Biomedical subjects

Michael G Dodds

Publications and source records attributed to Michael G Dodds.

5 recordsLinked to original sources

PubMed2005

Population pharmacokinetics of recombinant factor XIII in cynomolgus monkeys.

Hemostasis in humans and other animals is a complex process that controls blood loss after a vascular injury. Factor XIII (FXIII) stabilizes clots primarily by cross-linking fibrin, thus protecting a newly formed clot from fibrinolysis by plasmin. Congenital deficiencies in humans involving FXIII are associated with delayed bleeding and wound healing and severe spontaneous hemorrhaging. These symptoms can be alleviated by intravenous administration of enriched FXIII plasma fractions. Circulating plasma FXIII is found as a heterotetramer that dissociates in the presence of calcium and thrombin into an active dimer and 2 inactive monomers. The recombinant FXIII under investigation is the active dimer alone. A 3-compartment, nonlinear population pharmacokinetic model was implemented in NONMEM V and then used to analyze data from preclinical studies in cynomolgus monkeys. The model simultaneously describes endogenous production of dimer (0.622 microg kg(-1) hr(-1)) and monomer (12.1 microg kg(-1) hr(-1)), and the administration of recombinant dimer. The model incorporates the rate and extent of complexation of recombinant dimer with available endogenous monomer (6.59 mg(-1) kg hr(-1)) to form the heterotetramer. Half-lives for dimer, heterotetramer, and monomer (3.33 hours, 2.83 days, and 3.94 hours for A(2), A(2)B(2), and B, respectively) were estimated, along with their variability in the population studied.

Animals↗

PubMed2005

Integrated model of hepatic and peripheral glucose regulation for estimation of endogenous glucose production during the hot IVGTT.

We have developed a new model to describe endogenous glucose kinetics during a labeled (hot) intravenous glucose tolerance test (IVGTT) to derive a time profile of endogenous glucose production (EGP). We reanalyzed data from a previously published study (P. Vicini, J. J. Zachwieja, K. E. Yarasheski, D. M. Bier, A. Caumo, and C. Cobelli. Am J Physiol Endocrinol Metab 276: E285-E294, 1999), in which insulin-modified [6,6-2H2]glucose-labeled IVGTTs (0.33 g/kg glucose) were performed in 10 normal subjects. In addition, a second tracer ([U-13C]glucose) was infused in a variable rate to clamp the endogenous glucose tracer-to-tracee ratio (TTR). Our new model describing endogenous glucose kinetics was incorporated into the two-compartment hot minimal-model structure. The model gave estimates of glucose effectiveness [1.54 +/- 0.31 (SE) ml x kg(-1) x min(-1)], insulin sensitivity (37.74 +/- 5.23 10(4) dl x kg(-1) x min(-1) x microU(-1) x ml), and a new parameter describing the sensitivity of EGP to the inhibitory effect of insulin (IC50 = 0.0195 +/- 0.0046 min(-1)). The model additionally provided an estimate of the time course of EGP showing almost immediate inhibition, followed by a secondary inhibitory effect caused by infusion of insulin, and a large overshoot as EGP returns to its basal value. Our estimates show very good agreement with those obtained via deconvolution and the model-independent TTR clamp technique. These results suggest that the new integrated model can serve as a simple one-step approach to obtain metabolic indexes while also providing a parametric description of EGP.

Blood Glucose↗

PubMed2005

Robust population pharmacokinetic experiment design.

