3D-QSAR Design of New Escitalopram Derivatives for the Treatment of Major Depressive Disorders

Antidepressants are psychiatric agents used for the treatment of different types of depression being at present amongst the most commonly prescribed drug, while their effectiveness and adverse effects are the subject of many studies and competing claims. Having studied five QSAR models predicting the biological activities of 18 antidepressants, already approved for clinical treatment, in interaction with the serotonin transporter (SERT), we attempted to establish the membrane ions’ contributions (sodium, potassium, chlorine and calcium) supplied by donor/acceptor hydrogen bond character and electrostatic field to the antidepressant activity. Significant cross-validated correlation q2 (0.5–0.6) and the fitted correlation r2 (0.7–0.82) coefficients were obtained indicating that the models can predict the antidepressant activity of compounds. Moreover, considering the contribution of membrane ions (sodium, potassium and calcium) and hydrogen bond donor character, we have proposed a library of 24 new escitalopram structures, some of them probably with significantly improved antidepressant activity in comparison with the parent compound

MTD–CoMSIA modelling of HMG-CoA reductase inhibitors

The 3D quantitative structure–activity relationship for a series of hydroxymethylglutaryl-CoA (HMG-CoA) reductase inhibitors based on the pyrrolylethyl-tetrahydropyranone scaffold was examined using the Minimal Topological Difference (MTD) method and comparative molecular similarity index analysis (CoMSIA). The studied compounds were of the tetrahydro-4-hydroxy-6-[2-(1H-pyrrol-1-yl)ethyl]-2H-pyran-2-one type. In clinical practice, HMG-CoA reductase inhibitors are usually referred to by the generic name statins. The analysis performed using the MTD method showed that voluminous substituents produce a significant biological activity (= 0.677 > 0.5; SEECV = 0.319), while the CoMSIA method added useful information regarding the influence of the steric, electrostatic, hydrophobic, hydrogen bond donor, and acceptor properties on biological activity (= 0.60; r2 = 0.98)

Chemoinformatic Study of Benzodiazepines

Benzodiazepines are a type of psychotropic drugs with tranquilizing effect, originally used as ansiolytics. However, benzodiazepines are highly addictive, and a person who abuses them faces a host of symptoms. Moreover, the market for new psychoactive substances is growing. The aim of this study was to predict in silico chemical structures on benzodiazepine skeleton with affinity for the GABA A receptor. A set of 50 benzodiazepine-based compounds was analyzed to develop a QSAR training set with respect to published binding values to GABA receptors. To develop a mathematical model, physicochemical properties (partition coefficient, molar refractivity, molar volume) were used to correlate with biological activity (logIC50). We estimated the logIC50 and compared it with the observed values to test our model.  To create the ADMET profile, we used the ADMETlab2.0 program. The molecular target for diazepam and nitrazepam molecules was predicted using SwissTargetPrediction. The visualization of the target structure was achieved with the Chimera program. The SeamDock molecular docking program was used to study molecular target interactions with diazepam and nitrazepam drugs. The best results of univariate correlation are shown by molar volume. The best statistics were obtained for univariate correlation. Diazepam and nitrazepam are small molecules with good oral availability that cross the blood-brain barrier, affecting the nervous system. The target predicted most likely to interact with drugs is the alpha-1 subunit of the GABA A receptor. The molecular docking study showed a favorable interaction between the molecular target and the drugs diazepam and nitrazepam. This QSAR model will allow rapid prediction of the binding activity of emerging benzodiazepines in a rapid and economic way, compared with expensive in vitro/in vivo analysis. Diazepam and nitrazepam interact with GABA A receptors, potentiating their activity

Introducing Catastrophe-QSAR. Application on Modeling Molecular Mechanisms of Pyridinone Derivative-Type HIV Non-Nucleoside Reverse Transcriptase Inhibitors

