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Vector-Based Comparison and Average Slope Can Refine Bioequivalence Claims: A Machine and Deep Learning Approach.
Maria Kokkali1, Vangelis D Karalis1,2
1Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, Athens, Greece.
Biopharmaceutics & Drug Disposition
|February 13, 2026
Summary
Two new methods, absorption rates (AS) and vector-based clinical endpoints (VBC), enhance bioequivalence studies. Combining AS and VBC improves accuracy, reduces variability, and lowers costs for drug development.
Area of Science:
- Pharmacokinetics and Biopharmaceutics
- Statistical Modeling in Clinical Trials
Background:
- Bioequivalence (BE) studies are crucial for drug development, ensuring therapeutic equivalence.
- Current BE study methods face challenges in accuracy, efficiency, and cost.
Purpose of the Study:
- To evaluate the combined advantages of Absorption Rate (AS) and Vector-Based Clinical endpoints (VBC) in bioequivalence studies.
- To assess the performance of AS and VBC against standard statistical methods and advanced machine learning techniques.
Main Methods:
- Utilized 14 real-world datasets to assess the performance of AS and VBC.
- Applied standard regulatory statistical methods alongside machine learning and artificial neural networks.
- Analyzed absorption rates using AS and clinical endpoints using VBC, breaking them into independent components.
Main Results:
- The combination of AS and VBC accurately measures absorption rates while significantly reducing data variability.
- This integrated approach enhances statistical power and effectively addresses multiplicity issues.
- The methods support the use of smaller sample sizes, leading to simplified study designs.
Conclusions:
- The joint application of AS and VBC offers a precise and efficient approach to defining absorption rates in BE studies.
- These innovative methods improve study outcomes, reduce resource requirements, and shorten completion times.
- AS and VBC represent valuable tools for modern bioequivalence analysis, optimizing drug development processes.
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