Modeling of acetylcholinesterase inhibition by tacrine analogues using Bayesian-regularized Genetic Neural Networks and ensemble averaging

Five-Membered-Ring-Fused Tacrines as Anti-Alzheimer’s Disease Agents

Our endeavors in the design, synthesis, and biological assessment of five-membered-ring-fused tacrines as potential therapeutic agents for Alzheimer’s disease are summarized. Particularly, we have identified racemic 4-(2-methoxyphenyl)-3-methyl-2,4,6,7,8,9-hexahydropyrazolo[4′,3′:5,6]pyrano[2,3-b]quinolin-5-amine, a pyranopyrazolotacrine, as having the best nontoxic profile at the highest concentrations used (300 μM); this allows cell viability, is less hepatotoxic than tacrine, and is a potent noncompetitive AChE inhibitor (IC50 = 1.52 ± 0.49 μM). It is able to completely inhibit the EeAChE-induced Aβ1–40 aggregation in a statistically significant manner without affecting the Aβ1–40 self-aggregation at 25 μM, and shows strong neuroprotective effects (EC50 = 0.82 ± 0.17 μM).

1 Introduction

2 Furo-, Thieno-, and Pyrrolotacrines

3 Pyrazolo-, Oxazolo-, and Isoxazolotacrines

4 Indolotacrines

5 Pyrano- and Pyridopyrazolotacrines

6 Conclusions and Outlook

Palladium(ii)-catalyzed vinylic geminal double C-H activation and alkyne annulation reaction: synthesis of pentafulvenes

The first transition-metal-catalyzed vinylic geminal double C(sp²)-H activation and di-substituted alkyne annulation reaction is reported. This palladium(ii)-catalyzed, amide directed reaction of vinylic compounds with di-substituted alkynes offers an efficient synthetic path to pentafulvenes, which are very important compounds because of their bioactivity and interesting optical properties. A FeCl₃-mediated transformation of pentafulvenes to fluorescent cyclopenta[b]quinolines is also developed.

In Silico Studies in Drug Research Against Neurodegenerative Diseases

Background

Neurodegenerative diseases such as Alzheimer's disease (AD), amyotrophic lateral sclerosis, Parkinson's disease (PD), spinal cerebellar ataxias, and spinal and bulbar muscular atrophy are described by slow and selective degeneration of neurons and axons in the central nervous system (CNS) and constitute one of the major challenges of modern medicine. Computer-aided or in silico drug design methods have matured into powerful tools for reducing the number of ligands that should be screened in experimental assays.

Methods

In the present review, the authors provide a basic background about neurodegenerative diseases and in silico techniques in the drug research. Furthermore, they review the various in silico studies reported against various targets in neurodegenerative diseases, including homology modeling, molecular docking, virtual high-throughput screening, quantitative structure activity relationship (QSAR), hologram quantitative structure activity relationship (HQSAR), 3D pharmacophore mapping, proteochemometrics modeling (PCM), fingerprints, fragment-based drug discovery, Monte Carlo simulation, molecular dynamic (MD) simulation, quantum-mechanical methods for drug design, support vector machines, and machine learning approaches.

Results

Detailed analysis of the recently reported case studies revealed that the majority of them use a sequential combination of ligand and structure-based virtual screening techniques, with particular focus on pharmacophore models and the docking approach.

Conclusion

Neurodegenerative diseases have a multifactorial pathoetiological origin, so scientists have become persuaded that a multi-target therapeutic strategy aimed at the simultaneous targeting of multiple proteins (and therefore etiologies) involved in the development of a disease is recommended in future.

