Codeine Precipitating Serotonin Syndrome in a Patient in Therapy with Antidepressant and Triptan.

The serotonin syndrome is a serioius medical condition due due to an intensive stimulation of setonin receptors. It is a rare, but severe, consequence of interaction between serotomimetic agents. This is a report of a 70-year-old woman steadily in therapy with venlafaxine and rizatriptan for migraine and major depressive syndrome. She was admitted to neurology unit for decreased light reflex with miotic pupils, global hyperreflexia, tremor, anxiety, ataxia and incoordination. The patient was diagnosed as a probable case of serotonin syndrome due to a pharmacological interaction between venlafaxine and rizatriptan trigged by opioid intake. In this paper, the development of syntomatology, the clinical examination and the possible pharmacokinetics explanation were carefully discussed and analysed.

Moisture effects on dielectric relaxations of poly (ɛ-caprolactone)/starch biodegradable blends: Local, interfacial and segmental.

Poly(ɛ-caprolactone)/starch biocomposites in a wide composition range were studied by thermally stimulated depolarization currents, TSDC, during the drying process following the effect on the dielectric relaxations of moisture loss and blend composition. PCL's cooperative chain mobility is unaffected by the presence of water which is mainly located in the starch inclusions as its segmental relaxation is fixed at 207K. The heterogeneities found in the amorphous phase of neat starch are also observed here as a bimodal α relaxation which shifts to higher temperatures as it dries. The blend plasticization by water molecules also affects the blend secondary relaxations showing how the local motions are hindered by the loss of water which is lower in the blend than in pure starch. The interfacial peak which is intermediate between the α relaxations of the two components is affected by the inclusions change from liquid to glassy state as the blend dries.

Ligand-based discovery of novel trypanosomicidal drug-like compounds: in silico identification and experimental support.

Two-dimensional bond-based linear indices and linear discriminant analysis are used in this report to perform a quantitative structure-activity relationship study to identify new trypanosomicidal compounds. A database with 143 anti-trypanosomal and 297 compounds having other clinical uses, are utilized to develop the theoretical models. The best discriminant models computed using bond-based linear indices provides accuracies greater than 90 for both training and test sets. Our models identify as anti-trypanosomals five out of nine compounds of a set of already-synthesized substances. The in vitro anti-trypanosomal activity of this set against epimastigote forms of Trypanosoma cruzi is assayed. Both models show a perfect agreement between theoretical predictions and experimental results. The compounds identified as active ones show more than 98% of anti-epimastigote elimination (AE) at a concentration of 100 µg/mL. Besides, three compounds show more than 70% of AE at a concentration of 10 µg/mL. Finally, compounds with the best "activity against epimastigote forms/unspecific cytotoxicity" ratio are evaluated using an amastigote susceptibility assay. It should be noticed that, compound Va7-71 exhibit a 100% of intracellular amastigote elimination and shows similar activity when compared to a standard trypanosomicidal as nifurtimox. Finally, we can emphasize that, the present algorithm constitutes a step forward in the search for efficient ways of discovering new anti-trypanosomal compounds.

Computational discovery of novel trypanosomicidal drug-like chemicals by using bond-based non-stochastic and stochastic quadratic maps and linear discriminant analysis.

Herein we present results of a quantitative structure-activity relationship (QSAR) studies to classify and design, in a rational way, new antitrypanosomal compounds by using non-stochastic and stochastic bond-based quadratic indices. A data set of 440 organic chemicals, 143 with antitrypanosomal activity and 297 having other clinical uses, is used to develop QSAR models based on linear discriminant analysis (LDA). Non-stochastic model correctly classifies more than 93% and 95% of chemicals in both training and external prediction groups, respectively. On the other hand, the stochastic model shows an accuracy of about the 87% for both series. As an experiment of virtual lead generation, the present approach is finally satisfactorily applied to the virtual evaluation of 9 already synthesized in house compounds. The in vitro antitrypanosomal activity of this series against epimastigote forms of Trypanosoma cruzi is assayed. The model is able to predict correctly the behaviour for the majority of these compounds. Four compounds (FER16, FER32, FER33 and FER 132) showed more than 70% of epimastigote inhibition at a concentration of 100 microg/mL (86.74%, 78.12%, 88.85% and 72.10%, respectively) and two of these chemicals, FER16 (78.22% of AE) and FER33 (81.31% of AE), also showed good activity at a concentration of 10 microg/mL. At the same concentration, compound FER16 showed lower value of cytotoxicity (15.44%), and compound FER33 showed very low value of 1.37%. Taking into account all these results, we can say that these three compounds can be optimized in forthcoming works, but we consider that compound FER33 is the best candidate. Even though none of them resulted more active than Nifurtimox, the current results constitute a step forward in the search for efficient ways to discover new lead antitrypanosomals.