Relevance of Pharmacokinetic and Pharmacodynamic Modeling to Clinical Care of Critically Ill Patients

Jurgen      B. Bulitta; Cornelia      B. Landersdorfer; Alan      Forrest; Silvia      V. Brown; Michael      N. Neely; Brian      T. Tsuji; Arnold      Louie

doi:10.2174/138920111798808428

Abstract

Efficacious therapy is of utmost importance to save lives and prevent bacterial resistance in critically ill patients. This review summarizes pharmacokinetic (PK) and pharmacodynamic (PD) modeling methods to optimize clinical care of critically ill patients in empiric and individualized therapy. While these methods apply to all therapeutic areas, we focus on antibiotics to highlight important applications, as emergence of resistance is a significant problem. Nonparametric and parametric population PK modeling, multiple-model dosage design, Monte Carlo simulations, and Bayesian adaptive feedback control are the methods of choice to optimize therapy. Population PK can estimate between patient variability and account for potentially increased clearances and large volumes of distribution in critically ill patients. Once patient- specific PK data become available, target concentration intervention and adaptive feedback control algorithms can most precisely achieve target goals such as clinical cure of an infection or resistance prevention in stable and unstable patients with rapidly changing PK parameters. Many bacterial resistance mechanisms cause PK/PD targets for resistance prevention to be usually several-fold higher than targets for near-maximal killing. In vitro infection models such as the hollow fiber and one-compartment infection models allow one to study antibiotic-induced bacterial killing and emergence of resistance of mono- and combination therapies over clinically relevant treatment durations. Mechanism-based (and empirical) PK/PD modeling can incorporate effects of the immune system and allow one to design innovative dosage regimens and prospective validation studies. Mechanism-based modeling holds great promise to optimize mono- and combination therapy of anti-infectives and drugs from other therapeutic areas for critically ill patients.

Keywords: Adaptive feedback control, Bayesian updating methods, mathematical modeling, mechanism-based modeling, Monte Carlo simulation, population pharmacokinetics, pharmacodynamics, prevention of emergence of resistance, ill patients, pharmacokinetic (PK), optimize clinical care, bacterial resistance mechanisms, therapeutic areas, optimal therapy, immunocompetent patients