(T0930-11-62) Exploration of Predictive Bioequivalence: Comparison of Methods to Incorporate In Vitro Dissolution Data in Pharmacokinetic Modeling of BCS Class II Acidic Drugs

Purpose: The Biopharmaceutics Classification System (BCS) classifies drugs according to permeability, solubility, and dissolution. Currently, Class I (high solubility and permeability) and Class III (high solubility, low permeability) drugs are eligible for waiver of in vivo bioequivalence studies (biowaivers) as per guidelines described in ICH M9. BCS Class II drug substances are highly permeable but have low solubility. BCS Class II acidic (IIa) drugs, more specifically, behave like BCS Class I drugs at intestinal pH (6.5-7.0) in the gastrointestinal tract, despite having low solubility at acidic (gastric) pH values. Using a single drug substance as a starting point, the purpose of this project is to determine the best in vitro dissolution input approach for predicting pharmacokinetic area under the curve (AUC) and maximum concentration (Cmax) values and use this to establish boundaries for a “safe-space” range of dissolution rates that render a BCS Class IIa drug bioequivalent and thus potentially eligible for biowaiver considerations.

Methods: An in-silico model was built using GastroPlus simulation software, with pharmacokinetic inputs determined in a population study conducted using Phoenix—which incorporated data from an internal FDA database. Available dissolution data for two different products of the same drug substance in pH 5.6 phosphate buffer (USP apparatus II, 900mL, 50 rpm, 37°C) were incorporated into the model using 4 different methods—traditional z-factor, refined z-factor, theoretical particle size distribution (PSD), and Weibull function with a “CR: Dispersed” formulation using the Advanced Compartmental and Transit (ACAT) absorption model and two-compartment pharmacokinetic model. The ratio of Cmax and AUC for each method was calculated and compared with the reported values to determine the most predictive method.

Results: All methods were able to consistently provide good estimations of AUC infinity, while none of the strategies yielded consistent approximations of the time of maximum concentration (Tmax). The theoretical particle size distribution was able to accurately predict the Cmax and discriminate between the two products. Using the “CR: Dispersed” formulation with a Weibull fit on the dissolution profile resulted in a close prediction of Cmax for Product 1, but significantly underpredicted this value for Product 2. Due to Product 2’s incomplete dissolution, the dissolution profile was extended using the Weibull function until it reached completion at about 3 hours. The traditional z-factor consistently overpredicted the Cmax for both products by a small margin. The refined z-factor approach accounted for the possible disintegration process from the dissolution profile of Product 2 but resulted in an aggressive adjustment and thus a larger discrepancy between predicted and observed values.

Conclusion: Using pH 5.6 dissolution media allows for discrimination between different formulations of this drug. Incorporating dissolution data using particle size distribution demonstrated the best discriminating power while maintaining acceptable accuracy when predicting Cmax and AUCi. When compared to other approaches, the Weibull approach with “CR: Dispersed” formulation significantly underestimated Cmax when the initial dissolution rate is slow. This underprediction is likely due to the slow release in the initial phase of dissolution of Product 2 and suggests a possible disintegration process for this product. Due to the overall rapid disintegration of both products, the traditional z-factor method performed slightly better when predicting Cmax than the refined approach. It was, however, less accurate than the particle size distribution method. Moreover, the z-factor method may have limited applicability because it has a maximum limit for prediction of Cmax—any dissolution rate past a certain point does not lead to further increases in Cmax, which results in a plateau of the dissolution graph. Overall, all modeling strategies have strengths and weaknesses. It is likely that none of the strategies were able to consistently predict Tmax due to high variability in gastric emptying time and absorption patterns. Further research is ongoing to determine whether these findings are drug specific or can be generalized to determine bioequivalence and present a case for the expansion of BCS-based biowaivers for all BCS Class IIa drug products.

References: 1. Pavliv L, Voss B, Rock A. Pharmacokinetics, safety, and tolerability of a rapid infusion of i.v. ibuprofen in healthy adults. Am J Health Syst Pharm. 2011;68(1):47-51. Epub 2010/12/18. doi: 10.2146/ajhp100120. PubMed PMID: 21164065.
2. Pepin XJ, Flanagan TR, Holt DJ, Eidelman A, Treacy D, Rowlings CE. Justification of Drug Product Dissolution Rate and Drug Substance Particle Size Specifications Based on Absorption PBPK Modeling for Lesinurad Immediate Release Tablets. Mol Pharm. 2016;13(9):3256-69. Epub 2016/07/21. doi: 10.1021/acs.molpharmaceut.6b00497. PubMed PMID: 27438964.
3. Hofsass MA, Dressman J. Suitability of the z-Factor for Dissolution Simulation of Solid Oral Dosage Forms: Potential Pitfalls and Refinements. J Pharm Sci. 2020;109(9):2735-45. Epub 2020/06/05. doi: 10.1016/j.xphs.2020.05.019. PubMed PMID: 32497594.
4. Proposal to waive in vivo bioequivalence requirements for WHO Model List of Essential Medicines immediate-release, solid oral dosage forms, in WHO Technical Report Series. 2006, World Health Organization.[1]
5. Food and Drug Administration, C.D.E.R., Waiver of In Vivo Bioavailability and Bioequivalence Studies for Immediate-Release Solid Oral Dosage Forms Based on a Biopharmaceutics Classification System: Guidance for Industry, HHS, Editor. 2017: Silver Spring, MD.
6. Gordon L. Amidon, H.L., Vinod P. Shah, John R. Crison, A Theoretical Basis for a Biopharmaceutic Drug Classification: The Correlation of in Vitro Drug Product Dissolution and in Vivo Bioavailability. Pharmaceutical Research, 1995. 12(3).

Acknowledgements: The present work reflects the views of the author(s) and should not be construed to represent US-FDA’s views or policies.


Average dissolution profile of products 1 and 2. (Dissolution conditions: USP Type II apparatus, pH 5.6 phosphate buffer, 50rpm, 900mL, 37±0.5 °C)


Summary of reported and predicted Cmax, AUCi, and Tmax using baseline, CR: dispersed with Weibull (Weibull), theoretical particle size distribution (PSD), traditional z-factor (Traditional Z), and refined z-factor (Refined Z) methods.


Comparison of observed (pink dots) and predicted dissolution profiles using Weibull function (blue line), theoretical particle size distribution (green line), traditional z-factor (yellow-green line), and refined z-factor (red line) methods.