Probing the anticancer activity of nucleoside analogues: a QSAR model approach using an internally consistent training set

Computational identification of RNA functional determinants by three-dimensional quantitative structure-activity relationships

Anti-infection drugs target vital functions of infectious agents, including their ribosome and other essential non-coding RNAs. One of the reasons infectious agents become resistant to drugs is due to mutations that eliminate drug-binding affinity while maintaining vital elements. Identifying these elements is based on the determination of viable and lethal mutants and associated structures. However, determining the structure of enough mutants at high resolution is not always possible. Here, we introduce a new computational method, MC-3DQSAR, to determine the vital elements of target RNA structure from mutagenesis and available high-resolution data. We applied the method to further characterize the structural determinants of the bacterial 23S ribosomal RNA sarcin–ricin loop (SRL), as well as those of the lead-activated and hammerhead ribozymes. The method was accurate in confirming experimentally determined essential structural elements and predicting the viability of new SRL variants, which were either observed in bacteria or validated in bacterial growth assays. Our results indicate that MC-3DQSAR could be used systematically to evaluate the drug-target potentials of any RNA sites using current high-resolution structural data.

First report on development of quantitative interspecies structure-carcinogenicity relationship models and exploring discriminatory features for rodent carcinogenicity of diverse organic chemicals using OECD guidelines

Different regulatory agencies in food and drug administration and environmental protection worldwide are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. Carcinogenicity is a toxicity endpoint of major concern in recent times. Interspecies toxicity correlations may provide a tool for estimating sensitivity towards toxic chemical exposure with known levels of uncertainty for a diversity of wildlife species. In this background, we have developed quantitative interspecies structure-carcinogenicity correlation models for rat and mouse [rodent species according to the Organization for Economic Cooperation and Development (OECD) guidelines] based on the carcinogenic potential of 166 organic chemicals with wide diversity of molecular structures, spanning a large number of chemical classes and biological mechanisms. All the developed models have been assessed according to the OECD principles for the validation of QSAR models. Consensus predictions for carcinogenicity of the individual compounds are presented here for any one species when the data for the other species are available. Informative illustrations of the contributing structural fragments of chemicals which are responsible for specific carcinogenicity endpoints are identified by the developed models. The models have also been used to predict mouse carcinogenicities of 247 organic chemicals (for which rat carcinogenicities are present) and rat carcinogenicities of 150 chemicals (for which mouse carcinogenicities are present). Discriminatory features for rat and mouse carcinogenicity values have also been explored.

QSAR AND ACTION MECHANISM OF TROXACITABINE PRODRUGS WITH ANTITUMOR ACTIVITY

The quantitative structure–activity relationship (QSAR) of troxacitabine prodrugs with antitumor activity has been studied by using the density functional theory (DFT), molecular mechanics (MM2), and statistical methods. The established QSAR model shows not only significant statistical quality, but also predictive ability, with the square of adjusted correlation coefficient [Formula: see text] and the square of the cross-validation coefficient (q2 = 0.807). The antitumor activity is expressed as p IC50, which is defined as the negative value of the logarithm of necessary molar concentration of a compound to cause 50% growth inhibition against the human non-small-cell lung cancer cell line SW1573. It appears to be mainly governed by two factors (or original variables), i.e. the calculated hydrophobic coefficient (C log P) of whole molecule and the net charges of the first atom of substituent R (Q FR ), although three descriptors, i.e. C log P, (C log P)2, and Q FR , were selected in our multiple linear regression model. The factor C log P shows parabolic relation to p IC50 and its suitable range is around 5.6, and the other factor Q FR shows a significant negative correlation with p IC50. In this paper, a detailed discussion on these two factors was carried out, and their close correlation with the action mechanism of these prodrugs was reasonably revealed. Such results can offer some useful theoretical references for understanding the action mechanism and directing the molecular design of this kind of compound with antitumor activity.

