Funding Opportunities

Opportunity and Support Pool awards and applications

Various funding awards are available from 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
james.antoon@vumc.org

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
leena.choi@vumc.org 

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
ashley.leech@vanderbilt.edu

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
anna.e.patrick@vumc.org 

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.


Lead investigator
Monika Grabowska, MD, PhD
Vanderbilt University Medical Center

There is a critical need for high-throughput electronic health record (EHR) phenotyping tools designed for use in pediatric populations. Previous advances in EHR phenotyping have involved the development of phenotype codes (phecodes) by our group to aggregate ICD-9-CM, ICD-10-CM, and ICD-10 codes to better represent clinically meaningful diseases and traits, with superior performance demonstrated by phecodes in replicating known genetic associations compared to other coding systems (ICD-9-CM and
CCS). However, pediatric patients have different diseases and outcomes than adults, and existing phecodes, which were created using population-based diagnoses primarily from adult patients, do not capture the distinctive pediatric spectrum of disease. We developed specialized pediatric phecodes (Peds-Phecodes) using a hybrid data- and knowledgedriven approach combining patient diagnosis counts with clinician-led manual review. We found that Peds-Phecodes replicated more known pediatric genotype-phenotype associations than phecodes (248 versus 192 out of 687 SNPs, p<0.001), and may also
detect novel genotype-phenotype associations for pediatric conditions. Our results suggest that Peds-Phecodes capture higher-quality pediatric phenotypes and deliver superior outcomes in phenome-wide association studies (PheWAS) compared to phecodes. We expect that Peds-Phecodes will facilitate efficient, large-scale phenotypic analyses of pediatric patients. We developed the PedsPheWAS R package to perform PheWAS using the Peds-Phecodes (https://github.com/The-Wei-Lab/PedsPheWAS).

Lead investigator
Elizabeth Jasper, PhD
Research Assistant Professor, Department of obstetrics and Gynecology
Vanderbilt University Medical Center

Perinatal depression (PD) is a common complication of pregnancy, affecting roughly one in 10 birthing persons. It has profound adverse health effects for both birthing persons and their children. Though antidepressants, including several with known pharmacogenetic associations, are often used as a first line treatment for PD, there is limited evidence for their efficacy and safety in pregnant and lactating individuals. Treatment of PD is further complicated by an inadequate understanding of its etiology. There are a limited number of clinical risk factors linked to PD. Furthermore, there is continued debate about whether PD is the result of the exact same factors as major depressive disorder (MDD) or if the etiology of PD is distinct and simply results in symptoms like MDD. Current genetic research is inconclusive. To fill these gaps in knowledge, this proposal seeks to identify novel clinical and genetic risk factors for PD. In Aim 1, temporal phenome-wide association studies will be performed, using phecodes occurring prior to pregnancy or in the perinatal period. The study population, consisting of females with at least one live birth and records one year prior to and after pregnancy, will be obtained from the Synthetic Derivative. I will utilize the Phenotyping Core to identify PD using two definitions: one based on the Edinburgh Postnatal Depression Scale, and another based on diagnoses codes. As part of the study, I will compare these two methods to deduce the accuracy and usability of diagnosis codes. Additional analyses will be performed to investigate whether potential risk factors vary based on the PD subcategory (e.g., postpartum diagnosis, PD superimposed on existing MDD, etc.). In Aim 2, individuals will be obtained from several large databases linking electronic health records and biorepositories (BioVU, Electronic Medical Records and Genomics Network, UK Biobank, and AllofUs) and will include those who met Aim 1’s inclusion criteria and possess genotype data. I will investigate whether the genetic risk factors for MDD, PD, and PD superimposed on MDD differ by performing multiple genome-wide association studies using individuals with and without these conditions. To further investigate potential unique genetic risk factors for PD and lessen the possibility of bias due to misclassification, I will perform sensitivity analysis where individuals with a high polygenic risk scores for MDD will be excluded before comparing individuals without any history of depression to those with PD only. Functional Mapping and Annotation of Genome Wide Association Studies (FUMA) will be used to annotate results and provide biologic context. FUMA results will aid in identification of enriched pathways which could lead to discovery of unique drivers of disease and may suggest novel or preferred treatment modalities for future studies. For both aims, results will be compared to clinical and genetic risk factors for MDD with the hypothesis that, while they may share some clinical and genetic risk factors, there will be unique factors associated with PD, including number of prenatal care visits and changes in the hormonal and immune pathways that experience significant alterations throughout the perinatal period.



