2019
DOI: 10.1016/j.ijpharm.2019.01.007
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Reliability of the Hansen solubility parameters as co-crystal formation prediction tool

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Cited by 63 publications
(37 citation statements)
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“…For the theoretical miscibility assessment, if the solubility parameter difference ( Δδ ) is less than 7.0 MPa 1/2 , the components are considered to have good miscibility. If Δδ is less than 2.0 MPa 1/2 , the components may possess excellent miscibility and easily form a solid solution, while Δδ greater than 10 MPa 1/2 indicates immiscibility between them [ 30 , 42 ]. Theoretical predicted δ using the Fedors and Hoftyzer–Van Krevelen methods are given in Supplementary Materials Table S1 .…”
Section: Resultsmentioning
confidence: 99%
“…For the theoretical miscibility assessment, if the solubility parameter difference ( Δδ ) is less than 7.0 MPa 1/2 , the components are considered to have good miscibility. If Δδ is less than 2.0 MPa 1/2 , the components may possess excellent miscibility and easily form a solid solution, while Δδ greater than 10 MPa 1/2 indicates immiscibility between them [ 30 , 42 ]. Theoretical predicted δ using the Fedors and Hoftyzer–Van Krevelen methods are given in Supplementary Materials Table S1 .…”
Section: Resultsmentioning
confidence: 99%
“…This in vitro dissolution study indicates the significant contribution of multicomponent crystal engineering in solubility enhancement and drug release of LMG [Figure 5]. [28] Comparative in vitro dissolution study of tablet formulation of LMG and LMG-AA eutectics reveal the advantage of eutectics formation in solubility, drug release, and dissolution. LMG-AA eutectic tablet showed near about double drug release as compare to pure LMG tablet.…”
Section: Comparative In Vitro Dissolution Analysis Of Lmg and Lmg Eutectics Tabletsmentioning
confidence: 84%
“…However, the selection of coforms has still largely relied on trial and error, which is time consuming and labor-intensive. Recently, a variety of computer-assisted approaches have emerging as attractive tools to accelerate the screening process of cocrystals 214 , 215 , 216 , 217 , 218 . For example, the artificial neural network models are developed to predict cocrystal formation by analyzing a network of coformers extracted from the Cambridge Structural Database (CSD) 218 .…”
Section: Concluding Remarks and Future Perspectivesmentioning
confidence: 99%