PREPARATION, PHYSICAL CHARACTERIZATION, AND PHARMACOKINETIC STUDY OF DOCETAXEL NANOCRYSTALS

Objective: The main objective of this study was to prepare and evaluate the nanocrystal formulation of docetaxel. Methods: Docetaxel nanocrystals were formulated to improve the water solubility. Docetaxel nanocrystals were prepared by nanoprecipitation method using Tween 80, egg lecithin, and povidone C-12 as stabilizers and poly(lactic-co-glycolic acid) (PLGA) as polymer in acceptable limits. A total of 16 formulations were prepared by changing stabilizer and polymer ratios. The prepared nanocrystals were characterized by particle size, zeta potential, crystalline structure, surface morphology, assay, saturation solubility, and in vitro drug release. Results: Based on particle size, polydispersity index, and zeta potential data, four formulations were optimized. The formulation containing Tween 80 as stabilizer has shown lowest particle size and better drug release than the formulations containing egg lecithin and povidone C-12 as stabilizers. The formulation containing Tween 80 and PLGA has shown still lower sized particles than the Tween 80 alone and exhibited prolonged sustained drug release. The release kinetics of formulations containing Tween 80 and PLGA followed zero-order release kinetics and formulations containing egg lecithin and povidone C-12 followed Higuchi diffusion (non-Fickian). Conclusion: From the study, we concluded that as the type and concentration of stabilizer changed the size and shape of the crystals were also changed and the formulations showed sustained drug release with non-Fickian diffusion

Understanding the expression of Toll-like receptors in Asian Indians predisposed to coronary artery disease

Introduction: Toll-like receptors (TLRs) are an important link between innate and adaptive immunity. Material and methods: Expression of TLR-2, TLR-4, and TLR-9 genes was assessed in 60 coronary artery disease (CAD) patients and 79 controls by SYBR Green 1 based real time PCR assay

Integrative Bioinformatics Analysis of Genomic and Proteomic Approaches to Understand the Transcriptional Regulatory Program in Coronary Artery Disease Pathways

<div><p>Patients with cardiovascular disease show a panel of differentially regulated serum biomarkers indicative of modulation of several pathways from disease onset to progression. Few of these biomarkers have been proposed for multimarker risk prediction methods. However, the underlying mechanism of the expression changes and modulation of the pathways is not yet addressed in entirety. Our present work focuses on understanding the regulatory mechanisms at transcriptional level by identifying the core and specific transcription factors that regulate the coronary artery disease associated pathways. Using the principles of systems biology we integrated the genomics and proteomics data with computational tools. We selected biomarkers from 7 different pathways based on their association with the disease and assayed 24 biomarkers along with gene expression studies and built network modules which are highly regulated by 5 core regulators PPARG, EGR1, ETV1, KLF7 and ESRRA. These network modules in turn comprise of biomarkers from different pathways showing that the core regulatory transcription factors may work together in differential regulation of several pathways potentially leading to the disease. This kind of analysis can enhance the elucidation of mechanisms in the disease and give better strategies of developing multimarker module based risk predictions.</p> </div

Modulation of m-RNA and protein expression profiles.

<p>a. Fold change in the mRNA expression of biomarkers from microarray data. b: Fold change in the expression of biomarkers at protein level.</p

Methodology for regulome and network module analysis.

<p>Methodology for regulome and network module analysis.</p

Transcription factor and Protein network.

<p>Circled Transcription factors are common among the pathways. The dashed line represents the demarcation between the Transcription factor regulation in nucleus and biomarker expression in extracellular matrix.</p

Identification of core transcription factors.

<p>a. Venn-diagram of the 443 Transcription factors regulating the pathways in CVD and identification of 55 core transcription factors. b. Mean expression levels of significant transcription factors obtained from microarray between cases and controls. c. Number of binding sites of 34 expressed transcription factors in the biomarkers from 7 different pathways. d. Venn-diagram of the 34 significant Transcription factors regulating the pathways in CVD and identification of 5 core transcription factors as PPARG, EGR-1, ETV-1, KLF-7, and ESRRA.</p