Quantitative Structure-activity Relationship Equation Research Articles

Pharmacophore Modeling, Quantitative Structure–Activity Relationship Analysis, and in Silico Screening Reveal Potent Glycogen Synthase Kinase-3β Inhibitory Activities for Cimetidine, Hydroxychloroquine, and Gemifloxacin

The pharmacophoric space of glycogen synthase kinase-3beta (GSK-3beta) was explored using two diverse sets of inhibitors. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select optimal combination of pharmacophores and physicochemical descriptors that access self-consistent and predictive quantitative structure-activity relationship (QSAR) against 132 training compounds ( r (2) 123 = 0.663, F = 24.6, r (2) LOO = 0.592, r (2) PRESS against 29 external test inhibitors = 0.695). Two orthogonal pharmacophores emerged in the QSAR, suggesting the existence of at least two distinct binding modes accessible to ligands within GSK-3beta binding pocket. The validity of the QSAR equation and the associated pharmacophores was established by the identification of three nanomolar GSK-3beta inhibitors retrieved from our in-house-built structural database of established drugs, namely, hydroxychloroquine, cimetidine, and gemifloxacin. Docking studies supported the binding modes suggested by the pharmacophore/QSAR analysis. In addition to being excellent leads for subsequent optimization, the anti-GSK-3beta activities of these drugs should have significant clinical implications.

QSAR Studies and Molecular Design of Phenanthrene‐based Tylophorine Derivatives with Anticancer Activity

AbstractThe Quantitative Structure–Activity Relationship (QSAR) of a series of novel phenanthrene‐based tylophorine derivatives with anticancer activity has been studied by using the Density Functional Theory (DFT), Molecular Mechanics (MM2), and statistical methods. The established model shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient (R2A=0.813) and the square of the cross‐validation coefficient (q2=0.748). We find that the anticancer activity expressed as pIC50, which is defined as the negative value of the logarithm of necessary molar concentration of this series of compounds to cause 50% growth inhibition against the human A549 lung cancer cell line, closely relates with the energy (EHOMO) of the Highest Occupied Molecular Orbital (HOMO), the net charge of the terminal H atom of substituent R2 ($\rm{ Q_{{\rm{HR}}_{\rm{2}} } }$), the hydrophobic coefficient of substituent R2 (log $\rm{ P_{{\rm{R}}_{\rm{2}} } }$), and the net charges of the first atom of substituent R1 ($\rm{ Q_{{\rm{FR}}_{\rm{1}} } }$). The same model was further applied to predict the pIC50 for six recently reported congeneric compounds as external test set, and the predicted pIC50 values are close to the experimental ones, and thus it further confirms that this QSAR model has high predictive ability. The theoretical results can offer some useful references for understanding the action mechanism and designing new compounds with anticancer activity. Based on this QSAR equation, ten new compounds with higher anticancer activity have been theoretically designed and are expecting the experimental confirmation.

A QSAR study and molecular design of benzothiazole derivatives as potent anticancer agents

A quantitative structure-activity relationship (QSAR) of a series of benzothiazole derivatives showing a potent and selective cytotoxicity against a tumorigenic cell line has been studied by using the density functional theory (DFT), molecular mechanics (MM+) and statistical methods, and the QSAR equation was established via a correlation analysis and a stepwise regression analysis. A new scheme determining outliers by “leave-one-out” (LOO) cross-validation coefficient ( q 2 n - i ) was suggested and successfully used. In the established optimal equation (excluding two outliers), the steric parameter ( MR R) and the net charge ( Q FR) of the first atom of the substituent (R), as well as the square of hydrophobic parameter (lg P )2 of the whole molecule, are the main independent factors contributing to the anticancer activities of the compounds. The fitting correlation coefficient ( R 2) and the cross-validation coefficient (q2) values are 0.883 and 0.797, respectively. It indicates that this model has a significantly statistical quality and an excellent prediction ability. Based on the QSAR studies, 4 new compounds with high predicted anticancer activities have been theoretically designed and they are expected to be confirmed experimentally.