Drug development to protozoan diseases

The diseases caused by protozoan parasite are responsible for considerable mortality and morbidity, affecting more than 500 million of people in the world. The epidemiological control of protozoan is unsatisfactory due to difficulties of vector and reservoir control; while the progress in the development of vaccine tends to be slow and arduous. Currently, the chemotherapy remains essential component of both clinical management and disease control programmer in endemic areas. The drugs in use as anti-protozoan agents were discovered over 50 years and a number of factors limit their utility such as: high cost, poor compliance, drug resistance, low efficacy and poor safety. In the recent years, the searches about the development of new drugs against protozoa parasite have been increased. This special issue of The Open Medicinal Chemistry Journal will present some of developments in this field with the aim to shown the significant advances in the discovery of new anti-protozoan drugs

A classification study of respiratory Syncytial Virus (RSV) inhibitors by variable selection with random forest

Experimental pEC₅₀s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development.

Using entropy of drug and protein graphs to predict FDA drug-target network: theoretic-experimental study of MAO inhibitors and hemoglobin peptides from Fasciola hepatica

There are many drugs described with very different affinity to a large number of receptors. In this work, we selected Drug-Target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets like proteins. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately, most QSAR models predict activity against only one protein. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model. The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 32:32-15-1:1. This MLP classifies correctly 623 out of 678 DTPs (Sensitivity = 91.89%) and 2995 out of 3234 nDTPs (Specificity = 92.61%), corresponding to training Accuracy = 92.48%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 313 out of 338 DTPs (Sensitivity = 92.60%) and 1411 out of 1534 nDTP (Specificity = 91.98%) in validation series, corresponding to total Accuracy = 92.09% for validation series (Predictability). This model favorably compares with other LDA and ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. These mt-QSARs offer also a good opportunity to construct drug-protein Complex Networks (CNs) that can be used to explore large and complex drug-protein receptors databases. Finally, we illustrated two practical uses of this model with two different experiments. In experiment 1, we report prediction, synthesis, characterization, and MAO-A and MAO-B pharmacological assay of 10 rasagiline derivatives promising for anti-Parkinson drug design. In experiment 2, we report sampling, parasite culture, SEC and 1DE sample preparation, MALDI-TOF MS and MS/MS analysis, MASCOT search, MM/MD 3D structure modeling, and QSAR prediction for different peptides of hemoglobin found in the proteome of the human parasite Fasciola hepatica; which is promising for anti-parasite drug targets discovery.

Pyrrolyl-, 2-(2-thienyl)pyrrolyl- and 2,5-bis(2-thienyl)pyrrolyl-nucleosides: synthesis, molecular and electronic structure, and redox behaviour of C5-thymidine derivatives

A series of modified nucleosides based on thymidine have been prepared by Pd-catalysed cross-coupling between N-alkyl-alkynyl functionalised pyrrolyl- (py), 2-(2-thienyl)pyrrolyl- (tp) or 2,5-bis(2-thienyl)pyrrolyl (tpt) groups with 5-iodo-2'-deoxyuridine. The length of the alkyl chain linking the nucleoside and pyrrolyl-containing unit, N(CH(2))(n)C[triple bond, length as m-dash]C-nucleoside (where n = 1-3) was also varied. The compounds have been characterised by (1)H NMR, ES-MS, UV-vis, cyclic voltammetry (CV) and, in some cases, single-crystal X-ray diffraction. Cyclic voltammetry studies demonstrated that all the py-, tp- and tpt-alkynyl derivatives 1-7 can be electrochemically polymerised to form conductive materials. It was found that increasing the N-alkyl chain length in these cases resulted in only minor changes in the oxidation potential. The same behaviour was observed for the tp- and tpt-modified nucleosides 9-12; however, the py-derivative, 8, produced a poorly conducting material. DFT calculations on the one-electron oxidised cation of the modified nucleosides bearing tp or tpt showed that spin density is located on the pyrrolyl and thienyl units in all cases and that the coplanarity of adjacent rings increases upon oxidation. In contrast, in the corresponding pyrrolyl cases the spin density is distributed over the whole molecule, suggesting that polymerisation does not occur solely at the pyrrolyl-Cα position and the conjugation is interrupted.

