Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs
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| Publicado en: | Algorithms vol. 18, no. 5 (2025), p. 266 |
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| Autor principal: | |
| Otros Autores: | |
| Publicado: |
MDPI AG
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| Materias: | |
| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 1999-4893 | ||
| 024 | 7 | |a 10.3390/a18050266 |2 doi | |
| 035 | |a 3211847019 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231333 |2 nlm | ||
| 100 | 1 | |a Nikolopoulos Anastasios |u Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, 15784 Athens, Greece; anastnik@pharm.uoa.gr | |
| 245 | 1 | |a Generative Neural Networks for Addressing the Bioequivalence of Highly Variable Drugs | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a Bioequivalence assessment of highly variable drugs (HVDs) remains a significant challenge, as the application of scaled approaches requires replicate designs, complex statistical analyses, and varies between regulatory authorities (e.g., FDA and EMA). This study introduces the use of artificial intelligence, specifically Wasserstein Generative Adversarial Networks (WGANs), as a novel approach for bioequivalence studies of HVDs. Monte Carlo simulations were conducted to evaluate the performance of WGANs across various variability levels, population sizes, and data augmentation scales (2× and 3×). The generated data were tested for bioequivalence acceptance using both EMA and FDA scaled approaches. The WGAN approach, even applied without scaling, consistently outperformed the scaled EMA/FDA methods by effectively reducing the required sample size. Furthermore, the WGAN approach not only minimizes the sample size needed for bioequivalence studies of HVDs, but also eliminates the need for complex, costly, and time-consuming replicate designs that are prone to high dropout rates. This study demonstrates that using WGANs with 3× data augmentation can achieve bioequivalence acceptance rates exceeding 89% across all FDA and EMA criteria, with 10 out of 18 scenarios reaching 100%, highlighting the WGAN method potential to transform the design and efficiency of bioequivalence studies. This is a foundational step in utilizing WGANs for the bioequivalence assessment of HVDs, highlighting that with clear regulatory criteria, a new era for bioequivalence evaluation can begin. | |
| 610 | 4 | |a Food & Drug Administration--FDA | |
| 653 | |a Federal regulation | ||
| 653 | |a Datasets | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Generic drugs | ||
| 653 | |a Acceptance | ||
| 653 | |a Generative adversarial networks | ||
| 653 | |a Bioequivalence | ||
| 653 | |a Drug development | ||
| 653 | |a Pharmaceutical industry | ||
| 653 | |a Drugs | ||
| 653 | |a Automation | ||
| 653 | |a Statistical analysis | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Patients | ||
| 653 | |a Simulation | ||
| 653 | |a Gastrointestinal surgery | ||
| 653 | |a Data augmentation | ||
| 653 | |a Neural networks | ||
| 653 | |a Monte Carlo simulation | ||
| 653 | |a Decision making | ||
| 653 | |a Criteria | ||
| 653 | |a Pharmacokinetics | ||
| 653 | |a Design | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Regulatory agencies | ||
| 700 | 1 | |a Karalis, Vangelis D |u Department of Pharmacy, School of Health Sciences, National and Kapodistrian University of Athens, 15784 Athens, Greece; anastnik@pharm.uoa.gr | |
| 773 | 0 | |t Algorithms |g vol. 18, no. 5 (2025), p. 266 | |
| 786 | 0 | |d ProQuest |t Engineering Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3211847019/abstract/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3211847019/fulltextwithgraphics/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3211847019/fulltextPDF/embedded/CH9WPLCLQHQD1J4S?source=fedsrch |