The population approach to estimating mixed effects model parameters of interest in pharmacokinetic (PK) studies has been demonstrated to be an effective method in quantifying relevant population drug properties. The information available for each individual is usually sparse. As such, care should be taken to ensure that the information gained from each population experiment is as efficient as possible by designing the experiment optimally, according to some criterion. The classic approach to this problem is to design "good" sampling schedules, usually addressed by the D-optimality criterion. This method has the drawback of requiring exact advanced knowledge (expected values) of the parameters of interest. Often, this information is not available. Additionally, if such prior knowledge about the parameters is misspecified, this approach yields designs that may not be robust for parameter estimation. In order to incorporate uncertainty in the prior parameter specification, a number of criteria have been suggested. We focus on ED-optimality. This criterion leads to a difficult numerical problem, which is made tractable here by a novel approximation of the expectation integral usually solved by stochastic integration techniques. We present two case studies as evidence of the robustness of ED-optimal designs in the face of misspecified prior information. Estimates from replicate simulated population data show that such misspecified ED-optimal designs recover parameter estimates that are better than similarly misspecified D-optimal designs, and approach estimates gained from D-optimal designs where the parameters are correctly specified.

Algorithms↗

PubMed2004

Assessing convergence of Markov chain Monte Carlo simulations in hierarchical Bayesian models for population pharmacokinetics.

Advances in computer hardware and the associated computer-intensive algorithms made feasible by these advances [like Markov chain Monte Carlo (MCMC) data analysis techniques] have made possible the application of hierarchical full Bayesian methods in analyzing pharmacokinetic and pharmacodynamic (PK-PD) data sets that are multivariate in nature. Pharmacokinetic data analysis in particular has been one area that has seized upon this technology to refine estimates of drug parameters from sparse data gathered in a large, highly variable population of patients. A drawback in this type of analysis is that it is difficult to quantitatively assess convergence of the Markov chains to a target distribution, and thus, it is sometimes difficult to assess the reliability of estimates gained from this procedure. Another complicating factor is that, although the application of MCMC methods to population PK-PD problems has been facilitated by new software designed for the PK-PD domain (specifically PKBUGS), experts in PK-PD may not have the necessary experience with MCMC methods to detect and understand problems with model convergence. The objective of this work is to provide an example of a set of diagnostics useful to investigators, by analyzing in detail three convergence criteria (namely the Raftery and Lewis, Geweke, and Heidelberger and Welch methods) on a simulated problem and with a rule of thumb of 10,000 chain elements in the Markov chain. We used two publicly available software packages to assess convergence of MCMC parameter estimates; the first performs Bayesian parameter estimation (PKBUGS/WinBUGS), and the second is focused on posterior analysis of estimates (BOA). The main message that seems to emerge is that accurately estimating confidence regions for the parameters of interest is more demanding than estimating the parameter means. Together, these tools provide numerical means by which an investigator can establish confidence in convergence and thus in the estimated parameters derived from hierarchical full Bayesian pharmacokinetic data analysis.

Algorithms↗

PubMed2003

An evaluation of population D-optimal designs via pharmacokinetic simulations.

One goal of large scale clinical trials is to determine how a drug is processed by, and cleared from, the human body [i.e., its pharmacokinetic (PK) properties] and how these PK properties differ between individuals in a population (i.e., its population PK properties). Due to the high cost of these studies and the limited amount of data (e.g., blood samples) available from each study subject, it would be useful to know how many measurements are needed and when those measurements should be taken to accurately quantify population PK model parameters means and variances. Previous studies have looked at optimal design strategies of population PK experiments by developing an optimal design for an individual study (i.e., no interindividual variability was considered in the design), and then applying that design to each individual in a population study (where interindividual variability is present). A more algorithmically and informationally intensive approach is to develop a population optimal design, which inherently includes the assessment of interindividual variability. We present a simulation-based evaluation of these two design methods based on nonlinear Gaussian population PK models. Specifically, we compute standard individual and population D-optimal designs and compare population PK model parameter estimates based on simulated optimal design measurements. Our results show that population and standard D-optimal designs are not significantly different when both designs have the same number of samples per individual. However, population optimal designs allow for sampling schedules where the number of samples per individual is less than the number of model parameters, the theoretical limit allowed in standard optimal design. These designs with a low number of samples per individual are shown to be nearly as robust in parameter estimation as standard D-optimal designs. In the limit of just one sample per individual, however, population D-optimal designs are shown to be inadequate.

AIDS Vaccines↗