An ab initio molecular dynamics method for cocrystal prediction: validation of the approach

Abstract

Cocrystals offer exciting opportunities to the scientists, with options of tuning their physicochemical, biopharmaceutical, and mechanical properties simultaneously, which can expand the solid form diversity of drugs. Herein, for overcoming the need for exhaustive experimental work and improving the chances of success in the selection of coformers, a computational prediction approach has been developed. In this study, a new cocrystal prediction methodology employing hydrogen bonding tendency, evaluated with the aid of molecular dynamics, has been utilized. For validation, the experimental results of 145 coformers with 6 drugs have been used. The method was found to significantly reproduce the experimental results with attractive features of being a simple, easy-to-use protocol, with short computational time. Further, the developed model was used to predict the formation of cocrystals of nitrofurantoin against a library of new coformers. Three out of the four new cocrystals formed were correctly predicted by the developed prediction methodology. The cocrystal formation of 89 coformers out of a total of 145 coformers was correctly predicted. Thus, the reasonable degree of success obtained in predicting and experimentally generating new cocrystals of nitrofurantoin indicates that the developed computational methodology can play an important role in screening a large library of coformers in the future.