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.
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.
Lead investigator
Miranda K. Kiefer, MD
Maternal-Fetal medicine Fellow
The Ohio State University School of Medicine Department of Obstetrics and Gynecology
Preeclampsia is a major complication that affects ~5-8% of pregnancies. When preeclampsia is diagnosed prior to 34 weeks gestation, continuation of pregnancy is recommended to reduce the risks of infant morbidity and mortality associated with prematurity. It is possible to prolong pregnancy in this case by controlling severely elevated blood pressures with medications such as Nifedipine, a calcium channel blocker that is first-line in pregnancy. Little is known about the pharmacokinetics of this drug in pregnancy despite its widespread use, nor its optimal dosing.
This is a randomized controlled unblinded trial comparing the rates of elevated blood pressure between individuals on Nifedipine XL 60mg daily to 30mg twice daily in patients admitted for expectant management of severe preeclampsia. Secondarily, we are assessing the pharmacokinetic measures of Nifedipine in pregnancy by analyzing nifedipine concentations in plasma at different timepoints after Nifedipine XL administration.
Lead investigator
Tomoyuki Mizuno, PhD
Assistant Professor of Pediatrics
Cincinnati Children’s Hospital Medical Center
Babies exposed to opioids before birth often develop neonatal opioid withdrawal syndrome (NOWS) after birth. Buprenorphine is an emerging therapy for NOWS; however, establishing optimal dosing regimens in this population remains an unmet clinical need. This pilot project will employ a physiologically-based pharmacokinetic (PBPK) modeling approach to predict buprenorphine disposition in pregnant women and fetuses to evaluate the association of buprenorphine prenatal exposure with postnatal NOWS severity.
We will also investigate the effect of neonatal genetic variants on postnatal response to buprenorphine treatment. Our overarching goal is to inform postnatal buprenorphine treatment based on prenatally predicted NOWS severity and enable PK/PD and pharmacogenetics-guided buprenorphine dose tailoring.
Lead investigator
Kara M. Rood, MD
Assistant Professor
Division of Maternal-Fetal Medicine
The Ohio State University School of Medicine Department of Obstetrics and Gynecology
Preeclampsia (PE) occurs in 5-8% of pregnancies and is associated with significant maternal and neonatal morbidity and mortality. For over decades, the role of aspirin in the primary or secondary prevention of preeclampsia has been the subject of numerous trials using a variety doses. To date, professional societies recommend use of low-dose aspirin for preeclampsia prevention in pregnant individuals at high-risk of developing the disease, but the optimal dose is unknown.
Furthermore, there are limited biomarkers available as candidates for monitoring therapeutic response to aspirin treatment in pregnant individuals at high-risk for PE, which ultimately may be used to generate desirable data to optimize aspirin dosing and improve pregnancy outcomes. Recent advancement in isolating specific extracellular vesicles (exosomes; 40–160 nm) from maternal blood and characterizing their cargo content have helped to better understand the response to therapeutic interventions.
Although diagnostic potential of exosomes has been reported, no studies have examined the response to therapeutic interventions such as different doses of aspirin on exosomes. Therefore, we are proposing this proof-of-concept, exosome profiling study to characterize the response to aspirin therapy in patients at high-risk of PE.
Using previously collected samples from over 100 high-risk patients assigned to 81 or 162mg of aspirin daily, we plan to characterize the maternal plasma proteome profile in high-risk individuals receiving different doses of daily aspirin and demonstrate that the proteome profile from patients receiving the higher dose of aspirin is more likely to mimic what is seen in low-risk control group, compared with the profile in patients receiving the lower dose of aspirin.
Lead investigator
Tamorah Lewis MD, PhD
Associate Professor of Paediatrics, Univ of Toronto
Division Head, Clinical Pharmacology and Toxicology, SickKids
Tamorah.Lewis@sickkids.ca
Current weight-based dosing algorithms (mg/kg) in the Neonatal ICU lead to varying drug exposures and require multiple dose modifications for optimal efficacy. This imprecision leads to highly variable drug concentrations, therapeutic failure, and significant toxicity. Sub-optimal drug efficacy in neonates can lead to lifelong impairments and overdosing can lead to life-threatening complications requiring additional tests and intensive treatments. Two commonly prescribed drugs with narrow therapeutic windows, and variable and unpredictable pharmacokinetics are the low molecular weight heparin enoxaparin (anticoagulation) and fosphenytoin (seizure treatment). These drugs require dose escalations for efficacy or, when given as a standard loading dose for seizure control, fail to achieve efficacy.