Cell-Based and Biochemical Structure-Activity Analyses of Analogs of the Microtubule Stabilizer Dictyostatin

Compounds that bind to microtubules (MTs) and alter their dynamics are highly sought as a result of the clinical success of paclitaxel and docetaxel. The naturally occurring compound (-)-dictyostatin binds to MTs, causes cell cycle arrest in G(2)/M at nanomolar concentrations, and retains antiproliferative activity in paclitaxel-resistant cell lines, making dictyostatin an attractive candidate for development as an antineoplastic agent. In this study, we examined a series of dictyostatin analogs to probe biological and biochemical structure-activity relationships. We used a high-content multiparameter fluorescence-based cellular assay for MT morphology, chromatin condensation, mitotic arrest, and cellular toxicity to identify regions of dictyostatin that were essential for biological activity. Four analogs (6-epi-dictyostatin, 7-epi-dictyostatin, 16-normethyldictyostatin, and 15Z,16-normethyldictyostatin) retained low nanomolar activity in the cell-based assay and were chosen for analyses with isolated tubulin. All four compounds were potent inducers of MT assembly. Equilibrium binding constant (K(i)) determinations using [(14)C]epothilone B, which has a 3-fold higher affinity for the taxoid binding site than paclitaxel, indicated that 6-epi-dictyostatin and 7-epi-dictyostatin displaced [(14)C]epothilone B with K(i) values of 480 and 930 nM, respectively. 16-Normethyldictyostatin and 15Z,16-normethyldictyostatin had reduced affinity (K(i) values of 4.55 and 4.47 muM, respectively), consistent with previous reports showing that C16-normethyldictyostatin loses potency in paclitaxel-resistant cell lines that have a Phe270-to-Val mutation in the taxoid binding site of beta-tubulin. Finally, we developed a set of quantitative structure-activity relationship equations correlating structures with antiproliferative activity. The equations accurately predicted biological activity and will help in the design of future analogs.

QSAR modeling of the interaction of flavonoids with GABA(A) receptor

Experimentally assigned values to binding affinity constants of flavonoid ligands towards the benzodiazepine site of the GABA(A) receptor complex were compiled from several publications, and enabled to perform a predictive analysis based on Quantitative Structure–Activity Relationships (QSAR). The best linear model established on 78 molecular structures incorporated four molecular descriptors, selected from more than a thousand of geometrical, topological, quantum-mechanical and electronic types of descriptors and calculated by Dragon software. An application of this QSAR equation was performed by estimating the binding affinities for some newly synthesized flavonoids displaying 2-,7-substitutions in the benzopyrane backbone which still do not have experimentally measured potencies.

QSAR, action mechanism and molecular design of flavone and isoflavone derivatives with cytotoxicity against HeLa

The quantitative structure–activity relationship (QSAR) of 32 flavone and isoflavone derivatives with cytotoxicity expressed as pGC 50, which is defined as the negative value of the logarithm of necessary molar concentration of this series of compounds to cause 50% growth inhibition against the human cervical epithelioid carcinoma cell line (HeLa), has been studied by using the density functional theory (DFT), molecular mechanics (MM2) and statistical methods. In order to obtain QSAR model with high predictive ability, the original dataset was randomly divided into a training set comprising 26 compounds and a test set comprising the rest 6 compounds. An optimal model for the training set with significant statistical quality ( R A 2 = 0.852 ) and predictive ability ( q 2 = 0.818) was established. The same model was further applied to predict pGC 50 values of the 6 compounds in the test set, and the resulting predictive correlation coefficient R pred 2 reaches 0.738, further showing that this QSAR model has high predictive ability. It is very interesting to find that the cytotoxicities of these compounds against HeLa appear to be mainly governed by two quantum-chemical factors, i.e., the energy ( E LUMO) of the lowest unoccupied molecular orbital (LUMO) and the net charges of C atom at site 6 on aromatic rings ( Q C6). Here the possible action mechanism of these compounds was analyzed and discussed in detail, in particular, the fact why the flavone derivatives have considerably higher cytotoxicity than isoflavone derivatives was reasonably explained. Based on this QSAR equation, 5 new compounds with higher cytotoxicity have been theoretically designed. Such results can offer useful theoretical references for experimental works.

