"Cardiac glycosides"-quo vaditis?-past, present, and future?

Up to date, digitalis glycosides, also known as "cardiac glycosides", are inhibitors of the Na+/K+-ATPase. They have a long-standing history as drugs used in patients suffering from heart failure and atrial fibrillation despite their well-known narrow therapeutic range and the intensive discussions on their raison d'être for these indications. This article will review the history and key findings in basic and clinical research as well as potentially overseen pros and cons of these drugs.

Validation of fluid bed granulation utilizing artificial neural network.

Three innovative components (an annular gap spray system, a booster bottom and an outlet filter) have been developed by Innojet Technologies to improve fluid bed technology and to reduce the common interference factors (clogging of nozzles and outlet filters, spray loss, spray drying and fluidized bed heterogeneity). In a fluid bed granulator, three conventional components have been replaced with these innovative components. Validation of the modified fluid bed granulator has been conducted using a generalized regression neural network (GRNN). Under different operating conditions (by variation of inlet air temperature, liquid-binder spray rate, atomizing air pressure, air velocity, amount and concentration of binder solution and batch size), sucrose was granulated and the properties of size, size distribution, flow rate, repose angle and bulk and tapped volumes of granules were measured. To confirm the method's validity, the trained network has been used to predict new granulation parameters as well as granule properties. These forecasts were then compared with the corresponding experimental results. Good correlation has been obtained between the predicted and the experimental data. From these findings, we conclude that the GRNN may serve as a reliable method to validate the modified fluid bed apparatus.

Bayesian neural networks for aroma classification.

Bayesian Neural Networks (BNNs) are investigated to test their potential to distinguish between different aroma impressions. Special attention is thereby drawn on mixed aroma impressions, resulting from the flavor description of a single compound with more than one aroma quality. The structures of 133 pyrazine-derived aroma compounds as well as their aroma descriptions are selected for comparison. The information fed into the neural networks is based on molecular descriptors calculated from the geometrically optimized chemical structures. While in the case of the Probabilistic Neural Network (PNN) the networks' output consists of a categorical variable, the output for the General Regression Neural Network (GRNN) is defined in a numerical way. The best models attain comparable performance with a correct prediction of 90.8% of the cases for PNN and 89.9% for GRNN, respectively. Comparison of the BNN results to those obtained by Multiple Linear Regression (MLR) points out that the nonlinear methods work significantly better on the studied problem and that BNNs can be applied to multiple-category problems in structure-flavor relationships with good accuracy.

Aroma quality differentiation of pyrazine derivatives using self-organizing molecular field analysis and artificial neural network.

The encoding of various aroma impressions and the distinction between different aroma qualities are unsolved problems, as differences between aroma impressions can be described only in a qualitative but not in a quantitative manner. As a consequence, classifications of various aroma qualities cannot easily be performed by standard QSAR methods. To find a proper way to encode aroma impressions for SAR studies, a total of 50 pyrazine-based aroma compounds showing the aroma quality of earthy, green-earthy, or green are analyzed. Special attention is thereby turned on the mixed aroma impression green-earthy. Classifications on the whole data set as well as on smaller subsets are calculated using self-organizing molecular field analysis (SOMFA) and artificial neural networks (ANNs). SOMFA classifies between two or three aroma impressions, leading to models satisfying in predictive power. ANN analysis using multilayer perceptron network architecture with one hidden layer and nominal output as well as genetic regression neural network) with two hidden layers and numerical output both lead to a rather good performance rate of 94%.