There are pediatric pharmacokinetic (PK) models, which if implemented clinically, have the potential to more accurately dose these medications through a process called “Model Informed Precision Dosing”, or MIPD. Using an already established and validated PK model, individual patient-specific covariates can be used to devise a custom dose for an individual child, based on their age, stage of development, renal function and other variables relevant to the specific drug’s PK profile. A novel EPIC-embedded MIPD tool called Lyv was developed by the University of Maryland Baltimore and PUMAS-AI (http://lyv.ai/). It places the PK model within the EPIC electronic health record, so that real-time clinical decision support for individualized dosing is facilitated at the bedside. To date, this tool has not been used in the Neonatal ICU. The lack of research utilizing such a tool leaves neonates behind in the progress towards precision medicine.
In the funded research project, a feasibility trial of MIPD in neonates less than 44 weeks PMA will be conducted in the Neonatal ICU, focusing on two drugs: enoxaparin and fosphenytoin. Prospectively, infants at high risk for requiring these medications during their inpatient hospitalization will be recruited to participate. If the infant clinically requires the start of one of these drugs, the research team will assist the clinical team to determine a precision dose for the patient using the novel tool. A historical cohort of infants who received the drug with standard of care dosing in the previous three years will serve as control group. The primary outcome will be time to drug efficacy from the start of dosing to the point when the pharmacodynamic endpoint is achieved. Secondary outcomes feasibility and end-user experience of this MIPD tool in the ICU will be evaluated through an end-user semi-structured interview. After completion of this study, we expect to understand the feasibility and early efficacy of the MIPD tool in sick neonates, and will be able to proceed with optimal power and design of large-scale randomized controlled trials to assess safety and benefit of MIPD in neonates.
Lead investigator
Brian Overholser, PharmD, FCCP
Professor of Pharmacy, Purdue University
boverhol@purdue.edu
Fetal loss via miscarriages and stillbirth are catastrophic outcomes of pregnancy. Despite fetal loss in over 15% of pregnancies, the mechanisms are unknown in a majority of cases. Recently, it has been reported that the risk of fetal loss increases eight-fold in the presence of a maternal congenital disease, Long-QT Syndrome (LQTS). The most common forms of LQTS prolongs the QT interval on a surface ECG through inhibition of repolarizing potassium channels. In fact, multiple investigations have implicated the congenital loss-of-function of these potassium channels in cases of miscarriages and fetal death. Interestingly, paternal LQTS does not confer the same risk to the unborn fetus. Thus, it is widely believed that the increased risk of fetal loss from LQTS is due to the channelopathy prompting maternal myometrial dysfunction, as these channels are important during labor and delivery.
It has been estimated that inherited LQTS accounts for approximately 8% of stillbirths. However, acquired channelopathies could play an important role in unknown cases since several exogenous and endogenous compounds inhibit these potassium channels. For example, sex hormones are known to be important regulators of cardiac repolarization, but the effects on ion channels during pregnancy are largely unstudied. We have recently reported that exogenous progesterone is transformed to metabolites that inhibit repolarizing potassium currents at physiological concentrations during pregnancy. Similarly, the QT interval is lengthened in pregnant women to the greatest extent during second and third trimesters, when progesterone concentrations are highest.
The primary objective of this research direction is to characterize the effects of genetics and progesterone metabolite exposure on maternal cardiac repolarization and fetal loss. In the first aim, we will establish the impact of genetic variants on the variability in progesterone metabolite exposure and cardiac repolarization. Progesterone is metabolized by the polymorphically expressed CYP 3A enzymes. Therefore, we hypothesize that CYP3A genotype will predict metabolite exposure and maternal QT interval length. This hypothesis will be tested in pregnant women in a prospective pilot clinical trial. The association of genetics on progesterone metabolite exposure and cardiac repolarization will be assessed. In the second aim, we will determine the impact of the genetics of sex hormone metabolism on the risk of fetal loss using a multi-level targeted genome-wide association study with a case control design.
