A webinar series that was organised by the Academy of Pharmaceutical Sciences Biopharmaceutics focus group in 2021 focused on the challenges of developing clinically relevant dissolution specifications (CRDSs) for oral drug products. Industrial scientists, together with regulatory and academic scientists, came together through a series of six webinars, to discuss progress in the field, emerging trends, and areas for continued collaboration and harmonisation. Each webinar also hosted a Q&A session where participants could discuss the shared topic and information. Although it was clear from the presentations and Q&A sessions that we continue to make progress in the field of CRDSs and the utility/success of PBBM, there is also a need to continue the momentum and dialogue between the industry and regulators. Five key areas were identified which require further discussion and harmonisation.

In November 2024, a two-day meeting entitled “PBBM—Best Scientific Practices to Define Drug Product Performance: Latest Regulatory and Industry Perspectives” was organised by the Academy of Pharmaceutical Sciences Biopharmaceutics Focus group and hosted by MHRA in London, UK. Physiologically based biopharmaceutics modelling, referred to as PBBM, is used to inform drug product quality strategies and provide a more detailed understanding of how medicines can interact with the human body. Industrial, academic, regulatory, and software company scientists came together to discuss the latest developments in PBBM and to debate key topics relevant to the establishment of best practices and improved implementation. Case study presentations and breakout sessions highlighted how companies are using PBBM in their portfolio decision-making (early development through post-approval changes). Discussions highlighted how the exploration of drug product quality risks has evolved over time, moving from the more empirical BCS classification approach to a more detailed in vivo and mechanistic understanding, where sponsors have and continue to invest in building clinical drug product knowledge. Regulatory scientists shared how they are building experience in using PBBM to set clinically relevant drug product quality specifications, including how they would like to see the area grow in the future. Although significant progress has certainly been made in this field over the last 10 years, the need to continue to bring industry and regulators closer together in the future remains a key topic. Guideline evolution, training and continued dialogue will be essential in reaching a harmonised approach to the use of PBBM to develop drug product strategies and set quality specifications.

Background/Objectives: This paper describes the use of physiologically based biopharmaceutics modelling (PBBM) to predict the effect of food on diclofenac and ibuprofen absorption from ultra-rapid-release Surge Dose^® tablets. Methods: Fasted-state diclofenac pharmacokinetics (PK) were used with published IV data and biorelevant dissolution data for the diclofenac tablets to develop a mechanistic PBBM model which could be used to predict absorption. Results: The resultant model that best fitted the PK data showed that, in vivo, the ultra-rapid-release tablets behaved like a solution with a median time to peak plasma concentration (T_max) of 20 min. Incorporating a well-established model for gastric emptying in the fed state, the fed T_max for these tablets was predicted to be 21 min, similar to that seen in fasted subjects. Use of a PBBM model to predict absorption of ibuprofen in the fasted and fed states again showed that ultra-rapid-release tablets produced fast and consistent absorption independent of the presence of food. Predicted mean T_max values were 31.8 and 35.4 min in the fasted and fed states, respectively. Conclusions: Therefore, even if Surge Dose^® formulations are taken after food, as frequently recommended for NSAIDs, the speed of absorption and subsequent onset of action should not be impacted.

Background: Preclinical Oncology drug development is heavily reliant on xenograft studies to assess the anti-tumour effect of new compounds. Patient derived xenograft (PDX) have become popular as they may better represent the clinical disease, however variability is greater than in cell-line derived xenografts. The typical approach of analysing these studies involves performing an un-paired t-test on the mean tumour volumes between the treated and control group at the end of the study. This approach ignores the time-series and may result in false conclusions, especially when considering the increased variability of PDX studies. Aim: To test the hypothesis that a model-based analysis provides increased power than analysis of final day volumes and to provide insights into more efficient PDX study designs. Methods: Data was extracted from tumour xenograft time-series data from a large publicly available PDX drug treatment database released by Novartis. For all 2-arm studies the percent tumour growth inhibition (TGI) at two time-points, day 10 and day 14 was calculated. For each study, the effect of treatment was calculated using an un-paired t-test and also a model-based analysis using the likelihood ratio-test. In addition a simulation study was also performed to assess the difference in power between the two data-analysis approaches for different levels of TGI for PDX or standard cell-line derived xenografts (CDX). Results: The model-based analysis had greater statistical power than the un-paired t-test approach within the PDX data-set. The model-based approach was able to detect TGI values as low as 25 percent whereas the un-paired t-test approach required at least 50 percent TGI. These findings were confirmed within the simulation study performed which also highlighted that CDX studies require less animals than PDX studies which show the equivalent level of TGI. Conclusion: The analysis of 59 2-arm PDX studies highlighted that taking a model-based approach gave increased statistical power over simply performing an un-paired t-test on the final study day. Importantly the model-based approach was able to detect smaller size of effect compared to the un-paired t-test approach is which maybe common of such studies. These findings were confirmed within simulated studies which also highlighted the same sample size used for CDX studies would lead to inadequately powered PDX studies. Application of a model-based analysis should allow studies to use less animals and run experiments for a shorter period thus providing effective insight into compound anti-tumour activity

Demonstratingbioequivalence (BE) is important for the development of lower cost genericproducts, and also for approving post-submission manufacturing changes.However, for many complex parenteral products, BE demonstration can be verychallenging. For example, long-acting injectable products are engineered tohave an extended release over several weeks or months, but this also means thata traditional BE study can take many months or years to perform. Here, we summarisehow population PK modelling, which captures differences in PK profiles due topopulation variation, could be used explore hundreds of virtual formulations,and thus determine a range of products that are bioequivalent after bothmultiple and single dosing. This provides a guide for formulation developmentbut also opens alternative, more streamlined routes to BE assessment.