Experimental Design for Estimating Integrals by Numerical Quadrature, with Applications to Pharmacokinetic Studies

Katz, Darryl; D’Argenio, David Z.

doi:10.2307/2531089

Cited by 22 publications

(16 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The resulting optimal PK sampling schedules from the three approaches were compared to the model building data PK sampling schedules in terms of the scaled MSE using data from 10,000 hypothetical subjects. The residual variability has been reported to dominate the MSE [30,31] which may result in unintuitive time points, even when the variability is relatively negligible [32]. Moreover, the estimated residual variability for non-Asian and Asian subjects were small compared to the inter-subject variability.…”

Section: Datamentioning

confidence: 99%

Population pharmacokinetic modelling of S-warfarin to evaluate the design of drug–drug interaction studies for CYP2C9

Klein

Gueorguieva²,

Aarons

2012

J Pharmacokinet Pharmacodyn

View full text Add to dashboard Cite

This study is to assess pharmacokinetic (PK) sampling time schedules and trial size requirements of drug-drug interaction (DDI) studies for CYP2C9, based on S-warfarin population PK models. S-warfarin plasma concentrations from eight DDI studies were utilized to develop S-warfarin population PK models. Optimal PK sampling times were obtained that minimized mean squared error of geometric mean of the area under the concentration-time curve (AUC(0-∞)). The powers and type I error rates of testing the equivalences of the geometric means of AUC(0-∞) only and AUC(0-∞) and maximum concentration (C (max)), jointly, were assessed via simulation for two-by-two cross-over designs. The results were compared to those from three bioequivalence sample size calculation methods. Two-compartment population PK models with first order absorption were established for non-Asian and Asian subjects. The optimal PK sampling times of size 17 per individual per period were found to be 0.0, 0.5, 1.0, 2.0, 4.0, 6.0, 10.0, 12.0, 16.0, 24.0, 36.0, 48.0, 60.0, 72.0, 96.0, 120.0, 144.0 h post a single oral dose of 25 mg warfarin. For non-Asian subjects, the minimum numbers of subjects required per trial with the optimal PK sampling schedule to achieve 80% power and 5% type I error rate, ranged from 6 to 19 for the equivalence of AUC(0-∞) and C (max) jointly. It has been demonstrated that appropriately selected PK sampling time points can greatly increase the corresponding power of the study without increasing the number of subjects, especially when the true ratio is near the default bioequivalence boundary (0.8-1.25).

show abstract

Section: Datamentioning

confidence: 99%

Population pharmacokinetic modelling of S-warfarin to evaluate the design of drug–drug interaction studies for CYP2C9

Klein

Gueorguieva²,

Aarons

2012

J Pharmacokinet Pharmacodyn

View full text Add to dashboard Cite

show abstract

“…Several authors (Katz and D'Argenio, 1983;Katz, 1984;Bailer and Piegorsch, 1990;Wang, 2001) have noticed that the variance term tends to dominate the MSE. As a result, these objective functions often do not give intuitively reasonable solutions, even when the measurement error is relatively small.…”

Section: The Objective Functionsmentioning

confidence: 99%

“…Atkinson et al (1993) proposed an optimization method using a Doptimality criteria for BA parameters such as AUC, C max and t max . Katz and D'Argenio (1983) proposed an algorithm which used the mean squared error (MSE) of the AUC estimate as an objective function for finding optimal sampling times. Here, an objective function is a summary function of the underlying true concentration time-curve and sampling times which is minimized with respect to the sampling times to select an optimal placement of time points.…”

Section: Introductionmentioning

confidence: 99%

“…Here, an objective function is a summary function of the underlying true concentration time-curve and sampling times which is minimized with respect to the sampling times to select an optimal placement of time points. Katz and D'Argenio (1983) used quasi-Newton methods implemented in FORTRAN to minimize their objective function. Wang (2001) used MSE for estimating the concentration time-curve using linear interpolation instead of MSE of the AUC estimate.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Optimal sampling times in bioequivalence studies using a simulated annealing algorithm

2007

View full text Add to dashboard Cite

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.

show abstract

“…Katz and D’Argenio found optimal sampling times for estimating the integral of bi- and triexponential equations using the trapezoid rule [12]. In their original form these results are of limited usefulness, since the authors have assumed fixed values of parameters of those equations.…”

Section: Introductionmentioning

confidence: 99%

Searching for an optimal AUC estimation method: a never-ending task?

Jawień

2014

J Pharmacokinet Pharmacodyn

View full text Add to dashboard Cite

An effective method of construction of a linear estimator of AUC in the finite interval, optimal in the minimax sense, is developed and demonstrated for five PK models. The models may be given as an explicit C(t) relationship or defined by differential equations. For high variability and rich sampling the optimal method is only moderately advantageous over optimal trapezoid or standard numerical approaches (Gauss-Legendre or Clenshaw-Curtis quadratures). The difference between the optimal estimator and other methods becomes more pronounced with a decrease in sample size or decrease in the variability. The described estimation method may appear useful in development of limited-sampling strategies for AUC determination, as an alternative to the widely used regression-based approach. It is indicated that many alternative approaches are also possible.Electronic supplementary materialThe online version of this article (doi:10.1007/s10928-014-9392-y) contains supplementary material, which is available to authorized users.

show abstract

Experimental Design for Estimating Integrals by Numerical Quadrature, with Applications to Pharmacokinetic Studies

Cited by 22 publications

References 0 publications

Population pharmacokinetic modelling of S-warfarin to evaluate the design of drug–drug interaction studies for CYP2C9

Population pharmacokinetic modelling of S-warfarin to evaluate the design of drug–drug interaction studies for CYP2C9

Optimal sampling times in bioequivalence studies using a simulated annealing algorithm

Searching for an optimal AUC estimation method: a never-ending task?

Contact Info

Product

Resources

About