Lead investigator
Amelie Pham, MD, MFM
Vanderbilt University Medical Center

Major gaps exist in the obstetric and pharmacoepidemiology literature regarding epilepsy, anti-seizure medication (ASM) safety, and ASM medication adherence during pregnancy. The objective of our study is to address the two following aims. First, we will seek to test the hypothesis that newer ASM medication use in pregnancy is associated with higher medication adherence compared to older ASM medications. Second, we will determine whether poor ASM medication adherence in pregnancy is associated with increased adverse perinatal and childhood outcomes. Our study design will use a Tennessee Medicaid cohort of pregnant patients with linked infant data using a previously validated platform to identify patients with ASM use in pregnancy, with and without a diagnosis of epilepsy, who delivered between 2007-2019. Covariates will include CDC’s social vulnerability index score (SVI), SVI subthemes (socioeconomic, household composition, minority status and language, and housing and transportation), maternal age at delivery, and race/ethnicity. In the first aim, our exposure will be classified by ASM agent type (newer versus older) and stratified by patients with and without a diagnosis of epilepsy to ascertain any independent effect of epilepsy, regardless of ASM exposure, on outcomes. Our primary outcome will be medication adherence in pregnancy defined as the proportion of days covered. In our second aim, our exposure will be medication adherence and our outcomes will be perinatal and childhood outcomes. We will use multivariable linear regression analysis to model all of these associations adjusting for relevant covariates. Secondary analyses will examine the risk of outcomes associated with poor medication adherence stratified by newer and older ASM medications.

Lead investigator
Megan Shuey, MS, PhD
Research Instructor,
Vanderbilt University Medical Center


Medication use during pregnancy is common with many pregnant persons reporting use of one or more medications throughout pregnancy. While the teratogenic effects of various medications, such as valproic acid and thalidomide, are well known, there are many other medications where the risk to fetal development are uncertain. Using large-scale electronic health record databases with linked maternal and fetal records, such as the Vanderbilt University Medical Center’s Mom Baby Database (MBDB), has the potential to improve our understanding of medication exposure and risk profiles in real-world clinical populations.

 This proposal will curate the MBDB resource, to identify prescribed medication used prior to and throughout the perinatal period and determine associated variables relating to class-type, administration specific variables (e.g. dose, frequency), and concurrent medication. Using this curated data resource I will (a) identify novel medication exposures associated with adverse pregnancy related outcomes: congenital anomalies, extreme preterm birth, and miscarriage. Any medication found to be nominally associated with a given outcome (P<0.05) will be used to identify drug-gene target pairs using the Druggable Genome Resource. After identifying drug-gene pairs that demonstrate an association with an adverse pregnancy outcome, I will (b) use Mendelian Randomization to test whether the genetically predicted gene expression (GPGE) of the gene targets are causal for the three adverse outcomes.

 At study completion I will have a curated dataset with pregnancy-related medication exposures, maternal health variables, and related outcomes. I will also have developed new datasets for the three adverse pregnancy-related outcomes that will be available for future studies. The results of this study will be important to spear head future research endeavors and will be made available to other researchers by request or through public databases such as the GWAS Catalog.




Lead investigator
Sudeep Sunthankar, MD, MSCI
Instructor in Pediatrics, Cardiology,
Vanderbilt University Medical Center

Congenital heart disease affects 1 in 100 children. One of the most severe phenotypes of congenital heart disease is single ventricle physiology in which there is only one functional pumping chamber. Transplant-free survival for patients with hypoplastic left heart and single ventricle physiology is ~60% at six years of age. While studies have investigated clinical features associated with transplant-free survival, the reason those physiologic conditions, such as atrioventricular valve insufficiency and depressed ventricular function, occur is not always clear. Improvement in our ability to identify high-risk patients for closer monitoring or earlier intervention may improve outcomes for this vulnerable population. We propose to bridge this gap by investigating genetic differences leading to phenotypical changes, which are known to impact transplant free survival. In this study, we propose performing genetic testing for known cardiomyopathy causing genes in patients with single ventricle heart disease to elucidate genetic differences which may be associated with single ventricular dysfunction and transplant-free survival. Furthermore, could there be a relationship between the benefit of digoxin, a well-accepted protective medication against mortality, and the presence of cardiomyopathy causing genes. Incorporating this genetic information with known clinical risk factors may allow a more complete risk profile in this population.