METHODS: The complex has been characterized for its apparent solubility and in vitro dissolution. The solid state characterization has been carried out using Fourier Transform Infra-Red (FTIR) Spectroscopy, Elemental Analysis, X-Ray Powder Diffraction (XRD), Differential Scanning Calorimetry (DSC) analysis, Thermal Gravimetric Analysis (TGA) and Scanning Electron Microscopy (SEM).
RESULTS: Simvastatin-Arginine (SMV-ARG) complex exhibited massive solubility enhancement by 12,000 fold and significant improvement in both acidic and alkaline dissolution media. A conversion of coherent crystalline to non-coherent pattern, and certain extent of amorphization in SMV-ARG complex, fully justifies the enhanced solubility, and hence the dissolution profile.
CONCLUSION: The present study provides a significant evidence that ARG molecules are capable to form a complex with small molecules and increase their aqueous solubility which prove to be beneficial in drug formulation and development.
METHODS: We assessed sCD26/DPP-IV levels, active GLP-1 levels, body mass index (BMI), glucose, insulin, A1c, glucose homeostasis indices, and lipid profiles in 549 Malaysian subjects (including 257 T2DM patients with MetS, 57 T2DM patients without MetS, 71 non-diabetics with MetS, and 164 control subjects without diabetes or metabolic syndrome).
RESULTS: Fasting serum levels of sCD26/DPP-IV were significantly higher in T2DM patients with and without MetS than in normal subjects. Likewise, sCD26/DPP-IV levels were significantly higher in patients with T2DM and MetS than in non-diabetic patients with MetS. However, active GLP-1 levels were significantly lower in T2DM patients both with and without MetS than in normal subjects. In T2DM subjects, sCD26/DPP-IV levels were associated with significantly higher A1c levels, but were significantly lower in patients using monotherapy with metformin. In addition, no significant differences in sCD26/DPP-IV levels were found between diabetic subjects with and without MetS. Furthermore, sCD26/DPP-IV levels were negatively correlated with active GLP-1 levels in T2DM patients both with and without MetS. In normal subjects, sCD26/DPP-IV levels were associated with increased BMI, cholesterol, and LDL-cholesterol (LDL-c) levels.
CONCLUSION: Serum sCD26/DPP-IV levels increased in T2DM subjects with and without MetS. Active GLP-1 levels decreased in T2DM patients both with and without MetS. In addition, sCD26/DPP-IV levels were associated with Alc levels and negatively correlated with active GLP-1 levels. Moreover, metformin monotherapy was associated with reduced sCD26/DPP-IV levels. In normal subjects, sCD26/DPP-IV levels were associated with increased BMI, cholesterol, and LDL-c.
METHODS: The study involved 235 Malaysian subjects who were randomly selected (66 normal weight subjects, 97 overweight, 59 obese subjects, and 13 subjects who were underweight). Serum sDPP4 and active GLP-1 levels were examined by enzyme-linked immunosorbent assay (ELISA). Also, body mass index kg/m(2) (BMI), lipid profiles, insulin and glucose levels were evaluated. Insulin resistance (IR) was estimated via the homeostasis model assessment for insulin resistance (HOMA-IR).
RESULTS: Serum sDPP4 levels were significantly higher in obese subjects compared to normal weight subjects (p=0.034), whereas serum levels of active GLP-1 were lower (p=0.021). In obese subjects, sDPP4 levels correlated negatively with active GLP-1 levels (r(2)=-0.326, p=0.015). Furthermore, linear regression showed that sDPP4 levels were positively associated with insulin resistance (B=82.28, p=0.023) in obese subjects.
CONCLUSION: Elevated serum sDPP4 levels and reduced GLP-1 levels were observed in obese subjects. In addition, sDPP4 levels correlated negatively with active GLP-1 levels but was positively associated with insulin resistance. This finding provides evidence that sDPP4 and GLP-1 may play an important role in the pathogenesis of obesity, suggesting that sDPP4 may be valuable as an early marker for the augmented risk of obesity and insulin resistance.
Methods: Six different polymers were used to prepare FLU nanopolymeric particles: hydroxyl propyl methylcellulose (HPMC), poly (vinylpyrrolidone) (PVP), poly (vinyl alcohol) (PVA), ethyl cellulose (EC), Eudragit (EUD), and Pluronics®. A low-energy method, nanoprecipitation, was used to prepare the polymeric nanoparticles.
Results and conclusion: The combination of HPMC-PVP and EUD-PVP was found most effective to produce stable FLU nanoparticles, with particle sizes of 250 nm ±2.0 and 280 nm ±4.2 and polydispersity indices of 0.15 nm ±0.01 and 0.25 nm ±0.03, respectively. The molecular modeling studies endorsed the same results, showing highest polymer drug binding free energies for HPMC-PVP-FLU (-35.22 kcal/mol ±0.79) and EUD-PVP-FLU (-25.17 kcal/mol ±1.12). In addition, it was observed that Ethocel® favored a wrapping mechanism around the drug molecules rather than a linear conformation that was witnessed for other individual polymers. The stability studies conducted for 90 days demonstrated that HPMC-PVP-FLU nanoparticles stored at 2°C-8°C and 25°C were more stable. Crystallinity of the processed FLU nanoparticles was confirmed using differential scanning calorimetry, powder X-ray diffraction analysis and TEM. The Fourier transform infrared spectroscopy (FTIR) studies showed that there was no chemical interaction between the drug and chosen polymer system. The HPMC-PVP-FLU nanoparticles also showed enhanced dissolution rate (P<0.05) compared to the unprocessed counterpart. The in vitro antibacterial studies showed that HPMC-PVP-FLU nanoparticles displayed superior effect against gram-positive bacteria compared to the unprocessed FLU and positive control.