Pharmacokinetics / Pharmacodynamics Modelling and Simulation with MATLAB.

Abstract

Population pharmacokinetics has taken off with an exponential increase in published papers in the last decade. This has revolutionized how data from clinical studies is analyzed. Population pharmacokinetics methods are used almost exclusively for phase II and III studies and to summarize data across a drug development program. Advances in pharmacokinetic and pharmacodynamic modeling will allow fewer, more focused and informative clinical trials, and lead to significant cost savings. However, despite these advances, population methods are not routinely easy to employ. A major hindrance to implementing population methods is that it is mathematically and statistically complex, and compared to the number of pharmacokineticists in general there are few modelers who specialize in the methodology. Hence, in this study, we have used MATLAB computer program to obtain information on effective dosage regimens of doripenem by a modeling and simulation approach based on pharmacokinetic (PK)/pharmacodynamic (PD) theory. We have introduced a modification to the PK/PD model which explains in-vitro bactericidal kinetics of doripenem for several Pseudomonas aeruginosa strains. Time-course profiles of bacterial counts in patients infected with P. aeruginosa were simulated for typical clinical dosage regimens considering the variability of PK and the patients' backgrounds by a Monte Carlo simulation. Moreover, time-course profiles of probability achieving the criterion (log (CFU/mL) < 0) were predicted for the evaluation of antibacterial efficacy by renal function. The in-vitro bacterial profiles at various dosage regimens could be well explained by the PK/PD model. The simulations suggested the dependence of antibacterial efficacy on the frequency of administration, indicating time-dependent antibacterial activity. It was also suggested that 500 mg t.i.d. showed significant bacterial reduction in patients for any degree of renal function and any severities in two weeks after the start of treatment. Our approach to simulate time-course profiles of bacterial counts should be useful for determining and examining effective dosage regimens, including the treatment period in drug development.