PEMODELAN SENYAWA TURUNAN ASAM KARBAMAT SEBAGAI SENYAWA ANTIKANKER MENGGUNAKAN METODE SEMIEMPIRIS AM1

4-N-carbamic acid-4’-dimetylpipodopylotoxin and its derivatives are compounds which are synthesized from etoposide (VP 16). These compounds are used as anticancer medicine because they inhibit DNA topoisomerase II enzyme. The enzyme participates in controlling breaking process of DNA double helix bounding in cancer cell. It makes cancer growing cease and dies because cell can not replicate. However, the compound insoluble in water, make a medicine resistant, inhibit metabolism system and poison. It needs to design a modification of new compounds from carbamic acid derivatives which have higher activity. Structure modification was done using Quantitative Structure Activity Relationship (QSAR) which was a computational chemistry application in medicine design process. This research used semiempiris AM1 method to determine the best QSAR equation based on multilinear regression analysize, with log 1/IC50 as dependent variable and independent variables were atomic net charge of qN29, qC30, qO31, qO32, dipole moment, n-octanol-water coefficient partition (Log P), and polarity. The best QSAR equation in this research was : Log 1/IC50 = 4.871 + 12.738 qN29 + 33.183 qC30 + 28.015 qO31 – 3.6 x 10-2 polarity, with N = 13, r =0.907, SE = 0.13025, Fcount/Ftable = 1.901, PRESS = 0.1357. Based on the best QSAR equation, the prediction compounds were 1, 2, 3, 8, and 22 with each IC50 theoretical value were 0.032, 0.034, 0.036, and 0.098 µM.

Discovery of new MurF inhibitors via pharmacophore modeling and QSAR analysis followed by in-silico screening

The pharmacophoric space of streptococcal MurF was explored using a set of 39 known inhibitors. Subsequently, genetic algorithm and multiple linear regression analysis were employed to select an optimal combination of pharmacophoric models and physicochemical descriptors that access self-consistent quantitative structure–activity relationship (QSAR) ( r 2 = 0.93 ,F = 56.9 , r LOO 2 = 0.91 , r PRESS 2 against eight external test inhibitors = 0.75). Two orthogonal pharmacophores (of cross-correlation r 2 = 0.26) emerged in the QSAR equation suggesting the existence of at least two distinct binding modes accessible to ligands within MurF binding pocket. The validity of the QSAR equation and the associated pharmacophore models was experimentally established by the identification of three promising new MurF inhibitors retrieved from the NCI database. Docking studies conducted on active hits supported the binding modes suggested by the pharmacophore/QSAR analysis.

A Quantitative Structure Activity Analysis on the Relative Sensitivity of the Olfactory and the Nasal Trigeminal Chemosensory Systems

We have applied a quantitative structure-activity relationship (QSAR) approach to analyze the chemical parameters that determine the relative sensitivity of olfaction and nasal chemesthesis to a common set of volatile organic compounds (VOCs). We used previously reported data on odor detection thresholds (ODTs) and nasal pungency thresholds (NPTs) from 64 VOCs belonging to 7 chemical series (acetate esters, carboxylic acids, alcohols, aliphatic aldehydes, alkylbenzenes, ketones, and terpenes). The analysis tested whether NPTs could be used to separate out "selective" chemosensory effects (i.e., those resting on the transfer of VOCs from the gas phase to the receptor phase) from "specific" chemosensory effects in ODTs. Previous work showed that selective effects overwhelmingly dominate chemesthetic potency whereas both selective and specific effects control olfactory potency. We conclude that it is indeed possible to use NPTs to separate out selective from specific effects in ODTs. Among the series studied, aldehydes and acids, except for formic acid, show clear specific effects in their olfactory potency. Furthermore, for VOCs whose odor potency rests mainly on selective effects, we have developed a QSAR equation that can predict their ODTs based on their NPTs.

