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The visually indistinguishable acicular crystals of a (2S,3R/S)-3-ethyl-1-phenylhex-5-ene-2,3-diol (ephd) diastereomeric pair are separated via the mechanical response based on elastic (2S,3R, right) and brittle (2S,3S, left) crystals.

peer-reviewed In this work a method is developed for the extraction and purification of the three curcuminoids; Curcumin (CUR), Demethoxycurcumin (DMC) and Bisdemethoxycurcumin (BDMC) from commercially available crude curcumin. In a previous publication the extraction of pure curcumin by repeated crystallization has been described. The focus of this paper is on the following chromatographic treatment of the mother liquor from the crystallization to obtain pure DMC and BDMC, and to increase the yield of pure CUR. In the chromatographic process a mixture of chloroform and methanol is used as the mobile phase and silica gel is used as the stationary phase. Each fraction isolated in the chromatographic process was characterised by High Performance Liquid Chromatography (HPLC) and Mass Spectrometry (LC-MS) techniques, and the pure CUR, DMC and BDMC solid phases were fully characterized by powder Xray diffraction (PXRD), differential scanning calorimetry (DSC) and thermal gravimetric analysis (TGA). Stability studies were performed on the purified curcuminoids where the degradation products were observed and analysed by HPLC/LC-MS. Overall, the combined purification method recovered from the crude: 88.5%, 79.7% and 68.8% of CUR, DMC and BDMC respectively, in highly pure form CUR (100%), DMC (98.6%) and BDMC (98.3%).

A quantitative structure–activity relationship model has been constructed by artificial neural networks for estimation of melting temperature (Tm) of molecular cocrystals (CCs). On the basis of a literature analysis using SciFinder and Cambridge Structural Database softwares, a database has been created of CCs for four active pharmaceutical ingredients, namely, caffeine, theophylline (THP), nicotinamide (NA), and isonicotinamide (INA). In total, of 61 CCs were included: 14-CAF, 9-THP, 29-INA, and 9-NA. A good correlation was obtained with ANNs to quantify the Tm of the CCs with respect to various coformers. The training process was completed with an average relative error of 2.38%, whereas the relative error for the validation set was 2.89%.

peer-reviewed The full text of this article will not be available in ULIR until the embargo expires on the 07/11/2018 Using Artificial Neural Networks (ANNs), four distinct models have been developed for the prediction of solid-state properties of cocrystals: melting point, lattice energy, and crystal density. The models use three input parameters for the pure model compound (MC) and three for the pure coformer. In addition, as input parameter the model uses the pKa difference between the MC and the coformer, and a 1:1 MC–conformer binding energy as calculated by a force field method. Notably the models require no data for the actual cocrystals. In total, 61 CCs (two-component molecular cocrystals) were used to construct the models, and melting temperatures and crystal densities were extracted from the literature for four MCs: caffeine, theophylline, nicotinamide and isonicotinamide. The data set includes 14 caffein cocrystals, 9 theophylline cocrystals, 9 nicotinamide cocrystals and 29 isonicotinamide cocrystals. The model–I is trained using known cocrystal melting temperatures, lattice energies and crystal densities, to predict all three solid–state properties simultaneously. The average relative deviation for the training set is 2.49%, 6.21% and 1.88% for the melting temperature, lattice energy and crystal density, respectively, and correspondingly 6.26%, 4.58% and 0.99% for the valdation set. Model–II, model–III and model–IV were built using the same input neurons as in model–I, for separate prediction of each respective output solid–state property. For these models the average relative deviation for the traning sets becomes 1.93% for the melting temperature model-II, 1.29% for the lattice energy model-III and 1.03% for the crystal density model-IV, and correspondingly 2.23%, 2.40% and 1.77% for the respective validation sets