Lead investigator
Kendra Radtke, PharmD, PhD
Clinical Pharmacologist, University of San Francisco
Kendra.Radtke@ucsf.edu
Moxifloxacin is a fluoroquinolone antibiotic with borad-spectrum activity against Gram-negative, Gram-positive, and acid-fast bacteria. Despite many indications in adults, moxifloxacin use in patients under 18 years of age is entirely off-label and is generally limited to rare or last resort scenarios. Pediatric data on pharmacokinetics and safety are very limited lacking and dosing is unestablished. In the proposed study, we aim to leverage existing knowledge of moxifloxacin population pharmacokinetics in children to determine the optimal sampling times for moxifloxacin concentration evaluation and individualized dosing to support safe and effective clinical use. Additionally, we aim to use real-world data of moxifloxacin and other fluroquinolone use to generate real-world evidence of its cardiac safety. Our methodology includes a combination of machine learning, pharmacometrics, and standard statistical approaches applied in novel ways to learn the most from existing pharmacokinetic and outcomes data. Our real-world data sources include electronic health records from the Unversity of California Health System and The Ohio State University - the MPRINT real-world evidence core. We hypothesize that drug levels measured 6 hours after dose will predict AUC over 24 hours and that pediatric moxifloxacin use is safe from severe cardiac adverse events. The outputs of this proposed work will be (i) establishing a limited sampling strategy for moxifloxacin target assessment, (ii) a pediatric-data-driven tool for precision dosing of moxifloxacin, and (iii) real-world evidence of comparative safety of fluoroqinolones in children.
Lead investigator
Samuel Vaughn, MD, PhD
Associate Professor
Cincinnati Children's Hospital Medical Center
Former lead investigator
Laura B. Ramsey, PhD
Associate Professor
UC Department of Pediatrics
SSRIs are effective in children and adolescents with psychiatric disorders and their use is increasing, yet, pediatric patients often endure multiple antidepressant trials before finding an efficacious and well-tolerated medication. SSRI blood concentrations vary substantially and accumulating evidence suggests that side effects are associated with blood concentrations. In adults, variability in SSRI concentration is partially explained by pharmacogenetics. While there are dosing recommendations based on CYP2C19 and CYP2D6, studies in pediatric patients are lacking and it’s unclear whether the adult guidelines should be applied to pediatric patients since their CYP2C19 activity may be 30% higher than adults. Sertraline is an SSRI indicated for obsessive compulsive disorder in pediatric patients but is often prescribed off-label for depression and anxiety. It is metabolized by several enzymes in the liver, including CYP2C19, CYP2D6, and CYP2B6. Our first goal is to determine the influence of CYP2B6, CYP2D6 and CYP2C19 on sertraline exposure in children and adolescents. Our second goal is to determine the influence of sertraline exposure on tolerability in children and adolescents. The proposed study addresses the gaps in knowledge of the influence of pharmacogenetics on sertraline tolerability and exposure in pediatric patients. This population has been excluded from prior pharmacogenetic studies but thousands of pediatric patients are prescribed sertraline each year in the US. Thus, this project will provide much-needed data for improving the care of children and adolescents with psychiatric disorders.