Rhodium catalyzed cascade cyclization featuring B-H and C-H activation: one-step construction of carborane-fused N-polyheterocycles

A one-pot strategy for efficient and facile synthesis of C,B-substituted carborane-fused N-polyheterocycles is reported. A rhodium catalyzed cascade cyclization of carboranyl N-arylimines with vinyl ketones enables the effective construction of three new B-C and C-C bonds in one reaction. Both carboranyl B-H and aryl C-H bonds are sequentially activated, leading to a series of previously unavailable C,B-substituted carborane-fused cyclopenta[b]quinoline derivatives, for potential applications in pharmaceuticals and materials, in a step-economical manner. The successful isolation and structural identification of a key intermediate provide solid evidence for the reaction mechanism, involving a tandem sequence of regioselective B-H activation, alkene insertion, nucleophilic cyclization, C-H activation, nucleophilic cyclization, dehydration and oxidative aromatization.

Prediction of therapeutic potency of tacrine derivatives as BuChE inhibitors from quantitative structure-activity relationship modelling

Numerous studies show that tacrine derivatives exhibit increased inhibitory activity against butyrylcholinesterase (BuChE) and acetylcholinesterase (AChE). However, the screening assays for currently available BuChE inhibitors are expensive, time consuming and dependent on the inhibitory compound. It is therefore desirable to develop alternative methods to facilitate the screening of these derivatives in the early phase of drug discovery. In order to develop robust predictive models, three regression methods were chosen in this study: multiple linear regression (MLR), support vector regression (SVR) and multilayer perceptron network (MLP). Eight relevant descriptors were selected on a dataset of 151 molecules using a method based on genetic algorithms. Internal and external validation strategies play an important role. Also, to check the robustness of the selected models, all available validation strategies were used, and all criteria used to validate these models revealed the superiority of the SVR model. The statistical parameters obtained with the SVR model were RMSE = 0.197, r² = 0.969 and Q² = 0.964 for the training set, and r² = 0.906 and Q² = 0.891 for the test set. Therefore, the model developed in this study provides an excellent prediction of the inhibitory concentration of tacrine derivatives.

Benefits of statistical molecular design, covariance analysis, and reference models in QSAR: a case study on acetylcholinesterase

Scientific disciplines such as medicinal- and environmental chemistry, pharmacology, and toxicology deal with the questions related to the effects small organic compounds exhort on biological targets and the compounds' physicochemical properties responsible for these effects. A common strategy in this endeavor is to establish structure-activity relationships (SARs). The aim of this work was to illustrate benefits of performing a statistical molecular design (SMD) and proper statistical analysis of the molecules' properties before SAR and quantitative structure-activity relationship (QSAR) analysis. Our SMD followed by synthesis yielded a set of inhibitors of the enzyme acetylcholinesterase (AChE) that had very few inherent dependencies between the substructures in the molecules. If such dependencies exist, they cause severe errors in SAR interpretation and predictions by QSAR-models, and leave a set of molecules less suitable for future decision-making. In our study, SAR- and QSAR models could show which molecular sub-structures and physicochemical features that were advantageous for the AChE inhibition. Finally, the QSAR model was used for the prediction of the inhibition of AChE by an external prediction set of molecules. The accuracy of these predictions was asserted by statistical significance tests and by comparisons to simple but relevant reference models.

QSAR analysis on tacrine-related acetylcholinesterase inhibitors

Background

The evaluation of the clinical effects of Tacrine has shown efficacy in delaying the deterioration of the symptoms of Alzheimer’s disease, while confirming the adverse events consisting mainly in the elevated liver transaminase levels. The study of tacrine analogs presents a continuous interest, and for this reason we establish Quantitative Structure-Activity Relationships on their Acetylcholinesterase inhibitory activity.

Results

Ten groups of new developed Tacrine-related inhibitors are explored, which have been experimentally measured in different biochemical conditions and AChE sources. The number of included descriptors in the structure-activity relationship is characterized by ‘Rule of Thumb’. The 1502 applied molecular descriptors could provide the best linear models for the selected Alzheimer’s data base and the best QSAR model is reported for the considered data sets.

Conclusion

The QSAR models developed in this work have a satisfactory predictive ability, and are obtained by selecting the most representative molecular descriptors of the chemical structure, represented through more than a thousand of constitutional, topological, geometrical, quantum-mechanical and electronic descriptor types.