Design, Synthesis, and Evaluation of Antineoplastic Activity of Novel Carbocyclic Nucleosides

Cancer is the leading cause of death among men and women under age 85. Every year, millions of individuals are diagnosed with cancer. But finding new drugs is a complex, expensive, and very time-consuming task. Over the past decade, the cancer research community has begun to address the in silico modeling approaches, such as Quantitative Structure-Activity Relationships (QSAR), as an important alternative tool for targeting potential anticancer drugs. With the compilation of a large dataset of nucleosides synthesized in our laboratories, or elsewhere, and tested in a single cytotoxic assay under the same experimental conditions, we recognized a unique opportunity to attempt to build predictive QSAR models. Early efforts with 2D classification models built from part of this dataset were very encouraging. Here we report a further detailed evaluation of classification models to flag potential anticancer activities derived from a variety of 3D molecular representations. A quantitative 3D-model model that discriminates anticancer compounds from the inactive ones was attained, which allowed the correct classification of 82 % of compounds in such a large and diverse dataset, with only 5 % of false inactives and 11 % of false actives. The model developed here was then used to select and design a new series of nucleosides, by classifying beforehand them as active/inactive anticancer compounds. From the compounds so designed, 22 were synthesized and evaluated for their inhibitory effects on the proliferation of murine leukemia cells (L1210/0), of which 86 % were well-classified as active or inactive, and only two were false actives, corroborating the good predictive ability of the present discriminant model. The results of this study thus provide a valuable tool for the design of novel potent anticancer nucleoside analogues.

QSAR models to predict mutagenicity of acrylates, methacrylates and alpha,beta-unsaturated carbonyl compounds

OBJECTIVE

The purpose of this study is to develop a quantitative structure-activity relationship (QSAR) model that can distinguish mutagenic from non-mutagenic species with alpha,beta-unsaturated carbonyl moiety using two endpoints for this activity - Ames test and mammalian cell gene mutation test - and also to gather information about the molecular features that most contribute to eliminate the mutagenic effects of these chemicals.

METHODS

Two data sets were used for modeling the two mutagenicity endpoints: (1) Ames test and (2) mammalian cells mutagenesis. The first one comprised 220 molecules, while the second one 48 substances, ranging from acrylates, methacrylates to alpha,beta-unsaturated carbonyl compounds. The QSAR models were developed by applying linear discriminant analysis (LDA) along with different sets of descriptors computed using the DRAGON software.

RESULTS

For both endpoints, there was a concordance of 89% in the prediction and 97% confidentiality by combining the three models for the Ames test mutagenicity. We have also identified several structural alerts to assist the design of new monomers.

SIGNIFICANCE

These individual models and especially their combination are attractive from the point of view of molecular modeling and could be used for the prediction and design of new monomers that do not pose a human health risk.

Alignment-free prediction of a drug-target complex network based on parameters of drug connectivity and protein sequence of receptors

There are many drugs described with very different affinity to a large number of receptors. In this work, we selected drug-receptor pairs (DRPs) of affinity/nonaffinity drugs to similar/dissimilar receptors and we represented them as a large network, which may be used to identify drugs that can act on a receptor. Computational chemistry prediction of the biological activity based on quantitative structure-activity relationships (QSAR) substantially increases the potentialities of this kind of networks avoiding time- and resource-consuming experiments. Unfortunately, most QSAR models are unspecific or predict activity against only one receptor. To solve this problem, we developed here a multitarget QSAR (mt-QSAR) classification model. Overall model classification accuracy was 72.25% (1390/1924 compounds) in training, 72.28% (459/635) in cross-validation. Outputs of this mt-QSAR model were used as inputs to construct a network. The observed network has 1735 nodes (DRPs), 1754 edges or pairs of DRPs with similar drug-target affinity (sPDRPs), and low coverage density d = 0.12%. The predicted network has 1735 DRPs, 1857 sPDRPs, and also low coverage density d = 0.12%. After an edge-to-edge comparison (chi-square = 9420.3; p < 0.005), we have demonstrated that the predicted network is significantly similar to the one observed and both have a distribution closer to exponential than to normal.

