Demonstration of bioequivalence (BE) is mandatory while developing generic drugs. The scientific concept of BE applies equally to different regulatory agencies. However, the application of the concept may differ for each agency, which can affect the design of BE studies. To evaluate the study practices in terms of the BE concept in South Korea, we retrospectively analyzed BE study reports available from Ministry of Food and Drug Safety between 2013 and 2019. Statistical estimation of the pharmacokinetic parameters, including peak concentration and area under the concentration-time curve to the last measurable concentration, as well as study design, number of subjects in a study, study duration, fasting status, and formulation of specific drugs were obtained. The drugs were classified per World Health Organization Anatomical Therapeutic Chemical Classification and Biopharmaceutics Classification System. Post-hoc intrasubject coefficient of variation and corresponding sample sizes were calculated from the 90% confidence intervals of pharmacokinetic parameters. A total of 143 generic drugs in 588 BE studies were analyzed. The largest number of studies were performed in the area of Cardiovascular system (172 studies), followed by Nervous system (143 studies) and Alimentary tract and metabolism (92 studies). Overall, BE studies in South Korea were conducted in accordance with the global guideline despite the differences in details. BE studies were focused on the several therapeutic areas and conducted in a similar manner. The number of subjects was generally larger than that estimated with 90% power.

An investigation into the biopharmaceutics classification and a study of the in vitro bioavailability (permeability and solubility) of the antiviral compound enisamium iodide (4-(benzylcarbamoyl)-1-methylpyridinium iodide) were carried out. The solubility of enisamium iodide was determined in four different buffers. Apparent intestinal permeability (Papp) of enisamium iodide was assessed using human colon carcinoma (Caco-2) cells at three concentrations. The solubility of enisamium iodide in four buffer solutions from pH 1.2 to 7.5 is about 60 mg/mL at 25 °C, and ranges from 130 to 150 mg/mL at 37 °C, depending on the pH. Based on these results, enisamium iodide can be classified as highly soluble. Enisamium iodide demonstrated low permeability in Caco-2 experiments in all tested concentrations of 10-100 μM with permeability coefficients between 0.2 × 10-6 cm s-1 and 0.3 × 10-6 cm s-1. These results indicate that enisamium iodide belongs to class III of the Biopharmaceutics Classification System (BCS) due to its high solubility and low permeability. The bioavailability of enisamium iodide needs to be confirmed in animal and human studies.

The purpose of the present study was to provide a theoretical basis for the dissolution test criteria of a biowaiver scheme. The critical dissolution number (Dncrit) was defined as a value to show bioequivalence of AUC and Cmax against infinitely rapid dissolution (Dn = ∞). The gastrointestinal tract was represented by the one-compartment model. The dissolution of a drug was expressed by the Noyes-Whitney equation. The permeation of a drug was expressed by the first-order equation. The approximate analytical solutions of Dncrit were derived from the analytical solution for the fraction of a dose absorbed [Fa = 1 - exp(-1/(1/Dn + Do/Pn)]; Do, the dose number; Pn, the permeation number). Numerical integration was also performed to calculate Dncrit more accurately. Dncrit was found to become smaller as Pn and Do became smaller. Dncrit for Cmax was found to be dependent on the elimination half-life of a drug as well as Pn and Do. The Fa equation can be an appropriate theoretical basis for a biowaiver scheme.

In many absorption, distribution, metabolism, and excretion (ADME) modeling problems, imbalanced data could negatively affect classification performance of machine learning algorithms. Solutions for handling imbalanced dataset have been proposed, but their application for ADME modeling tasks is underexplored. In this paper, various strategies including cost-sensitive learning and resampling methods were studied to tackle the moderate imbalance problem of a large Caco-2 cell permeability database. Simple physicochemical molecular descriptors were utilized for data modeling. Support vector machine classifiers were constructed and compared using multiple comparison tests. Results showed that the models developed on the basis of resampling strategies displayed better performance than the cost-sensitive classification models, especially in the case of oversampling data where misclassification rates for minority class have values of 0.11 and 0.14 for training and test set, respectively. A consensus model with enhanced applicability domain was subsequently constructed and showed improved performance. This model was used to predict a set of randomly selected high-permeability reference drugs according to the biopharmaceutics classification system. Overall, this study provides a comparison of numerous rebalancing strategies and displays the effectiveness of oversampling methods to deal with imbalanced permeability data problems.