In silico preformulation studies, including drug-excipient interaction or compatibility studies, using an AI-based computational model and expert system

Abstract A comprehensive understanding of drugs and excipients is crucial for developing a stable pharmaceutical formulation. Pre-formulation studies include the selection of compatible excipients for selected drugs (one or more) based on theoretical and physicochemical interaction risk factors. At present, a knowledge-driven expert system (PharmDE) and a machine-learning-based computational model (DE_Interact) are available for in silico drug-excipient compatibility or interaction studies. Here, we evaluated two systems to assess the compatibility of the selected drugs and excipients for the future development of a solid oral dosage form. The interaction results of the chosen drugs and excipients, as determined by DE_Interact and the PharmDE expert system, were interpreted. According to the results, atenolol and amlodipine displayed incompatibility or interactions with lactose and polyvinyl pyrrolidone on PharmDE. However, atenolol was found to be compatible with polyvinyl pyrrolidone and incompatible with the remaining excipients (mannitol and lactose), as determined using the DE_Interact computational model. Amlodipine displayed possible compatibility and a medium level of recommended risk with mannitol in DE_Interact and PharmDE, respectively. The comprehensive results of interactions between both drugs and their individual excipients are evaluated side-by-side using in silico tools. The comparative study results conclude that the final solid oral dosage form will contain amlodipine as the active pharmaceutical ingredient and mannitol as the primary excipient. Through benchmarking and analysing two prominent in silico tools, we aimed to establish a validated framework for interpreting their predictions. We have demonstrated how their discrepancies (between the results of PharmDE and DE_Interact) can be informative and propose a strategic workflow for utilising them as pre-screening aids to prioritise excipients and mitigate risks associated with formulation development.