On the role of polarizability in QSAR

A quantitative structure-activity relationship study of tetrabutylphosphonium bromide analogs as muscarinic acetylcholine receptors

Quantitative structure-activity relationship (QSAR) of tetrabutylphosphonium bromide (TBPB) analogs as muscarinic acetylcholine receptors (mAChRs) was studied. A suitable set of molecular descriptors was calculated and stepwise multiple linear regression (SW-MLR) was employed to select those descriptors that resulted in the best fitted models. A MLR model with three selected descriptors was obtained. Furthermore, the MLR model was validated using the leave-one-out (LOO) and leave-group-out (LGO) crossvalidation, and the Y-randomization test. This model, with high statistical significance (R2 train = 0.982, F = 388.715, Q2 LOO = 0.973, Q2 LGO = 0.977 and R2 test = 0.986) could predict the activity of the molecules with a percentage prediction error lower than 5 %.

Syntheses, antiproliferative activity and theoretical characterization of acitretin-type retinoids with changes in the lipophilic part

Acitretin analogs, incorporating changes in the lipophilic part, were efficiently synthesized from commercially available aromatic aldehydes or methyl ketones using the Wittig or Horner-Wadsworth-Emmons reaction. Their antiproliferative activity was evaluated against human breast MCF-7 epithelial cells. Analogs 3, 4, 8 and 11 exhibited strong, dose-dependent, antiproliferative activity on the tested cell line. Analog 3, incorporating three methoxy groups in the aromatic ring, exhibited the strongest inhibitory effect at 10 μM. High-level all electron conventional ab initio and density functional theory quantum chemical calculations were performed to obtain the molecular structure, electron charge distribution and polarization properties of all compounds of interest in this work. The most active analogs were planar and were characterized by larger dipole moments than the other synthesized molecules. Another factor of importance to the analysis of the activity of these molecules is the dipole polarizability.

Application of a genetic algorithm and an artificial neural network for global prediction of the toxicity of phenols to Tetrahymena pyriformis

ABSTRACT

Genetic algorithm (multiparameter linear regression; GA-MLR) and genetic algorithm-artificial neural network (GA-ANN) global models have been used for prediction of the toxicity of phenols to Tetrahymena pyriformis. The data set was divided into 150 molecules for training, 50 molecules for validation, and 50 molecules for prediction sets. A large number of descriptors were calculated and the genetic algorithm was used to select variables that resulted in the best-fit to models. The six molecular descriptors selected were used as inputs for the models. The MLR model was validated using leave-one-out, leave-group-out cross-validation and external test set. A three-layered feed forward ANN with back-propagation of error was generated using six molecular descriptors appearing in the MLR model. Comparison of the results obtained using the ANN model with those from the MLR revealed the superiority of the ANN model over the MLR. The root mean square error of the training, validation, and prediction sets for the ANN model were calculated to be 0.224, 0.202, and 0.224 and correlation coefficients (r2) of 0.926, 0.943, and 0.925 were obtained. The improvements are because of non-linear correlations of the toxicity of the compounds with the descriptors selected. The prediction ability of the GA-ANN global model is much better than that of previously proposed models.

GRAPHICAL ABSTRACT

High-throughput pKa screening and prediction amenable for ADME profiling

Recent technological advances have made it possible for several new pK(a) assays to be used in drug screening. In this review, a critical overview is provided of current new methodologies for high-throughput screening and prediction of pK(a). Typical applications of using pK(a )constants and charge state for absorption, distribution, metabolism and excretion (ADME) profiling and quantitative structure-activity relationship modelling complements the methodological comparisons and discussions. The experimental methods discussed include high-throughput screening of pK(a) by multiplexed capillary with ultraviolet absorbance detection on a 96-capillary format instrument, capillary electrophoresis and mass spectrometry (CEMS) based on sample pooling, determination of pK(a) by pH gradient high-performance liquid chromatography, and measurement of pK(a) by a mixed-buffer liner pH gradient system. Comparisons of the different experimental assays are made with emphasis on the newly developed CEMS method. The current status and recent progress in computational approaches to pK(a) prediction are also discussed. In particular, the accuracy limits of simple fragment-based approaches as well as quantum mechanical methods are addressed. Examples of pK(a) prediction from in-house drug candidates as well as commercially available drug molecules are shown and an outline is provided for how drug discovery companies can integrate experiments with computational approaches for increased applications for ADME profiling.