Natural/random protein classification models based on star network topological indices

The development of the complex network graphs permits us to describe any real system such as social, neural, computer or genetic networks by transforming real properties in topological indices (TIs). This work uses Randic's star networks in order to convert the protein primary structure data in specific topological indices that are used to construct a natural/random protein classification model.

The set of natural proteins contains 1046 protein chains selected from the pre-compiled CulledPDB list from PISCES Dunbrack's Web Lab. This set is characterized by a protein homology of 20%, a structure resolution of 1.6 Å and R-factor lower than 25%. The set of random amino acid chains contains 1046 sequences which were generated by Python script according to the same type of residues and average chain length found in the natural set.

A new Sequence to Star Networks (S2SNet) wxPython GUI application (with a Graphviz graphics back-end) was designed by our group in order to transform any character sequence in the following star network topological indices: Shannon entropy of Markov matrices, trace of connectivity matrices, Harary number, Wiener index, Gutman index, Schultz index, Moreau–Broto indices, Balaban distance connectivity index, Kier–Hall connectivity indices and Randic connectivity index. The model was constructed with the General Discriminant Analysis methods from STATISTICA package and gave training/predicting set accuracies of 90.77% for the forward stepwise model type.

In conclusion, this study extends for the first time the classical TIs to protein star network TIs by proposing a model that can predict if a protein/fragment of protein is natural or random using only the amino acid sequence data. This classification can be used in the studies of the protein functions by changing some fragments with random amino acid sequences or to detect the fake amino acid sequences or the errors in proteins. These results promote the use of the S2SNet application not only for protein structure analysis but also for mass spectroscopy, clinical proteomics and imaging, or DNA/RNA structure analysis.

Highly efficient Suzuki-Miyaura coupling of heterocyclic substrates through rational reaction design

A dicyclohexyl(2-sulfo-9-(3-(4-sulfophenyl)propyl)-9H-fluoren-9-yl)phosphonium salt was synthesized in 64% overall yield in three steps from simple commercially available starting materials. The highly water-soluble catalyst obtained from the corresponding phosphine and [Na(2)PdCl(4)] enabled the Suzuki coupling of a broad variety of N- and S-heterocyclic substrates. Chloropyridines (-quinolines) and aryl chlorides were coupled with aryl-, pyridine- or indoleboronic acids in quantitative yields in water/n-butanol solvent mixtures in the presence of 0.005-0.05 mol % of Pd catalyst at 100 degrees C, chloropurines were quantitatively Suzuki coupled in the presence of 0.5 mol % of catalyst, and S-heterocyclic aryl chlorides and aryl- or 3-pyridylboronic acids required 0.01-0.05 mol % Pd catalyst for full conversion. The key to the high activity of the Pd-phosphine catalyst is the rational design of the reaction parameters (i.e., the presence of water in the reaction mixture, good solubility of reactants and catalyst in n-butanol/water (3:1), and the electron-rich and sterically demanding nature of the phosphine ligand).

Modeling resistance index of taxoids to MCF-7 cell lines using ANN together with electrotopological state descriptors

AIM

To develop an artificial neural network model for predicting the resistance index (RI) of taxoids.

METHODS

A dataset of 63 experimental data points were compiled from published studies and randomly subdivided into training and external test sets. Electrotopological state (E-state) indices were calculated to characterize molecular structure together with a principle component analysis to reduce the variable space and analyze the relative importance of E-state indices. Back propagation neural network technique was used to build the models. Five-fold cross-validation was performed and 5 models with different compound composition in training and validation sets were built. The independent external test set was used to evaluate the predictive ability of models.

RESULTS

The final model proved to be good with the cross-validation Q2cv0.62, external testing R2 0.84, and the slope of the regression line through the origin for the testing set at 0.9933.

CONCLUSION

The quantitative structure-activity relationship model can predict the RI to a relative nicety, which will aid in the development of new anti-multidrug resistance taxoids.