Open Access

Rafael Leal Monteiro Paraiso

• Computational oral absorption models, in particular PBBM models, provide a powerful tool for researchers and pharmaceutical scientists in drug discovery and formulation development, as they mimic and can describe the physiologically processes relevant to the oral absorption. PBBM models provide in vivo context to in vitro data experiments and allow for a dynamic understanding of in vivo drug disposition that is not typically provided by data from standard in vitro assays. Investigations using these models permit informed decision-making, especially regarding to formulation strategies in drug development. PBBM models, but can also be used to investigate and provide insight into mechanisms responsible for complex phenomena such as food effect in drug absorption. Although there are obviously still some gaps regarding the in silico construction of the gastrointestinal environment, ongoing research in the area of oral drug absorption (e.g. the UNGAP, AGE-POP and InPharma projects) will increase knowledge and enable improvement of these models. PBBM can nowadays provide an alternative approach to the development of in vitro–in vivo correlations. The case studies presented in this thesis demonstrate how PBBM can address a mechanistic understanding of the negative food effect and be used to set clinically relevant dissolution specification for zolpidem immediate release tablets. In both cases, we demonstrated the importance of integrating drug properties with physiological variables to mechanistically understand and observe the impact of these parameters on oral drug absorption. Various complex physiological processes are initiated upon food consumption, which can enhance or reduce a drug’s dissolution, solubility, and permeability and thus lead to changes in drug absorption. With improvements in modeling and simulation software and design of in vitro studies, PBBM modeling of food effects may eventually serve as a surrogate for clinical food effect studies for new doses and formulations or drugs. Furthermore, the application of these models may be even more critical in case of compounds where execution of clinical studies in healthy volunteers would be difficult (e.g., oncology drugs). In the fourth chapter we have demonstrated the establishment of the link between biopredictive in vitro dissolution testing (QC or biorelevant method) PBBM coupled with PD modeling opens the opportunity to set truly clinically relevant specifications for drug release. This approach can be extended to other drugs regardless of its classification according to the BCS. With the increased adoption of PBBM, we expect that best practices in development and verification of these models will be established that can eventually inform a regulatory guidance. Therefore, the application of Physiologically Based Biopharmaceutical Modelling is an area with great potential to streamline late-stage drug development and impact on regulatory approval procedures.