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. One must monitor the behaviour of the drug by measuring serum concentrations or other responses, hopefully obtained at optimally chosen times, not only to see the raw results, but to also make an individualised (Bayesian posterior) model of how the drug is behaving in that patient. Only then can one see the relationship between the dose and the absorption, distribution, effect and elimination of the drug, and the patient's clinical sensitivity to it; one must always look at the patient. Only by looking at both the patient and the model can it be judged whether the target goal was correct or needs to be changed. The adjusted dosage regimen is again developed to hit that target most precisely starting with the very next dose, not just for some future steady state. Nonparametric population models have discrete, not continuous, parameter distributions. These lead naturally into the multiple model method of dosage design, specifically to hit a desired target with the greatest possible precision for whatever past experience and present data are available on that drug--a new feature for this goal-oriented, model-based, individualised drug therapy. As clinical versions of this new approach become available from several centers, it should lead to further improvements in patient care, especially for bacterial and viral infections, cardiovascular therapy, and cancer and transplant situations.

[Aminoglycoside determinations calculated in endocarditis vegetations. Relations with clinical practices during infectious endocarditis treatment with amikacin]. / Concentrations calculées en aminoside dans des végétations d'endocardites. Relations avec les pratiques cliniques lors du traitement d'endocardites infectieuses par l'amikacine.

Using a new computer program SPHERE, amikacin concentrations have been computed at various layers of simulated endocardial vegetations. Inputs are the computed serum (central compartment) concentrations of either population pharmacokinetic models or of individualized patient-specific models utilizing Bayesian fitting to data of doses given and measured serum levels, using the USC*PACK PC Clinical Programs. The vegetation is modeled as an isotropic homogeneous sphere. Fick's second law of radial diffusion was applied to compute the in situ antibiotic concentrations. Examination of factors affecting concentrations in vegetations shows that in situ peak concentrations are less when the vegetation is larger, and when the antibiotic dose, serum concentrations and diffusivity are all less. The results show that early and aggressive treatment of infectious endocarditis is required with high doses of concentration-dependent antibiotics, such as aminoglycosides, to achieve the desired high peak serum levels and to reach effective concentrations deep inside the vegetations.

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. Patients who received therapy with aminoglycosides or/and vancomycin were selected. For each patient, after estimation of individual pharmacokinetic parameters, the computed outputs of the linear compartmental pharmacokinetic model were used as inputs for 2 different a priori nonlinear dynamic models: 1) the EFFECT modeling program, using a Hill model, and 2) the BACTCIDE program, which is a combination of a simple growth model for the organism and a Hill effect model considering both the microorganism, the antibiotic, and the patient's minimal inhibitory concentration (MIC). The programs (1) and (2) can use as inputs the computed concentrations from any of three compartments: central, peripheral, or a spherical diffusion compartment to compute drug diffusion into endocardial vegetations or abscesses. The EFFECT program can be used alone for the evaluation of drug effects. The BACTCIDE program illustrates differences in activity between concentration-dependent and time-dependent antibiotics. Such nonlinear programs are very sensitive to the MIC values.

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). The computed relative number of CFU was plotted against each pharmacodynamic index, with each axis parameterized over time. We assumed that a good pharmacodynamic index should present a clear and continuous relationship between the time course of its values and the time course of the bacterial killing as seen with the Zhi model. Preliminary work showed that some pharmacodynamic indices were very similar. A good sensitivity to the change in the values of the MIC was shown for AUC/MIC and also for T > MIC. In addition, the time courses of some other pharmacodynamic indices were very similar. Since AUC/MIC is easily calculated and shows more sensitivity, it appeared to be the best of the indices mentioned above for the concentration-dependent drug, because it incorporated and used the MIC the best. T > MIC appeared to be the best index for a concentration-independent drug. We also propose a new composite index, weighted AUC (WAUC), which appears to be useful for both concentration-dependent and concentration-independent drugs.

Individualizing drug dosage regimens: roles of population pharmacokinetic and dynamic models, Bayesian fitting, and adaptive control.

The role of population pharmacokinetic modeling is to store experience with drug behavior. The behavior of the model is then correlated with the clinical behavior of the patients studied, permitting selection of a specific serum level therapeutic goal that is based on each individual patient's need for the drug and on the risk of adverse reactions, both of which must be considered. A dosage regimen is then computed to achieve that goal with maximum precision. The patient should not run a greater risk of toxicity than is justified, and should obtain the maximum possible benefit within the acceptable risk. The regimen is given and the patient monitored.

Computation of drug concentrations in endocardial vegetations in patients during antibiotic therapy.

The treatment of endocarditis often requires prolonged antibiotic therapy. Individualized drug dosage regimens have made such therapy possible even in patients with impaired renal function. However, the problem of efficacy remains. Especially for aminoglycosides, it would be a useful guide to have at least an approximate idea of the concentration of an antibiotic within an endocardial vegetation. This study was designed to develop software to model the drug concentrations at different layers within spherical vegetations to provide a guide during clinical therapy of patients with endocarditis. A general model describing the diffusion of antibiotics in spheres has now been developed and interfaced with the USC*PACK PC Clinical Programs in order to compute and plot concentrations, within the vegetation, based on the regimen given to the patient and the diffusitivity of the antibiotic into the vegetation. Some preliminary results of this research, which are still in progress, are presented. Diffusion into simulated spherical vegetations has been computed for different treatment regimens for endocarditis: amikacin or netilmicin and vancomycin were given to three elderly patients (3 women, 74, 75 and 92 years old, with initial estimated creatinine clearances of 51, 36, and 31 ml/min/1.73 m2, respectively). Although Amikacin has a low diffusivity, the concentrations, even in the center of the vegetation, appear to be effective. The effects of various regimens, including a 'once-a-day' aminoglycoside regimen, are presented.