Quantitative structure-antitumor activity relationships of camptothecin analogues: cluster analysis and genetic algorithm-based studies

In silico screening of FDA approved drugs on AXL kinase and validation for breast cancer cell line

AXL kinase has been over expressed in many tumors and its involvement in cell proliferation, migration, survival, and resistance makes the kinase as attractive therapeutic target for many cancers. In this study, we performed a virtual screening of the food and drug administration (FDA) approved drug molecule database against AXL kinase for repurposing studies of breast cancer. We have identified three non-cancer drugs with good binding energies were subjected to in vitro breast cancer MCF-7 cell lines. Three drug molecules showing the activity with good IC50 values toward the cancer cell line. We also carried out a 2 dimensional (2 D) quantitative structure activity relation (QSAR) studies on N-[4-(Quinolin-4-yloxy)phenyl]benzenesulfonamides derivatives to design potent inhibitors for AXL kinase. The final QSAR equation was robust with good predictivity and the statistical validation having R² and Q² values are 0.91 and 0.86, respectively. QSAR equation descriptors informs about the chemical properties of AXL inhibitors and helpful for designing novel inhibitors. Communicated by Ramaswamy H. Sarma.

Synthesis, Anticancer Evaluation and Structure-Activity Analysis of Novel (E)- 5-(2-Arylvinyl)-1,3,4-oxadiazol-2-yl)benzenesulfonamides

To learn more about the structure-activity relationships of (E)-3-(5-styryl-1,3,4-oxadiazol-2-yl)benzenesulfonamide derivatives, which in our previous research displayed promising in vitro anticancer activity, we have synthesized a group of novel (E)-5-[(5-(2-arylvinyl)-1,3,4-oxadiazol-2-yl)]-4-chloro-2-R1-benzenesulfonamides 7-36 as well as (E)-4-[5-styryl1,3,4-oxadiazol-2-yl]benzenesulfonamides 47-50 and (E)-2-(2,4-dichlorophenyl)-5-(2-arylvinyl)-1,3,4-oxadiazols 51-55. All target derivatives were evaluated for their anticancer activity on HeLa, HCT-116, and MCF-7 human tumor cell lines. The obtained results were analyzed in order to explain the influence of a structure of the 2-aryl-vinyl substituent and benzenesulfonamide scaffold on the anti-tumor activity. Compound 31, bearing 5-nitrothiophene moiety, exhibited the most potent anticancer activity against the HCT-116, MCF-7, and HeLa cell lines, with IC50 values of 0.5, 4, and 4.5 µM, respectively. Analysis of structure-activity relationship showed significant differences in activity depending on the substituent in position 3 of the benzenesulfonamide ring and indicated as the optimal meta position of the sulfonamide moiety relative to the oxadizole ring. In the next stage, chemometric analysis was performed basing on a set of computed molecular descriptors. Hierarchical cluster analysis was used to examine the internal structure of the obtained data and the quantitative structure-activity relationship (QSAR) analysis with multiple linear regression (MLR) method allowed for finding statistically significant models for predicting activity towards all three cancer cell lines.

Multivariate image analysis-quantitative structure-retention relationship study of polychlorinated biphenyls using partial least squares and radial basis function neural networks

Polychlorinated biphenyls belong to a class of hazardous and environmental pollutants. Gas chromatography separation and experimental relative retention time evaluation of these compounds on a poly (94% methyl/5% phenyl) silicone-based capillary non-bonded and cross-linked column are time consuming and expensive. In this study, relative retention times were estimated using two-dimensional images of molecules based on a newly implemented rapid and simple quantitative structure retention relationship methodology. The resulting descriptors were subjected to partial least square and principal component-radial basis function neural networks as linear and nonlinear models, respectively, to attain a statistical explanation of the retention behavior of the molecules. The high numerical values of correlation coefficients and low root mean square errors in the case of the partial least square model, confirm the supremacy of this model as well as the linear dependency of images of molecules to their relative retention times. Evaluation of the best correlation model performed using internal and external tests and its good applicability domain was checked using a distance to the model in the X-Space plot. This study provides a practical and effective method for analytical chemists working with chromatographic platforms to improve predictive confidence of studies that seek to identify unknown molecules or impurities.

