| ORIGINAL ARTICLE |
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| Year : 2022 | Volume
: 11
| Issue : 2 | Page : 182-191 |
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Prediction of cocrystal formation between drug and coformer by simple structural parameters
Shadi Shayanfar1, Abolghasem Jouyban2, Sitaram Velaga3, Ali Shayanfar4
1 Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran
2 Pharmaceutical Analysis Research Center, Tabriz University of Medical Sciences, Tabriz, Iran
3 Pharmaceutical Engineering Group, Department of Health Sciences, Lulea University of Technology, Lulea, Sweden
4 Drug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, Iran; Editorial Office of Pharmaceutical Sciences Journal, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz, Iran
Correspondence Address:
Dr. Ali Shayanfar
Editorial Office of Pharmaceutical Sciences Journal, Faculty of Pharmacy, Tabriz University of Medical Sciences, Tabriz
Iran
 Source of Support: None, Conflict of Interest: None  | Check |
DOI: 10.4103/jrptps.JRPTPS_172_21
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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. |
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