Hyperoxaluria leads to dysbiosis and drives selective enrichment of oxalate metabolizing bacterial species in recurrent kidney stone endures.

Hyperoxaluria due to endogenously synthesized and exogenously ingested oxalates is a leading cause of recurrent oxalate stone formations. Even though, humans largely rely on gut microbiota for oxalate homeostasis, hyperoxaluria associated gut microbiota features remain largely unknown. Based on 16S rRNA gene amplicons, targeted metagenomic sequencing of formyl-CoA transferase (frc) gene and qPCR assay, we demonstrate a selective enrichment of Oxalate Metabolizing Bacterial Species (OMBS) in hyperoxaluria condition. Interestingly, higher than usual concentration of oxalate was found inhibitory to many gut microbes, including Oxalobacter formigenes, a well-characterized OMBS. In addition a concomitant enrichment of acid tolerant pathobionts in recurrent stone sufferers is observed. Further, specific enzymes participating in oxalate metabolism are found augmented in stone endures. Additionally, hyperoxaluria driven dysbiosis was found to be associated with oxalate content, stone episodes and colonization pattern of Oxalobacter formigenes. Thus, we rationalize the first in-depth surveillance of OMBS in the human gut and their association with hyperoxaluria. Our findings can be utilized in the treatment of hyperoxaluria associated recurrent stone episodes.

Pharmacophore Identification and QSAR Studies on Substituted Benzoxazinone as Antiplatelet Agents: kNN-MFA Approach.

The three-dimensional quantitative structure-activity relationship (3D-QSAR) and pharmacophore identification studies on 28 substituted benzoxazinone derivatives as antiplatelet agents have been carried out. Multiple linear regression (MLR) method was applied for QSAR model development considering training and test set approaches with various feature selection methods. Stepwise (SW), simulated annealing (SA) and genetic algorithm (GA) were applied to derive QSAR models which were further validated for statistical significance and predictive ability by internal and external validation. The results of pharmacophore identification studies showed that hydrogen bond accepters, aromatic and hydrophobic, are the important features for antiplatelet activity. The selected best 3D kNN-MFA model A has a training set of 23 molecules and test set of 5 molecules with validation (q(2)) and cross validation (pred_r(2)) values 0.9739 and 0.8217, respectively. Additionally, the selected best 3D QSAR (MLR) model B has a training set of 23 molecules and test set of 5 molecules with validation (r(2)) and cross validation (pred_r(2)) values of 0.9435 and 0.7663, respectively, and four descriptors at the grid points S_123, E_407, E_311 and H_605. The information rendered by 3D-QSAR models may lead to a better understanding and designing of novel potent antiplatelet molecules.

Synthesis, characterization and quantification of simvastatin metabolites and impurities.

Simvastatin is used in treatment of hypercholesterolemia because it regulates cholesterol synthesis as a result of its ß-hydroxy acid acting as an inhibitor of 3-hydroxy-methylglutaryl coenzyme A (HMG-CoA). The present communication deals with synthesis, characterization and development of accurate, precise and sensitive Reverse Phase High Performance Liquid Chromatography (RP-HPLC) method for simultaneous estimation of simvastatin and its synthetic impurities. The impurities methyl ether and ß-hydroxy acid of simvastatin were synthesized in the laboratory and characterized by MS, NMR and FT-IR spectroscopy. The separation of simvastatin and its impurities was carried out on an isocratic JASCO RP-HPLC system using KYA TECH HIQ SIL C(18) column (150 × 4.6 mm internal diameter, particle size 5 µm) operating at ambient temperature using acetonitrile:water (80:20 v/v) with 0.1% orthophosphoric acid as mobile phase. The method developed for HPLC analysis of three impurities along with simvastatin was validated using ICH Q2B (R1) guidelines and it complied with these guidelines. The results of analysis were found to be in the range of 98.14% to 101.89% for all analytes with acceptable accuracy and precision. The method can be used for detection and quantification of synthetic impurities in bulk or formulations of simvastatin.