Model-Based Individualized Treatment of Chemotherapeutics: Bayesian Population Modeling and Dose Optimization

摘要: 6-Mercaptopurine (6-MP) is one of the key drugs in treatment many pediatric cancers, auto immune diseases and inflammatory bowel disease. 6-MP a prodrug, converted to an active metabolite 6-thioguanine nucleotide (6-TGN) through enzymatic reaction involving thiopurine methyltransferase (TPMT). Pharmacogenomic variation observed TPMT enzyme produces significant drug response among patient population. Despite 6-MP’s widespread use response, efforts at quantitative optimization dose regimens for individual patients are limited. In addition, research devoted on pharmacogenomics predict clinical responses proving far from ideal. this work, we present Bayesian population modeling approach develop pharmacological model metabolism humans. face scarcity data settings, global sensitivity analysis based reduction used minimize parameter space. For accurate estimation sensitive parameters, robust optimal experimental design D-optimality criteria was exploited. With patient-specific model, predictive control algorithm optimize scheduling with objective maintaining 6-TGN concentration within its therapeutic window. More importantly, first time, show how incorporation information different levels biological chain-of (i.e. gene expression-enzyme phenotype-drug phenotype) plays critical role determining uncertainty predicting target. The can be utilized setting individualize dosing patient’s ability metabolize instead traditional standard-dose-for-all approach.