QSAR AND MOLECULAR DESIGN OF BENZO[B]ACRONYCINE DERIVATIVES AS ANTITUMOR AGENTS

Quantitative structure-activity relationship (QSAR) studies of a series of benzo[b]acronycine derivatives as a novel class of antitumor agents have been carried out using the density functional theory (DFT), molecular mechanics (MM+) and statistical methods. Some calculated parameters of geometric structures, electronic structures and molecular properties of the compounds were adopted as generalized descriptors (variables). Via a stepwise regression analysis, some main independent factors affecting the activities of the compounds were selected out, and then the quantitative structure-activity relationship (QSAR) equation was established. The results suggest that the energy difference (Δ εL-H) between the lowest unoccupied molecular orbital and the highest occupied molecular orbital, the net charges of the nitrogen atom N 11 and the first atom of the substituent R2, and the hydrophobic parameter (log P1) of the substituent R1 are the main independent factors contributing to the antitumor activities of the compounds. The fitting correlation coefficient (r2) and the cross-validation coefficient (q2) for the model established by this study are 0.865 and 0.721, respectively, showing this model with a good predictability. The QSAR equation can be used to estimate unknown antitumor activity of this kind of compound, and thus design new compounds with high antitumor activities. Here, based on this QASR study, 4 new compounds with predicted high antitumor activities have been theoretically designed and they are expecting experimental verification.

QSAR study on permeability of hydrophobic compounds with artificial membranes

We previously reported a classical quantitative structure–activity relationship (QSAR) equation for permeability coefficients ( P app-pampa) by parallel artificial membrane permeation assay (PAMPA) of structurally diverse compounds with simple physicochemical parameters, hydrophobicity at a particular pH (log P oct and |p K a − pH|), hydrogen-accepting ability ( SA HA), and hydrogen-donating ability ( SA HD); however, desipramine, imipramine, and testosterone, which have high log P oct values, were excluded from the derived QSAR equation because their measured P app-pampa values were lower than calculated. In this study, for further investigation of PAMPA permeability of hydrophobic compounds, we experimentally measured the P app-pampa of more compounds with high hydrophobicity, including several pesticides, and compared the measured P app-pampa values with those calculated from the QSAR equation. As a result, compounds having a calculated log P app-pampa > −4.5 showed lower measured log P app-pampa than calculated because of the barrier of the unstirred water layer and the membrane retention of hydrophobic compounds. The bilinear QSAR model explained the PAMPA permeability of the whole dataset of compounds, whether hydrophilic or hydrophobic, with the same parameters as the equation in the previous study. In addition, PAMPA permeability coefficients correlated well with Caco-2 cell permeability coefficients. Since Caco-2 cell permeability is effective for the evaluation of human oral absorption of compounds, the proposed bilinear model for PAMPA permeability could be useful for not only effective screening for several drug candidates but also the risk assessment of chemicals and agrochemicals absorbed by humans.

Comparison of QSAR and QSPR Based Aquatic Toxicity for Mixed Surfactants

AbstractIn the present study, quantitative structure‐activity relationship (QSAR) equations were derived using the logarithm of the octanol/water partition co‐efficient for the prediction of acute aquatic toxicity of mixed surfactant systems. Further mixed surfactant systems of an anionic surfactant (sodium lauryl sulfate) and several nonionic surfactants (alkyl polyglucoside) of different hydrophobic chain lengths were taken together to calculate the parameter pEC50. Quantitative structure‐properties relationship (QSPR) equations based on pC20, and Amin were developed from the surface tension data to predict pEC50 values and compared with QSAR derived pEC50 values to understand the probable mechanisms of action of the mixed surfactants blends for aquatic toxicity. The established QSAR and QSPR equations for mixed surfactants indicate that given blends of surfactants act as a polar narcotic.

Quantitative structure-activity relationship study of human A3 adenosine receptor antagonists: Derivatives of 2-aryl-1,2,4-triazolo[4,3-α]quinoxaline

A quantitative structure-activity relationship (QSAR) study was conducted on the antagonistic activities of derivatives of 2-aryl-1,2,4-triazolo[4,3-α]quinoxaline at the human A3 adenosine receptor. As per the structural framework, the title analogues were subdivided into two congeneric series, namely the 1,4-dione and the 4-amino-1-one series. A majority of substituents occurred at the R- and a limited number at the X-positions in both of these series. In the case of the 1,4-dione series, the derived significant QSAR equation revealed that those substituents exhibiting a larger field effect at R renders the molecule to more efficiently bind at the receptor site. The study also extrapolated the requirement of electron-donor substituents at the X-position which, at present, is regarded as insensitive to any interaction due to limited substitution. However, the X-position may be explored in a further synthetic study. From the derived correlation equation for the 4-amino-1-one series, it appeared that a strong electron-withdrawing substituent at R will enhance the pKi value of a compound while a strong electron-donor at this position will have a detrimental effect on it. Based on correlation equations, derived using different electronic parameters, it may be interpreted that the two series of compounds attain different orientation inside the recognition site of the receptor.

