Gene/Protein Disease Symptom Drug Enzyme Compound
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We conducted a series of studies aimed at investigating the effect of beta-blockers on exercise physiology. On the basis of these and other existing studies, it is possible to draw the following conclusions and to make the following tentative recommendations for patients engaged in exercise training who receive beta-blocker therapy: i) CAD patients treated with beta-blockers are capable of deriving the expected enhancement of cardiorespiratory fitness during training, irrespective of the type of drug used; ii) beta1-selective blockers are preferable to nonselective agents for hypertensive patients engaged in exercise training; iii) because beta1-selective blockers impair exercise tolerance in some hypertensive patients, physicians should look out for this adverse reaction and, if present, consider alternative antihypertensive therapy; iv) intrinsic sympathomimetic activity confers no advantage during exercise training; v) exercise intensity prescription for patients receiving beta-blockers should be in accordance with traditional guidelines and based on results of individualized exercise testing performed on medication; vi) exercise training is desirable during beta-blocker therapy in that it appears to offset adverse alterations in lipoprotein metabolism; and vii) nonselective beta-blockers may increase predisposition to exertional hyperthermia, and patients must therefore be encouraged to adhere strictly to accepted guidelines for heat injury prevention.
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PMID:Effect of beta-blockers on exercise physiology: implications for exercise training. 167 17

The purpose of this study was to evaluate four commercially available artificial neural network (ANN) software programs: NeuroShell2 v3.0, BrainMaker v3.7, CAD/Chem v5.0, and NeuralWorks Professional II/Plus for prediction of in vitro dissolution-time profiles of controlled-release tablets containing a model sympathomimetic drug. Seven independent formulation variables and three other tablet variables (moisture content of granules, granule particle size, and tablet hardness), for 22 tablet formulations, were used as the ANN model input. In vitro dissolution time-profiles at 10 different sampling times were used as the output. The models' optimum architectures were determined for each ANN software by varying the number of hidden layers and number of nodes in hidden layer(s). The ANN developed from the four software programs were validated by predicting the in vitro dissolution time-profiles of each of the 19 formulations, which were excluded from the training process. Although the same data set was used, the optimum ANN architectures generated from the four software programs were different. Using the four optimum ANN models, the plots of predicted vs. observed percentage of drug dissolved gave slopes ranging from 0.95 to 1.01 and r2 values ranging from 0.95 to 0.99 for all 190 dissolution data points for the 19 training formulations. The difference factors (f1) and similarity factors (f2) between the ANN predicted and the observed in vitro dissolution profiles were also used to compare the predictions for the four software programs. It was concluded that the four programs provided reasonable predictions of in vitro dissolution profiles for the data set employed in this study, with NeuralShell2 showing the best overall prediction.
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PMID:Comparison of four artificial neural network software programs used to predict the in vitro dissolution of controlled-release tablets. 1222 68