Towards predictive computational platform for drug solubilization in the gastrointestinal tract

Objective

Improving the oral absorption of poorly water-soluble drug molecules remains one of the major challenges in the pharmaceutical industry. A key bottleneck in this process is the solubilization of administered lipophilic compounds in the small intestines, where bile salts (BS) and lipids assemble into colloidal particles. Therefore, understanding the mechanisms and controlling factors of this process is critical for drug development. The advent of powerful supercomputers has enabled the integration of in silico techniques into the investigation of complex, realistic systems. However, no existing computational platform explicitly accounts for intermolecular interactions to predict drug solubilization in gastrointestinal fluids (GIF). In this project, I will employ coarse-grained (CG) molecular dynamics (MD) to characterize the particles in GIF, and develop a python based computational platform to predict drug solubilization. The time and size scales involved, and the complexity of multi-component systems, make CG MD the most appropriate modeling tool to my project. An important initial step will be the development of required molecular models within the widely used and state-of-the-art Martini 3 framework. This task will leverage mine and the supervisor’s expertise to fill an important gap in CG MD bile salts and drug parameterization, and will expand on its own the applicability of CG MD to entire new areas. I will simulate GIF containing all scales of micelles/vesicles/droplets that are experimentally detected but not yet computationally studied in the entire range. Finally, I will be able to follow the solubilization mechanisms of drug molecules in the created GI environments, also leveraging a non-academic placement with an important pharmaceutical partner. Ultimately, this work will enable efficient in silico screening of intestinal drug solubility before in vitro and in vivo studies and guiding the identification of promising candidates for drug discovery.