QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer's Disease.

Alzheimer's disease is one of the most common neurodegenerative disorders in elder population. The ß-site amyloid cleavage enzyme 1 (BACE1) is the major constituent of amyloid plaques and plays a central role in this brain pathogenesis, thus it constitutes an auspicious pharmacological target for its treatment. In this paper, a QSAR model for identification of potential inhibitors of BACE1 protein is designed by using classification methods. For building this model, a database with 215 molecules collected from different sources has been assembled. This dataset contains diverse compounds with different scaffolds and physical-chemical properties, covering a wide chemical space in the drug-like range. The most distinctive aspect of the applied QSAR strategy is the combination of hybridization with backward elimination of models, which contributes to improve the quality of the final QSAR model. Another relevant step is the visual analysis of the molecular descriptors that allows guaranteeing the absence of information redundancy in the model. The QSAR model performances have been assessed by traditional metrics, and the final proposed model has low cardinality, and reaches a high percentage of chemical compounds correctly classified.

Imidazole Derivatives as Promising Agents for the Treatment of Chagas Disease.

More than 100 years after being first described, Chagas disease remains endemic in 21 Latin American countries and has spread to other continents. Indeed, this disease, which is caused by the protozoan parasite Trypanosoma cruzi, is no longer just a problem for the American continents but has become a global health threat. Current therapies, i.e., nifurtimox and benznidazole (Bz), are far from being adequate, due to their undesirable effects and their lack of efficacy in the chronic phases of the disease. In this work, we present an in-depth phenotypic evaluation in T. cruzi of a new class of imidazole compounds, which were discovered in a previous phenotypic screen against different trypanosomatids and were designed as potential inhibitors of cAMP phosphodiesterases (PDEs). The confirmation of several activities similar or superior to that of Bz prompted a synthesis program of hit optimization and extended structure-activity relationship aimed at improving drug-like properties such as aqueous solubility, which resulted in additional hits with 50% inhibitory concentration (IC50) values similar to that of Bz. The cellular effects of one representative hit, compound 9, on bloodstream trypomastigotes were further investigated. Transmission electron microscopy revealed cellular changes, after just 2 h of incubation with the IC50 concentration, that were consistent with induced autophagy and osmotic stress, mechanisms previously linked to cAMP signaling. Compound 9 induced highly significant increases in both cellular and medium cAMP levels, confirming that inhibition of T. cruzi PDE(s) is part of its mechanism of action. The potent and selective activity of this imidazole-based PDE inhibitor class against T. cruzi constitutes a successful repurposing of research into inhibitors of mammalian PDEs.

Chameleon-like behavior of indolylpiperidines in complex with cholinesterases targets: Potent butyrylcholinesterase inhibitors.

Alzheimer's disease (AD) is the most common form of dementia worldwide with an increasing prevalence for the next years. The multifactorial nature of AD precludes the design of new drugs directed to a single target being probably one of the reasons for recent failures. Therefore, dual binding site acetylcholinesterase (AChE) inhibitors have been revealed as cognitive enhancers and ß-amyloid modulators offering an alternative in AD therapy field. Based on the dual ligands NP61 and donepezil, the present study reports the synthesis of a series of indolylpiperidines hybrids to optimize the NP61 structure preserving the indole nucleus, but replacing the tacrine moiety of NP61 by benzyl piperidine core found in donepezil. Surprisingly, this new family of indolylpiperidines derivatives showed very potent and selective hBuChE inhibition. Further studies of NMR and molecular dynamics have showed the capacity of these hybrid molecules to change their bioactive conformation depending on the binding site, being capable to inhibit with different shapes BuChE and residually AChE.

From dual binding site acetylcholinesterase inhibitors to allosteric modulators: A new avenue for disease-modifying drugs in Alzheimer's disease.

The lack of an effective treatment for Alzheimer' disease (AD), an increasing prevalence and severe neurodegenerative pathology boost medicinal chemists to look for new drugs. Currently, only acethylcholinesterase (AChE) inhibitors and glutamate antagonist have been approved to the palliative treatment of AD. Although they have a short-term symptomatic benefits, their clinical use have revealed important non-cholinergic functions for AChE such its chaperone role in beta-amyloid toxicity. We propose here the design, synthesis and evaluation of non-toxic dual binding site AChEIs by hybridization of indanone and quinoline heterocyclic scaffolds. Unexpectely, we have found a potent allosteric modulator of AChE able to target cholinergic and non-cholinergic functions by fixing a specific AChE conformation, confirmed by STD-NMR and molecular modeling studies. Furthermore the promising biological data obtained on human neuroblastoma SH-SY5Y cell assays for the new allosteric hybrid 14, led us to propose it as a valuable pharmacological tool for the study of non-cholinergic functions of AChE, and as a new important lead for novel disease modifying agents against AD.

Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery.

Quantitative structure-activity relationship modeling using machine learning techniques constitutes a complex computational problem, where the identification of the most informative molecular descriptors for predicting a specific target property plays a critical role. Two main general approaches can be used for this modeling procedure: feature selection and feature learning. In this paper, a performance comparative study of two state-of-art methods related to these two approaches is carried out. In particular, regression and classification models for three different issues are inferred using both methods under different experimental scenarios: two drug-like properties, such as blood-brain-barrier and human intestinal absorption, and enantiomeric excess, as a measurement of purity used for chiral substances. Beyond the contrastive analysis of feature selection and feature learning methods as competitive approaches, the hybridization of these strategies is also evaluated based on previous results obtained in material sciences. From the experimental results, it can be concluded that there is not a clear winner between both approaches because the performance depends on the characteristics of the compound databases used for modeling. Nevertheless, in several cases, it was observed that the accuracy of the models can be improved by combining both approaches when the molecular descriptor sets provided by feature selection and feature learning contain complementary information.