Novel distance-based atom-type topological indices DAI for QSPR/QSAR studies of alcohols

In this work, we propose a distance-based atom-type topological index (DAI) for quantitative structure-property/activity relationship (QSPR/QSAR) studies. The newly constructed index, which codes the structural environment of each atom type in a molecule, can be calculated simply. These atom-type topological indices, along with our recently proposed Lu index, were used to construct QSPR/QSAR models for several representative physical properties and biological activities of several data sets of alcohols with a range of non-hydrogen atoms by using multiple linear regression (MLR) analysis. The efficiency of these indices is verified by high quality QSPR models. The results indicate that the combined use of Lu and DAI indices promises to be a useful method for QSPR/QSAR analysis of complex compounds.      

MIA-QSAR,PCA-ranking and least-squares support-vector machines in the accurate prediction of the activities of phosphodiesterase type 5 (PDE-5) inhibitors

Phosphodiesterase type-5 (PDE-5) is a key enzyme involved in the erection process. PDE-5 inhibitors, such as Sildenafil (Viagra^TM), Vardenafil (Levitra^TM) and Tadalafil (Cialis^TM), are used for the treatment of erectile dysfunction. Computer-assisted modelling of biological activities of PDE-5 inhibitors may make quantitative structure–activity relationship (QSAR) models useful for the development of safer (low side effects) and more potent drugs. The multivariate image analysis applied to QSAR (MIA-QSAR) method, coupled to partial least-squares (PLS) regression, has provided highly predictive QSAR models. Nevertheless, regression methods which take into account nonlinearity, such as least-squares support-vector machines (LS-SVMs), are supposed to predict biological activities more accurately than the usual linear methods. Thus, together with prior variable selection using principal component analysis ranking, MIA-QSAR and LS-SVM regression were applied to model the bioactivities of a series of cyclic guanine derivatives (PDE-5 inhibitors), and the results were compared with those based on linear methodologies. MIA-QSAR/LS-SVM was found to improve greatly the prediction performance when compared with MIA-QSAR/PLS, MIA-QSAR/N-PLS, CoMFA/PLS and CoMSIA/PLS models.     

QSAR studies on the activation of the human carbonic anhydrase cytosolic isoforms I and II and secretory isozyme VI with amino acids and amines

The first QSAR study on the activation of the human secretory isoform of the metalloenzyme carbonic anhydrase (CA, EC 4.2.1.1), CA VI, with a series of amines and amino acids is reported. A large set of topological indices have been used to obtain several tri-/tetra-parametric models. We compared the CA VI activating QSAR models with those calculated for activation of the cytosolic human isozymes hCA I and hCA II. In addition, the effect of D- and L-amino acids as activators of hCA I, hCA II and of hCA VI as compared to those of structurally related biogenic amines was investigated for obtaining statistically significant and predictive QSAR equations. The obtained models are discussed using a variety of statistical parameters. The best models were obtained for hCA II activation, followed by hCA I, whereas the QSAR models for the activation of hCA VI were statistically weaker.     

Design and preliminary structure–activity relationship of redox-silent semisynthetic tocotrienol analogues as inhibitors for breast cancer proliferation and invasion

Vitamin E (VE) is a generic term that represents a family of compounds composed of various tocopherol and tocotrienol isoforms. Tocotrienols display potent anti-angiogenic and antiproliferative activities. Redox-silent tocotrienol analogues also display potent anticancer activity. The ultimate objective of this study was to develop semisynthetically C-6-modified redox-silent tocotrienol analogues with enhanced antiproliferative and anti-invasive activities as compared to their parent compound. Examples of these are carbamate and ether analogues of α-, γ-, and δ-tocotrienols (1–3). Various aliphatic, olefinic, and aromatic substituents were used. Steric limitation, electrostatic, hydrogen bond donor (HBD) and hydrogen bond acceptor (HBA) properties were varied at this position and the biological activities of these derivatives were tested. Three-dimensional quantitative structure–activity relationship (3D QSAR) studies were performed using Comparative Molecular Field (CoMFA) and Comparative Molecular Similarity Indices Analyses (CoMSIA) to better understand the structural basis for biological activity and guide the future design of more potent VE analogues.     

Quantitative Proteome-Property Relationships (QPPRs). Part 1: finding biomarkers of organic drugs with mean Markov connectivity indices of spiral networks of blood mass spectra

Numerical parameters of the molecular networks, also referred as Topological Indices or Connectivity Indices (CIs), have been used in Bioorganic and Medicinal Chemistry to find Quantitative Structure-Activity, Property or Toxicity Relationship (QSAR, QSPR and QSTR) models. QSPR models generally use CIs as inputs to predict the biological activity of compounds. However, the literature does not evidence a great effort to find QSAR-like models for other biologically and chemically relevant systems. For instance, blood proteome constitutes a protein-rich information reservoir, since the serum proteome Mass Spectra (MS) represents a potential information source for the early detection of Biomarkers for diseases and/or drug-induced toxicities. The concept of mass spectrum network (MS network) for a single protein is already well-known. However, there are no reported results on the use of CIs for a MS network of a whole proteome to explore MS patterns. In this work, we introduced for the first time a novel network representation and the CIs for the MS of blood proteome samples. The new network bases on Randic's Spiral network have been previously introduced for protein sequences. The new MS CIs, called here Spiral Markov Connectivity (SMC(k)) of the MS Spiral graph can be calculated with the software MARCH-INSIDE, combining network and Markov model theory. The SMC(k) values could be used to seek QSAR-like models, called in this work Quantitative Proteome-Property Relationships (QPPRs). We calculate the SMC(k) values for 62 blood samples and fit a QPPR model by discriminating proteome MS, typical of individuals susceptible to suffer drug-induced cardiotoxicity from control samples. The accuracy, sensitivity, and specificity values of the QPPR model were between 73.08% and 87.5% in training and validation series. This work points to QPPR models as a powerful tool for MS detection of biomarkers in proteomics.     

QSAR studies on carbonic anhydrase inhibitors: a case of ureido and thioureido derivatives of aromatic/heterocyclic sulfonamides

QSAR studies on modelling of biological activity (hCAI) for a series of ureido and thioureido derivatives of aromatic/heterocyclic sulfonamides have been made using a pool of topological indices. Regression analysis of the data showed that excellent results were obtained in multiparametric correlations upon introduction of indicator parameters. The predictive abilities of the models are discussed using cross-validation parameters.     

2 D - QSAR studies on CYP26A1 inhibitory activity of 1-[benzofuran-2-yl-(4-alkyl/aryl-phenyl)-methyl]- 1 H-triazoles

The Quantitative Structure Activity Relationship (QSAR) study is performed over a set of 15, 4-alkyl/aryl-substituted 1- [benzofuran-2-yl-phenylmethyl]-1 H-triazoles derivatives. This study is based on the application of physicochemical parameters in QSAR. The parameters include (MR (molar refractivity), MW (molecular weight), Pc (parachor), St (surface tension), D (density), Ir (index of refraction) and log P (partition coefficient). The parameters describing physiochemical properties are used as independent variables and the biological activity (IC(50)) is considered as dependent variable in multiple regression analysis. Different models were generated with high co-efficient of determination (R(2)). The 2D-QSAR study identified compounds capable of inhibiting the metabolic breakdown of the retinoid (trans-retinoic acid (ATRA)) involved in the activation of specific nuclear Retinoic acid receptors (RARs). This study identifies R115866 as a potential inhibitor of the cytochrome P450 (CYP) mediated metabolism with increased RA levels for retinoid actions.