Quantitative Structure-Activity Relationship Study of Camptothecin Derivatives as Anticancer Drugs Using Molecular Descriptors

Aim and Objective:

A Quantitative Structure-Activity Relationship (QSAR) has been widely developed to derive a correlation between chemical structures of molecules to their known activities. In the present investigation, QSAR models have been carried out on 76 Camptothecin (CPT) derivatives as anticancer drugs to develop a robust model for the prediction of physicochemical properties.

Materials and Methods:

A training set of 60 structurally diverse CPT derivatives was used to construct QSAR models for the prediction of physiochemical parameters such as Van der Waals surface area (SvdW), Van der Waals Volume (VvdW), Molar Refractivity (MR) and Polarizability (α). The QSAR models were optimized using Multiple Linear Regression (MLR) analysis. A test set of 16 compounds was evaluated using the defined models.

:

The Genetic Algorithm And Multiple Linear Regression Analysis (GA-MLR) were used to select the descriptors derived from the Dragon software to generate the correlation models that relate the structural features to the studied properties.

Results:

QSAR models were used to delineate the important descriptors responsible for the properties of the CPT derivatives. The statistically significant QSAR models derived by GA-MLR analysis were validated by Leave-One-Out Cross-Validation (LOOCV) and test set validation methods. The multicollinearity and autocorrelation properties of the descriptors contributed in the models were tested by calculating the Variance Inflation Factor (VIF) and the Durbin–Watson (DW) statistics.

Conclusion:

The predictive ability of the models was found to be satisfactory. Thus, QSAR models derived from this study may be helpful for modeling and designing some new CPT derivatives and for predicting their activity.

Topoisomerase Inhibitors and Targeted Delivery in Cancer Therapy

DNA topoisomerases are enzymes that catalyze the alteration of DNA topology with transiently induced DNA strand breakage, essential for DNA replication. Topoisomerases are validated cancer chemotherapy targets. Anticancer agents targeting Topoisomerase I and II have been in clinical use and proven to be highly effective, though with significant side effects. There are tremendous efforts to develop new generation of topoisomerase inhibitors. Targeted delivery of topoisomerase inhibitors is another way to reduce the side effects. Conjugates of topoisomerases inhibitors with antibody, polymer, or small molecule are developed to target these inhibitors to tumor sites.

Construction of a Quantitative Structure Activity Relationship (QSAR) Model to Predict the Absorption of Cephalosporins in Zebrafish for Toxicity Study

Cephalosporins are beta-lactam antibiotics that are widely used in China. Five generations of cephalosporins have been introduced in clinical practice to date; moreover, some new candidates are also undergoing clinical evaluations. To improve the success rates of new drug development, we need to have a comprehensive understanding about the relationship between the structure of cephalosporins and the toxicity that it induces at an early stage. In the cephalosporins toxicity study using zebrafish, the drug absorption is a key point. In this study, we determined the absorption of cephalosporins in zebrafish during toxicity test. The internal concentrations of 19 cephalosporins in zebrafish were determined using a developed liquid chromatography-tandem mass spectrometry (LC-MS/MS) method. Furthermore, a quantitative structure-activity relationship (QSAR) model was established by multilinear regression; moreover, it was used to predict the absorption of cephalosporins in zebrafish. During leave-one-out cross-validation, a satisfactory performance was obtained with a predictive ability (q ²) of 0.839. The prediction ability of the model was further confirmed when the predictive ability (q ²) was 0.859 in external prediction. The best QSAR model, which was based on five molecular descriptors, exhibited a promising predictive performance and robustness. In experiments involving drug toxicity, the developed QSAR model was used to estimate internal concentrations of cephalosporins. Thus, the toxicity results were correlated with the internal concentration of the drug within the larvae. The developed model served as a new powerful tool in zebrafish toxicity tests.

QSPR modelling for prediction of glass transition temperature of diverse polymers

The glass transition temperature is a vital property of polymers with a direct impact on their stability. In the present study, we built quantitative structure-property relationship models for the prediction of the glass transition temperatures of polymers using a data set of 206 diverse polymers. Various 2D molecular descriptors were computed from the single repeating units of polymers. We derived five models from different combinations of six descriptors in each case by employing the double cross-validation technique followed by partial least squares regression. The selected models were subsequently validated by methods such as cross-validation, external validation using test set compounds, the Y-randomization (Y-scrambling) test and an applicability domain study of the developed models. All of the models have statistically significant metric values such as r² ranging from 0.713-0.759, Q² ranging from 0.662-0.724 and [Formula: see text] ranging 0.702-0.805. Finally, a comparison was made with recently published models, though the previous models were based on a much smaller data set with limited diversity. We also used a true external set to demonstrate the performance of our developed models, which may be used for the prediction and design of novel polymers prior to their synthesis.

