We have performed virtual screening to identify new lead trypanothione reductase inhibitor (TRI) compounds, enzyme present in Tripanozoma cruzi, the agent responsible of Chagas disease. From a training set of 58 compounds, linear discriminant analysis (LDA) was performed using 2D and 3D descriptors as discriminating variables in order to find out which function of descriptors characterizes the active TRI. The values of the statistical parameters F--Snedecor and Wilk's lambda for the discriminant function (DF) showed good statistical significance, as long as the rate of success in the prediction for both the training and the test set: 91.38% and 88.63%, in that order. Internal validation through the Leave--Group--Out methodology was performed with good results, assuring the stability of the DF. Afterwards, the DF was applied in virtual screening of 422,367 compounds. The optimum range of values of octanol--water partition coefficient for a compound to develop trypanothione reductase inhibition was applied as a second filtering criteria. 739 structurally heterogeneous drugs of the virtual library were selected as promissory TRI.
A similarity-based algorithm based on a previously developed model is applied in the classification of two sets of anticonvulsant and non-anticonvulsant drugs. Each set is composed of a) anticonvulsant compounds that have shown moderate to high activity in the Maximal Electroshock Seizure (MES) test and b) drugs with other biological activities or poor activity in the MES test. The results from the analysis of variance (ANOVA) indicate that the proposed algorithm is able to differentiate anticonvulsant from non-anticonvulsant drugs. The proposed model may then be useful in the identification of new anticonvulsant agents through virtual screening of large virtual libraries of chemical structures.
About 50 million people in the world suffer from epilepsy, especially in childhood, adolescence and old age. Available treatment fails to control epilepsy in up to 30% of affected people. In developing countries, however, the amount of patients that do not receive adequate treatment climbs up to 75%. Moreover, the new generation of antiepileptic drugs (AEDs) causes important central and peripheral side effects, including ataxia, diplopia, dizziness, headache, nausea, allergies and sedation. A mathematical model previously developed by Bagchi and Maiti, involving Carhart atom pairs and similarity measures, is applied in the prediction of anticonvulsant activity of two sets of compounds which have shown to be active in the Maximal Electroshock Seizure (MES) test, meaning that their mechanism of action can be at least partially explained through sodium channels blockade. Nine structurally heterogeneous molecules define the first set of compounds, with Carhart similarities to carbamazepine ranging from 0.005 to 0.593. The second set is defined by four benzodiazepines derivatives with Carhart similarities to THIQ-10c ranging from 0.533 to 0.570. A new, more specific, model is constructed based on the one from Bagchi and Maiti and a pharmacophore previously identified in our laboratory through an active analog approach. Applied to both sets of compounds, our model shows smaller average percentage error and average absolute error in the prediction than the one form Bagchi and Maiti and smaller SD as well. Accuracy and precision in the prediction also increases compared to those obtained when using bare similarity coefficients as relative activity indicators.
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