Projection To Latent Structures-discriminant Analysis Model Research Articles

Toward In Silico Prediction of Glass-Forming Ability from Molecular Structure Alone: A Screening Tool in Early Drug Development

We present a novel computational tool which predicts the glass-forming ability of drug compounds solely from their molecular structure. Compounds which show solid-state limited aqueous solubility were selected, and their glass-forming ability was determined upon spray-drying, melt-quenching and mechanical activation. The solids produced were analyzed by differential scanning calorimetry (DSC) and powder X-ray diffraction. Compounds becoming at least partially amorphous on processing were classified as glass-formers, whereas those remaining crystalline regardless of the process method were classified as non-glass-forming compounds. A predictive model of the glass-forming ability, designed to separate between these two classes, was developed through the use of partial least-squares projection to latent structure discriminant analysis (PLS-DA) and calculated molecular descriptors. In total, ten of the 16 compounds were determined experimentally to be good glass-formers and the PLS-DA model correctly sorted 15 of the compounds using four molecular descriptors only. An external test set was predicted with an accuracy of 75%, and, hence, the PLS-DA model developed was shown to be applicable for the identification of compounds that have the potential to be designed as amorphous formulations. The model suggests that larger molecules with a low number of benzene rings, low level of molecular symmetry, branched carbon skeletons and electronegative atoms have the ability to form a glass. To conclude, we have developed a predictive, transparent and interpretable computational model for the identification of drug molecules capable of being glass-formers. The model allows an assessment of amorphization as a formulation strategy in the early drug development process, and can be applied before compound synthesis.

NMR and Chemometric methods: A powerful combination for characterization of Balsamic and Traditional Balsamic Vinegar of Modena

This work presents the capability of NMR spectroscopy combined with Chemometrics in predicting the ageing of Balsamic and Traditional Balsamic Vinegar of Modena. The need of an analytical method is an important requirement for both research oriented and commercial evaluation of these very valuable products. 1H NMR spectroscopy, based on the advantage of rapid sample analysis without any manipulation or derivatization, is here proposed as a valid tool to describe Balsamic and Traditional Balsamic Vinegar of Modena. For this purpose, 72 reliable samples, were divided into three different groups according to their ageing process: young (<12 years), old (>12 and <25 years) and extra old (>25 years). Hierarchical Projection to Latent Structures Discriminant Analysis (PLS-DA) allowed us to characterize the ageing process. Variables showing the largest VIP (Variable Importance in the Projection) were extracted from PLS-DA model, thus shedding lights onto the role played by specific compounds in this complex ageing process. Two robust classification models, were built by PLS-DA and Naïve Bayes classifier and compared to prove the accuracy of the representation on both training and test sets. The predictions obtained for 41 “unknown” vinegar samples with these both methods gave more than 80% agreement among them.