Chemometric modeling of odor threshold property of diverse aroma components of wine

We have modelled here odor threshold properties (OTP) of various aroma components present in different types of wine using quantitative structure–property relationship (QSPR) studies employing both two-dimensional and three-dimensional descriptors.

PLS regression-based chemometric modeling of odorant properties of diverse chemical constituents of black tea and coffee

We investigate the key structural features regulating the odorant properties of constituents present in black tea and coffee, the most attractive non-alcoholic beverages.

Computer-aided drug design of capuramycin analogues as anti-tuberculosis antibiotics by 3D-QSAR and molecular docking

Capuramycin and a few semisynthetic derivatives have shown potential as anti-tuberculosis antibiotics.To understand their mechanism of action and structureactivity relationships a 3D-QSAR and molecular docking studies were performed. A set of 52 capuramycin derivatives for the training set and 13 for the validation set was used. A highly predictive MFA model was obtained with crossvalidated q2 of 0.398, and non-cross validated partial least-squares (PLS) analysis showed a conventional r2 of 0.976 and r2pred of 0.839. The model has an excellent predictive ability. Combining the 3D-QSAR and molecular docking studies, a number of new capuramycin analogs with predicted improved activities were designed. Biological activity tests of one analog showed useful antibiotic activity against Mycobacterium smegmatis MC2 155 and Mycobacterium tuberculosis H37Rv. Computer-aided molecular docking and 3D-QSAR can improve the design of new capuramycin antimycobacterial antibiotics.

Endocrine-disrupting activity of per- and polyfluoroalkyl substances: Exploring combined approaches of ligand and structure based modeling

Exposure to perfluorinated and polyfluoroalkyl substances (PFCs/PFASs), endocrine disrupting halogenated pollutants, has been linked to various diseases including thyroid toxicity in human populations across the globe. PFASs can compete with thyroxine (T4) for binding to the human thyroid hormone transport protein transthyretin (TTR) which may lead to reduce thyroid hormone levels leading to endocrine disrupting adverse effects. Environmental fate and endocrine-disrupting activity of PFASs has initiated several research projects, but the amount of experimental data available for these pollutants is limited. In this study, experimental data for T4-TTR competing potency of 24 PFASs obtained in a radioligand-binding assay were modeled using classification- and regression-based quantitative structure-activity relationship (QSAR) tools with simple molecular descriptors obtained from chemical structure of these compounds in order to identify the responsible structural features and fragments of the studied PFASs for endocrine disruption activity. Additionally, docking studies were performed employing the crystal structure complex of TTR with bound 2', 6'-difluorobiphenyl-4-carboxylic acid (PDB: 2F7I) in order to constitute the receptor model for human TTR. The results corroborate evidence for these binding interactions and indicate multiple high-affinity modes of binding. The developed in silico models therefore advance our understanding of important structural attributes of these chemicals and may provide important information for the design of future synthesis of PFASs as well as may serve as an efficient query tool for virtual screening of large PFAS databases to check their endocrine toxicity profile.

QSAR model for prediction of the therapeutic potency of N-benzylpiperidine derivatives as AChE inhibitors

A new family of AChE inhibitors, N-benzylpiperidines, showed exceptional efficacy in vitro and in vivo, minimal side effects and high selectivity for acetylcholinesterase (AChE). Three regression methods were chosen in this work to develop robust predictive models, namely multiple linear regression (MLR), genetic function approximation (GFA) and multilayer perceptron network (MLP). Ten descriptors were selected for a dataset of 99 molecules, using a genetic algorithm. The best results were obtained for MLP with a 10-6-1 artificial neural network model trained with the Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithm. Statistical prediction for MLR and GFA were r² = 0.882 and r² = 0.875, respectively. Because internal and external validation strategies play an important role, we adopted all available validation strategies to check the robustness of the models. All criteria used to validate these models revealed the superiority of the GFA model. Therefore, the models developed in this study provide an excellent prediction of the inhibitory concentration of a new family of AChE inhibitors.

