Biomedical subjects

M Van Guilder

Publications and source records attributed to M Van Guilder.

8 recordsLinked to original sources

PubMed2000

Achieving target goals most precisely using nonparametric compartmental models and "multiple model" design of dosage regimens.

Multiple model (MM) design and stochastic control of dosage regimens permit essentially full use of all the information contained in either a Bayesian prior nonparametric EM (NPEM) population pharmacokinetic model or in an MM Bayesian posterior updated parameter set, to achieve and maintain selected therapeutic goals with optimal precision (least predicted weighted squared error). The regimens are visibly more precise in the achievement of desired target goals than are current methods using mean or median population parameter values. Bayesian feedback has now also been incorporated into the MM software. An evaluation of MM dosage design using an NPEM population model versus dosage design based on conventional mean population parameter values is presented, using a population model of vancomycin. Further feedback control was also evaluated, incorporating realistic simulated uncertainties in the clinical environment such as those in the preparation and administration of doses.

Anti-Bacterial Agents↗

PubMed2000

Population pharmacokinetics/pharmacodynamics modeling: parametric and nonparametric methods.

As clinicians acquire experience with the clinical and pharmacokinetic behavior of a drug, it is usually optimal to record this experience in the form of a population pharmacokinetic model, and then to relate the behavior of the model to the clinical effects of the drug or to a linked pharmacodynamic model. The role of population modeling is thus to describe and record clinical experience with the behavior of a drug in a certain group or population of patients or subjects.

Algorithms↗

PubMed2000

A population pharmacokinetic analysis of the penetration of the prostate by levofloxacin.

Prostatitis has remained a pathological entity that is difficult to treat. Part of the difficulty revolves about the putative offending pathogens. For acute prostatitis, members of the Enterobacteriaceae, particularly Escherichia coli, play a central role, while intracellular pathogens such as Chlamydia are more frequently seen in chronic prostatitis. Consequently, a drug needs to be able to penetrate to this specialized site in both the acute and chronic infection forms of the disease and also have potent activity against the most common causative pathogens, both intracellular and extracellular. Levofloxacin has such an activity profile. We wished to document its ability to penetrate to the site of infection. Patients undergoing prostatectomies were administered 500 mg of levofloxacin orally every 24 h for 2 days prior to surgery, and then on the day of surgery, 500 mg was administered as an hour-long, constant-rate intravenous (i.v.) infusion. A set of blood samples was obtained as guided by stochastic optimal design theory. Prostate biopsy times were determined by randomizing subjects into one of four groups, based on the interval after the i.v. dose. All plasma and prostate drug concentrations were comodeled by a population modeling program, BigNPEM, implemented on the Cray T3E Supercomputer housed at the Supercomputer Center at the University of California at San Diego. Penetration was determined as the ratio of the area under the concentration-time curve (AUC) of levofloxacin in the prostate to the plasma levofloxacin AUC. When calculated from the mean population parameters, this penetration ratio was 2.96. We also performed a 1,000-subject Monte Carlo simulation from the mean parameter vector and covariance matrix. The mean penetration ratio here was 4.14 with a 95% confidence interval of 0.20 to 19.6. Over 70% of the population had a penetration ratio in excess of 1.0. Levofloxacin adequately penetrates a noninflamed prostate and should be evaluated for the therapy of prostatitis.

Aged↗

PubMed1998

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.

Anti-Arrhythmia Agents↗

PubMed1995

Pharmaco-informatics: more precise drug therapy from 'multiple model' (MM) adaptive control regimens: evaluation with simulated vancomycin therapy.

A multiple model (MM) stochastic control of dosage regimens permits essentially optimal use of the information contained in either a population pharmacokinetic model or in a MM Bayesian updated parameter set to achieve and maintain selected therapeutic goals with optimal precision. The regimens are visibly more precise than those achieved using mean parameter values. Feedback has now also been incorporated into the MM software. An evaluation of MM adaptive control precision versus control achieved using population mean parameter values is presented using a real population model (Vancomycin). Further feedback control was evaluated, incorporating simulated clinical errors in the preparation and administration of doses.

Bayes Theorem↗

PubMed1994

Pharmaco-informatics: more precise drug therapy from "multiple model" (MM) stochastic adaptive control regimens: evaluation with simulated vancomycin therapy.

MM stochastic control of dosage regimens permits essentially full use of information, either in a population pharmacokinetic model or a Bayesian updated MM parameter set, to achieve and maintain selected therapeutic goals with optimal precision. The regimens are visibly more precise than those developed using mean parameter values. Bayesian MM feedback has now also been implemented.

Bayes Theorem↗

PubMed1993

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.

Bayes Theorem↗

PubMed1989

Estimation of parameters and missing values under a regression model with non-normally distributed and non-randomly incomplete data.

We carried out a simulation study to compare the performance of three algorithms (complete cases, ALLVALUE, and expectation maximization, EM) in estimating regression parameters and missing values for situations that have varying amounts of missing data, distributions (normal, mixture of normals and lognormal), patterns of incomplete data (random, related and censored), and degrees of correlational structure among the dependent and independent variables. We found that the EM and complete cases algorithms performed equally well regardless of the correlational structure, when the percentage of incomplete data was only 5 per cent. When this percentage increased to 25 per cent, the EM algorithm was generally best for estimation, but the complete cases algorithm was safe and conservative. This finding may be attributed to the study design, which required that the slopes be the same in the population of all cases, and in the population of complete cases. In addition, the one-step imputing method (ALLVALUE) was competitive only for situations with weak correlational structure and/or little missing data. In that situation the bias caused with use of all available information was less than that caused with use of only complete cases. On the other hand, for imputation, the EM algorithm performed optimally, even in situations of censored or log-normally distributed data.

Algorithms↗