Electronic supplementary material

The online version of this article (doi:10.1186/s12929-014-0084-0) contains supplementary material, which is available to authorized users.

Docking and quantitative structure-activity relationship of oxadiazole derivates as inhibitors of GSK3β

The binding modes of 42 oxadiazole derivates inside glycogen synthase kinase 3 beta (GSK3β were determined using docking experiments; thus, the preferred active conformations of these inhibitors are proposed. We found that these compounds adopt a scorpion-shaped conformation and they accept a hydrogen bond (HB) from the residue Val135 of the GSK3β ATP-binding site hinge region. In addition, quantitative structure-activity relationship (QSAR) models were constructed to explain the trend of the GSK3β inhibitory activities for the studied compounds. In a first approach, three-dimensional (3D) vectors were calculated using docking conformations and, by using multiple-linear regression, we assessed that GETAWAY vectors were able to describe the reported biological activities. In other QSAR approach, SMILES-based optimal descriptors were calculated. The best model included three-SMILES elements SSSβ leading to the identification of key molecular features that contribute to a high GSK3β inhibitory activity.

Identification in silico and experimental validation of novel phosphodiesterase 7 inhibitors with efficacy in experimental autoimmune encephalomyelitis mice

A neural network model has been developed to predict the inhibitory capacity of any chemical structure to be a phosphodiesterase 7 (PDE7) inhibitor, a new promising kind of drugs for the treatment of neurological disorders. The numerical definition of the structures was achieved using CODES program. Through the validation of this neural network model, a novel family of 5-imino-1,2,4-thiadiazoles (ITDZs) has been identified as inhibitors of PDE7. Experimental extensive biological studies have demonstrated the ability of ITDZs to inhibit PDE7 and to increase intracellular levels of cAMP. Among them, the derivative 15 showed a high in vitro potency with desirable pharmacokinetic profile (safe genotoxicity and blood brain barrier penetration). Administration of ITDZ 15 in an experimental autoimmune encephalomyelitis (EAE) mouse model results in a significant attenuation of clinical symptoms, showing the potential of ITDZs, especially compound 15, for the effective treatment of multiple sclerosis.

Docking and quantitative structure-activity relationship studies for 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline, 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline, and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine derivatives as c-Met kinase inhibitors

We have performed docking of 3-fluoro-4-(pyrrolo[2,1-f][1,2,4]triazin-4-yloxy)aniline (FPTA), 3-fluoro-4-(1H-pyrrolo[2,3-b]pyridin-4-yloxy)aniline (FPPA), and 4-(4-amino-2-fluorophenoxy)-2-pyridinylamine (AFPP) derivatives complexed with c-Met kinase to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 103 compounds with variations both in structure and activity. Docking helped to analyze the molecular features which contribute to a high inhibitory activity for the studied compounds. In addition, the predicted biological activities of the c-Met kinase inhibitors, measured as IC(50) values were obtained by using quantitative structure-activity relationship (QSAR) methods: Comparative molecular similarity analysis (CoMSIA) and multiple linear regression (MLR) with topological vectors. The best CoMSIA model included steric, electrostatic, hydrophobic, and hydrogen bond-donor fields; furthermore, we found a predictive model containing 2D-autocorrelation descriptors, GETAWAY descriptors (GETAWAY: Geometry, Topology and Atom-Weight AssemblY), fragment-based polar surface area (PSA), and MlogP. The statistical parameters: cross-validate correlation coefficient and the fitted correlation coefficient, validated the quality of the obtained predictive models for 76 compounds. Additionally, these models predicted adequately 25 compounds that were not included in the training set.

