Speranta Avram

Chondroitin Sulfate Proteoglycans: Structure-Function Relationship with Implication in Neural Development and Brain Disorders

Chondroitin sulfate proteoglycans (CSPGs) are extracellular matrix components that contain two structural parts with distinct functions: a protein core and glycosaminoglycan (GAG) side chains. CSPGs are known to be involved in important cell processes like cell adhesion and growth, receptor binding, or cell migration. It is recognized that the presence of CSPGs is critical in neuronal growth mechanisms including axon guidance following injury of nervous system components such as spinal cord and brain. CSPGs are upregulated in the central nervous system after injury and participate in the inhibition of axon regeneration mainly through their GAG side chains. Recently, it was shown that some CSPGs members like aggrecan, versican, and neurocan were strongly involved in brain disorders like bipolar disorder (BD), schizophrenia, and ADHD. In this paper, we present the chemical structure-biological functions relationship of CSPGs, both in health state and in genetic disorders, addressing methods represented by genome-wide and crystallographic data as well as molecular modeling and quantitative structure-activity relationship.

More effective dithiocarbamate derivatives inhibiting carbonic anhydrases, generated by QSAR and computational design

Dithiocarbamates (DTC) are promising compounds with potential applications in antitumoral and glaucoma therapy. Our aim is to understand molecular features affecting DTC interaction with carbonic anhydrases (CAs), zinc-containing enzymes maintaining acid-base balance in blood and other tissues. To this end, we generate QSAR models based on a compound series containing 25 DTC, inhibitors of four human (h) CAs isoforms: hCA I, II, IX and XII. We establish that critical physicochemical parameters for DTC inhibitory activity are: hydrophobic, electronic, steric, topological and shape. The predictive power of our QSAR models is indicated by significant values of statistical coefficients: cross-validated correlation q2 (0.55–0.73), fitted correlation r2 (0.75–0.84) and standard error of prediction (0.47-0.23). Based on the established QSAR equations, we analyse 22 new DTC derivatives and identify DTC dicarboxilic acids derivatives and their esters as potentially improved inhibitors of CA I, II, IX and XII.

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 R2 (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.

Correlation between the predicted and the observed biological activity of the symmetric and nonsymmetric cyclic urea derivatives used as HIV‐1 protease inhibitors. A 3D‐QSAR‐CoMFA method for new antiviral drug design

The predicted inhibition constant (Ki) and the predicted inhibitor concentration (IC90) of the HIV‐1 protease (HIV‐1 PR) inhibitors: symmetric and nonsymmetric ‐ benzyl, ketone, oxime, pyrazole, imidazole, and triazole cyclic urea derivatives, were obtained by the 3D‐CoMFA (Comparative Molecular Field Analysis) method. The CoMFA statistical parameters: cross‐validate correlation coefficient (q2), higher than 0.5, and the fitted correlation coefficient (r2), higher than 0.90 validated the predicted biological activities. The best predictions were found for the trifluoromethyl ketoxime derivative (log 1/Ki predict = 8.42), the m‐pyridineCH2 pyrazole derivative (log 1/Ki predict = 9.77) and the 1,2,3 triazole derivative (log 1/Ki predict = 7.03). We attempted to design a new potent HIV‐1 protease inhibitor by addition of o‐benzyl to the (p‐HOPhCH2) pyrazole 12f derivative inhibitor. A favorable steric area surrounded the o‐benzyl, suggesting a possible new potent HIV‐1 protease inhibitor.

Comparative study of some energetic and steric parameters of the wild type and mutants HIV-1 protease: a way to explain the viral resistance.

Because, in vivo, the HIV‐1 PR (HIV‐1 protease) present a high mutation rate we performed a comparative study of the energetic behaviors of the wild type HIV‐1 PR and four type of mutants: Val82/Asn; Val82/Asp; Gln7/Lys, Leu33/Ile, Leu63/Ile; Ala71/Thr, Val82/Ala. We suggest that the energetic fluctuation (electrostatic, van der Waals and torsion energy) of the mutants and the solvent accessible surface (SAS) values can be useful to explain the viral resistance process developed by HIV‐1 PR. The number and localization of enzyme mutations induce important modifications of the van der Waals and torsional energy, while the electrostatic energy has an insignificant fluctuation. We showed that the viral resistance can be explored if the solvent accessible surfaces of the active site for the mutant structures are calculated. In this paper we have obtained the solvent accessible surface for a group of 15 mutants (11 mutants obtained by Protein Data Bank (PDB) file, 4 mutants modeled by CHARMM software) and for the wild type HIV‐1 PR). Our study try to show that the number and localization of the mutations are factors which induce the HIV‐1 PR viral resistance. The larger solvent accessible surface could be recorded for the point mutant Val 82/Phe.

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.

