Mark McAllister

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: Biorelevant in vitro dissolution testing is used increasingly to predict complex mechanisms in the gastrointestinal (GI) tract that determine oral bioavailability. However, the limited use of non-compendial systems is driven by the lack of widely accepted, standardized validation frameworks. This ongoing gap continues to restrict their adoption relative to United States Pharmacopeia (USP) apparatus. While the physiological relevance and biopredictive capabilities of the tiny-TIM and TIM-1 in vitro GI models have been demonstrated in previous studies, their inter-laboratory reproducibility has not been systematically established. Therefore, this international ring study evaluates the reproducibility of in vitro simulations of GI transit and absorption of paracetamol in fasted- and fed-state conditions in tiny-TIM and TIM-1. Methods: Three laboratories used TIM-1 and five used tiny-TIM to simulate oral administration of a 500 mg paracetamol solution to a healthy adult. Paracetamol solution was selected as a well-characterized and widely available BCS I compound to minimize formulation and solubility effects and focus on system performance, enabling the generation of a generic validation dataset for the reproducibility of TIM experiments. Results: Paracetamol bioaccessibility profiles were repeatable and reproducible (all pairwise f₂ > 50). Maximum differences in total bioaccessible paracetamol were 0.9% (TIM-1) and 2.8% (tiny-TIM) within laboratories and 3.4 and 5.9% between laboratories. Inter-lab variability at individual time points remained max. Gastric and intestinal environments showed repeatable pH, temperature, and GI transit characteristics, with fluctuations across transit stages that mirrored reported in vivo patterns. Conclusions: These results demonstrate that TIM systems can reproducibly simulate gastrointestinal conditions across laboratories and generate consistent measurements of drug product performance, despite the complexity of the dynamic processes involved. While this evaluation involving a single BCS I drug solution should not be directly extrapolated to experiments with poorly soluble compounds or different formulations, it supports the use of TIM systems as robust in vitro models in drug product development. This study provides a standardized, inter-laboratory, baseline performance dataset to support regulatory submissions incorporating TIM data and enable more confident interpretation of TIM experiments.