The Quantitative Structure-Mutagenicity Relationship of Polycylic Aromatic Hydrocarbon Metabolites

Quantitative structure-activity relationships (QSARs) for benz[a]anthracene (BA)mutagens using 73 descriptors were searched. The mutagenicity data was obtained fromAmes assays for Mycobacterium vanbaalenii, Mycobacterium gilvum and Mycobacteriumflavescens strains. These data were fitted using a mutagenicity-cytotoxicity competitionmodel which defines the mutagenic potencies of BA metabolites, and include oxides,phenols, quinones, and dihydrodiols. The QSAR equations were derived using the moleculardescriptor set (charged partial surface area, spatial, thermodynamic and electronicdescriptors) and semi-empirical energetic and charge descriptors. Genetic functionapproximation was used to reduce and fit independent variables, including linear- andquadratic-based functions. Multiple QSAR equations were generated and a separate QSARequation was chosen and evaluated for each strain using conventional r2, F-test, and cross-validated r2. Each strain exhibited its own characteristic descriptors.

DFT‐based QSAR study and molecular design of AHMA derivatives as potent anticancer agents

AbstractA quantitative structure–activity relationship (QSAR) of 3‐(9‐acridinylamino)‐5‐hydroxymethylaniline (AHMA) derivatives and their alkylcarbamates as potent anticancer agents has been studied using density functional theory (DFT), molecular mechanics (MM+), and statistical methods. In the best established QSAR equation, the energy (ENL) of the next lowest unoccupied molecular orbital (NLUMO) and the net charges (QFR) of the first atom of the substituent R, as well as the steric parameter (MR2) of subsituent R2 are the main independent factors contributing to the anticancer activity of the compounds. A new scheme determining outliers by “leave‐one‐out” (LOO) cross‐validation coefficient (q) was suggested and successfully used. The fitting correlation coefficient (R2) and the “LOO” cross‐validation coefficient (q2) values for the training set of 25 compounds are 0.881 and 0.829, respectively. The predicted activities of 5 compounds in the test set using this QSAR model are in good agreement with their experimental values, indicating that this model has excellent predictive ability. Based on the established QSAR equation, 10 new compounds with rather high anticancer activity much greater than that of 34 compounds have been designed and await experimental verification. © 2006 Wiley Periodicals, Inc. Int J Quantum Chem, 2007

Triazoloquinazolines as Human A3 Adenosine Receptor Antagonists: A QSAR Study

Multiple linear regression analysis was performed on the quantitative structure-activity relationships (QSAR) of the triazoloquinazoline adenosine antagonists for human A3receptors. The data set used for the QSAR analysis encompassed the activities of 33triazoloquinazoline derivatives and 72 physicochemical descriptors. A template moleculewas derived using the known molecular structure for one of the compounds when bound tothe human A2B receptor, in which the amide bond was in a cis-conformation. All the testcompounds were aligned to the template molecule. In order to identify a reasonable QSARequation to describe the data set, we developed a multiple linear regression program thatexamined every possible combination of descriptors. The QSAR equation derived from thisanalysis indicates that the spatial and electronic effects is greater than that of hydrophobiceffects in binding of the antagonists to the human A3 receptor. It also predicts that a largesterimol length parameter is advantageous to activity, whereas large sterimol widthparameters and fractional positive partial surface areas are nonadvatageous.

QSAR Studies on a Series of 7,8‐Dialkyl‐1,3‐diaminopyrrolo‐[3,2‐f]quinazolines with Anticancer Activity

AbstractThe quantitative structure‐activity relationship (QSAR) studies on a series of 7,8‐dialkyl‐1,3‐diaminopyrrolo‐[3,2‐f]quinazolines, dihydrofolate reductase (DHFR) inhibitors as potential anticancer agents, have been carried out by using the density functional theory (DFT) method, molecular mechanics method (MM+) and statistical method. Some QSAR models based on their lipophilic and steric parameters were built up via a stepwise regression analysis. It is very interesting to find that the established optimal QSAR equation involves only two descriptors: lipophilicity indexes Clog P and Clog P2. However, such descriptors can quite well describe a significant statistic quality and have a remarkable predictive activity according to the square of adjusted correlation coefficient (R2A=0.937) and the square of cross‐validation coefficient (q2=0.911) of this equation. The results show that the lipophilicity is a main factor affecting the anticancer activity of this series of antimetastatic agents, and the obtained equation describes a parabolic correlation between pIC50 and Clog P, and indicates a suitable range of Clog P (around 4.43) being very important for optimal pIC50 values. These QSAR studies can offer some useful references for understanding the action mechanism and performing the molecular design or modification of this series of antimetastatic agents.

