Bioequivalence Tests Based on Individual Estimates Using Non-compartmental or Model-Based Analyses: Evaluation of Estimates of Sample Means and Type I Error for Different Designs

Dubois, Anne; Gsteiger, Sandro; Pigeolet, Etienne; Mentré, France

doi:10.1007/s11095-009-9980-5

Cited by 34 publications

(48 citation statements)

References 35 publications

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“…Indeed, the modelling approach allows separating the different phases of the PK process (absorption, distribution, metabolism, excretion), improving the interpretation of the genetic effects by the comprehension of mechanisms behind the association between a genetic and a primary PK parameter. The benefits provided by this approach compared to the NCA in terms of power have also been shown in other areas (41,42), especially when the number of samples per subject is limited, but remained to be demonstrated in the field of pharmacogenetics.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates

Tessier

Bertrand

Chenel

et al. 2015

AAPS J

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Abstract. Genetic data is now collected in many clinical trials, especially in population pharmacokinetic studies. There is no consensus on methods to test the association between pharmacokinetics and genetic covariates. We performed a simulation study inspired by real clinical trials, using the pharmacokinetics (PK) of a compound under development having a nonlinear bioavailability along with genotypes for 176 single nucleotide polymorphisms (SNPs). Scenarios included 78 subjects extensively sampled (16 observations per subject) to simulate a phase I study, or 384 subjects with the same rich design. Under the alternative hypothesis (H 1 ), six SNPs were drawn randomly to affect the log-clearance under an additive linear model. For each scenario, 200 PK data sets were simulated under the null hypothesis (no gene effect) and H 1 . We compared 16 combinations of four association tests, a stepwise procedure and three penalised regressions (ridge regression, Lasso, HyperLasso), applied to four pharmacokinetic phenotypes, two observed concentrations, area under the curve estimated by noncompartmental analysis and model-based clearance. The different combinations were compared in terms of true and false positives and probability to detect the genetic effects. In presence of nonlinearity and/or variability in bioavailability, model-based phenotype allowed a higher probability to detect the SNPs than other phenotypes. In a realistic setting with a limited number of subjects, all methods showed a low ability to detect genetic effects. Ridge regression had the best probability to detect SNPs, but also a higher number of false positives. No association test showed a much higher power than the others.

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Section: Discussionmentioning

confidence: 99%

Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates

Tessier

Bertrand

Chenel

et al. 2015

AAPS J

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“…Other researchers have shown, through the analysis of simulated data or data obtained from real clinical trials, that the compartmental approach can be used to assess bioequivalence (33,34,35,36,37,38,39,40,41). However, the PK models employed in previously published analyses were relatively simple ones (one or two compartment models).…”

Section: Discussionmentioning

confidence: 99%

Novel Population Pharmacokinetic Method Compared to the Standard Noncompartmental Approach to Assess Bioequivalence of Iron Gluconate Formulations

Yue

Gallicano²,

Labbé

et al. 2013

J Pharm Pharm Sci

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-Purpose: Iron-containing products are atypical in terms of their pharmacokinetic properties because iron is only removed by plasma sampling and is non-linear. This study aims to present a novel way of assessing the relative bioavailability of two sodium ferric gluconate complex (SFGC) formulations and compare this approach to a standard previously published noncompartmental approach. Methods: Data were from openlabel, randomized, single-dose studies (Study 1 was parallel whereas Study 2 was crossover). Subjects with low but normal iron levels were infused IV SFGC in sucrose by GeneraMedix Inc. and/or Ferrlecit ® Injection (Watson Laboratories Inc.). In Study 1 (n=240), 125 mg was infused over 10 minutes. In Study 2 (n=29), 62.5 mg was infused over 30 minutes. Samples were assayed for total iron (TI) and transferrin-bound iron (TBI) over 36 hours (Study 1) or 72 hours (Study 2) post-dose. Studies 1 and 2 used standard noncompartmental analysis. Study 2 also used population PK (PPK) analyses with ADAPT 5®. The final model predicted SFGC area-underthe-curve (AUC pred ) and maximal concentration (Cmax pred ). Analyses of variance was conducted on lntransformed PK parameters. Ratios of means and 90% confidence intervals (CIs) were estimated. Bioequivalence was demonstrated if values were within 80-125%. Results: For Study 1, ratios and 90% CIs for TI baseline-corrected Cmax and AUC 0-36 were 100.4 (96.5 -104.5) and 99.7 (94.2 -105.5). For TBI, results for TI baseline-corrected Cmax and AUC 0-36 were 86.8 (82.7 -91.1) and 92.4 (85.6 -99.7). For Study 2, a multicompartmental model simultaneously described the PK of TI, TBI and SFGC. Ratios and 90% CIs for SFGC Cmax pred and AUC pred were 89.9 (85.9 -94.0) and 89.7 (85.7 -93.9), while ratios and 90% CI obtained from the noncompartmental analysis of Study 2 did not meet BE criteria because of low power. Conclusions: Both the standard and PPK modeling approach suggested bioequivalence between the iron products. However, with the PPK method, less subjects were required to meet study objectives compared to the standard noncompartmental approach which required considerably more subjects (29 vs 240).

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“…An alternative approach for testing the equivalence of nonlinear mixed effects model estimates is the application of the Wald test, where parameters of interest (e.g., AUC, C max ) are derived from model estimates and are compared. Simulation exercises have shown that these two approaches provide very similar results …”

Section: Dose–exposure Relationships: Demonstrating Bioequivalencementioning

confidence: 93%

“…Applying the same dose–exposure model to data for originator and biosimilar would then focus on quantifying differences between extents and rates (e.g., 19 day half‐life vs. 22 day half‐life) for the purposes of bioequivalence testing. Model‐based approaches to establish bioequivalence have been known for over two decades . The statistical approaches used to establish bioequivalence differ in this setting .…”

Section: Dose–exposure Relationships: Demonstrating Bioequivalencementioning

confidence: 99%

The Use of Pharmacometrics to Optimize Biosimilar Development

Dodds

Chow

Márkus

et al. 2013

Journal of Pharmaceutical Sciences

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Bioequivalence Tests Based on Individual Estimates Using Non-compartmental or Model-Based Analyses: Evaluation of Estimates of Sample Means and Type I Error for Different Designs

Abstract: For small n or data with high residual error, tests based on a global data analysis should be considered instead of those based on individual estimates.

Cited by 34 publications

References 35 publications

Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates

Comparison of Nonlinear Mixed Effects Models and Noncompartmental Approaches in Detecting Pharmacogenetic Covariates

Novel Population Pharmacokinetic Method Compared to the Standard Noncompartmental Approach to Assess Bioequivalence of Iron Gluconate Formulations

The Use of Pharmacometrics to Optimize Biosimilar Development

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