AUTHOR=Shao Huarui , Tao Yi , Tang Chengyong 

TITLE=Factors influencing bioequivalence evaluation of insulin biosimilars based on a structural equation model

JOURNAL=Frontiers in Pharmacology

VOLUME=14

YEAR=2023

URL=https://www.frontiersin.org/journals/pharmacology/articles/10.3389/fphar.2023.1143928

DOI=10.3389/fphar.2023.1143928

ISSN=1663-9812

ABSTRACT=<p><bold>Objective:</bold> This study aimed to explore the factors affecting the bioequivalence of test and reference insulin preparations so as to provide a scientific basis for the consistency evaluation of the quality and efficacy of insulin biosimilars.</p><p><bold>Methods:</bold> A randomized, open, two-sequence, single-dose, crossover design was used in this study. Subjects were randomly divided into TR or RT groups in equal proportion. The glucose infusion rate and blood glucose were measured by a 24-h glucose clamp test to evaluate the pharmacodynamic parameters of the preparation. The plasma insulin concentration was determined by liquid chromatography–mass spectrometry (LC-MS/MS) to evaluate pharmacokinetic parameters. WinNonlin 8.1 and SPSS 23.0 were applied for PK/PD parameter calculation and statistical analysis. The structural equation model (SEM) was constructed to analyze the influencing factors of bioequivalence by using Amos 24.0.</p><p><bold>Results:</bold> A total of 177 healthy male subjects aged 18–45 years were analyzed. Subjects were assigned to the equivalent group (N = 55) and the non-equivalent group (N = 122) by bioequivalence results, according to the EMA guideline. Univariate analysis showed statistical differences in albumin, creatinine, T<sub>max</sub>, bioactive substance content, and adverse events between the two groups. In the structural equation model, adverse events (<italic>β</italic> = 0.342; <italic>p</italic> &lt; 0.001) and bioactive substance content (<italic>β</italic> = −0.189; <italic>p</italic> = 0.007) had significant impacts on the bioequivalence of two preparations, and the bioactive substance content significantly affected adverse events (<italic>β</italic> = 0.200; <italic>p</italic> = 0.007).</p><p><bold>Conclusion:</bold> A multivariate statistical model was used to explore the influencing factors for the bioequivalence of two preparations. According to the result of the structural equation model, we proposed that adverse events and bioactive substance content should be optimized for consistency evaluation of the quality and efficacy of insulin biosimilars. Furthermore, bioequivalence trials of insulin biosimilars should strictly obey inclusion and exclusion criteria to ensure the consistency of subjects and avoid confounding factors affecting the equivalence evaluation.</p>