Previous TL1 Fellows
Brian T. Burmeister, PhD
Senior Research Analyst at CMIC
Determining the Mechanism of Pediatric Doxorubicin-Induced Cardiotoxicity
Dr. Burmeister will be studying patient-specific responses to chemotherapy agents, specifically doxorubicin which is used to treat pediatric cancer patients. Doxorubicin is very effective in treating pediatric cancer, however one of the drug’s side effects, cardiotoxicity, limits it’s usefulness. As a result, Dr. Burmeister will be working to determine the molecular mechanisms that regulate doxorubicin-induced cardiotoxicity in order to identify new targets for drug discovery.
Computer-Controlled Pediatric Regional Anesthesia to Improve Patient Safety
Matthieu Chardon, PhD, will be developing a tool to measure the effectiveness of local anesthetic dosage in children with the goal of reducing dosage and increasing safety. “Regional anesthesia in pediatrics has dramatically increased over the past 30 years because it provides localized pain relief, diminishes opioid use and facilitates earlier mobilization, internal feeding and hospital discharge,” Dr. Chardon says. “It is also used frequently due to concerns about the effect of general anesthesia on brain development.” Nonetheless, Dr. Chardon explains that there are several concerns surrounding local anesthesia, and that there are no tools to determine its effectiveness or toxicity in real time. As a result, he is seeking to develop a solution in partnership with his mentors Santhanam Suresh, MD, Professor of Anesthesiology and Pediatrics, and Charles Heckman, MD, Professor of Physiology/Physical Medicine and Rehabilitation and Physical Therapy and Human Movement Sciences.
Electronic Medical Record Integration of Genomic Testing Results
Bimal Chaudhari, MD, MPH, is developing a knowledge base that will deliver genomic testing results to non-geneticist clinicians via an electronic medical record. Many patients may benefit from genomic testing, but not all physicians feel prepared to take action based on the results of genetic testing. However, this new knowledge base will address this problem by offering clinical decision support that is patient specific and compatible with a physician’s workflow. “Right now, methods for DNA sequencing are fairly robust and well-developed but methods of sequence interpretation are evolving,” Dr. Chaudhari says. “The result is that, unlike an X-ray or blood culture, the interpretation of a sequencing test result can change over time. Results may also be more or less relevant at different points in time. The TL-1 program provides me the support I need to develop my knowledge base in this area and gain practical experience. I'm developing collaborations in fields I didn't even know existed a year ago. My hope is that this relatively novel collaboration is the foundation for sustained extra-mural research funding.”
Michael Dominick DiVito, PhD
Differentiating Hepatocytes from iPSCs to Treat Pediatric Metabolic Liver Disease
Dr. DiVito will be studying ways to treat pediatric metabolic liver deficiencies using induced pluripotent (iPS) stem cells derived from liver cells. He plans to improve iPS cell differentiation protocols and to develop an infusion method for these cells with the overall goal of making both in vitro and living models of metabolic liver diseases that can be used to develop stem cell therapies and other treatments for these diseases. “I am excited at the potential of using stem cells with our lab’s bioreactor systems to develop disease models and cell therapies for pediatric metabolic diseases,” Dr. DiVito says. “With this award and the resources it offers, I feel I am in a great position to fulfill these goals. I am excited to see the output of this project after two years under the TL1 training program” Because the TL1 program promotes interdisciplinary mentorship, Dr. DiVito will be able to not only have the opportunity to enhance his engineering knowledge but also acquire new knowledge of hepatology, stem cell biology and other topics important to his research.
Daniel Giles Fort, MPH PhD
Director of Data Science/Biomedical Informatics Research at Ochsner Health System
Informatics Interventions to Accelerate Pediatric Clinical Trial Recruitment
Dan Fort, PhD, MPH, will be researching ways to use informatics in order to accelerate pediatric clinical trial recruitment. To do this, he will be looking at patterns in clinical trial recruitment success and failure as well as digging into historical cohort selection queries for insights into how they might be modified to increase the number of patients identified for recruitment. “This award is vital early career support, allowing me to build a solid body of investigatory, preliminary work for R-scale funding proposals as well as address important skill gaps in team and decision sciences,” said Dr. Fort.
A Novel siRNA Approach for Targeting Immunosuppresive IDO1 in Pediatric Brain Tumors
Alicia Lenzen, MD, will be studying a novel form of treatment for pediatric brain tumors that will use immunotherapy combined with nanotechnology. She will be researching the inflammation-induced expression of indoleamine 2,3 deoxygenase 1 (IDO1), its role in suppressing tumor immunity and therefore its promising therapeutic reactivation of the immune response against brain tumors. Dr. Lenzen will also integrate nanotechnology as a direct delivery mode into central nervous system tumors in order to create precise targeting. “This TL1 training will facilitate the building blocks of that research foundation now, obtaining findings related to the development of novel treatment regimens using mouse brain tumor models, with the possibility of direct translation into patients,” Dr. Lenzen says. “It will also allow me a look into multi-disciplinary work in order to create the best projects.”
Susan M. Slattery, MD
Process Vulnerabilities in Hospital Discharge after Neonatal Intensive Care Unit Stays
Dr. Slattery will be focusing her research on the timing of a safe transition home from the Neonatal Intensive Care Unit. Along with her mentors, she hopes to develop a valid, reliable predictive tool using data captured in the Electronic Health Record.