DEVELOPING A WORKFLOW TO EVALUATE MEDICATIONS FOR REPURPOSING USING HEALTH CLAIMS DATA: APPLICATION TO SUBSTANCE USE DISORDERS

Healthcare big data are a growing source of real-world data with which to identify and validate medications with repurposing potential. Previously, we developed a claims-based workflow to evaluate medications with potential to treat stimulant use disorders. In order to test the workflow, the framework was applied in the context of opioid use disorders (OUDs), for which there are medications with known efficacy. Using the Truven Marketscan Commercial Claims Database, a nested case-control analysis was conducted to determine the association between OUD medications (buprenorphine, naltrexone) and remission. Cases were defined as enrollees with a remission diagnosis and matched (1:4) to controls (individuals without remission) using incidence density sampling, with age group, sex, region, and index year as additional matching variables. After adjusting for behavioral health visits, polysubstance use disorders, and psychiatric disorders using conditional logistic regression, the odds of OUD medication exposure were 3.8 (99% confidence interval: 3.0 – 4.9) times higher in cases than controls. Evaluation of angiotensin converting enzyme inhibitors (e.g. lisinopril) as a negative control revealed no significant association between the medication and remission. This work demonstrates the feasibility of using administrative health claims data to evaluate the effectiveness of medications to treat substance use disorders

Age- and sex-specific effects of amphetamines on cognition and serotonin in the orbitofrontal cortex

Use of amphetamines is a global health problem associated with significant social and financial burdens. Worldwide, amphetamines are second only to marijuana and opiates, respectively, for most abused illicit drug and highest prevalence of addiction. Epidemiological data reveal that adolescent and female drug users are at higher risk to develop addiction and have worse treatment outcomes than adults or males. Drug-induced cognitive deficits and neuroadaptations, in combination with different patterns of drug-seeking, represent possible mechanisms by which heightened vulnerability to addiction may be conferred in adolescents and females. Using a rodent model, this hypothesis was tested with two specific aims: (1) by assessing the impact of exposure to amphetamines on cognitive flexibility and 5-HT2C receptor structure and function (Experiments 1, 3, 4) and (2) by examining age and sex differences in intravenous methamphetamine self-administration (Experiment 2). In Experiment 1, male and female Sprague-Dawley rats were treated with amphetamine (3 mg/kg i.p.) during adolescence or young adulthood and tested in a Pavlovian outcome devaluation task at the same age in adulthood. Subsequently, the impact of systemic 5-HT2C receptor antagonism on devaluation was tested in a separate group of amphetamine-treated rats. In Experiment 2, male and female Sprague-Dawley rats were trained to self-administer methamphetamine at 3 doses (0.02, 0.05, 0.08 mg/kg/inf) during adolescence or adulthood and, subsequently, tested for motivation to work for four doses of methamphetamine using a progressive ratio schedule of reinforcement. Rats that acquired methamphetamine self-administration at the highest dose were used in Experiments 3 and 4. In Experiment 3, rats with a history of self-administration were tested for cognitive flexibility in an operant strategy shifting task. Subsequently, in Experiment 4, immunohistochemical analysis of the brains examined colocalization of 5-HT2C receptors with parvalbumin-immunoreactive interneurons in the orbitofrontal cortex. These studies revealed complex interactions of age and sex on drug-induced changes and heightened drug-seeking in adult rats. Taken together and in the context of broader literature, this work supports the assertion that pervasive, and sometimes subtle, drug-induced changes in cognition and neurobiology along with different patterns of drug-seeking may be mechanisms of heightened vulnerability in adolescent and female users

X-search: An Open Access Interface for Cross-Cohort Exploration of the National Sleep Research Resource

Background: The National Sleep Research Resource (NSRR) is a large-scale, openly shared, data repository of de-identified, highly curated clinical sleep data from multiple NIH-funded epidemiological studies. Although many data repositories allow users to browse their content, few support fine-grained, cross-cohort query and exploration at study-subject level. We introduce a cross-cohort query and exploration system, called X-search, to enable researchers to query patient cohort counts across a growing number of completed, NIH-funded studies in NSRR and explore the feasibility or likelihood of reusing the data for research studies. Methods: X-search has been designed as a general framework with two loosely-coupled components: semantically annotated data repository and cross-cohort exploration engine. The semantically annotated data repository is comprised of a canonical data dictionary, data sources with a data dictionary, and mappings between each individual data dictionary and the canonical data dictionary. The cross-cohort exploration engine consists of five modules: query builder, graphical exploration, case-control exploration, query translation, and query execution. The canonical data dictionary serves as the unified metadata to drive the visual exploration interfaces and facilitate query translation through the mappings. Results: X-search is publicly available at https://www.x-search.net/ with nine NSRR datasets consisting of over 26,000 unique subjects. The canonical data dictionary contains over 900 common data elements across the datasets. X-search has received over 1800 cross-cohort queries by users from 16 countries. Conclusions: X-search provides a powerful cross-cohort exploration interface for querying and exploring heterogeneous datasets in the NSRR data repository, so as to enable researchers to evaluate the feasibility of potential research studies and generate potential hypotheses using the NSRR data