Browsing Theses and Dissertations School of Pharmacy by Title "From Data to Decisions: Utilizing Pharmacometrics to Optimize Clinical Therapeutics and Drug Development in Neuropsychiatry"
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From Data to Decisions: Utilizing Pharmacometrics to Optimize Clinical Therapeutics and Drug Development in NeuropsychiatryAt least 50% of clinical trials of neuropsychiatric compounds fail due to an unclear understanding of disease pathophysiology and drug pharmacology. Further, lack of dosing information in special patient populations for approved neuropsychiatric drugs could contribute to suboptimal outcomes. The current research highlights the role of pharmacometrics in (i) optimizing therapeutics in patients receiving antiepileptics and continuous renal replacement therapy (CRRT) and (ii) informing efficient trial design for binge eating disorder (BED). Currently, no dosing recommendations exist for CRRT patients receiving antiepileptics. Real-world clinical studies were conducted to characterize the pharmacokinetics of levetiracetam and lacosamide in patients (N=18) receiving CRRT at the University of Maryland Medical Center. Major determinants for drug clearance were drug-specific extraction coefficient (EC) approximated to fraction unbound (levetiracetam: 0.89, lacosamide: 0.80), effluent flow rate, and preserved non-renal clearance. Ex-vivo models of CRRT were developed using human plasma and normal saline containing albumin solutions. The developed ex-vivo in-vivo correlation model demonstrated an average bias of <15% in predicting in-vivo CRRT clearance for levetiracetam and lacosamide. Similarity in ECs justified the ability to bridge dosing information across CRRT modalities. This research, in combination with a priori knowledge of drug pharmacokinetics, confirms the use of ex-vivo CRRT models to establish dosing recommendations and alleviate the need for CRRT pharmacokinetic studies. The development of BED therapies are challenged by high placebo response and high dropout rates in clinical trials. A comprehensive disease-drug-trial (DDT) model was developed using data from 12 different investigator-led BED clinical trials (N = 578; 6 to 16-week duration) to inform optimal clinical trial design elements. Baseline BED severity metrics were predictors for placebo response and dropouts. Stimulants and anticonvulsants demonstrated 1.8 times higher effect differences as compared to antidepressants. Among the clinical trial designs (placebo run-in, drug run-in, sequential parallel comparison design) evaluated in-silico, placebo-controlled trial of shorter (6-week) duration with model-based analysis demonstrated superior trial design properties (40% lower sample size with 50% lower dropouts) as compared to current 12-week registration trials for BED. The proposed DDT framework can inform efficient trial design and potentially increase the number of therapeutic options for BED.