Related Experiment Video
Updated: Mar 31, 2026

In Vitro Methods for Comparing Target Binding and CDC Induction Between Therapeutic Antibodies: Applications in Biosimilarity Analysis
Published on: May 4, 2017
Evaluation of a Scenario in Which Estimates of Bioequivalence Are Biased and a Proposed Solution: tlast (Common)
Dennis Fisher1, William Kramer2, Elise Burmeister Getz3
1P Less Than, San Francisco, CA, USA.
Abstract:
In bioequivalence (BE) testing, it is the convention to identify tlast separately for each concentration-vs-time profile. Within-subject differences in tlast between treatments can arise when assay sensitivity is reached during washout, causing profiles to fall below the limit of quantitation (LOQ) at different sampling times. The resulting tlast difference may be systematic, due to true differences in exposure, and/or random, due to measurement noise. The conventional profile-specific tlast approach assumes that concentrations in the terminal phase are sufficiently low that use of different tlast values between treatments within a subject causes negligible bias in the AUC0-t geometric mean ratio (GMR). Here we investigate the validity of this assumption. Using concentration-vs-time data following oral inhalation of 50 μg salmeterol as an example data set, we conducted simulations to evaluate whether use of different test/reference AUC timeframes arising from a systematic difference in exposure causes sufficient AUC0-t GMR bias to influence the determination of BE. To ensure that results would be relevant to BE testing, we considered only test/reference relative systemic exposures within the BE window (80.00%-125.00%). We show that use of conventional profile-specific tlast exaggerates true differences in systemic exposure; the resulting AUC0-t ratios are biased from true relative exposure by an amount large enough to impact the conclusion of BE. Thus, drugs whose concentrations fall below LOQ during washout may fail BE inappropriately using conventional methods. AUC0-t calculated over a common timeframe within each subject (tlast [common]) minimizes this bias and harmonizes the statistical analysis of BE.
More Related Videos
11:38High Content Screening Analysis to Evaluate the Toxicological Effects of Harmful and Potentially Harmful Constituents HPHC
Published on: May 10, 2016
04:53A Clinical Trial Assessing the Safety, Efficacy, and Delivery of Olive-Oil-Based Three-Chamber Bags for Parenteral Nutrition
Published on: September 20, 2019
Related Concept Videos
Bioequivalence Data: Statistical Interpretation
Bioequivalence of Drugs: Drugs with Multiple Indications
Bioequivalence: Overview
Bioequivalence studies: Biowaivers
Bioequivalence Experimental Study Designs: Repeated Measures, Cross-Over, Carry-Over, and Latin Square Designs
Equivalence: In Vitro and In Vivo Bioequivalence