Optimal statistical design for toxicokinetic studies

Beatty, D A; Piegorsch, Walter W.

doi:10.1177/096228029700600405

Cited by 13 publications

(3 citation statements)

References 34 publications

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“…Measurement errors on the experimental data, sparseness of those, and model simplifications or mispecifications are translated into uncertainties in the parameter values determined by statistical fitting to such data. Uncertainty may be reduced by further experiments, the design of which can be formally optimized (72,73), or by a better understanding of the actual processes under study.…”

Section: Calibration Uncertainty and Variabilitymentioning

confidence: 99%

Statistical Issues in Toxicokinetic Modeling: A Bayesian Perspective

Bernillon¹,

Bois²

2000

Environ Health Perspect

116

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Determining the relationship between an exposure and the resulting target tissue dose is a critical issue encountered in quantitative risk assessment (QRA). Classical or physiologically based toxicokinetic (PBTK) models can be useful in performing that task. Interest in using these models to improve extrapolations between species, routes, and exposure levels in QRA has therefore grown considerably in recent years. In parallel, PBTK models have become increasingly sophisticated. However, development of a strong statistical foundation to support PBTK model calibration and use has received little attention. There is a critical need for methods that address the uncertainties inherent in toxicokinetic data and the variability in the human populations for which risk predictions are made and to take advantage of a priori information on parameters during the calibration process. Natural solutions to these problems can be found in a Bayesian statistical framework with the help of computational techniques such as Markov chain Monte Carlo methods. Within such a framework, we have developed an approach to toxicokinetic modeling that can be applied to heterogeneous human or animal populations. This approach also expands the possibilities for uncertainty analysis. We present a review of these efforts and other developments in these areas. Appropriate statistical treatment of uncertainty and variability within the modeling process will increase confidence in model results and ultimately contribute to an improved scientific basis for the estimation of occupational and environmental health risks.

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Section: Calibration Uncertainty and Variabilitymentioning

confidence: 99%

Statistical Issues in Toxicokinetic Modeling: A Bayesian Perspective

Bernillon¹,

Bois²

2000

Environ Health Perspect

116

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“…Uncertainty may be reduced by new experiments, the design of which can be optimised to bring a maximum of information (Beatty and Piegorsch, 1997) , or by a better understanding of the biology and hence better models. This may take much efforts and decisions routinely need to be quickly made despite a fair amount of uncertainty about the situation examined.…”

Section: Introductionmentioning

confidence: 99%

Applications of population approaches in toxicology

Bois

2001

Toxicology Letters

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Many experimental or observational studies in toxicology are best analysed in a population framework.Recent examples include investigations of the extent and origin of intra-individual variability in toxicity studies, incorporation of genotypic information to address intra-individual variability, optimal design of experiments, and extension of toxicokinetic modelling to the analysis of biomarker studies in industrial hygiene or epidemiological contexts. Bayesian statistics provide powerful numerical methods for fitting population models, particularly when complex mechanistic toxicokinetic or toxicodynamic models are involved. Challenges and limitations to the use of population models, in terms of basic structure, computational burden, ease of implementation and data accessibility, are identified and discussed.

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“…That is, replicates of animals may be assigned at each time point, but sampled only once often after sacrificing them. A review methodology for study design in toxicokinetics/pharmacokinetics can be found in Beatty and Piegorsch (1997).…”

Section: Introductionmentioning

confidence: 99%

Optimal sampling times in bioequivalence studies using a simulated annealing algorithm

2007

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In pharmacokinetic (PK) studies, blood samples are taken over time on subjects after the administration of a drug to measure the time-course of the plasma drug concentrations. In bioequivalence studies, the trapezoidal rule on the sampled time points is often used to estimate the area under the plasma concentration-time curve, a quantity of principle interest. This manuscript investigates the choice of sampling time points to estimate the area under the curve. In particular, we explore the relative merits of several objective functions, those functions which are minimized with respect to the sampling times to obtain an optimal study design. We propose an objective function which overcomes some of the deficits of existing choices. We also present a simulated annealing algorithm to perform the minimization. The main benefits of the simulated annealing algorithm are the ease in which it can handle constraints on the sampling schedules and its ability to accommodate a variety of models and objective functions. The manuscript presents optimal sampling times for some key examples of true underlying models.

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Optimal statistical design for toxicokinetic studies

Cited by 13 publications

References 34 publications

Statistical Issues in Toxicokinetic Modeling: A Bayesian Perspective

Statistical Issues in Toxicokinetic Modeling: A Bayesian Perspective

Applications of population approaches in toxicology

Optimal sampling times in bioequivalence studies using a simulated annealing algorithm

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