Preclinical toxicity of innovative molecules: In vitro, in vivo and metabolism prediction.

The lack of predictivity of animal's models has increased the failure rate of drug candidates. Thus, the reversion of this scenario using preliminary in vitro assays and metabolism prediction can reduce the unnecessary use of animals, as well as predict toxic effects at preclinical and clinical stages. The present study aimed to evaluate safety of four biologically active molecules (RN104, RI78, ICH, PCH) with potential therapeutic applications synthesized in our laboratory. Initially, we used MTT cytotoxicity against A549, H9C2, HepG2, LLC-PK1 and NEURO-2 cell lines. RN104 showed the lowest cytotoxicity and further studies were conducted with it. The neutral red (NR) test was performed according to OECD-129 and then acute toxicity test (OECD-423). According to NR results we administered at 300 mg/kg on animals; however, no toxic effect was observed, while 2,000 mg/kg resulted in the death of one animal per group. After, metabolism prediction studies, performed using both ligand-based and structure-based, suggests three potential metabolites. In silico results suggested that potential metabolites could be fast eliminated and, then, this could be an explanation for lower observed toxicity in in vivo experiments. The results showed limitations of the NR as a predictor of the initial dose for the acute toxicity study, which may be related to metabolism. Therefore, the combination of theoretical and experimental studies is relevant to a general understanding of new molecule's toxicity.

Medicinal electrochemistry: integration of electrochemistry, medicinal chemistry and computational chemistry.

Over the last centuries, there were many important discoveries in medicine that were crucial for gaining a better understanding of several physiological processes. Molecular modelling techniques are powerful tools that have been successfully used to analyse and interface medicinal chemistry studies with electrochemical experimental results. This special combination can help to comprehend medicinal chemistry problems, such as predicting biological activity and understanding drug action mechanisms. Electrochemistry has provided better comprehension of biological reactions and, as a result of many technological improvements, the combination of electrochemical techniques and biosensors has become an appealing choice for pharmaceutical and biomedical analyses. Therefore, this review will briefly outline the present scope and future advances related to the integration of electrochemical and medicinal chemistry approaches based on various applications from recent studies.

A novel phospholipase A2 (D49) from the venom of the Crotalus oreganus abyssus (North American Grand canyon rattlesnake).

Currently, Crotalus viridis was divided into two species: Crotalus viridis and Crotalus oreganus. The current classification divides "the old" Crotalus viridis into two new and independent species: Crotalus viridis (subspecies: viridis and nuntius) and Crotalus oreganus (subspecies: abyssus, lutosus, concolor, oreganus, helleri, cerberus, and caliginis). The analysis of a product from cDNA (E6d), derived from the gland of a specie Crotalus viridis viridis, was found to produce an acid phospholipase A2. In this study we isolated and characterized a PLA2 (D49) from Crotalus oreganus abyssus venom. Our studies show that the PLA2 produced from the cDNA of Crotalus viridis viridis (named E6d) is exactly the same PLA2 primary sequence of amino acids isolated from the venom of Crotalus oreganus abyssus. Thus, the PLA2 from E6d cDNA is actually the same PLA2 presented in the venom of Crotalus oreganus abyssus and does not correspond to the venom from Crotalus viridis viridis. These facts highlight the importance of performing more studies on subspecies of Crotalus oreganus and Crotalus viridis, since the old classification may have led to mixed results or mistaken data.

Molecular features related to the binding mode of PPARδ agonists from QSAR and docking analyses.

Diabetes affects approximately 4% of world's population and metabolic syndrome has been directly related to obesity. There is a class of nuclear receptors, peroxisome proliferator-activated receptors (PPARs), which controls the metabolism of carbohydrates and lipids. It has been considered an attractive target to treat diabetes and metabolic syndrome. Accordingly, the primary objective of this study was to employ molecular modelling techniques to understand the factors involved in PPARδ activation. The QSAR models obtained showed good internal and external consistency and presented good validation coefficients (QSAR: q(2) = 0.83, r(2) = 0.87; HQSAR: q(2) = 0.73, r(2) = 0.90; CoMFA: q(2) = 0.88, r(2) = 0.94). The selected properties and the contour maps described the possible interactions between the PPARδ receptor and its agonists. From these findings, it is possible to propose molecular modifications to design new compounds with improved biological properties.

Machine learning techniques and drug design.

The interest in the application of machine learning techniques (MLT) as drug design tools is growing in the last decades. The reason for this is related to the fact that the drug design is very complex and requires the use of hybrid techniques. A brief review of some MLT such as self-organizing maps, multilayer perceptron, bayesian neural networks, counter-propagation neural network and support vector machines is described in this paper. A comparison between the performance of the described methods and some classical statistical methods (such as partial least squares and multiple linear regression) shows that MLT have significant advantages. Nowadays, the number of studies in medicinal chemistry that employ these techniques has considerably increased, in particular the use of support vector machines. The state of the art and the future trends of MLT applications encompass the use of these techniques to construct more reliable QSAR models. The models obtained from MLT can be used in virtual screening studies as well as filters to develop/discovery new chemicals. An important challenge in the drug design field is the prediction of pharmacokinetic and toxicity properties, which can avoid failures in the clinical phases. Therefore, this review provides a critical point of view on the main MLT and shows their potential ability as a valuable tool in drug design.