Abstract
Background: Cocrystal formation between an active pharmaceutical ingredient (API) and coformer is an applicable technique to change the physicochemical and pharmacokinetic properties. Computational methods can overcome the need for extensive experiments and improve the chances of success in the coformer selection. In this method, two compounds connect by non-covalent interactions that form a unique crystalline structure. Prediction of a cocrystal formation between API and coformer can help in the screening and design of new cocrystals.
Methods: In this study, available data in the literature were applied to develop a prediction method based on binary logistic regression to screen cocrystal formation by sum and absolute difference of structural parameters (the number of rotatable bonds, Abraham solvation parameters, and topological polar surface area) of the two involved compounds.
Results: The results showed various factors (eight structural parameters of the two compounds) could affect cocrystal formation, and the developed model can predict cocrystallization with a probability of about 90%.
Conclusion: The related parameter to hydrogen bonding basicity and volume of compounds has the most significant effect on cocrystal formation.
Methods: In this study, available data in the literature were applied to develop a prediction method based on binary logistic regression to screen cocrystal formation by sum and absolute difference of structural parameters (the number of rotatable bonds, Abraham solvation parameters, and topological polar surface area) of the two involved compounds.
Results: The results showed various factors (eight structural parameters of the two compounds) could affect cocrystal formation, and the developed model can predict cocrystallization with a probability of about 90%.
Conclusion: The related parameter to hydrogen bonding basicity and volume of compounds has the most significant effect on cocrystal formation.
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Copyright
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