Lead investigator
Theresa Marie Casey, PhD
Research Assistant Professor
Purdue University
Breast milk is recommended as the sole source of nutrition for infants to 6 months of age. However, only 25% of infants in the US meet this goal. Although infant formula is critical when breastfeeding is not possible, it lacks bioactive components of breastmilk. Milk is a rich source of lipids, and growing evidence supports, PPAR mediated metabolic pathways must be initated shortly after birth so neonates can catabolize fats. Our studies of swine found suboptimal intake of maternal milk impairs PPAR activation and peroxisome function, as indicated by accumulation of lipid substrates metabolized in peroxisomes. We hypothesize that components of maternal milk fat stimulate fatty acid oxidation through PPAR and peroxisomal biogenesis which is needed to initiate β-oxidation, and that if intake of milk is not adequate, eicosanoids, cholesterol esters, and very long chain fatty acids accumulate in tissues. Accumulation of these lipids likely elicit a proinflammatory signaling environment that can affect developmental pathways. Extreme cases of peroxisome dysfunction result in leukodystrophies like Zellweger’s syndrome. The overall goal of the proposed project is to determine if the preclinical data collected is translatable to humans. A longitudinal observational study of maternal-infant dyads beginning at birth to three weeks postnatal will be conducted to: SA1. Determine if neonate’s buccal cell lipidome is related to perinatal feeding, maternal buccal cell lipidome, and maternal milk or formula lipid profiles. This aim will test the working hypothesis that low or no intake (i.e., formula feeding) of maternal milk the first 48 h to 3 weeks postnatal will result in a greater abundance of peroxisome substrates (e.g., very long chain fatty acids) in buccal cells than those of exclusively breastfed counterparts. SA2. Assess the efficacy of isolating cellular DNA from buccal swabs to measure changes in DNA methylation in response to postnatal feeding. In rodents, postnatal intake of milk resulted in the demethylation of PPAR DNA-binding regions. This aim will generate preliminary data to determine the efficacy of using DNA isolated from neonatal buccal swabs to study changes in methylation patterns of the whole genome, which is necessary for applications for external funding. Preliminary data analysis aims to test the working hypothesis that infant feeding practices affect postnatal chromatin remodeling and that formula feeding will be associated with higher levels of methylation on PPAR targets. A primary goal of the National Institute of Child Health and Human Development is to optimize infant survival and health by optimizing formulas to more closely match the composition of human milk. Data collected will provide fundamental information regarding the ontogeny of neonatal metabolism and establish targets for development of breast milk alternatives to better mimic mothers’ milk bioactivity.
Lead investigator
Janet Markle, PhD
Assistant Professor
Vanderbilt University Medical Center
This project aims to better understanding the causes of pediatric inflammatory bowel disease (IBD) to enable more effective, personalized treatments. For most patients with IBD, the peak age at onset is between 20-40 years. However, a small but growing percentage of IBD patients have very early onset IBD (VEOIBD) at age 6 or younger, and disease in these children is especially severe and often refractory to treatment. Various immunosuppressive drugs are available for VEOIBD treatment, but these therapies are not equally effective in all children and overall efficacy remains low (between 30-50%). The timely selection of an optimal treatment plan is critical for these children, since VEOIBD is associated with poor long-term outcomes including surgical resection, increased risk of colorectal cancer, and even death. Our central hypothesis is that a greater understanding of inter-individual disease determinants through identification of genetic drivers and the impact of these genetic variants on the gut microbiome will lead to more effective, personalized treatments for VEOIBD patients. To this end, our project focuses on identifying genetic influencers of VEOIBD and how human genetic factors shape the gut microbiome in VEOIBD patients.
Lead investigator
Elizabeth Jaclyn Thompson, MD
Clinical Fellow
Duke University
The benefits of breastfeeding are well documented yet only ~50% of US infants are breastfed at 6 months of age. This goes against recommendations from the American Academy of Pediatrics and the American College of Obstetricians and Gynecologists. A common barrier to breastfeeding is the perceived and unquantified risk of infant exposure to maternal prescription drugs through breastmilk. Up to 1.5 million lactating women in the US take prescription drugs yet <2% of drugs have adequate safety data in lactation. This is particularly important for mothers with postpartum depression who are prescribed sertraline. Due to a lack of data, these women may either forego breastfeeding or discontinue sertraline with dire health consequences for the mother and her baby. Systematic strategies to determine the safety of commonly used drugs in lactating women and breastfeeding infants are urgently needed to improve postpartum maternal therapy and increase breastfeeding rates. In this proposal, we will evaluate a systematic approach for quantifying drug transfer into breastmilk over time to provide guidance for safe breastfeeding during maternal drug therapy. Our objective is to estimate sertraline and N-desmethylsertraline (DMS), its active metabolite, exposure through breastmilk using pairs of maternal and infant blood and maternal breastmilk samples and validate a real-world collection method for the study of drugs in lactation. This research will directly improve the health of mothers and infants by determining the safety of sertraline for treatment of postpartum depression. Additionally, this approach will validate real-world sample collection methods to increase available data for PK studies and enable ongoing collaboration between the Pediatric Trials Network and MPRINT hub.
