Open AccessQuantitative methods and modeling to assess COVID‐19‐interrupted in vivo pharmacokinetic bioequivalence studies with two reference batches
Author(s) -
Gong Yuqing,
Feng Kairui,
Zhang Peijue,
Lee Jieon,
Pan Yuzhuo,
Zhang Zhen,
Ni Zhanglin,
Bai Tao,
Yoon Miyoung,
Li Bing,
Kim Carol Y.,
Fang Lanyan,
Zhao Liang
Publication year - 2022
Publication title -
cpt: pharmacometrics and systems pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.53
H-Index - 37
ISSN - 2163-8306
DOI - 10.1002/psp4.12795
Subject(s) - bioequivalence , covid-19 , crossover study , food and drug administration , medicine , pharmacokinetics , protocol (science) , equivalence (formal languages) , statistics , computer science , pharmacology , mathematics , infectious disease (medical specialty) , disease , alternative medicine , pathology , discrete mathematics , placebo
The coronavirus disease 2019 (COVID‐19) has presented unprecedented challenges to the generic drug development, including interruptions in bioequivalence (BE) studies. Per guidance published by the US Food and Drug Administration (FDA) during the COVID‐19 public health emergency, any protocol changes or alternative statistical analysis plan for COVID‐19‐interrupted BE study should be accompanied with adequate justifications and not lead to biased equivalence determination. In this study, we used a modeling and simulation approach to assess the potential impact of study outcomes when two different batches of a Reference Standard (RS) were to be used in an in vivo pharmacokinetic BE study due to the RS expiration during the COVID‐19 pandemic. Simulations were performed with hypothetical drugs under two scenarios: (1) uninterrupted study using a single batch of an RS, and (2) interrupted study using two batches of an RS. The acceptability of BE outcomes was evaluated by comparing the results obtained from interrupted studies with those from uninterrupted studies. The simulation results demonstrated that using a conventional statistical approach to evaluate BE for COVID‐19‐interrupted studies may be acceptable based on the pooled data from two batches. An alternative statistical method which includes a “batch” effect to the mixed effects model may be used when a significant “batch” effect was found in interrupted four‐way crossover studies. However, such alternative method is not applicable for interrupted two‐way crossover studies. Overall, the simulated scenarios are only for demonstration purpose, the acceptability of BE outcomes for the COVID19‐interrupted studies could be case‐specific.