Promises and pitfalls of quantitative structure-activity relationship approaches for predicting metabolism and toxicity

The description of quantitative structure-activity relationship (QSAR) models has been a topic for scientific research for more than 40 years and a topic within the regulatory framework for more than 20 years. At present, efforts on QSAR development are increasing because of their promise for supporting reduction, refinement, and/or replacement of animal toxicity experiments. However, their acceptance in risk assessment seems to require a more standardized and scientific underpinning of QSAR technology to avoid possible pitfalls. For this reason, guidelines for QSAR model development recently proposed by the Organization for Economic Cooperation and Development (OECD) [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. OECD Environment Health and Safety Publications: Series on Testing and Assessment No. 69, Paris] are expected to help increase the acceptability of QSAR models for regulatory purposes. The guidelines recommend that QSAR models should be associated with (i) a defined end point, (ii) an unambiguous algorithm, (iii) a defined domain of applicability, (iv) appropriate measures of goodness-of-fit, robustness, and predictivity, and (v) a mechanistic interpretation, if possible [Organization for Economic Cooperation and Development (OECD) (2007) Guidance document on the validation of (quantitative) structure-activity relationships [(Q)SAR] models. The present perspective provides an overview of these guidelines for QSAR model development and their rationale, as well as the promises and pitfalls of using QSAR approaches and these guidelines for predicting metabolism and toxicity of new and existing chemicals.

QSAR study of 2-(1-Propylpiperidin-4-yl)-1H-benzimidazole-4-carboxamide as PARP inhibitors for treatment of cancer

Quantitative structure-activity relationship of the 2-(1-propylpiperidin-4-yl)-1H-benzimidazole-4-carboxamide as a potent inhibitor of poly(ADP-ribose) polymerase for cancer treatment was studied. A suitable set of molecular descriptors was calculated and the genetic algorithm was employed to select those descriptors that resulted in the best fitted models. Excellent results were obtained employing multiple linear regressions and critically discussed using a variety of statistical parameters. Furthermore, the model was validated using leave-one-out and leave-group-out cross-validation, external test set and chance correlation. A genetic algorithm-multiple linear regression model with seven selected descriptors was obtained. This model, with high statistical significance (R(2) = 0.935, Q(2)_(LOO)= 0.894, Q(2)_(LGO)= 0.875, F = 53.481), could be used to predict poly(ADP-ribose) polymerase inhibitor activity of the molecules.

Exploring QSARs for antiviral activity of 4-alkylamino-6-(2-hydroxyethyl)-2-methylthiopyrimidines by support vector machine

The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitative structure activity relationship models to predict the antiviral activity of 4-alkylamino-6-(2-hydroxyethyl)-2-methylthiopyrimidines. The genetic algorithm was employed to select the variables that resulted in the best-fitted models. A comparison between the obtained results using support vector machine with those of multiple linear regression revealed that support vector machine model was much better than multiple linear regression. The root mean square errors of the training set and the test set for support vector machine model were calculated to be 0.102 and 0.205, and the correlation coefficients (r2) were 0.956 and 0.852, respectively. Furthermore, the obtained statistical parameter of leave-one-out (LOO) and leave-group-out (LGO) cross-validation test on support vector machine model were 0.893 and 0.881, respectively, which prove the reliability of this model. The results suggest that branching, volume and lipophilicity are the main independent factors contributing to the antiviral activities of the studied compounds.

