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A meeting that was organized by the Academy of Pharmaceutical Sciences Biopharmaceutics and Regulatory Sciences focus groups focused on the challenges of Developing Clinically Relevant Dissolution Specifications (CRDS) for Oral Drug Products. Industrial Scientists that were involved in product development shared their experiences with in vitro dissolution and in silico modeling approaches to establish clinically relevant dissolution specifications. The regulators shared their perspectives on the acceptability of these different strategies for the development of acceptable specifications. The meeting also reviewed several collaborative initiatives that were relevant to regulatory biopharmaceutics. Following the scientific presentations, a roundtable session provided an opportunity for delegates to discuss the information that was shared during the presentations, debate key questions, and propose strategies to make progress in this critical area of regulatory biopharmaceutics. It was evident from the presentations and subsequent discussions that progress continues to be made with approaches to establish robust CRDS. Further dialogue between industry and regulatory agencies greatly assisted future developments and key areas for focused discussions on CRDS were identified.

This publication summarizes the proceedings of day 3 of a 3-day workshop on "Dissolution and Translational Modeling Strategies Enabling Patient-Centric Product Development." Specifically, this publication discusses the current approaches in building clinical relevance into drug product development for solid oral dosage forms, along with challenges that both industry and regulatory agencies are facing in setting clinically relevant drug product specifications (CRDPS) as presented at the workshop. The concept of clinical relevance is a multidisciplinary effort which implies an understanding of the relationship between the critical quality attributes (CQAs) and their impact on predetermined clinical outcomes. Developing this level of understanding, in many cases, requires introducing deliberate but meaningful variations into the critical material attributes (CMAs) and critical process parameters (CPPs) to establish a relationship between the resulting in vitro dissolution/release profiles and in vivo PK performance, a surrogate for clinical outcomes. Alternatively, with the intention of improving the efficiency of the drug product development process by limiting the burden of conducting in vivo studies, this understanding can be either built, or at least enhanced, through in silico efforts, such as IVIVC and physiologically based pharmacokinetic (PBPK) absorption modeling and simulation (M&S). These approaches enable dissolution testing to establish safe boundaries and reject drug product batches falling outside of the established safe range (e.g., due to inadequate in vivo performance) enabling the method to become clinically relevant. Ultimately, these efforts contribute towards patient-centric drug product development and allow regulatory flexibility throughout the lifecycle of the drug product.

In drug development, comparability of dissolution profiles of 2 different formulations is usually assessed using the similarity factor f . In practice, the drug dissolution profiles are deemed similar if the f exceeds 50, which occurs when a 10% maximum difference in the mean percentage of the dissolved drug at each time point between test and reference formulation is obtained. According to the Guideline on the Investigation of Bioequivalence (CPMP/EWP/QWP/1401/98 Rev. 1/ Corr **) use of the f is however restricted by a set of validity conditions. If some of these conditions are not satisfied, the f is not considered suitable, and alternative statistical methods are needed. In this article, we propose an inferential framework based on the maximum deviation between curves to test the comparability of drug dissolution profiles. The new methodology is applicable regardless whether the validity criteria of the f are met or not. Contrary to the f , this approach also integrates the variability of the measurements over time and not only their average. To benchmark our method, we performed simulations informed by 3 real case studies provided by the European Medicines Agency and extracted from dossiers submitted to the Centralised Procedure for Marketing Authorisation Application. In the scenarios of the simulation study, the new method controlled its type I error rate when the maximum deviation was greater than the similarity acceptance limit of 10%. The power exceeded 80% for small values of the maximum deviation, while the test was more conservative for intermediate ones. Our results were also very robust to sampling variations. Based on these positive findings, we encourage applicants to consider the new maximum deviation-based method as a valid alternative to the f , especially when the validity criteria of the latter are not met.

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