The classical method of quantitative structure-activity relationships (QSAR) is enriched using non-linear models, as Thom’s polynomials allow either uni- or bi-variate structural parameters. In this context, catastrophe QSAR algorithms are applied to the anti-HIV-1 activity of pyridinone derivatives. This requires calculation of the so-called relative statistical power and of its minimum principle in various QSAR models. A new index, known as a statistical relative power, is constructed as an Euclidian measure for the combined ratio of the Pearson correlation to algebraic correlation, with normalized t-Student and the Fisher tests. First and second order inter-model paths are considered for mono-variate catastrophes, whereas for bi-variate catastrophes the direct minimum path is provided, allowing the QSAR models to be tested for predictive purposes. At this stage, the max-to-min hierarchies of the tested models allow the interaction mechanism to be identified using structural parameter succession and the typical catastrophes involved. Minimized differences between these catastrophe models in the common structurally influential domains that span both the trial and tested compounds identify the “optimal molecular structural domains” and the molecules with the best output with respect to the modeled activity, which in this case is human immunodeficiency virus type 1 HIV-1 inhibition. The best molecules are characterized by hydrophobic interactions with the HIV-1 p66 subunit protein, and they concur with those identified in other 3D-QSAR analyses. Moreover, the importance of aromatic ring stacking interactions for increasing the binding affinity of the inhibitor-reverse transcriptase ligand-substrate complex is highlighted

Design of Anti-HIV Ligands by Means of Minimal Topological Difference (MTD) Method

Molecular modeling and MTD methods are useful tools to assess both qualitative(SAR) and quantitative (QSAR) chemical structure-biological activity relationships. The 1-[(2-hydroxiethoxi)-methyl]-6-(phenylthio)thymine congeners (HEPT ligands) show in vitroanti-viral activity against the type-1 human immunodeficiency virus (HIV-1), which is theetiologic agent of AIDS. This work shows an extensive QSAR study performed upon a largeseries of 79 HEPT ligands using the MTD and HyperChem molecular modeling methods.The studied HEPT ligands are HIV reverse-transcriptase inhibitors. Their geometries wereoptimized and conformational analysis was carried out to build the hypermolecule, whichallowed applying the MTD method. The hypermolecule was used for space mapping of thereceptor’s interaction site. The obtained results show that there are three 3D molecular zonesimportant for the anti-HIV biological activity of the HEPT ligands under study

Evaluation of Antimicrobial Activity of New Mastoparan Derivatives Using QSAR and Computational Mutagenesis

Antimicrobial peptides, also called body defense peptides, are used against a wide range of pathogens, such as negative- and positive-gram bacteria, mycobacteria, fungi, viruses, etc. Contrary to antibiotics, antimicrobial peptides do not develop resistance. Their wide antimicrobial spectrum situates them as important and attractive targets in research and pharmaceutical industry in order to obtain new structures using modern drug design techniques. We present here eleven QSAR models in which antimicrobial activity expressed as minimal inhibitory concentration values at Bacillus subtilis of 37 mastoparan analogs was correlated with different physicochemical parameters like: number of hydrophobic centers, molecular area and volume, internal dipole moment, refractivity, RPCG (relative positive charges) and number of donor and acceptor atoms generating by use of the computational software Sybyl. Significant R 2 (0.68-0.72) correlation coefficients and standard error of prediction SEE (0.199-0.230) were obtained, indicating that the established equations can be used. Thus, these linear models allowed us to create a library of 19 derivatives of mastoparan analogs obtained through computational mutagenesis. We propose this library of compounds as a source of possible derivatives with a more potent antimicrobial activity

Chemical Structure-Biological Activity Models for Pharmacophores’ 3D-Interactions

Within medicinal chemistry nowadays, the so-called pharmaco-dynamics seeks for qualitative (for understanding) and quantitative (for predicting) mechanisms/models by which given chemical structure or series of congeners actively act on biological sites either by focused interaction/therapy or by diffuse/hazardous influence. To this aim, the present review exposes three of the fertile directions in approaching the biological activity by chemical structural causes: the special computing trace of the algebraic structure-activity relationship (SPECTRAL-SAR) offering the full analytical counterpart for multi-variate computational regression, the minimal topological difference (MTD) as the revived precursor for comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analysis (CoMSIA); all of these methods and algorithms were presented, discussed and exemplified on relevant chemical medicinal systems as proton pump inhibitors belonging to the 4-indolyl,2-guanidinothiazole class of derivatives blocking the acid secretion from parietal cells in the stomach, the 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine congeners’ (HEPT ligands) antiviral activity against Human Immunodeficiency Virus of first type (HIV-1) and new pharmacophores in treating severe genetic disorders (like depression and psychosis), respectively, all involving 3D pharmacophore interactions