Population-based investigations of drug relative clearance using nonlinear mixed-effect modelling from information generated during the routine clinical care of patients.

Interpatient variability in drug disposition and response is a therapeutic premise, and thus evaluation and management of such variability are the basis for individualized pharmacotherapy. If the mathematical approach to determining drug doses were accurate and practical, the use of calculated doses could reduce the potential for toxicity and decrease the need for repetitious drug assays. The major strength of the population pharmacokinetics approach is that useful information can be extracted from sparse data collected during routine clinical care. Population pharmacokinetics can be defined as the study of the variability in serum drug concentrations between individuals when standard dosage regimens are administered. An approach to population pharmacokinetic data analysis has been implemented in the Nonlinear Mixed Effects Model (NONMEM) computer program. This report shows the feasibility of using a simple pharmacokinetic screen approach to estimate the population mean relative drug clearance and detecting drug-drug interaction by use of NONMEM. In clinical application of multiple trough screen or multiple peak screen, the variability of drug relative clearance within the population is assessed and a mathematical relationship between drug relative clearance and individual patient characteristics, such as age, body weight, gender, disease state or drug interaction with concomitant drug is derived. In this report I describe this approach and its application using several examples previously reported by us and others.