Investigation of DNA‐Binding Properties of Organic Molecules Using Quantitative Structure‐Activity Relationship (QSAR) Models

Due to the great potential of DNA as a receptor, many classes of synthetic and naturally occurring molecules exert their anticancer activities through DNA-binding. In the field of antitumor DNA-binding agents, a number of acridine and anthracycline derivatives are in the market as chemotherapeutic agents. However, the clinical application of such classes of compounds has encountered problems such as multi-drug resistance and secondary and/or collateral effects. Thus, there has been increasing interest in discovering and developing small molecules that are capable of DNA-binding, which will be expected to be used either in place of or in conjunction with, the existing compounds. The interest in the application of the QSAR paradigm has steadily increased in recent decades and we hope it may be useful in the design and development of DNA-binding molecules as new anticancer agents. In the present review, an attempt has been made to understand the DNA-binding properties of different compound series and discussed using 27 QSAR models, which reveal a number of interesting points. The most important determinants for the activity in these models are Hammett electronic (sigma and sigma+), hydrophobic, molar refractivity, and Sterimol width parameters.

Evaluation of electronic, lipophilic and membrane affinity effects on antiproliferative activity of 5-substituted-2-(2,4-dihydroxyphenyl)-1,3,4-thiadiazoles against various human cancer cells

The QSAR studies of 5-substituted-2-(2,4-dihydroxyphenyl)-1,3,4-thiadiazoles set of antiproliferative activity against human cancer cell lines have been performed. Electronic properties of compounds were estimated by the Hartree-Fock method at 6-31G** level. Lipophilicity and membrane affinity parameters were determined by the chromatographic methods RP-8 OPLC and IAM HPLC, respectively. Mono- and multivariable regression analyses were performed. The principle factor for determination of activity of compounds is partial charge of nitrogen (q(N3), q(N4)) and carbon (q(C5)) atoms of the 1,3,4-thiadiazole ring. Biological effect is also connected with molar refractivity (CMR) and lipophilicity of derivatives obtained by RP-8 chromatography. The analysis of the QSAR equations for individual cell lines indicates both similarities and differences of electron, steric factors and hydrophobic-hydrophilic character of the analogues of the tested set affecting the antiproliferative activity.

QSPR modeling of hyperpolarizabilities

The polarizabilities and the first and second hyperpolarizabilities of 219 conjugated organic compounds are modeled by QSPR (quantitative structure activity relationship) based on a large pool of constitutional, topological, electronic and quantum chemical descriptors calculated by CODESSA Pro (comprehensive descriptors for structural and statistical analysis) derived solely from molecular structure. Multilinear models were developed using the BMLR (best multilinear regression) algorithm to relate the experimental (hyper)polarizabilities to their predicted values. The regression equations include AM1 (Austin model 1) calculated (hyper)polarizabilities together with the size, electrostatic and quantum chemical descriptors to compensate for the imprecision of the AM1 computational method. The results emphasize the main factors that influence (hyper)polarizability. All models were validated by the "leave-one-out" method and internal validations that confirmed the stability and good predictive ability.

Matrix metalloproteinases (MMPs): chemical-biological functions and (Q)SARs

Matrix metalloproteinases (MMPs) are a large family of calcium-dependent zinc-containing endopeptidases, which are responsible for the tissue remodeling and degradation of the extracellular matrix (ECM), including collagens, elastins, gelatin, matrix glycoproteins, and proteoglycan. They are regulated by hormones, growth factors, and cytokines, and are involved in ovarian functions. MMPs are excreted by a variety of connective tissue and pro-inflammatory cells including fibroblasts, osteoblasts, endothelial cells, macrophages, neutrophils, and lymphocytes. These enzymes are expressed as zymogens, which are subsequently processed by other proteolytic enzymes (such as serine proteases, furin, plasmin, and others) to generate the active forms. Matrix metalloproteinases are considered as promising targets for the treatment of cancer due to their strong involvement in malignant pathologies. Clinical/preclinical studies on MMP inhibition in tumor models brought positive results raising the idea that the development of strategies to inhibit MMPs may be proved to be a powerful tool to fight against cancer. However, the presence of an inherent flexibility in the MMP active-site limits dramatically the accurate modeling of MMP-inhibitor complexes. The interest in the application of quantitative structure-activity relationships (QSARs) has steadily increased in recent decades and we hope it may be useful in elucidating the mechanisms of chemical-biological interactions for this enzyme. In the present review, an attempt has been made to explore the in-depth knowledge from the classification of this enzyme to the clinical trials of their inhibitors. A total number of 92 QSAR models (44 published and 48 new formulated QSAR models) have also been presented to understand the chemical-biological interactions. QSAR results on the inhibition of various compound series against MMP-1, -2, -3, -7, -8, -9, -12, -13, and -14 reveal a number of interesting points. The most important of these are hydrophobicity and molar refractivity, which are the most important determinants of the activity.

