University of North Carolina at Chapel Hill Graduate School
Doi
Abstract
Christoph P. Hornik: Application of Pharmacometric Methods to Optimize Trial Design and Dosing in Critically Ill Infants (under the direction of Daniel Gonzalez) Drug development in critically ill infants is challenging. Limited number of eligible trial participants, low consent rates, inability to perform or tolerate trial assessments, and ethical considerations all contribute to a low rate of successful clinical trials in this population. As a result, drugs administered to infants are often incompletely studied to ensure their efficacy and safety, and administered without a US Food and Drug Administration (FDA)-approved indication (off-label). Off-label drug use is associated with increased risk of unwanted drug toxicities or therapeutic failures, which can result in poor infant outcomes. To improve infant outcomes, innovative strategies in drug development are needed to generate the data necessary to identify safe and effective drug dosing regimens. The work performed in this dissertation provides 3 examples of innovative approaches to drug development in critically ill infants. Central to these innovations is leveraging pharmacometric methods to address 3 common obstacles: (1) sample size determination of infant pharmacokinetic (PK) trials; (2) characterization of the relationship between drug exposure and efficacy to identify efficacious doses; and (3) evaluation of the association between drug exposure and safety to identify safe doses. Each of these 3 obstacles is overcome with the help of a specific pharmacometric approach. In aim 1, populationPK (popPK) modeling and simulation is applied to determine optimal sample sizes for various infant PK trial designs. In aim 2, popPK/pharmacodynamic (PD) modeling is used to characterize the exposure response relationship between methylprednisolone and antiinflammatory changes in neonates undergoing cardiac surgery on cardiopulmonary bypass (CPB). In aim 3, popPK models are combined with electronic health record (EHR)-derived real-world data (RWD) sources to develop a novel platform to study the relationship between predicted drug exposures and safety events captured during routine clinical care.Doctor of Philosoph
