iTransform: Wearable Biosensors to Detect the Evolution of Opioid Tolerance in Opioid Naïve Individuals

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PROJECT SUMMARY The integrated research and training plans outlined in this K23 submission will prepare me for a career as a clinician-scientist conducting translational substance abuse research. My career goal is to perform hypothesis- driven original research investigations directed toward reducing morbidity and mortality from opioid overdose. In this proposal, I intend to deploy wearable biosensors (small devices that continuously record physiology) to study the effects of therapeutic administration of opioid analgesics. I have already studied wearable biosensors in individuals receiving opioids; my preliminary data demonstrates that opioid-tolerant individuals have different biometric signals than non-tolerant individuals. This observation suggests that biosensors can be used to identify the onset of tolerance, an important event that correlates with higher doses of opioid analgesics, and higher risk of death from opioid overdose. Biosensor data management and analysis requires signal processing, data analytic, and machine learning techniques; these approaches are beyond the areas of traditional medical training. My short-term goal is to utilize this K23 award to fill my knowledge gaps in wearable biosensing and advanced data analysis so that I can generate ever more innovative responses to the problem of opioid prescribing, tolerance, misuse, addiction, and overdose. To optimize this important line of investigation, I have developed a training plan that includes: 1) completing a PhD through the Millennium PhD program; 2) expanding my skills in wearable biosensing and behavioral health-based research; 3) developing an understanding of signal processing and machine learning; 4) developing data analytic and data science skills; and 5) expanding my research presentation and dissemination skills. I will achieve these goals through directed coursework, focused seminars, and practical experience. My mentorship team of expert investigators who will ensure my productivity and success includes E. Boyer (primary mentor), D. Smelson, J. Fang, and P. Indic (secondary mentors), and D. Ganesan (advisor) My research plan has three specific aims: 1) to deploy a wearable biosensor technology to detect digital biomarkers associated with the initiation of opioid analgesic therapy in an opioid naïve population; 2) to use signal-processing analytics to identify transitions in digital biomarkers with progressive opioid use and to identify individual characteristics associated with this transition; and, 3) to apply and explore supervised learning algorithms that can predict transitions in digital biomarkers that herald the onset of opioid tolerance. To identify dynamic patterns in response to opioids, I will study the digital biomarkers of opioid-naïve patients with acute fractures who are prescribed opioid analgesics. Results will be used to develop “big data” approaches to apply predictive algorithms to identify the onset of opioid tolerance. This work has the potential to prevent development of problematic opioid use and will provide the basis for subsequent R01 submissions to implement sensor-based interventions triggered by the onset of tolerance in individuals receiving opioid analgesics.