Pharmacophore modeling and in silico toxicity assessment of potential anticancer agents from African medicinal plants

Molecular modeling has been employed in the search for lead compounds of chemotherapy to fight cancer. In this study, pharmacophore models have been generated and validated for use in virtual screening protocols for eight known anticancer drug targets, including tyrosine kinase, protein kinase B β, cyclin-dependent kinase, protein farnesyltransferase, human protein kinase, glycogen synthase kinase, and indoleamine 2,3-dioxygenase 1. Pharmacophore models were validated through receiver operating characteristic and Güner–Henry scoring methods, indicating that several of the models generated could be useful for the identification of potential anticancer agents from natural product databases. The validated pharmacophore models were used as three-dimensional search queries for virtual screening of the newly developed AfroCancer database (~400 compounds from African medicinal plants), along with the Naturally Occurring Plant-based Anticancer Compound-Activity-Target dataset (comprising ~1,500 published naturally occurring plant-based compounds from around the world). Additionally, an in silico assessment of toxicity of the two datasets was carried out by the use of 88 toxicity end points predicted by the Lhasa’s expert knowledge-based system (Derek), showing that only an insignificant proportion of the promising anticancer agents would be likely showing high toxicity profiles. A diversity study of the two datasets, carried out using the analysis of principal components from the most important physicochemical properties often used to access drug-likeness of compound datasets, showed that the two datasets do not occupy the same chemical space.

Global Performance and Trend of QSAR/QSPR Research: A Bibliometric Analysis

A bibliometric analysis based on the Science Citation Index Expanded was conducted to provide insights into the publication performance and research trend of quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) from 1993 to 2012. The results show that the number of articles per year quadrupled from 1993 to 2006 and plateaued since 2007. Journal of Chemical Information and Modeling was the most prolific journal. The internal methodological innovations in acquiring molecular descriptors and modeling stimulated the articles' increase in the research fields of drug design and synthesis, and chemoinformatics; while the external regulatory demands on model validation and reliability fueled the increase in environmental sciences. "Prediction endpoints", "statistical algorithms", and "molecular descriptors" were identified as three research hotspots. The articles from developed countries were larger in number and more influential in citation, whereas those from developing countries were higher in output growth rates.

Nano-quantitative structure-activity relationship modeling using easily computable and interpretable descriptors for uptake of magnetofluorescent engineered nanoparticles in pancreatic cancer cells

As experimental evaluation of the safety of nanoparticles (NPs) is expensive and time-consuming, computational approaches have been found to be an efficient alternative for predicting the potential toxicity of new NPs before mass production. In this background, we have developed here a regression-based nano quantitative structure-activity relationship (nano-QSAR) model to establish statistically significant relationships between the measured cellular uptakes of 109 magnetofluorescent NPs in pancreatic cancer cells with their physical, chemical, and structural properties encoded within easily computable, interpretable and reproducible descriptors. The developed model was rigorously validated internally as well as externally with the application of the principles of Organization for Economic Cooperation and Development (OECD). The test for domain of applicability was also carried out for checking reliability of the predictions. Important fragments contributing to higher/lower cellular uptake of NPs were identified through critical analysis and interpretation of the developed model. Considering all these identified structural attributes, one can choose or design safe, economical and suitable surface modifiers for NPs. The presented approach provides rich information in the context of virtual screening of relevant NP libraries.

Development of predictive quantitative structure–activity relationship model and its application in the discovery of human leukotriene A4 hydrolase inhibitors

Background: Human LTA4H catalyzes the conversion of LTA4 to LTB4 and plays a key role in innate immune responses. Inhibition of this enzyme can be a valid method in the treatment of inflammatory response exhibited through LTB4. Results & discussion: The quantitative structure–activity relationship (QSAR) models were developed using genetic function approximation and validated. A training set of 26 diverse compounds and their molecular descriptors were used to develop highly correlating QSAR models. A six-descriptor model explaining the biological activity of the training and test sets with correlation values of 0.846 and 0.502, respectively, was selected as the best model and used in a database screening of drug-like Maybridge database followed by molecular docking. Conclusion: Based on the predicted potent inhibitory activities, expected binding mode and molecular interactions at the active site of hLTA4H final leads were selected as to be utilized in designing future hLTA4H inhibitors.