Free-energy force-field three-dimensional quantitative structure–activity relationship analysis of a set of p38-mitogen activated protein kinase inhibitors

The p38-mitogen-activated protein kinases (p38-MAPKs) belong to a family of serine-threonine kinases activated by pro-inflammatory or stressful stimuli that are known to be involved in several diseases. Their biological importance, related to the release of inflammatory pro-cytokines such as tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 (IL-1), has generated many studies aiming at the development of selective inhibitors for the treatment of inflammatory diseases. In this work, we developed receptor-based three dimensional (3D) quantitative structure-activity relationship (QSAR) models for a series of 33 pyridinyl imidazole compounds [Liverton et al. (1999) 42:2180], using a methodology named free-energy force-field (FEFF) [Tokarski and Hopfinger (1997) 37:792], in which scaled intra- and intermolecular energy terms of the Assisted Model Building Energy Refinement (AMBER) force field combined with a hydration-shell solvation model are the independent variables used in the QSAR studies. Multiple temperature molecular-dynamics simulations (MDS) of ligand-protein complexes and genetic-function approximation (GFA) were employed using partial least squares (PLS) as the fitting functions to develop FEFF-3D-QSAR models for the binding process. The best model obtained in the FEFF-3D-QSAR receptor-dependent (RD) method shows the importance of the van der Waals energy change upon binding and the electrostatic energy in the interaction of ligands with the receptor. The QSAR equations described here show good predictability and may be regarded as representatives of the binding process of ligands to p38-MAPK. Additionally, we have compared the top FEFF-3D-QSAR model with receptor independent (RI) 4D-QSAR models developed in a recent study [Romeiro et al. (2005) 19:385].

Quantitative Structure−Activity Relationship Based Quantification of the Impacts of Enzyme−Substrate Binding on Rates of Peroxidase-Mediated Reactions of Estrogenic Phenolic Chemicals

The initial rates of horseradish peroxidase (HRP)-mediated enzymatic reactions of 15 assorted aqueous phase phenolic chemicals were studied. The associated reaction rate constants were found to correlate quantitatively with two independent variables: the highest-occupied molecular orbital energy (E(HOMO)) defining the intrinsic redox reactivities of the phenolic substrates and the distance between a substrate and the deltaN of HIS42's imidazole ring in an HRP/substrate binding complex, obtained through molecular simulations. Highly correlated quantitative structure-activity relationship (QSAR) equations were thus developed. This work provides insights into the impacts that HRP/substrate binding may have on HRP-mediated reactions. Additionally, the QSAR equations developed in the work may serve as a basis to further explore the potential use of HRP-mediated reactions in the treatment of estrogenic contaminants, and they constitute an important tool for redesign and screening of potential proteomic modifications to the wild-type HRP structure intended to enhance reactivity toward selected substrates.

Studies on synthesis and evaluation of quantitative structure–activity relationship of 10-methyl-6-oxo-5-arylazo-6,7-dihydro-5 H-[1,3]azaphospholo[1,5- d][1,4]benzodiazepin-2-phospha-3-ethoxycarbonyl-1-phosphorus dichlorides

A new series of 10-methyl-6-oxo-5-arylazo-6,7-dihydro-5 H-[1,3]azaphospholo[1,5- d][1,4]benzodiazepin-2-phospha-3-ethoxycarbonyl-1-phosphorus dichlorides 11a– p has been synthesized and evaluated as antimicrobial agents. Structures of all the synthesized compounds were established on the basis of elemental analysis and spectroscopic data. Quantitative structure–activity relationship (QSAR) investigations were applied to find out the correlation between the experimentally evaluated activity with various parameters of the compounds studied. QSAR equations showed that the molecular refractivity correlates significantly with the antimicrobial activity.