Simple method for the prediction of the separation of racemates with high-performance liquid chromatography on Whelk-O1 chiral stationary phase

Modeling and predicting chiral stationary phase enantioselectivity: An efficient random forest classifier using an optimally balanced training dataset and an aggregation strategy

Predicting whether a chiral column will be effective is a daily task for many analysts. Moreover, finding the best chiral column for separating a particular racemic compound is mostly a matter of trial and error that may take up to a week in some cases. In this study we have developed a novel prediction approach based on combining a random forest classifier and an optimized discretization method for dealing with enantioselectivity as a continuous variable. Using the optimization results, models were trained on data sets divided into four enantioselectivity classes. The best model performances were achieved by over-sampling the minority classes (α ≤ 1.10 and α ≥ 2.00), down-sampling the majority class (1.2 ≤ α < 2.0), and aggregating multicategory predictions into binary classifications. We tested our method on 41 chiral stationary phases using layered fingerprints as descriptors. Experimental results show that this learning methodology was successful in terms of average area under the Receiver Operating Characteristic curve, Kappa indices and F-measure for structure-based prediction of the enantioselective behavior of 34 chiral columns.

Resolution, determination of enantiomeric purity and chiral recognition mechanism of new xanthone derivatives on (S,S)-whelk-O1 stationary phase

The enantioresolution and determination of the enantiomeric purity of 32 new xanthone derivatives, synthesized in enantiomerically pure form, were investigated on (S,S)-Whelk-O1 chiral stationary phase (CSP). Enantioselectivity and resolution (α and R_S ) with values ranging from 1.41-6.25 and from 1.29-17.20, respectively, were achieved. The elution was in polar organic mode with acetonitrile/methanol (50:50 v/v) as mobile phase and, generally, the (R)-enantiomer was the first to elute. The enantiomeric excess (ee) for all synthesized xanthone derivatives was higher than 99%. All the enantiomeric pairs were enantioseparated, even those without an aromatic moiety linked to the stereogenic center. Computational studies for molecular docking were carried out to perform a qualitative analysis of the enantioresolution and to explore the chiral recognition mechanisms. The in silico results were consistent with the chromatographic parameters and elution orders. The interactions between the CSP and the xanthone derivatives involved in the chromatographic enantioseparation were elucidated.

Gene expression profiling in whole blood identifies distinct biological pathways associated with obesity

Background

Obesity is reaching epidemic proportions and represents a significant risk factor for cardiovascular disease, diabetes, and cancer.

Methods

To explore the relationship between increased body mass and gene expression in blood, we conducted whole-genome expression profiling of whole blood from seventeen obese and seventeen well matched lean subjects. Gene expression data was analyzed at the individual gene and pathway level and a preliminary assessment of the predictive value of blood gene expression profiles in obesity was carried out.

Results

Principal components analysis of whole-blood gene expression data from obese and lean subjects led to efficient separation of the two cohorts. Pathway analysis by gene-set enrichment demonstrated increased transcript levels for genes belonging to the "ribosome", "apoptosis" and "oxidative phosphorylation" pathways in the obese cohort, consistent with an altered metabolic state including increased protein synthesis, enhanced cell death from proinflammatory or lipotoxic stimuli, and increased energy demands. A subset of pathway-specific genes acted as efficient predictors of obese or lean class membership when used in Naive Bayes or logistic regression based classifiers.

Conclusion

This study provides a comprehensive characterization of the whole blood transcriptome in obesity and demonstrates that the investigation of gene expression profiles from whole blood can inform and illustrate the biological processes related to regulation of body mass. Additionally, the ability of pathway-related gene expression to predict class membership suggests the feasibility of a similar approach for identifying clinically useful blood-based predictors of weight loss success following dietary or surgical interventions.