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Predictive Potential of BCS and Pharmacokinetic Parameters on Study Outcome: Analysis of 198 In Vivo Bioequivalence Studies

Eur J Drug Metab Pharmacokinet. 2023 Mar 5. doi: 10.1007/s13318-023-00821-z. Online ahead of print.


BACKGROUND AND OBJECTIVES: Understanding predictive potential of parameters to perform early bioequivalence (BE) risk assessment is crucial for good planning and risk mitigation during product development. The objective of the present study was to evaluate predictive potential of various biopharmaceutical and pharmacokinetic parameters on the outcome of BE study.

METHODS: Retrospective analysis was performed on 198 Sandoz (Lek Pharmaceuticals d.d., A Sandoz Company, Verovskova 57, 1526 Ljubljana, Slovenia) sponsored BE studies [52 active pharmaceutical ingredients (API)] where characteristics of BE study and APIs were collected for immediate-release products and their predictive potential on the study outcome was assessed using univariate statistical analysis.

RESULTS: Biopharmaceutics Classification System (BCS) was confirmed to be highly predictive of BE success. BE studies with poorly soluble APIs were riskier (23% non-BE) than with highly soluble APIs (0.1% non-BE). APIs with either lower bioavailability (BA), presence of first-pass metabolism, and/or being substrate for P-glycoprotein substrate (P-gP) were associated with higher non-BE occurrence. In silico permeability and time at peak plasma concentrations (Tmax) were shown as potentially relevant features for predicting BE outcome. In addition, our analysis showed significantly higher occurrence of non-BE results for poorly soluble APIs with disposition described by multicompartment model. The conclusions for poorly soluble APIs were the same on a subset of fasting BE studies; for a subset of fed studies there were no significant differences between factors in BE and non-BE groups.

CONCLUSION: Understanding the association of parameters and BE outcome is important for further development of early BE risk assessment tools where focus should be first in finding additional parameters to differentiate BE risk within a group of poorly soluble APIs.

PMID:36872388 | DOI:10.1007/s13318-023-00821-z

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