Predictive Potential of BCS and Pharmacokinetic Parameters on Study Outcome: Analysis of 198 In Vivo Bioequivalence
Dejan Krajcar1,2, Iztok Grabnar3, Rebeka Jereb4
1Lek Pharmaceuticals d.d., A Sandoz Company, Verovskova 57, 1526, Ljubljana, Slovenia. dejan.krajcar@sandoz.com.
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.
More Related Videos
07:25In Vitro Methods for Comparing Target Binding and CDC Induction Between Therapeutic Antibodies: Applications in Biosimilarity Analysis
Published on: May 4, 2017
15:04Potentiation of Anticancer Antibody Efficacy by Antineoplastic Drugs: Detection of Antibody-drug Synergism Using the Combination Index Equation
Published on: January 19, 2019
Related Concept Videos
01:16Bioequivalence: Overview
01:26Pharmacokinetic Models: Comparison and Selection Criterion
Physiological models take a detailed approach by considering specific molecular processes. They can predict drug distribution, metabolism, and elimination changes, providing a comprehensive understanding of how drugs interact with the body.
01:23Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
01:20Pharmacokinetic Models: Overview
There are three primary types of models: empirical, compartment, and physiological. Empirical models, with minimal...
01:12Analysis of Population Pharmacokinetic Data
01:07Physiological Pharmacokinetic Models: Incorporating Hepatic Transporter-Mediated Clearance
A recent model describes pravastatin's hepatobiliary excretion,...