Leveraging Oral Drug Development to a Next Level: Impact of the IMI-Funded OrBiTo Project on Patient Healthcare

A thorough understanding of the behavior of drugs in the human gastrointestinal (GI) tract is essential when working in the field of oral drug development in a pharmaceutical company. For orally administered systemic drugs, GI processes, including drug release, dissolution, precipitation, degradation, and permeation, dictate absorption into the systemic circulation. These processes are not always fully captured in predictive in vitro and in silico tools, as commonly applied in the non-clinical stage of formulation drug development. A collaborative initiative between academic institutions and industrial companies was set up in 2012 to optimize and validate these in vitro and in silico tools. From that perspective, the predictive power of these models can be revised and, if necessary, optimized in order to fully rely on their predictiveness towards the in vivo performance of a drug after oral administration in patients. The IMI/EFPIA-funded OrBiTo project aimed to improve our fundamental understanding of the GI absorption process. The gathered information was integrated into the development of new (or already existing) laboratory tests and computer-based methods in order to deliver more accurate predictions of drug product behavior in a real-life setting. The new methods were validated with the use of industrial data. Crucially, the ultimate goal of the project was to set up a scientific framework (i.e., decision tree) to guide the use of these new tools in drug development. The project will facilitate and speed up the formulation development process and significantly reduce the need for animal experiments in this area as well as for human clinical studies in the future. With respect to patients, high-quality oral medicines will be developed where the required dose is well calculated and consistently provides an optimal clinical effect. In a first step, this manuscript will summarize the setup of the project and how data were collected across the different work packages. In a second step, case examples of how this project contributed to improved patient healthcare will be thoroughly illustrated.