Human Genetic Variation, Population Pharmacokinetic - Dynamic Models, Bayesian Feedback Control, and Maximally Precise Individualized Drug Dosage Regimens
Variation in the behavior of drugs between people, and variation in drug behavior in a given patient over time, have both presented us with challenging problems in optimal description of such behavior as well as challenges of how best to act on such information. New high-throughput genotyping methods and measurement of variations in gene expression over time present us with issues of 1) how best to use such information in the overall process of planning drug dosage regimens for individual patients, especially if the drug is potentially toxic; 2) how to further refine our knowledge about the patient during the course of pharmacotherapy; and 3) how best to adjust the dosage regimen to the new information we obtain about him/her as a unique individual. Human genetic variation, in the form of gene sequence or expression variability, provides us with important covariate information to help further individualize our dosage regimen for a particular patient based on that information, just as does information about smoking status, age, gender, body weight, and renal function, for example. It helps us consider the patient as an individual rather than as a member of a larger group. Variation in gene expression over time (i.e., transcriptomic biomarkers) in an individual patient presents another problem, as it can cause significant differences in drug behavior over time. However, just as variation over time can occur in other covariates such as body weight and renal function, so can such changes in genetic expression over time be incorporated into models of drug behavior in individual patients, and used thoughtfully to optimize each patient's drug dosage regimen. The overall structure of optimally precise Bayesian adaptive control is presented in this paper, to define explicitly the context in which human genetic/genomic information can be incorporated and used to optimize drug therapy for patients.
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Document Type: Research Article
Publication date: December 1, 2009