An Exact Procedure for the Evaluation of Reference-Scaled Average Bioequivalence

Tóthfalusi, László; Endrényi, László

doi:10.1208/s12248-016-9873-6

Cited by 20 publications

(41 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are essentially 3 different ways to evaluate RSABE according to Equation , namely the ABEL, Hyslop's, and the Exact algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…The confidence interval of the sum of the individual confidence intervals is obtained by the method of Howe . The squared lengths of the individual confidence intervals are:

L_{m} = {({normalC}_{normalm} - {normalE}_{normalm})}^{2} and L_{s} = {({normalC}_{normals} - {normalE}_{normals})}^{2}

…”

Section: Methodsmentioning

confidence: 99%

“…The Exact algorithms were initially described in Tothfalusi and Endrenyi . A short background of these methods is presented in the following together with its suggested modification.…”

Section: Methodsmentioning

confidence: 99%

“…2—Compute k according to Equation , based on the design. Reference provides k's for common designs. For example, k equals (1.5/n) 0.5 for a 2‐sequence, 3‐period TRT‐RTR design, where n is the number of subjects in the study.…”

Section: Methodsmentioning

confidence: 99%

“…The main purpose of the present communication is to demonstrate that the step of estimating the formulation‐dependent within‐subject variances can be avoided. Eliminating this step not only makes the Exact algorithms computationally simpler but also allows extensions to a much wider class of designs than proposed earlier …”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Algorithms for evaluating reference scaled average bioequivalence: power, bias, and consumer risk

Tóthfalusi

Endrényi

2017

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

The determination of the bioequivalence between highly variable drug products involves the evaluation of reference scaled average bioequivalence. The European and US regulatory authorities suggest different algorithms for the implementation of this approach. Both algorithms are based on approximations reflected in lower than the achievable power or higher than the nominal consumer risk of 5%. To overcome these deficiencies, a new class of algorithms, the so-called Exact methods, was earlier introduced. However, their applicability was limited. We propose 2 modifications which make their computation simpler and also applicable with any study design. Four algorithms were evaluated in simulated 3-period and 4-period bioequivalence studies: Hyslop's approach recommended by the US FDA, the method of average bioequivalence with expanding limits requested by the European EMA, and 2 versions of the new Exact methods. At small sample sizes, the Exact methods had substantially higher statistical power than Hyslop's algorithm and had lower consumer risk than the method of average bioequivalence with expanding limits. Similarly to the Hyslop's algorithm, higher than 5% consumer risk was observed only with either unbalanced study design or with additional regulatory requirements. The improved Exact algorithms compare favorably with the alternative procedures. They are based on the bias correction method of Hedges. The recognition that the scaled difference statistics is measured with bias has important practical implications when results of pilot bioequivalence studies are evaluated and, at the same time, calls for the revision of the statistical theory of RSABE and its related methods.

show abstract

“…There are essentially 3 different ways to evaluate RSABE according to Equation , namely the ABEL, Hyslop's, and the Exact algorithms.…”

Section: Methodsmentioning

confidence: 99%

“…The confidence interval of the sum of the individual confidence intervals is obtained by the method of Howe . The squared lengths of the individual confidence intervals are:

L_{m} = {({normalC}_{normalm} - {normalE}_{normalm})}^{2} and L_{s} = {({normalC}_{normals} - {normalE}_{normals})}^{2}

…”

Section: Methodsmentioning

confidence: 99%

“…The Exact algorithms were initially described in Tothfalusi and Endrenyi . A short background of these methods is presented in the following together with its suggested modification.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Algorithms for evaluating reference scaled average bioequivalence: power, bias, and consumer risk

Tóthfalusi

Endrényi

2017

Statistics in Medicine

Self Cite

View full text Add to dashboard Cite

show abstract

Pharmacological Parameters and Pharmacokinetic Variability Derived from Bioequivalence Trials in a Mexican Population