Genetic algorithm optimization in drug design QSAR: Bayesian-regularized genetic neural networks (BRGNN) and genetic algorithm-optimized support vectors machines (GA-SVM)

Many articles in "in silico" drug design implemented genetic algorithm (GA) for feature selection, model optimization, conformational search, or docking studies. Some of these articles described GA applications to quantitative structure-activity relationships (QSAR) modeling in combination with regression and/or classification techniques. We reviewed the implementation of GA in drug design QSAR and specifically its performance in the optimization of robust mathematical models such as Bayesian-regularized artificial neural networks (BRANNs) and support vector machines (SVMs) on different drug design problems. Modeled data sets encompassed ADMET and solubility properties, cancer target inhibitors, acetylcholinesterase inhibitors, HIV-1 protease inhibitors, ion-channel and calcium entry blockers, and antiprotozoan compounds as well as protein classes, functional, and conformational stability data. The GA-optimized predictors were often more accurate and robust than previous published models on the same data sets and explained more than 65% of data variances in validation experiments. In addition, feature selection over large pools of molecular descriptors provided insights into the structural and atomic properties ruling ligand-target interactions.

Docking and quantitative structure-activity relationship studies for sulfonyl hydrazides as inhibitors of cytosolic human branched-chain amino acid aminotransferase

We have performed the docking of sulfonyl hydrazides complexed with cytosolic branched-chain amino acid aminotransferase (BCATc) to study the orientations and preferred active conformations of these inhibitors. The study was conducted on a selected set of 20 compounds with variation in structure and activity. In addition, the predicted inhibitor concentration (IC(50)) of the sulfonyl hydrazides as BCAT inhibitors were obtained by a quantitative structure-activity relationship (QSAR) method using three-dimensional (3D) vectors. We found that three-dimensional molecule representation of structures based on electron diffraction (3D-MoRSE) scheme contains the most relevant information related to the studied activity. The statistical parameters [cross-validate correlation coefficient (Q(2) = 0.796) and fitted correlation coefficient (R(2) = 0.899)] validated the quality of the 3D-MoRSE predictive model for 16 compounds. Additionally, this model adequately predicted four compounds that were not included in the training set.

Computational neural network analysis of the affinity of N-n-alkylnicotinium salts for the alpha4beta2* nicotinic acetylcholine receptor

Based on an 85 molecule database, linear regression with different size datasets and an artificial neural network approach have been used to build mathematical relationships to fit experimentally obtained affinity values (K(i)) of a series of mono- and bis-quaternary ammonium salts from [(3)H]nicotine binding assays using rat striatal membrane preparations. The fitted results were then used to analyze the pattern among the experimental K(i) values of a set of N-n-alkylnicotinium analogs with increasing n-alkyl chain length from 1 to 20 carbons. The affinity of these N-n-alkylnicotinium compounds was shown to parabolically vary with increasing numbers of carbon atoms in the n-alkyl chain, with a local minimum for the C(4) (n-butyl) analogue. A decrease in K(i) value between C(12) and C(13) was also observed. The statistical results for the best neural network fit of the 85 experimental K(i) values are r(2) = 0.84, rmsd = 0.39; r(cv)(2) = 0.68, and loormsd = 0.56. The generated neural network model with the 85 molecule training set may also be of value for future predictions of K(i) values for new virtual compounds, which can then be identified, subsequently synthesized, and tested experimentally.

Network models in drug discovery and regenerative medicine

Network motifs and modelling paradigms are attracting increasing attention as modelling tools in drug design and development, and in regenerative medicine. There is a gradual but inexorable convergence between these hitherto disparate disciplines. This review summarizes some very recent work in these areas, leading to an understanding of the complementary roles networks play and factors driving this convergence: network paradigms can be excellent ways of modelling and understanding drug molecules and their action, an understanding of the robustness and vulnerabilities of biological targets may improve the efficacy of drug design and discovery, drug design has an increasingly large role to play in directing stem cell properties, stem cell regulatory networks can be modelled in useful ways using network models at a reasonable level of scale, and the network tools of drug design are also very useful for the design of biomaterials used in regenerative medicine.