Theoretical Investigation of Dissolution Test Criteria for Waiver of Clinical Bioequivalence Study

Bioequivalence Dissolution Test Criteria for Formulation Development of High Solubility-Low Permeability Drugs

The purpose of the present study was to provide the experimental and theoretical basis of bioequivalence (BE) dissolution test criteria for formulation development of high solubility-low permeability drugs. According to the biowaiver scheme based on the biopharmaceutics classification system (BCS), for BCS class III drugs, a test formulation and a reference formulation are predicted to be BE when 85% of the drug dissolves within 15 min (T_85% < 15 min) in the compendial dissolution test. However, previous theoretical simulation studies have suggested that this criterion may possibly be relaxed for use in practical formulation development. In the present study, the dissolution profiles of 14 famotidine formulations for which BE has been clinically confirmed were evaluated by the compendial dissolution test at pH 1.2 and 6.8. The plasma concentration-time profiles of famotidine formulations were simulated using the dissolution data. In addition, virtual simulations were performed to estimate the range of dissolution rates to be bioequivalent. The fastest and slowest dissolution rates among the famotidine formulations were T_85% = 10 min and T_85% = 60 min at pH 6.8, respectively. The virtual simulation BE study suggested that famotidine formulations can be bioequivalent when T_85% < 99 min. In the case of BCS III drugs, the rate-limiting step of oral drug absorption is the membrane permeation process rather than the dissolution process. Therefore, a difference in the dissolution process has less effect on BE. These results contribute to a better understanding of the biowaiver approach and would be of great help in the formulation development of BCS class III drugs.

Lowly-buffered biorelevant dissolution testing is not necessarily biopredictive of human bioequivalence study outcome: Relationship between dissolution and pharmacokinetics

It has been revealed that buffer capacity of aspirated human intraluminal fluid is much lower than that of in vitro compendial dissolution media. Since buffer capacity significantly alters the dissolution profile of certain drug products, dissolution testing in highly buffered media dictates poor predictability of in vivo drug performance. To mitigate this inconsistency, low buffer capacity medium was suggested as an in vivo representation (biorelevant dissolution testing). The purpose of this study was to characterize the dissolution profiles of enteric-coated drug products in different buffer capacity media in a flow through cell dissolution apparatus, and to evaluate the in vivo predictability of human bioequivalence study outcomes conducted in the fasted state. It was confirmed that the lower the buffer capacity of dissolution media, the higher the discriminatory power of esomeprazole magnesium hydrate enteric-coated pellets, reflecting human bioequivalence failure. In the meantime, two duloxetine hydrochloride enteric-coated pellets also exhibited distinct dissolution profiles in such a lowly buffered medium despite the fact that these two are bioequivalent in human. Biopharmaceutical and pharmacokinetic characteristics comparison suggested that low intestinal permeability and small systemic elimination rate of duloxetine hinders the clear impact of different dissolution profile on its in vivo performance. These data suggest that dissolution comparison in physiologically-relevant low buffer capacity media is not always indicative of human bioequivalence. Instead, biopharmaceutical and pharmacokinetic aspects must be taken into consideration to make biorelevant dissolution testing biopredictive.

An Investigation into the Factors Governing Drug Absorption and Food Effect Prediction Based on Data Mining Methodology

Drug absorption is a complex process governed by a number of interrelated physicochemical, biopharmaceutical, and pharmacokinetic factors. In order to explore complex relationships among these factors, multivariate exploratory analysis was performed on the dataset of drugs with diverse bioperformance. The investigated dataset included subset of drugs for which bioequivalence between solid dosage form and oral solution has been reported, and subset of drugs described in the literature as low solubility/low permeability compounds. Discriminatory power of hierarchical clustering on principal components was somewhat higher when applied on the data subsets of drugs with similar bioperformance, while analysis of the integrated dataset indicated existence of two groups of drugs with the boundaries reflected in P_eff value of approximately 2 × 10^-4 cm/s and Fa and Fm values higher than 85% and 50%, respectively. Majority of the investigated drugs within the integrated dataset were grouped within their initial subset indicating that overall drug bioperformance is closely related to its physicochemical, biopharmaceutical and pharmacokinetic properties. Classification models constructed using the random forest (RF) and support vector machine with polynomial kernel function were able to predict food effect based on drug dose/solubility ratio (D/S), effective permeability (P_eff), percent of dose metabolized (Fm), and elimination half-life (τ_1/2). Although both models performed well during training and testing, only RF kept satisfying performance when applied on the external dataset (kappa value > 0.4). The results obtained indicate that data mining can be employed as useful tool in biopharmaceutical drug characterization which merits further investigation.

Application of data mining approach to identify drug subclasses based on solubility and permeability

Solubility and permeability are recognized as key parameters governing drug intestinal absorption and represent the basis for biopharmaceutics drug classification. The Biopharmaceutics Classification System (BCS) is widely accepted and adopted by regulatory agencies. However, currently established low/high permeability and solubility boundaries are the subject of the ongoing scientific discussion. The aim of the present study was to apply data mining analysis on the selected drugs data set in order to develop a human permeability predictive model based on selected molecular descriptors, and to perform data clustering and classification to identify drug subclasses with respect to dose/solubility ratio (D/S) and effective permeability (P_eff ). The P_eff values predicted for 30 model drugs for which experimental human permeability data are not available were in good agreement with the reported fraction of drug absorbed. The results of clustering and classification analysis indicate the predominant influence of P_eff over D/S. Two P_eff cut-off values (1 × 10^-4 and 2.7 × 10^-4  cm/s) have been identified indicating the existence of an intermediate group of drugs with moderate permeability. Advanced computational analysis employed in the present study enabled the recognition of complex relationships and patterns within physicochemical and biopharmaceutical properties associated with drug bioperformance.