Novel and versatile methodology for synthesis of cyclic imides and evaluation of their cytotoxic, DNA binding, apoptotic inducing activities and molecular modeling study

Versatile method has been developed for synthesis of N-substituted imides. Thus, acid anhydrides, imides and dicarboxylic acids were successfully subjected to dehydrative cyclization with substituted amines using DPPOx and Et(3)N to afford N-substituted imides under mild conditions. The DNA binding and apoptosis induction were investigated with regard to their potential utility as cytotoxic agents. Molecular modeling methods are used to study the cytotoxic activity of the active compounds by means of molecular and quantum mechanics.

Understanding human rhinovirus infections in terms of QSAR

The human rhinoviruses (HRVs) are the single most important cause of common colds. The widespread nature of this affliction, the economic consequences, and the well-known impracticality of vaccine development due to the large number of HRV serotypes (>100) have justified the search for chemotherapeutic agents. The interest in the application of quantitative structure-activity relationships has steadily increased in recent decades and we hope it may be useful in the search for anti-HRV agents. In the present paper, we have discussed the inhibition of various six compound series against HRV-1A, -1B, -2, -9, -14, -21, -22, -25, -64, and -89 by the formulation of a total number of 14 QSAR. Hydrophobicity is found to be one of the most important determinants of activity. Parabolic correlation with the hydrophobic parameter (Eq. ) is an encouraging example, where the optimal hydrophobicity is well defined. We believe that this may be the predictive model to narrow the synthetic challenges in order to yield very specific HRV-2 inhibitors. On the basis of this model, we have predicted eleven compounds (I-1 to I-11) that may be the next synthetic target. The proposed molecules (I-1 to I-11) also fulfill the conditions of Lipinski's "rule of five".

A QSAR study on influenza neuraminidase inhibitors

Influenza is a major respiratory infection associated with significant morbidity in the general population and mortality in elderly and high-risk patients. It is an RNA virus that contains two major surface glycoproteins, neuraminidase and hemagglutinin. These proteins are essential for infection. Neuraminidase has been found to be a potential target to control influenza virus. In this paper, we have developed 17 quantitative structure-activity relationships (QSAR) for different sets of compounds to understand chemical-biological interactions governing their activities toward influenza neuraminidase.

Lewis acid-promoted transformation of 2-alkoxypyridines into 2-aminopyridines and their antibacterial activity. Part 2: Remarkably facile C–N bond formation

2-Alkoxy-3-cyano-4,6-diarylpyridines 1a,b which were synthesized by condensation of alpha,beta-unsaturated ketones with malononitrils were subjected to Lewis acid-catalyzed nucleophilic displacement reaction with various amines to afford the corresponding 2-aminopyridines 3-21. The potency of the results as antibacterial agents has been evaluated. The structure of the newly prepared compounds was assessed by microanalysis, IR, and NMR spectra. Molecular modeling and QSAR methods are used to study the antibacterial activity of the active compounds by means of the molecular mechanic method.

An approach toward the problem of outliers in QSAR

Compounds that have unexpected biological activity and are unable to fit in a QSAR model are known as outliers. These are valuable in defining the limitations under which compounds act by a common molecular mechanism modeled by one or more descriptors, and also in defining the experimental limitations of the biological test data. Thus, the outliers should be submitted to particular attention to see if the reason for their peculiarity can be determined. Separating these outliers from the main data set and formulating another QSAR can resolve the problem. Our result shows that these outliers may be acting by a different mechanism or interacting with the receptor in different modes.