The Hill equation: a review of its capabilities in pharmacological modelling.

The Hill equation was first introduced by A.V. Hill to describe the equilibrium relationship between oxygen tension and the saturation of haemoglobin.

Comparisons between antimicrobial pharmacodynamic indices and bacterial killing as described by using the Zhi model.

Various suggestions have been made for empirical pharmacodynamic indices of antibiotic effectiveness, such as areas under the drug concentration-time curve in serum (AUC), AUC > MIC, AUC/MIC, area under the inhibitory curve (AUIC), AUC above MIC, and time above MIC (T > MIC). In addition, bacterial growth and killing models, such as the Zhi model, have been developed. The goal of the present study was to compare the empirical behavior of the Zhi model of bacterial growth and killing with the other empirical pharmacodynamic indices described above by using simulated clinical data analyzed with the USC*PACK PC clinical programs for adaptive control of drug therapy, with one model describing a concentration-dependent antibiotic (tobramycin) and another describing a concentration-independent antibiotic (ticarcillin).

Model-based, goal-oriented, individualised drug therapy. Linkage of population modelling, new 'multiple model' dosage design, bayesian feedback and individualised target goals.

This article examines the use of population pharmacokinetic models to store experiences about drugs in patients and to apply that experience to the care of new patients. Population models are the Bayesian prior. For truly individualised therapy, it is necessary first to select a specific target goal, such as a desired serum or peripheral compartment concentration, and then to develop the dosage regimen individualised to best hit that target in that patient.

Adaptive control of therapeutic drug regimens relations between clinical situations: outcomes and simulations using nonlinear dynamic models.

With Bayesian modeling and adaptive control of drug dosage regimens, serum and peripheral drug concentrations can be predicted in clinical situations using linear pharmacokinetic compartmental models (PK). Recently, several pathophysiologic and pharmacodynamic nonlinear models (PD) have been developed. The present report illustrates both their utility and limits for the computation of effects in clinical situations in the setting of actual routine and acute patient care.