Computer-Aided Drug Design for Typical and Atypical Antipsychotic Drugs: A Review of Application of QSAR and Combinatorial Chemistry Methods - Tools for New Antipsychotics Design

The central nervous system (CNS) is endowed with very efficient protection mechanisms. However, the same mechanisms that protect it, sometimes can be an enemy for therapeutic applications. In this way, many antipsychotic drugs used, are ineffective in the treatment of cerebral diseases such as schizophrenia. Many typical and atypical neuroleptics are very efficient against the positive symptoms, but not against the negative symptoms. To reduce the inefficiency of known neuroleptics, many research efforts have recently focused on the development of new strategies for new neuroleptics drug design. For this reason it was necessary to apply very fast and precise techniques, such as: QSAR (Quantitative Structure-Activity Relationships) and combinatorial chemistry methods, capable to analyze and predict the biological activity for these structures, taking in account the possible changes of the molecular structures. This review intends to detail recent advances in the field of structure-activity relationship and combinatorial chemistry applied to neuroleptics. The antipsychotic activities (log ED50) of potent neuroleptics as indole derivatives, were correlated with pharmacokinetic parameters namely: molecular volume (V), globularity (G), Octanol/water partition coefficient (logP), solubility(S), dipole moment, polarizability. QSAR studies of benzothiazepine derivatives as potent neuroleptics are presented. In addition, the 3D-QSAR methods such as Comparative Molecular Field Analysis (CoMFA) and Comparative Molecular Similarity Indices Analysis (CoMSIA) were applied for a set of dopamine D4 receptor antagonists. The combinatorial chemistry was used to develop a large chemical library starting from 5-hydroxytryptamine2A receptor antagonists used as antipsychotics.

PLS and shape-based similarity analysis of maleimides – GSK-3 inhibitors

Abstract Context: Glycogen synthase kinase-3 (GSK-3) overactivity was correlated with several pathologies including type 2 diabetes mellitus, Alzheimer’s disease, cancer, inflammation, obesity, etc. Objective: The aim of the current investigation was to model the inhibitory activity of maleimide derivatives – inhibitors of GSK-3, to evaluate the impact of alignment on statistical performances of the Quantitative Structure--Activity Relationship (QSAR) and the effect of the template on shape-similarity – binding affinity relationship. Materials and methods: Dragon descriptors were used to generate Projection to Latent Structures (PLS) models in order to identify the structural prerequisites of maleimides to inhibit GSK-3. Additionally, shape/volume structural analysis of binding site interactions was evaluated. Results: Reliable statistics  = 0.938/0.920,  = 0.866/0.838 for aligned and alignment free QSAR models and significant (Pearson, Kendall and Spearman) correlations between shape/volume similarity and affinities were obtained. Discussion and conclusions: The crucial structural features modulating the activity of maleimides include topology, charge, geometry, 2D autocorrelations, 3D-MoRSE as well as shape/volume and molecular flexibility.

Evaluation of the Pharmacological Descriptors Related to the Induction of Antidepressant Activity and its Prediction by QSAR/QRAR Methods

Antidepressants are psychiatric agents used for the treatment of different types of depression, being at present amongst the most commonly prescribed drugs, while their effectiveness and adverse effects are still the subject of many studies. To reduce the inefficiency of known antidepressants caused by their side-effects, many research efforts have recently focused on the development of improved strategies for new antidepressants drug design. For this reason it is necessary to apply very fast and precise techniques, such as QSAR (Quantitative Structure-Activity Relationships) and QRAR (Quantitative Retention-Activity Relationship), which are capable to analyze and predict the biological activity for these structures, taking in account the possible changes of the molecular structures and chromatographic parameters. We discuss the pharmaceutical descriptors (van der Waals, electrostatic, hydrophobicity, hydrogen donor/acceptor bond, Verloops parameters, polar area) involved in QSAR and also chromatographic parameters involved in QRAR studies of antidepressants. Antidepressant activities of alkanol piperazine, acetamides, arylpiperazines, thienopyrimidinone derivatives (as preclinical antidepressants) and also the antidepressants already used in clinical practice are mentioned.

Calcium Channel Blockers – Benefits Upon Vascular Biology in Hypertensive Patients

Calcium channel blockers (CCB) are widely used in cardiovascular medicine expressing high hopes upon decreasing cardiovascular risk, morbidity and mortality. Here, the potency of CCBs on 58 Romanian asymptomatic hypertensive patients, with no atherothrombotic cardiovascular disease, was studied by clinical and in silico methods. In our study, arterial elasticity/stiffness was assessed; anthropometric, metabolic (lipidic) parameters were quantified. We concluded that lercanidipine 10 mg once daily, during three weeks, is able to dramatically improve central aortic systolic blood pressure, aortic pulse wave velocity. Lipid profile improvement is an essential condition to improve elastic vascular properties in order to decrease the risk for further cardiovascular events. Besides, the potency of lercanidipine is expressed as the contribution of molecular descriptors (van der Waals and solvent accessible surface areas), electronic (molecular polarisability) and hydrophobic (water/octanol partition coefficient) by means of blocker effect on calcium channel, compared with cilnidipine and other 30 dihydropyridines, using molecular simulation techniques.