Díaz‐Tufinio,

Gonzalez‐Covarrubias,

Palma‐Aguirre

2023

Clinical Pharm in Drug Dev

View full text Add to dashboard Cite

Biomedical data from controlled randomized clinical trials (RCTs), from both early pharmacological phase stages and bioequivalence testing, is a valuable resource for obtaining the primary pharmacological characteristics of the studied drugs. Insights on drug safety, and its efficacy to some extent, might be explored as secondary endpoints. The clinical and analytical laboratories for pharmaceutical products' interchangeability evaluation in Mexico are trustful sources of pharmacokinetic information, given that their thorough internal quality management systems, regulatory requirements, and sponsor audits contribute to the generation of standardized experimental data, from the design of the trial to its final statistical analysis.Bioequivalence studies aim to assess the interchangeability between pharmaceutical products by comparing reference versus test drug pharmacokinetic (PK) parameters -C max and AUC -with slightly different evaluation methodologies among countries. 1 These studies are conducted mainly with healthy participants, so it is possible to control the experimental design with strict schedules, dosing, meals, and sampling times, as well as the demographic and clinical variables of the participant subjects. Since these sources of variation are controlled and blocked in the statistical analysis, the PK variability of the tested drugs can be estimated with high confidence using the pharmacological data derived from these trials.Drug PK variability, defined as a coefficient of variation (CV%), can be split into an intrasubject (IaSV) component and an intersubject (IeSV) component to quantify the within-and among individuals PK variation, respectively. These values are usually reported in the drug technical assessment documentation of regulatory agencies, such as those from the Center of Drug Evaluation and Research of the US Federal Drug Administration and the European Public Assessment Reports of the European Medicines Agency (EMA). Nevertheless, these data might vary among different formulations, study designs, clinical trial conduction, geographical regions, and tested populations due to potential ethnic differences in drug absorption and metabolism, 1 which are partly explained by genetic background. This last explains why mandatory bioequivalence testing has to be conducted regionally as a regulatory requirement in several countries.The authors consider that it is important to compile and promote access to clinical and PK data derived from RCTs for bioequivalence purposes, respecting sponsors' confidentiality non-disclosure agreements, and protecting sensitive information from the participants. These data could be useful by contributing to future study design and justifying sufficient sample

show abstract

Evaluation of Bioequivalence for Avapritinib Tablets in Chinese Participants Under Fasting Conditions Using a Reference‐Scaled Average Bioequivalence Method

Yue,

Wang,

et al. 2024

Clinical Pharm in Drug Dev

View full text Add to dashboard Cite

This study aimed to assess the bioequivalence of 2 avapritinib tablets formulations. A randomized, open‐label, single‐center trial was conducted on fasting, healthy Chinese participants. The study utilized a partial replicated design with 3 sequences and 3 periods. Participants were assigned to 1 of 3 sequences, with each sequence receiving the reference formulation twice and the test formulation once. Plasma samples were collected and analyzed to determine pharmacokinetic parameters. The bioequivalence of the 2 avapritinib formulations was assessed using reference‐scaled average bioequivalence for the maximum plasma concentration (Cmax) and the average bioequivalence analysis for the area under the concentration‐time curve (AUC). Out of 39 participants, 38 completed the study. For Cmax, the 1‐sided 95% upper confidence interval (CI) bound from the scaled approach was −0.035 (<0) and the point estimate value was 0.958, falling inside the acceptance range of 0.8‐1.25. For both the AUC over all concentrations measured (AUC0‐t) and the AUC from time 0 to infinity (AUC0‐inf), the 90% CIs of geometric mean ratios (0.87‐1.01) also met the bioequivalence criteria of 0.8‐1.25. Consequently, the study demonstrated that the 2 avapritinib formulations were bioequivalent under fasting conditions.

show abstract

An Exact Procedure for the Evaluation of Reference-Scaled Average Bioequivalence

Cited by 20 publications

References 21 publications

Algorithms for evaluating reference scaled average bioequivalence: power, bias, and consumer risk

Algorithms for evaluating reference scaled average bioequivalence: power, bias, and consumer risk

Pharmacological Parameters and Pharmacokinetic Variability Derived from Bioequivalence Trials in a Mexican Population

Evaluation of Bioequivalence for Avapritinib Tablets in Chinese Participants Under Fasting Conditions Using a Reference‐Scaled Average Bioequivalence Method

Contact Info

Product

Resources

About