title: Adaptive control of therapeutic drug regimens relations between clinical situations: outcomes and simulations using nonlinear dynamic models.
abstract: 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.
authors.affiliation: ADCAPT, Hopital A. Charial, Hospices Civils de Lyon, 40 Avenue de la Table de Pierre, 69340 Francheville, France.
authors.forename: P H
authors.initials: PH
authors.lastname: Maire
authors.forename: X
authors.initials: X
authors.lastname: Barbaut
authors.forename: J C
authors.initials: JC
authors.lastname: Thalabard
authors.forename: J M
authors.initials: JM
authors.lastname: Vergnaud
authors.forename: D
authors.initials: D
authors.lastname: Roux
authors.forename: M
authors.initials: M
authors.lastname: Roy
authors.forename: R W
authors.initials: RW
authors.lastname: Jelliffe
mesh_terms: D000368:Aged; D000617:Aminoglycosides; D000900:Anti-Bacterial Agents / Q000008:administration & dosage / Q000097:blood / Q000493:pharmacokinetics / Q000494:pharmacology / Q000627:therapeutic use*; D001825:Body Fluid Compartments / Q000502:physiology; D003198:Computer Simulation; D004360:Drug Therapy, Computer-Assisted; D004696:Endocarditis / Q000188:drug therapy; D005260:Female; D006801:Humans; D008826:Microbial Sensitivity Tests; D008875:Middle Aged; D008954:Models, Biological; D017711:Nonlinear Dynamics; D011704:Pyelonephritis / Q000188:drug therapy; D012984:Software; D016896:Treatment Outcome; D014640:Vancomycin / Q000097:blood / Q000627:therapeutic use
chemical_list: D000617:Aminoglycosides; D000900:Anti-Bacterial Agents; D014640:Vancomycin