Prediction of multi-target networks of neuroprotective compounds with entropy indices and synthesis, assay, and theoretical study of new asymmetric 1,2-rasagiline carbamates.

In a multi-target complex network, the links (L(ij)) represent the interactions between the drug (d(i)) and the target (t(j)), characterized by different experimental measures (K(i), K(m), IC50, etc.) obtained in pharmacological assays under diverse boundary conditions (c(j)). In this work, we handle Shannon entropy measures for developing a model encompassing a multi-target network of neuroprotective/neurotoxic compounds reported in the CHEMBL database. The model predicts correctly >8300 experimental outcomes with Accuracy, Specificity, and Sensitivity above 80%-90% on training and external validation series. Indeed, the model can calculate different outcomes for >30 experimental measures in >400 different experimental protocolsin relation with >150 molecular and cellular targets on 11 different organisms (including human). Hereafter, we reported by the first time the synthesis, characterization, and experimental assays of a new series of chiral 1,2-rasagiline carbamate derivatives not reported in previous works. The experimental tests included: (1) assay in absence of neurotoxic agents; (2) in the presence of glutamate; and (3) in the presence of H2O2. Lastly, we used the new Assessing Links with Moving Averages (ALMA)-entropy model to predict possible outcomes for the new compounds in a high number of pharmacological tests not carried out experimentally.

TOPS-MODE model of multiplexing neuroprotective effects of drugs and experimental-theoretic study of new 1,3-rasagiline derivatives potentially useful in neurodegenerative diseases.

The interest on computational techniques for the discovery of neuroprotective drugs has increased due to recent fail of important clinical trials. In fact, there is a huge amount of data accumulated in public databases like CHEMBL with respect to structurally heterogeneous series of drugs, multiple assays, drug targets, and model organisms. However, there are no reports of multi-target or multiplexing Quantitative Structure-Property Relationships (mt-QSAR/mx-QSAR) models of these multiplexing assay outcomes reported in CHEMBL for neurotoxicity/neuroprotective effects of drugs. Accordingly, in this paper we develop the first mx-QSAR model for multiplexing assays of neurotoxicity/neuroprotective effects of drugs. We used the method TOPS-MODE to calculate the structural parameters of drugs. The best model found correctly classified 4393 out of 4915 total cases in both training and validation. This is representative of overall train and validation Accuracy, Sensitivity, and Specificity values near to 90%, 98%, and 80%, respectively. This dataset includes multiplexing assay endpoints of 2217 compounds. Every one compound was assayed in at least one out of 338 assays, which involved 148 molecular or cellular targets and 35 standard type measures in 11 model organisms (including human). The second aim of this work is the exemplification of the use of the new mx-QSAR model with a practical case of study. To this end, we obtained again by organic synthesis and reported, by the first time, experimental assays of the new 1,3-rasagiline derivatives 3 different tests: assay (1) in absence of neurotoxic agents, (2) in the presence of glutamate, and (3) in the presence of H2O2. The higher neuroprotective effects found for each one of these assays were for the stereoisomers of compound 7: compound 7b with protection=23.4% in assay (1) and protection=15.2% in assay (2); and for compound 7a with protection=46.2% in assay (3). Interestingly, almost all compounds show protection values >10% in assay (3) but not in the other 2 assays. After that, we used the mx-QSAR model to predict the more probable response of the new compounds in 559 unique pharmacological tests not carried out experimentally. The results obtained are very significant because they complement the pharmacological studies of these promising rasagiline derivatives. This work paves the way for further developments in the multi-target/multiplexing screening of large libraries of compounds potentially useful in the treatment of neurodegenerative diseases.

Multi-Target Mining of Alzheimer Disease Proteome with Hansch's QSBR-Perturbation Theory and Experimental-Theoretic Study of New Thiophene Isosters of Rasagiline.

