Does Price Really Matter for Generic Alternatives? Supervised learning Approaches in Deciding the Right Price for Acceptable Quality Attributes of Amlodipine Besylate among Generic Alternatives.

The price and safety of finished pharmaceutical preparations are two major concerns while prescribing medicine. In this work, machine learning-based classification models were developed with respect to the quality attributes of 258 samples covering 9 marketed amlodipine (AMLO) formulations. The quantitation of AMLO and its three sulfonate ester genotoxic impurities of besylate counter ion was settled using a validated high-performance liquid chromatography-diode-array detection method. The classification of correlation between dependent and independent variables was exercised using linear discriminant analysis models. The linear dispersion of acceptable quality attributes was significantly different for AMLO besylate formulation with unit price per tablet "<1 Rs." Although the correlations between price and quality are well-understood associations group centroid distance for price group "2-3 Rs." and "1-2 Rs." reveal that acceptable quality dispersion was similar for both groups. Nonetheless, a higher price could allow storage of the finished formulation to be kept on the shelf for a longer period.

Optimization of atorvastatin and quercetin-loaded solid lipid nanoparticles using Box-Behnken design.

Aim: The study explores the synergistic potential of atorvastatin (ATR) and quercetin (QUER)- loaded solid lipid nanoparticles (SLN) in combating breast cancer. Materials & methods: SLNs were synthesized using a high-shear homogenization method and optimized using Box-Behnken design. The SLNs were characterized and evaluated for their in vitro anticancer activity. Results: The optimized SLN exhibited narrow size distribution (PDI = 0.338 ± 0.034), a particle size of 72.5 ± 6.5 nm, higher entrapment efficiency (<90%), sustained release and spherical surface particles. The in vitro cytotoxicity studies showed a significant reduction in IC50 values on MDA-MB-231 cell lines. Conclusion: We report a novel strategy of repurposing well-known drugs and encapsulating them into SLNs as a promising drug-delivery system against breast cancer.

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Effect of glycation-induced concentration-dependent change in albumin structure and alteration in its binding capacity.

Reducing sugars causes confirmatory alterations in albumin structure by the nonenzymatic glycation of the amino group of serum albumin. In this study, glucose and its hazardous metabolic products like glyoxal and methylglyoxal were incubated with bovine serum albumin (BSA). The confirmational changes in BSA molecule's structure by glycating substances was investigated using a variety of spectroscopic methods, including deconvolutionFourier Transform Infra-red (FT-IR) spectroscopy, fluorescence spectroscopy, UV spectroscopy and circular dichroism (CD) spectroscopy. Dynamic fluorescence quenching was observed in the case of glucose, while static quenching was observed in the case of methyl glyoxal and glyoxal. Similarly, employing deconvolution FT-IR spectroscopy and CD spectroscopy for determination of change in secondary structures in terms signature of α-helix, ß-turn, ß-sheet and random coil modifications. Destabilization or unfolding of the albumin structure, due to the disruption of the hydrogen bonding pattern that stabilizes the albumin manifold, causes a 25-50% reduction in α-helix and a 2-fold increase in ß-sheet and turns in glycated BSA. The competitive displacement interaction studies with warfarin were performed using the ultrafiltration technique and quantitative determination of free drug in ultrafiltrate using LC-MS/MS. The binding of carbamazepine (CBZ) or its active metabolite to proteins was unaffected by the glycation of BSA with glucose and methyl glyoxal. Nevertheless, with glyoxal-modified BSA, it changed the binding of selected analytes significantly. Based on in vitro observations and results, it could be anticipated that the serum CBZ concentration variation may be worsened in uncontrolled diabetes circumstances, with an overall variance of 30-40% possible.Communicated by Ramaswamy H. Sarma.

Application of Supervised and Unsupervised Learning Approaches for Mapping Storage Conditions of Biopharmaceutical Product-A Case Study of Human Serum Albumin.

The stability of biopharmaceutical therapeutics over the storage period/shelf life has been a challenging concern for manufacturers. A noble strategy for mapping best and suitable storage conditions for recombinant human serum albumin (rHSA) in laboratory mixture was optimized using chromatographic data as per principal component analysis (PCA), and similarity was defined using hierarchical cluster analysis. In contrast, separability was defined using linear discriminant analysis (LDA) models. The quantitation was performed for rHSA peak (analyte of interest) and its degraded products, i.e., dimer, trimer, agglomerates and other degradation products. The chromatographic variables were calculated using validated stability-indicating assay method. The chromatographic data mapping was done for the above-mentioned peaks over three months at different temperatures, i.e., 20°C, 5-8°C and at room temperature (25°C). The PCA had figured out the ungrouped variable, whereas supervised mapping was done using LDA. As an outcome result of LDA, about 60% of data were correctly classified with the highest sensitivity for 25°C (Aq), 25°C and 5-8°C (Aq with 5% glucose as a stabilizer), whereas the highest specificity was observed for samples stored at 5-8°C (Aq with 5% glucose as a stabilizer).