Lead investigator
Jonathan Wagner, DO
Associate Professor of Pediatrics, Department of Pediatrics
University of Missouri-Kansas City
Despite significant improvements in post-operative survival for children born with functional single ventricle congenital heart disease (SVCHD), there is increasing recognition of end-organ damage, specifically liver fibrosis, in these patients. The Fontan operation creates passive systemic venous return of deoxygenated blood to the lungs. Over time, this passive system leads to elevated central venous pressure and hepatic venous stasis, congestion, and subsequent liver fibrosis/cirrhosis, known as Fontan-Associated Liver Disease (FALD). While advanced liver imaging can screen for and quantify degrees of liver stiffness, there remains a persistent disconnect between the degree of liver congestion/fibrosis and its impact on intrinsic hepatic function, making clinical decisions, such as drug dosing, difficult. Although it is known that moderate to severe hepatic damage in adults impairs drug disposition, there is a critical information gap in how liver congestion and fibrosis in patients with Fontan circulation affects hepatocellular processes, such as hepatic drug metabolism and transport. The consequence of this unmet need is suboptimal medical management of Fontan-related comorbidities, which is imperative given the known gradual decline in survival following Fontan palliation and the challenges related to and limited availability of solid organ transplantation.
The objective here is to determine the role FALD has on two independent drug disposition pathways through a single dose pharmacokinetic study with administered liver metabolism and transport probe substrates (CYP3A4: sildenafil; OATP1B1: pravastatin). These data will subsequently be utilized in the future to develop and validate the first physiologically based pharmacokinetic (PBPK) model based in the Fontan population that will serve as an in silico foundation to develop future model-informed precision dosing (MIPD) in this vulnerable population. The central hypothesis is that increased liver congestion and fibrosis leads to cellular dysfunction resulting in altered drug disposition. Therefore, the primary goal in this proposal is to address the effect of FALD, specifically liver congestion and fibrosis as measured on magnetic resonance imaging (MRI) 4-dimensional flow and elastography, on drug disposition in SVCHD by addressing these two aims: Aim 1) Determine the contribution of liver congestion and fibrosis on hepatocellular drug metabolism in patients with Fontan circulation, Aim 2) Evaluate the role of liver congestion and fibrosis on hepatocellular drug transport in patients with Fontan circulation. IMProving DRug dosing and Outcomes for single VEntricle patients with Fontan Associated Liver Disease (IMPROVE FALD) study will develop a drug pharmacokinetic evaluation as an end-organ phenotypic biomarker that establishes the liver congestion and fibrosis thresholds where intrinsic hepatic function begins to decline as a way to enhance prediction, prevention, and treatment of FALD. Collectively, the IMPROVE FALD study will serve as a guide to refine not only current CV drug dosing strategies, but also dosing for other drugs that may be used in those with Fontan circulation that have comparable drug disposition pathways.
Lead investigator
Sydney Thomas, PhD
Post Doctural Research Scholar
UC of San Diego
Human milk is the optimal nutrition for the developing infant. In addition to energy sources such as fat and protein, human milk contains a rich diversity of other factors that play crucial roles in infant development. These factors include immunomodulatory components, hormones, and prebiotics, which help to prime the infant’s immune system and seed a healthy infant microbiota. This intricate combination of metabolites, sugars, and microbes underpin many of the positive health outcomes linked to exclusive breastfeeding. These outcomes include short-term effects, such as lowered risk of necrotizing enterocolitis, and long-term outcomes, such as reduced risk of obesity and improved cognitive development. However, while the benefits of human milk are clear, the exact mechanisms underlying these effects are still unknown. Many studies focus on a single factor or class of factors in milk that may influence health outcomes, but this may obscure the complex interactions that exist in a matrix as diverse as human milk. In addition, few studies replicate results across studies or geographical locations, leading to potential biases from genetic or environmental homogeneity. Here, we apply a multi-omics approach to holistically study the composition of milk in approximately 800 volunteers across three continents. Using three methods - 16S milk microbiome sequencing, untargeted milk metabolomics, and human milk oligosaccharide (HMO) profiling - we identify an HMO signature associated with adverse birth outcomes. This HMO signature is associated with a handful of bacterial genera and metabolites across geographical locations, and may influence infant developmental outcomes up to 30 months of age.