Hansch's model is a classic approach to Quantitative Structure-Binding Relationships (QSBR) problems in Pharmacology and Medicinal Chemistry. Hansch QSAR equations are used as input parameters of electronic structure and lipophilicity. In this work, we perform a review on Hansch's analysis. We also developed a new type of PT-QSBR Hansch's model based on Perturbation Theory (PT) and QSBR approach for a large number of drugs reported in CheMBL. The targets are proteins expressed by the Hippocampus region of the brain of Alzheimer Disease (AD) patients. The model predicted correctly 49312 out of 53783 negative perturbations (Specificity = 91.7%) and 16197 out of 21245 positive perturbations (Sensitivity = 76.2%) in training series. The model also predicted correctly 49312/53783 (91.7%) and 16197/21245 (76.2%) negative or positive perturbations in external validation series. We applied our model in theoretical-experimental studies of organic synthesis, pharmacological assay, and prediction of unmeasured results for a series of compounds similar to Rasagiline (compound of reference) with potential neuroprotection effect.

Brain-inspired cheminformatics of drug-target brain interactome, synthesis, and assay of TVP1022 derivatives.

The use of Cheminformatics tools is gaining importance in the field of translational research from Medicinal Chemistry to Neuropharmacology. In particular, we need it for the analysis of chemical information on large datasets of bioactive compounds. These compounds form large multi-target complex networks (drug-target interactome network) resulting in a very challenging data analysis problem. Artificial Neural Network (ANN) algorithms may help us predict the interactions of drugs and targets in CNS interactome. In this work, we trained different ANN models able to predict a large number of drug-target interactions. These models predict a dataset of thousands of interactions of central nervous system (CNS) drugs characterized by > 30 different experimental measures in >400 different experimental protocols for >150 molecular and cellular targets present in 11 different organisms (including human). The model was able to classify cases of non-interacting vs. interacting drug-target pairs with satisfactory performance. A second aim focus on two main directions: the synthesis and assay of new derivatives of TVP1022 (S-analogues of rasagiline) and the comparison with other rasagiline derivatives recently reported. Finally, we used the best of our models to predict drug-target interactions for the best new synthesized compound against a large number of CNS protein targets.

Model for high-throughput screening of multitarget drugs in chemical neurosciences: synthesis, assay, and theoretic study of rasagiline carbamates.

The disappointing results obtained in recent clinical trials renew the interest in experimental/computational techniques for the discovery of neuroprotective drugs. In this context, multitarget or multiplexing QSAR models (mt-QSAR/mx-QSAR) may help to predict neurotoxicity/neuroprotective effects of drugs in multiple assays, on drug targets, and in model organisms. In this work, we study a data set downloaded from CHEMBL; each data point (>8000) contains the values of one out of 37 possible measures of activity, 493 assays, 169 molecular or cellular targets, and 11 different organisms (including human) for a given compound. In this work, we introduce the first mx-QSAR model for neurotoxicity/neuroprotective effects of drugs based on the MARCH-INSIDE (MI) method. First, we used MI to calculate the stochastic spectral moments (structural descriptors) of all compounds. Next, we found a model that classified correctly 2955 out of 3548 total cases in the training and validation series with Accuracy, Sensitivity, and Specificity values>80%. The model also showed excellent results in Computational-Chemistry simulations of High-Throughput Screening (CCHTS) experiments, with accuracy=90.6% for 4671 positive cases. Next, we reported the synthesis, characterization, and experimental assays of new rasagiline derivatives. We carried out three different experimental tests: assay (1) in the absence of neurotoxic agents, assay (2) in the presence of glutamate, and assay (3) in the presence of H2O2. Compounds 11 with 27.4%, 8 with 11.6%, and 9 with 15.4% showed the highest neuroprotective effects in assays (1), (2), and (3), respectively. After that, we used the mx-QSAR model to carry out a CCHTS of the new compounds in >400 unique pharmacological tests not carried out experimentally. Consequently, this model may become a promising auxiliary tool for the discovery of new drugs for the treatment of neurodegenerative diseases.