A biowaiver is accepted by the Brazilian Health Surveillance Agency (ANVISA) for immediate-release solid oral products containing Biopharmaceutics Classification System (BCS) class I drugs showing rapid drug dissolution. This study aimed to simulate plasma concentrations of fluconazole capsules with different dissolution profiles and run population simulation to evaluate their bioequivalence. The dissolution profiles of two batches of the reference product Zoltec^® 150 mg capsules, A1 and A2, and two batches of other products (B1 and B2; C1 and C2), as well as plasma concentration–time data of the reference product from the literature, were used for the simulations. Although products C1 and C2 had drug dissolutions < 85% in 30 min at 0.1 M HCl, simulation results demonstrated that these products would show the same in vivo performance as products A1, A2, B1, and B2. Population simulation results of the ln-transformed 90% confidence interval for the ratio of C_max and AUC_0–t values for all products were within the 80–125% interval, showing to be bioequivalent. Thus, even though the in vitro dissolution behavior of products C1 and C2 was not equivalent to a rapid dissolution profile, the computer simulations proved to be an important tool to show the possibility of bioequivalence for these products.

Long-acting injectable (LAI) formulations provide sustained drug release over an extended period ranging from weeks to several months to improve efficacy, safety, and compliance. Nevertheless, many challenges arise in the development and regulatory assessment of LAI drug products due to a limited understanding of the tissue response to injected particles (e.g., inflammation) impacting in vivo performance. Mechanism-based in silico methods may support the understanding of LAI–physiology interactions. The objectives of this study were as follows: (1) to use a mechanistic modeling approach to delineate the in vivo performance of DepoSubQ Provera^® and formulation variants in preclinical species; (2) to predict human exposure based on the knowledge gained from the animal model. The PBPK model evaluated different elements involved in LAI administration and showed that (1) the effective in vivo particle size is potentially larger than the measured in vitro particle size, which could be due to particle aggregation at the injection site, and (2) local inflammation is a key process at the injection site that results in a transient increase in depot volume. This work highlights how a mechanistic modeling approach can identify critical physiological events and product attributes that may affect the in vivo performance of LAIs.

Dissolution testing is a major tool used to assess a drug product’s performance and as a quality control test for solid oral dosage forms. However, compendial equipment and methods may lack discriminatory power and the ability to simulate aspects of in vivo dissolution. Using low buffer capacity media combined with an absorptive phase (biphasic dissolution) increases the physiologic relevance of in vitro testing. The purpose of this study was to use non-compendial and compendial dissolution test conditions to evaluate the in vitro performance of different formulations. The United States Pharmacopeia (USP)-recommended dissolution method greatly lacked discriminatory power, whereas low buffer capacity media discriminated between manufacturing methods. The use of an absorptive phase in the biphasic dissolution test assisted in controlling the medium pH due to the drug removal from the aqueous medium. Hence, the applied non-compendial methods were more discriminative to drug formulation differences and manufacturing methods than conventional dissolution conditions. In this study, it was demonstrated how biphasic dissolution and a low buffer capacity can be used to assess in vitro drug product performance differences. This can be a valuable approach during the early stages of drug product development for investigating in vitro drug release with improved physiological relevance.

The tissue response to long-acting injectables (LAIs) suspension injection may impact the product in vivo performance. One such response is the formation of an inflammatory cell layer (ICL) resulting in an envelope around the injected particles. This study aims to use a mechanistic model to describe the clinical in vivo exposure and performance of an intramuscular LAI suspension and evaluate impact of ICL physiological response at the injection site in humans. Aripiprazole lauroxil (AR-L) was used as the model drug. A baseline pharmacokinetics model was built and validated for aripiprazole. The impact of inflammation on the LAI in vivo performance was assessed by including an ICL model. The developed pharmacokinetic model adequately described the observed plasma profiles of AR following intravenous and oral administration in humans. The initial intramuscular predictions assumed that the absorption rate is dependent on the dissolution and partitioning of AR-L into the systemic circulation from the intramuscular (IM) depot. The simulation resulted in a shape mismatch between the simulated and observed data and an earlier predicted T _max. The inclusion of an ICL in the model resulted in adequate predictions (fold errors less than 25%) of the exposure and shape of the plasma concentration–time profiles. Utilizing a time-dependent change in ICL thickness resulted in reasonable predictions of AR pharmacokinetic profiles following IM administration of multiple strengths of the AR-L suspension. This shows the utility of physiologically based pharmacokinetic (PBPK) model in mechanistically describing the in vivo performance of LAIs. Graphical Abstract

Context: In response to the COVID-19 pandemic, many educational adjustments had to be made to move in-person teaching to online classrooms. This report showcases the use of the software GastroPlus in an undergraduate-level pharmacy course. Programme description: This course aimed for the students to learn how to perform a mechanistically based simulation to predict the oral absorption pattern, pharmacokinetics, and biopharmaceutics properties of compounds in humans. The computer simulation offered the opportunity to teach concepts about bioavailability providing all kinds of experience with major biopharmaceutic determinants that affect systemic drug exposure. Evaluation: The advantage of this approach was seen by the enhanced performance on the biopharmaceutics questions on the final exam compared with the previous year where the laboratory was not implemented: An increase from 2019 (where no laboratory was implemented) through 2021 incorrect scores from 52, 76 to 75%, respectively. Conclusion: There is great benefit in using computer programs and simulations as a technique to enhance active learning and to educate pharmacy students in salient aspects of biopharmaceutics.

Allometric scaling provides a practical framework for predicting human pharmacokinetic (PK) parameters from animal data by relating physiological processes to body size through power-law equations. Despite its simplicity and widespread use in first-in-human (FIH) dose selection, its predictive performance is limited by species-specific differences in absorption, distribution, metabolism, and excretion (ADME). This review summarizes the mathematical foundations, workflows, and diagnostics of allometric scaling, while critically examining where the approach succeeds and where it fails. Core concepts, including clearance, volume of distribution, correction factors, and the rule of exponents, are discussed alongside complementary methods: in vitro–in vivo extrapolation (IVIVE), physiologically based pharmacokinetic (PBPK) modelling, and the Wajima normalized time-course method. Historical clinical failures, including fialuridine, TGN1412, BIA 10-2474, and rofecoxib, illustrate the limits of relying solely on allometry, while thalidomide and the fenfluramine combination exemplify toxicodynamic species-selection failures. Modern advances, including the Extended Clearance Classification System (ECCS), target-mediated drug disposition, FcRn recycling, and emerging artificial intelligence and machine-learning methods, are integrated within a framework. Overall, the review treats allometric scaling as a disciplined starting hypothesis that must be triangulated with mechanistic, experimental, and regulatory evidence to support safer and more reliable human translation.