(654c) Computational Strategies in Pharmaceutical Materials Sciences to Derisk Solid Form

A plethora of organic molecules exhibit polymorphism, which refers to the ability of chemical compounds to pack in to different 3D crystalline motifs. This phenomenon is of special importance to both industry and academia since the physical & chemical properties, such a stability, bioavailability & manufacturability are largely related to the crystalline motifs and may vary tremendously between polymorphs. Hence, it becomes crucial for the pharmaceutical industry to understand and flag the risk of finding another polymorph associated to guide the design of a particular medicine. With the advancements in employing predictive sciences for derisking & design of solid forms, several state-of-the-art computational tools have emerged in the past decades. In particular, energetics-based (QM/MD) crystal structure prediction (CSP) and informatics-based health checks have been quite popular across all pharmaceutical research.

CSP has emerged has a pivotal resource within Pfizer’s de-risking workflow enabling us to determine the most stable and pharmaceutically desirable crystalline motifs using advanced atomistic modeling approaches. Informatics based health checks, on the other hand, leverages information available through the vast crystallographic structural database available through Cambridge Structural Database (CSD) and Pfizer’s inhouse database to flag potential risks in terms of intra- or inter-molecular geometry, hydrogen bonding network, crystal packing, etc. The combination of both the approaches are embedded within Pfizer’s derisking workflow and employed to guide design studies.

In this presentation, using case-studies, consisting of both computational & experimental outcomes, we highlight the application of tools like CSP and health checks for designing & derisking of the nominated solid form.