Funding Opportunities

Opportunity Pool support awards and applications

Various Opportunity Pool funding awards are open to applications with the MPRINT Hub. Browse previously awarded research awards below. To apply for current funding opportunities please click here.

Vanderbilt University MPRINT-funded support pool projects

Lead investigator
James Antoon, MD, PhD
Assistant Professor of Pediatrics, Hospital Medicine
Vanderbilt University Medical Center

Oseltamivir is the only FDA approved medication for the treatment of influenza in children. While oseltamivir is known to improve influenza outcomes in children, there is high variability in prescribing patterns for oseltamivir in children with influenza. Furthermore, some medications could increase the levels of oseltamivir metabolites and corresponding toxicity when used concurrently with oseltamivir. Yet, the prevalence of oseltamivir use overall, and concurrently with medications with potential drug-drug interactions is unknown. The objective of the study is to determine the prevalence of oseltamivir use alone, and concurrently with medications with potential drug-drug interactions that may increase the risk of drug related adverse events among children.

The overall objective of this research is to determine the association between oseltamivir and neuropsychiatric adverse events (NPAEs) using newly validated NPAE identification algorithms. We will perform a retrospective observational cohort study using the TennCare database to evaluate oseltamivir exposure among Tennessee children.

The TennCare database is ideal for this study as medications. Medication exposures are captured using automated pharmacy files of dispensed medications, an excellent source for prescribed medications. None of the proposed study drugs are over-the-counter for pediatric use.

Furthermore, while medications paid out of pocket may not be captured by the automated TennCare pharmacy files, the underserved nature of the study population should minimize concerns about this possibility.

Lead investigator
Leena Choi, PhD
Professor of Biostatistics
Vanderbilt University Medical Center 

Many medications prescribed to children are off-label and dosing is not optimal due to lack of data. Pharmacokinetic (PK) and pharmacodynamic (PD) studies can provide critical data for determining dosing regimens. We recently developed a dexmedetomidine population PK model for a large pediatric cohort of 354 children. Using this PK model, we plan to develop a PK/PD-model guided clinical decision support system for dexmedetomidine, embedded in our electronic health record (EHR) system.

This clinical decision support system would help providers to find optimal dose for dexmedetomidine in children undergoing surgery. There are several software platforms that were developed for therapeutic drug monitoring available in market such as InsightRX, DoseMe, and MwPharm. As the first step, we will evaluate these software platforms to select the most appropriate software that meets our criteria (e.g., accuracy of dose prediction algorithm, comparability with our EHR system, flexibility for customization, etc.).

With this support pool fund, we will buy at least 3 different PK software platforms and will select the most appropriate one. Once the software is selected, we will implement our developed PK model in the software.

Lead investigator
Ashley Leech, PhD, MS
Assistant Professor of Health Policy
Vanderbilt University Medical Center

Women with substance use disorder (SUD) or who use opioids in pregnancy represent a vulnerable population; however, less is known about how social vulnerability affects relapse and death in the year after delivery. The purpose of this study is to examine the association between the CDC Social Vulnerability Index (SVI) and overdose or late maternal death among women with (or at-risk for) substance use disorder in pregnancy. We plan to examine the association between the CDC Social Vulnerability Index (SVI) and overdose or late maternal death among women with (or at-risk for) substance use disorder in pregnancy. We will perform a retrospective cohort study using TN Medicaid (TennCare) files linked to TN birth certificate data, US census reports, TN Joint Annual Report data, American Hospital Association annual hospital survey, and TN death certificate data.

Outcomes include (1) maternal death from any cause identified from 42 to 365 days after delivery; and (2) overdose diagnosis. A Cox proportional hazard model will be used for analysis, comparing women with SUD or on treatment for SUD (methadone, buprenorphine, naltrexone) to women without SUD who filled > 2 opioid prescriptions in pregnancy.

Lead investigator
Anna Patrick, MD, PhD
Assistant Professor of Pediatrics, Rheumatology
Vanderbilt University Medical Center 

Juvenile idiopathic arthritis (JIA) is an autoimmune arthritis in children with pathogenesis involving genetic risk and the environment. JIA patients with prepubescent diagnosis and more joints involved at onset or over time have a chronic disease course with longstanding medication use and often medication failures. A major hurdle for personalized medicine in JIA is that we do not understand how genetics impact disease outcomes and therapy choices. An essential area of JIA research is to define relationships between genetics, molecular phenotypes, and clinical characteristics to improve diagnosis, clinical outcomes, and medication choices. The purpose of this non-human subject research is to use de-identified information from the Synthetic Derivative to study characteristics of a cohort of juvenile idiopathic arthritis patients. Our hypothesis is that the genetic contribution to gene expression in JIA predisposes to increased immune activation and exhibition of inflammatory cytokines. To test this hypothesis, we will use publicly available genetic data for JIA and genetic data obtained from a cohort of JIA patients identified in a de-identified electronic medical record, the Synthetic Derivative.

We have 2 approaches. 1) GWAS summary statistics are available for 3,308 subjects with JIA and 2,816 subjects with the rheumatoid negative polyarticular and oligoarticular subtypes of JIA. We will perform a GWAS meta-analysis of this data and use S-PrediXcan to impute genetically regulated gene expression. 2) Extensive, longitudinal clinical data from the Synthetic Derivative is available for a cohort of over 1,100 JIA subjects that we identified in the Synthetic Derivative using a sensitive and specific algorithm. We will obtain Illumina MEGA-ex array genetic data for JIA subjects diagnosed at less than 10 years old from DNA available in the BioVU DNA repository. We will use PrediXcan to impute genetically regulated gene expression in this JIA cohort. We will compare the JIA group 4:1 to a cohort with similar heritage. In this study, we use two approaches that leverage the existing data in JIA and builds an extremely well characterized and growing EMR JIA cohort to identify differences in the genetic contribution to immune pathway activation in JIA.