Antihyperlipidemic and antiperoxidative effect of Diasulin, a polyherbal formulation in alloxan induced hyperglycemic rats

BACKGROUND: This study was undertaken to investigation the effect of Diasulin, a poly herbal drug composed of ethanolic extract of ten medicinal plants on blood glucose, plasma insulin, tissue lipid profile, and lipidperoxidation in alloxan induced diabetes. METHODS: Ethanolic extract of Diasulin a, poly herbal drug was administered orally (200 mg/kg body weight) for 30 days. The different doses of Diasulin on blood glucose and plasma insulin in diabetic rats were studied and the levels of lipid peroxides [TBARS, and Hydroperoxide] and tissue lipids [cholesterol, triglyceride, phospholipides and free fatty acids] were also estimated in alloxan induced diabetic rats. The effects were compared with glibenclamide. RESULT: Treatment with Diasulin and glibenclamide resulted in a significant reduction of blood glucose and increase in plasma insulin. Diasulin also resulted in a significant decrease in tissue lipids and lipid peroxide formation. The effect produced by Diasulin was comparable with that of glibenclamide. CONCLUSION: The decreased lipid peroxides and tissue lipids clearly showed the antihyperlipidemic and antiperoxidative effect of Diasulin apart from its antidiabetic effect

Informatics: The fuel for pharmacometric analysis

The current informal practice of pharmacometrics as a combination art and science makes it hard to appreciate, the role that informatics can and should play in the future of the discipline and to comprehend the gaps that exist because of its absence. The development of pharmacometric informatics has important implications for expediting decision making and for improving the reliability of decisions made in model-based development. We argue that well-defined informatics for pharmacometrics can lead to much needed improvements in the efficiency, effectiveness, and reliability of the pharmacometrics process

Importance of Shrinkage in Empirical Bayes Estimates for Diagnostics: Problems and Solutions

Empirical Bayes (“post hoc”) estimates (EBEs) of ηs provide modelers with diagnostics: the EBEs themselves, individual prediction (IPRED), and residual errors (individual weighted residual (IWRES)). When data are uninformative at the individual level, the EBE distribution will shrink towards zero (η-shrinkage, quantified as 1-SD(ηEBE)/ω), IPREDs towards the corresponding observations, and IWRES towards zero (ε-shrinkage, quantified as 1-SD(IWRES)). These diagnostics are widely used in pharmacokinetic (PK) pharmacodynamic (PD) modeling; we investigate here their usefulness in the presence of shrinkage. Datasets were simulated from a range of PK PD models, EBEs estimated in non-linear mixed effects modeling based on the true or a misspecified model, and desired diagnostics evaluated both qualitatively and quantitatively. Identified consequences of η-shrinkage on EBE-based model diagnostics include non-normal and/or asymmetric distribution of EBEs with their mean values (“ETABAR”) significantly different from zero, even for a correctly specified model; EBE–EBE correlations and covariate relationships may be masked, falsely induced, or the shape of the true relationship distorted. Consequences of ε-shrinkage included low power of IPRED and IWRES to diagnose structural and residual error model misspecification, respectively. EBE-based diagnostics should be interpreted with caution whenever substantial η- or ε-shrinkage exists (usually greater than 20% to 30%). Reporting the magnitude of η- and ε-shrinkage will facilitate the informed use and interpretation of EBE-based diagnostics