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- Shayanfar, Ali, et al.
(author)
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Solubility of carbamazepine, nicotinamide and carbamazepine-nicotinamide cocrystal in ethanol-water mixtures
- 2014
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In: Fluid Phase Equilibria. - : Elsevier BV. - 0378-3812 .- 1879-0224. ; 363, s. 97-105
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Journal article (peer-reviewed)abstract
- Solubility is an important physiochemical property of pharmaceutical compounds, and cocrystallization is one method used to improve the solubility of drugs. Carbamazepine is a drug from class II, according to the biopharmaceutical classification system, and it forms a cocrystal with nicotinamide. Carbamazepine cocrystallized with nicotinamide was synthesized using the solvent evaporation approach, and its characteristics were determined using differential scanning calorimetry and powder X-ray diffractometry. The solubility of various solid phases in ethanol. +. water mixtures was investigated at different temperatures using the shake-flask method, and the resulting precipitates were characterized. The solubility of carbamazepine was increased with the addition of ethanol up to a mass fraction of 0.8. Nevertheless, maximum solubility of NIC is observed in neat solvent (water). While the solubility of a cocrystal depends on the concentration of the coformer and its stability in the solution.
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| 2. |
- Shayanfar, Shadi, et al.
(author)
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Prediction of Cocrystal Formation Between Drug and Coformer by Simple Structural Parameters
- 2022
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In: Journal of Reports in Pharmaceutical Science. - : Medknow Publications. - 2322-1232 .- 2322-5106. ; 11:2, s. 182-191
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Journal article (peer-reviewed)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.
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