DESIGN AND EVALUATION OF DOMPERIDONE SUBLINGUAL TABLETS

Objective: The aim of this work was to enhance the bioavailability of poorly soluble, anti-emetic drug; domperidone (DMP) having a poor oral bioavailability (13-17%) due to extensive first pass metabolism. The goal of this study was achieved through solubilization of DMP using solid dispersion technology followed by incorporation of solid dispersions into sublingual tablets to bypass pre-systemic metabolism.Methods: Solid dispersions of DMP with Pluronic F-68 were prepared in different weight ratios by fusion method and they were evaluated for their in vitro dissolution rate to select the best ratio for final formulation. Then, solid dispersions were formulated into sublingual tablets in combination with various soluble excipients. Sublingual tablets were prepared by direct compression technique and evaluated for their physical properties, in vitro dissolution rate and kinetics of drug release. The best formulae were selected for in vivo studies in rabbits in comparison with marketed oral tablets; Motinorm®.Results: Solid dispersions of DMP with Pluronic F-68 in a weight ratio of 1:7 (w/w) showed the highest dissolution rate and were selected for sublingual tablets formulation. Sublingual tablets formulae S16 (containing Fructose and 10% w/w Ac-Di-Sol) and S20 (containing Fructose and 10% w/w Explotab) showed the best results and were selected for in vivo studies in rabbits. The selected formulae showed marked enhancement of DMP bioavailability compared with the commercial oral tablets; Motinorm®, with relative bioavailability values of 432.49±10.13% and 409.32±11.59 % for S16 and S20, respectively.Conclusion: The results confirmed that sublingual tablets were an effective tool for DMP delivery with marked enhancement of bioavailability.Keywords: Domperidone, Solubility, Solid dispersions, Sublingual tablets, First-pass metabolism, Bioavailabilit

Type IV pili interactions promote intercellular association and moderate swarming of Pseudomonas aeruginosa

Pseudomonas aeruginosa is a ubiquitous bacterium that survives in many environments, including as an acute and chronic pathogen in humans. Substantial evidence shows that P. aeruginosa behavior is affected by its motility, and appendages known as flagella and type IV pili (TFP) are known to confer such motility. The role these appendages play when not facilitating motility or attachment, however, is unclear. Here we discern a passive intercellular role of TFP during flagellar-mediated swarming of P. aeruginosa that does not require TFP extension or retraction. We studied swarming at the cellular level using a combination of laboratory experiments and computational simulations to explain the resultant patterns of cells imaged from in vitro swarms. Namely, we used a computational model to simulate swarming and to probe for individual cell behavior that cannot currently be otherwise measured. Our simulations showed that TFP of swarming P. aeruginosa should be distributed all over the cell and that TFP−TFP interactions between cells should be a dominant mechanism that promotes cell−cell interaction, limits lone cell movement, and slows swarm expansion. This predicted physical mechanism involving TFP was confirmed in vitro using pairwise mixtures of strains with and without TFP where cells without TFP separate from cells with TFP. While TFP slow swarm expansion, we show in vitro that TFP help alter collective motion to avoid toxic compounds such as the antibiotic carbenicillin. Thus, TFP physically affect P. aeruginosa swarming by actively promoting cell-cell association and directional collective motion within motile groups to aid their survival.National Institutes of HealthIndiana Clinical and Translational Sciences Institut

Improvement of heart failure by human amniotic mesenchymal stromal cell transplantation in rats

Background: Recently, stem cells have been considered for the treatment of heart diseases, but no marked improvement has been recorded. This is the first study to examine the functional and histological effects of the transplantation of human amniotic mesenchymal stromal cells (hAMSCs) in rats with heart failure (HF). Methods: This study was conducted in the years 2014 and 2015. 35 male Wistar rats were randomly assigned into 5 equal experimental groups (7 rats each) as 1-Control 2-Heart Failure (HF) 3-Sham 4-Culture media 5-Stem Cell Transplantation (SCT). Heart failure was induced using 170 mg/kg/d of isoproterenol subcutaneously injection in 4 consecutive days. The failure confirmed by the rat cardiac echocardiography on day 28. In SCT group, 3�106 cells in 150 μl of culture media were transplanted to the myocardium. At the end, echocardiographic and hemodynamic parameters together with histological evaluation were done. Results: Echocardiography results showed that cardiac ejection fraction in HF group increased from 58/73 � 9 to 81/25 � 6/05 in SCT group (p value < 0.001). Fraction shortening in HF group was increased from 27/53 � 8/58 into 45/55 � 6/91 in SCT group (p value < 0.001). Furthermore, hAMSCs therapy significantly improved mean diastolic blood pressure, mean arterial pressure, left ventricular systolic pressure, rate pressure product, and left ventricular end-diastolic pressure compared to those in the HF group, with the values reaching the normal levels in the control group. A marked reduction in fibrosis tissue was also found in the SCT group (p value < 0.001) compared with the animals in the HF group. Conclusion: The transplantation of hAMSCs in rats with heart failure not only decreased the level of fibrosis but also conferred significant improvement in heart performance in terms of echocardiographic and hemodynamic parameters. � 2016, Tehran Heart Center. All rights reserved

EGC: Sparse covariance estimation in logit mixture models

This paper introduces a new data-driven methodology for estimating sparse covariance matrices of the random coefficients in logit mixture models. Researchers typically specify covariance matrices in logit mixture models under one of two extreme assumptions: either an unrestricted full covariance matrix (allowing correlations between all random coefficients), or a restricted diagonal matrix (allowing no correlations at all). Our objective is to find optimal subsets of correlated coefficients for which we estimate covariances. We propose a new estimator, called MISC (mixed integer sparse covariance), that uses a mixed-integer optimization (MIO) program to find an optimal block diagonal structure specification for the covariance matrix, corresponding to subsets of correlated coefficients, for any desired sparsity level using Markov Chain Monte Carlo (MCMC) posterior draws from the unrestricted full covariance matrix. The optimal sparsity level of the covariance matrix is determined using out-of-sample validation. We demonstrate the ability of MISC to correctly recover the true covariance structure from synthetic data. In an empirical illustration using a stated preference survey on modes of transportation, we use MISC to obtain a sparse covariance matrix indicating how preferences for attributes are related to one another