Lead investigator
Simone Zuffa, PhD
Post Doctural Research Scholar
UC of San Diego
Indirect exposure to antibiotics during early life is considered relatively safe for infants. Nevertheless, neonatal consumption of antibiotics has been shown to severely alter gut microbiota profiles, leading to an increased risk of developing non-communicable diseases later in life, including effects on the developing brain. The investigation of indirect exposure to antibiotics is gaining momentum since a recent study showed that maternal antibiotic administration led to altered neurobehavior in murine offspring, but an investigation of gut microbiota composition and functionality was missing. Additionally, although antibiotic studies mainly focus on determining the impact on the bacterial population, these drugs have also been shown to affect the gut mycobiome. For example, a reduced presence of bacteria allows specific fungi to overgrow, such as the Candida genus, which has been associated with different neurobehavioral disorders, like autism spectrum disorder (ASD).
This Support Pool project will enhance Aim 1 of the MPRINT Basic Science Project, mirroring its study design to further investigate the gut-brain axis and neurodevelopment in a murine model. Collected stool samples will be investigated using untargeted metabolomics, shallow shotgun, and ITS2 sequencing to determine molecular, bacterial, and fungal profiles, respectively. Blood metabolic and cytokine profiles will also be collected, along with the metabolic profiles of the frontal cortex and hippocampus regions of the brain. Finally, state-of-the-art behavioral experiments will be used to evaluate the neurodevelopment of the animals. This integrative multi-omics approach will be used to unlock a holistic view of the interplay between bacterial, fungal, and host metabolism and highlight how complex metabolic interactions can influence brain metabolism and behavior.
Lead investigator
George Liu, MD, PhD
Professor and Chief of Pediatric Infectious Diseases Division
UC of San Diego
Maternal milk confers broad immune benefit to infants, but the specific contributions of milk immune factors to infant microbiome and immune development are only beginning to be understood. Among immune components, both pro- and anti-inflammatory cytokines are abundantly found in human colostrum and milk. We propose that these cytokines directly modulate infant microbiome and metabolome and contribute to the infant’s immune resistance to infections. Here we propose cross-fostering experiments using wild-type pups and wild-type and IL-6, IL-1β, IL1RA or IL-10 deficient dams. We will measure the effect of milk cytokine absence on infant gut microbiome and metabolome as well as their effect on the infant’s susceptibility to enterocolitis. Identification of critical milk cytokines will inform on immune supplementation strategies that ameliorate the health of infants at increased risk for infectious diseases.
Lead investigator
Pia Pannaraj, MD, MPH
Professor of Pediatrics
UC of San Diego
Mounting evidence suggest that maternal secretory IgA (SIgA) from human milk bind to fecal bacteria, shape infant microbiota development, and modulate the homeostatic level of regulatory T cells (Tregs) in the gut. Tregs suppress immune responses to maintain homeostasis and prevent autoimmune diseases. However, by suppressing antigen presentation, T and B cells, Tregs may also inhibit responses to vaccines. The live oral Rotavirus (RV) vaccines administered to infants are less effective in low socioeconomic settings for unclear reasons. Our team has an ongoing study of gut microbial and metabolic mediators of RV vaccine response. We enrolled 526 mother-infant pairs in the US, Panama, and Peru from whom we have collected maternal milk and infant blood and stool at multiple time points pre- and post-vaccination through 12 months of age. In our preliminary analysis, we found that infants with a higher proportion of FOXP3+ regulatory T cells at the time of the first vaccination dose at 2 months of age had reduced oral vaccine response, as measured by infant serum RV-specific IgA. We also found that maternal milk RV-specific IgA does not predict infant RV vaccine response. In this proposal, we seek to investigate the roles of non-RV-specific IgA in milk and IgA binding to fecal bacteria in gut microbiome development, Treg frequency, and oral vaccine response. These questions were not included in the original funded proposal. Using samples collected in the newborn period and at 2 months of age pre-vaccination, we will stain human milk and corresponding infant stool pellets with anti-IgA antibodies, measure the IgA-bound populations using flow cytometry, and employ magnetic sorting of IgA+ and IgA- bacteria and 16S rRNA sequencing to characterize the differential binding of the infant microbiota. We will incorporate this analysis with the microbial and metabolic changes in early life and the immunologic response to the oral vaccine.
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.