interventional Response Phenotyping describes the need in any precision medicine initiative to phenotype participants based on what therapies they do and do not respond to so that one can later link mechanistically distinct disease endophenotypes with those who preferentially respond to therapies targeting those mechanisms.

Scientists do not know the details of how the nervous system interacts with the temporomandibular joint (TMJ) that connects the lower jaw with the skull. This project aims to comprehensively explain the functions, types, neuroanatomical distributions, and adaptability (plasticity) of specific nerve cells in the brain (trigeminal neurons) that connect with the TMJ. The research will analyze nerve-TMJ connections associated with chewing muscles and other structures that form the TMJ such as cartilage and ligaments. The project will analyze samples from both sexes of aged mice, primates, and humans with and without painful TMJ disorders. This research aims to uncover potential treatment and prevention targets for managing TMJ pain.

A complex network of different nerve cell subtypes connects to joints in different ways throughout body regions, such as the knee and the temporomandibular joint (TMJ) that connects the lower jaw and skull. This project aims to identify disease-specific pain symptoms using clinically relevant rat models of TMJ and knee osteoarthritis – and compare findings with disease-specific pain symptoms in human patients with the same conditions. This research may lead to a better understanding of how different nerve cell subtypes contribute to joint pain as well as how these nerve cell subtypes change with age and disease.

Every year, more than 330,000 Americans are hospitalized for hip fractures. Rapid surgical intervention and pain treatment is critical to recover mobility and reduce other health complications. Ultrasound-guided regional anesthesia techniques are an effective alternative to opioid medication, but require specialized training for use in the emergency department. This project will develop and validate an easy-to-use ultrasound-based regional anesthesia guidance system, to ultimately improve access to non-opioid-pain treatment for hip fracture pain.

The University of California, San Francisco, as part of the Back Pain Consortium (BACPAC) Research Program, has established a Core Center for Patient-centric Mechanistic Phenotyping in Chronic Low Back Pain (REACH). The main goal of REACH is to define different subtypes (phenotypes) of chronic low back pain as well as to identify underlying pain mechanisms that can lead to effective, personalized treatments for patients across all population subgroups. To achieve this goal, REACH is, or will be, participating in several clinical trials, and it is imperative that the patients participating in these trials reflect the diversity of the U.S. population. Therefore, this project seeks to adapt methods that have successfully improved minority participation in other settings as well as to develop and deploy digital strategies that can promote recruitment and engagement of patients from marginalized populations.

Digital health interventions, such as virtual reality (VR) applications, have become available for the treatment and monitoring of numerous health conditions, including pain management. A current HEAL-funded study is evaluating the role of a therapeutic VR approach for chronic low back pain. However, racial and ethnic disparities exist in patient access and response to such VR applications, as well as in the incidence and reporting of pain. For example, non-Hispanic Blacks and Hispanics are more likely to report severe pain than non-Hispanic Whites, yet are less likely to have access to digital health information and interventions. To address these disparities, this project will develop a framework to advance diversity and inclusion in digital health trials and will seek to increase the proportion of non-Hispanic Black and Hispanic participants in the ongoing VR trial by tailoring recruitment materials and using novel artificial intelligence-driven cohort building tools.

 Non-Invasive Brain Stimulation (NIBS) has been successfully applied for the treatment of chronic pain (CP) in some disease states, where treatment induced changes in brain activity revert maladaptive plasticity associated with the perception/sensation of CP [25-28]. However, the most common NIBS methods, e.g., transcranial direct current stimulation, have shown limited, if any, efficacy in treating neuropathic pain. It has been postulated that limitations in conventional NIBS techniques’ focality, penetration, and targeting control limit their therapeutic efficacy . Electrosonic Stimulation (ESStim™) is an improved NIBS modality that overcomes the limitations of other technologies by combining independently controlled electromagnetic and ultrasonic fields to focus and boost stimulation currents via tuned electromechanical coupling in neural tissue . This proposal is focused on evaluating whether our noninvasive ESStim system can effectively treat CP in carpal tunnel syndrome (CTS), both as a lone treatment and in conjunction with physical therapy (PT). Investigators hypothesize ESStim can be provided synergistically with PT, as both can encourage plasticity-dependent changes which could maximally improve a CTS patient’s pain free mobility. In parallel with the CTS treatments, the team will build multivariate linear and generalized linear regression models to predict the CTS patient outcomes related to pain, physical function, and psychosocial assessments as a function of baseline disease characteristics. The computational work will be used to develop an optimized CTS ESStim dosing model.