(337cn) Solubility Prediction and Cocrystal Screening By COSMO-RS: From Theory to Experimental Validation

Research Interests Computational Chemistry, Structure-Based Drug Discovery, Computer-Aided Drug Design - Virtual Screening , Molecular Modeling and Simulation

The growing insolubility of new drug candidates presents a significant challenge for the current drug development process. Recent years have witnessed growing interests in the design of pharmaceutical cocrystals, a solid form technology to improve the physicochemical properties of drugs including the bioavailability of drugs with low aqueous solubility. Cocrystals are structurally homogeneous crystalline materials containing two or more components including the Active Pharmaceutical Ingredient (API) and a coformer (a safe substance for human use) present in definite stoichiometric amounts¹. Over the last decade, High-throughput screening experiments have successfully led to the development of novel cocrystals of low solubility APIs. However, due to the large number of potential coformers (hundreds of molecules), experimental screening and confirmation are time-consuming, complicated, and resource intensive. Therefore, experimental screening would benefit from a complementary screening tool that predicts a priori which compounds are likely to form cocrystals with a given API. COSMO-RS² (COnductor like Screening MOdel for Realistic Solvents), a fluid-phase thermodynamics approach, can be used to guide selection of potential coformers based on likelihood of forming a cocrystal with a given API. COSMO-RS theory utilizes the screening charge densities obtained from quantum chemical calculations in combination with fast statistical thermodynamics to compute the chemical potential of a drug in its pure liquid state, in a solvent, or in a solvent mixture. In this work, we have investigated the potential of COSMO-RS theory to assess the suitability of solvents and identify potential coformers for caffeine as a pharmaceutical model compound. Three platforms based on COSMO-RS theory have been applied to screen solvents and coformers for caffeine; various input types including experimental solubility reference, experimental fusion data, and fusion data from the QSPR (Quantitative Structure Property Relationship) model have been used in these simulations. The results from the three platforms were compared to comment on the accuracy, limitations, and advantages of each COSMO-RS implementation in solvent and cocrystal screening. Finally, to validate the predictions made using virtual screening by COSMO-RS, top caffeine solvents and coformer candidates were investigated experimentally. Through these combined computational and experimental investigations by employing the insights enabled by use of COSMO-RS, we have successfully identified a novel cocrystal of caffeine. The use of COSMO-RS has the potential to speed up drug development by offering a cost-effective means of predicting drug solubility, cocrystal formation, and other thermodynamic properties in liquid solutions.

Reference:

(1) Aakeröy, C. B.; Salmon, D. J. Building co-crystals with molecular sense and supramolecular sensibility. CrystEngComm 2005, 7, 439-448.

(2) Klamt, A.; Eckert, F. COSMO-RS: a novel and efficient method for the a priori prediction of thermophysical data of liquids. Fluid Phase Equilibria 2000, 172 (1), 43-72.