Perspective on the Relationship between Melting Points and Chemical Structure

Katritzky, Alan R.; Jain, Ritu; Lomaka, Andre; Petrukhin, Ruslan; Maran, Uko; Karelson, Mati

doi:10.1021/cg010009s

Cited by 178 publications

(144 citation statements)

References 32 publications

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“…1, FEL had a sharp endothermic peak at 160°C, and all FEL complexes had lower melting temperatures. According to literatures [19], it was probably due to the different arrangement of molecules in the crystal lattice, and FEL complexes may have lower crystalline lattice energy [20]. Figure 2 showed the pXRD patterns of solid-state forms of FEL and its complexes.…”

Section: Dsc and Pxrd Characterizationmentioning

confidence: 85%

Design and Evaluation of a Novel Felbinac Transdermal Patch: Combining Ion-Pair and Chemical Enhancer Strategy

Liu

Song

et al. 2015

AAPS PharmSciTech

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Abstract. The aim of this study was to design a novel felbinac (FEL) patch with significantly higher (P<0.05) skin permeation amount than the commercial product SELTOUCH® using ion-pair and chemical enhancer strategy, overcoming the disadvantage of the large application area of SELTOUCH®. Six complexes of FEL with organic amines diethylamine (DEA), triethylamine (TEA), N-(2′-hydroxyethanol)-piperdine (HEPP), monoethanolamine (MEtA), diethanolamine (DEtA), and triethanolamine (TEtA) were prepared by ion-pair interaction, and their formation were confirmed by differential scanning calorimetry (DSC), powder X-ray diffraction (pXRD), infared spectroscopy (IR), and proton nuclear magnetic resonance spectroscopy ( 1 H-NMR). Subsequently, the effect of ion-pair complexes and chemical enhancers were investigated through in vitro and in vivo experiments using rabbit abdominal skin. Results showed that FEL-TEA was the most potential candidate both in isopropyl palmitate (IPP) solution and transdermal patches. Combining use of 10% N-dodecylazepan-2-one (Azone), the optimized FEL-TEA patch achieved a flux of 18.29±2.59 μg/cm 2 /h, which was twice the amount of the product SELTOUCH® (J=9.18±1.26 μg/cm 2 /h). Similarly, the area under the concentration curve from time 0 to time t (AUC 0-t ) in FEL-TEA patch group (15.94±3.58 h.μg/mL) was also twice as that in SELTOUCH® group (7.31±1.16 h.μg/mL). Furthermore, the in vitro skin permeation results of FEL-TEA patch was found to have a good correlation with the in vivo absorption results in rabbit. These findings indicated that a combination of ion-pair and chemical enhancer strategy could be useful in developing a novel transdermal patch of FEL.KEY WORDS: chemical enhancer; felbinac-triethylamine (FEL-TEA); in vitro/in vivo correlation (IVIVC); ion-pair; transdermal patch.

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Section: Dsc and Pxrd Characterizationmentioning

confidence: 85%

Design and Evaluation of a Novel Felbinac Transdermal Patch: Combining Ion-Pair and Chemical Enhancer Strategy

Liu

Song

et al. 2015

AAPS PharmSciTech

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“…Not surprisingly, several reports relating to the prediction of melting point from structure have emerged in recent years. 10 , 61-65 We will not review melting point prediction in depth here; instead, we refer the reader to the fairly recent perspective by Katritzky et al 66 More recently, Yalkowsky has derived new parameters for predicting the entropy of melting, which presumably could be used in the GSE to remove the assumption of the applicability of Walden's rule. 63 Bergstrom and coworkers 61 generated a consensus model using 277 diverse drugs for melting point prediction that focuses on descriptors of polarity and molecular fl exibility.…”

Section: E29mentioning

confidence: 99%

Recent progress in the computational prediction of aqueous solubility and absorption

Johnson

Zheng

2006

AAPS J

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The computational prediction of aqueous solubility and/or human absorption has been the goal of many researchers in recent years. Such an in silico counterpart to the biopharmaceutical classifi cation system (BCS) would have great utility. This review focuses on recent developments in the computational prediction of aqueous solubility, P-glycoprotein transport, and passive absorption. We fi nd that, while great progress has been achieved, models that can reliably affect chemistry and development are still lacking. We briefl y discuss aspects of emerging scientifi c understanding that may lead to breakthroughs in the computational modeling of these properties.

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“…As noted above, melting point affects solubility, and solubility controls toxicity in that, if a compound is only poorly soluble, its concentration in the aqueous environment may be too low for it to exert a toxic effect. 4,5 Thus, it would be helpful to be able to estimate the melting point of a compound from its chemical structure. 6,7 Prediction methods for melting point, mainly can be categorized as property-property relationship (PPR), group contribution, and quantitative structure-property relationship (QSPR).…”

Section: Introductionmentioning

confidence: 99%

Prediction of Melting Point for Drug-like Compounds Using Principal Component-Genetic Algorithm-Artificial Neural Network

Habibi‐Yangjeh

Pourbasheer²,

Danandeh-Jenagharad³

2008

Bulletin of the Korean Chemical Society

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Principal component-genetic algorithm-multiparameter linear regression (PC-GA-MLR) and principal component-genetic algorithm-artificial neural network (PC-GA-ANN) models were applied for prediction of melting point for 323 drug-like compounds. A large number of theoretical descriptors were calculated for each compound. The first 234 principal components (PC's) were found to explain more than 99.9% of variances in the original data matrix. From the pool of these PC's, the genetic algorithm was employed for selection of the best set of extracted PC's for PC-MLR and PC-ANN models. The models were generated using fifteen PC's as variables. For evaluation of the predictive power of the models, melting points of 64 compounds in the prediction set were calculated. Root-mean square errors (RMSE) for PC-GA-MLR and PC-GA-ANN models are 48.18 and 12.77 ºC, respectively. Comparison of the results obtained by the models reveals superiority of the PC-GA-ANN relative to the PC-GA-MLR and the recently proposed models (RMSE = 40.7 ºC). The improvements are due to the fact that the melting point of the compounds demonstrates non-linear correlations with the principal components.

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Perspective on the Relationship between Melting Points and Chemical Structure

Cited by 178 publications

References 32 publications

Design and Evaluation of a Novel Felbinac Transdermal Patch: Combining Ion-Pair and Chemical Enhancer Strategy

Design and Evaluation of a Novel Felbinac Transdermal Patch: Combining Ion-Pair and Chemical Enhancer Strategy

Recent progress in the computational prediction of aqueous solubility and absorption

Prediction of Melting Point for Drug-like Compounds Using Principal Component-Genetic Algorithm-Artificial Neural Network

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