QSAR study and the hydrolysis activity prediction of three alkaline lipases from different lipase-producing microorganisms

The hydrolysis activities of three alkaline lipases, L-A1, L-A2 and L-A3 secreted by different lipase-producing microorganisms isolated from the Bay of Bohai, P. R. China were characterized with 16 kinds of esters. It was found that all the lipases have the ability to catalyze the hydrolysis of the glycerides, methyl esters, ethyl esters, especially for triglycerides, which shows that they have broad substrate spectra, and this property is very important for them to be used in detergent industry. Three QSAR models were built for L-A1, L-A2 and L-A3 respectively with GFA using Discovery studio 2.1. The models equations 1, 2 and 3 can explain 95.80%, 97.45% and 97.09% of the variances (R(2)(adj)) respectively while they could predict 95.44%, 89.61% and 93.41% of the variances (R(2)(cv)) respectively. With these models the hydrolysis activities of these lipases to mixed esters were predicted and the result showed that the predicted values are in good agreement with the measured values, which indicates that this method can be used as a simple tool to predict the lipase activities for single or mixed esters.

Development of classification and regression based QSAR models to predict rodent carcinogenic potency using oral slope factor

Carcinogenicity is among the toxicological endpoints posing the highest concern for human health. Oral slope factors (OSFs) are used to estimate quantitatively the carcinogenic potency or the risk associated with exposure to the chemical by oral route. Regulatory agencies in food and drug administration and environmental protection are employing quantitative structure-activity relationship (QSAR) models to fill the data gaps related with properties of chemicals affecting the environment and human health. In this background, we have developed quantitative structure-carcinogenicity regression models for rodents based on the carcinogenic potential of 70 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 Organization for Economic Cooperation and Development (OECD) principles for the validation of QSAR models. We have also attempted to develop a carcinogenicity classification model based on Linear Discriminant Analysis (LDA). Developed regression and LDA models are rigorously validated internally as well as externally. Our in silico studies make it possible to obtain a quantitative interpretation of the structural information of carcinogenicity along with identification of the discriminant functions between lower and higher carcinogenic compounds by LDA. Pharmacological distribution diagrams (PDDs) are used as a visualizing technique for the identification and selection of chemicals with lower carcinogenicity. Constructive, informative and comparable interpretations have been observed in both cases of classification and regression based modeling.

The use of Bayesian nonlinear regression techniques for the modelling of the retention behaviour of volatile components of Artemisia species

The main aim of this work was to assess the ability of Bayesian multivariate adaptive regression splines (BMARS) and Bayesian radial basis function (BRBF) techniques for modelling the gas chromatographic retention indices of volatile components of Artemisia species. A diverse set of molecular descriptors was calculated and used as descriptor pool for modelling the retention indices. The ability of BMARS and BRBF techniques was explored for the selection of the most relevant descriptors and proper basis functions for modelling. The results revealed that BRBF technique is more reproducible than BMARS for modelling the retention indices and can be used as a method for variable selection and modelling in quantitative structure-property relationship (QSPR) studies. It is also concluded that the Markov chain Monte Carlo (MCMC) search engine, implemented in BRBF algorithm, is a suitable method for selecting the most important features from a vast number of them. The values of correlation between the calculated retention indices and the experimental ones for the training and prediction sets (0.935 and 0.902, respectively) revealed the prediction power of the BRBF model in estimating the retention index of volatile components of Artemisia species.

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.

Structural Insights into the Active Site of Human Sodium Dependent Glucose Co-Transporter 2: Homology Modelling, Molecular Docking, and 3D - QSAR Studies

Human sodium dependent glucose co-transporter 2 (hSGLT2) is a target for diabetes mellitus type 2 (T2DM). The 3D (three dimensional) homology model of hSGLT2 comprising 14 transmembrane helical domains was constructed and molecular docking of the inhibitors, C-aryl glucoside analogues, into the active site was studied. The 3D-QSAR (quantitative structure activity relationship) analysis was carried out on 43 C-aryl glucoside analogues as a training set. The molecular field analysis (MFA) with G/PLS (genetic partial least-squares) method was used to generate statistically significant 3D-QSAR (r2 = 0.857) based on a molecular field generated using electrostatic and steric probes. The QSAR model was validated using leave-one-out cross-validation, bootstrapping, and randomisation methods, and finally with an external test set comprising 10 inhibitors. The molecular docking studies provide structural insights into the active site and key interactions involved in the binding of inhibitors to hSGLT2 and these results corroborate with the 3D-QSAR analysis that provide the active conformation of inhibitors and the nature of interactive fields important for activity.

Quantitative structure–property relationships of camptothecins in humans

PURPOSE

To develop quantitative structure property relationships (QSPR) for the pharmacokinetics and the susceptibility to BCRP-mediated efflux of ten drugs in the camptothecin family of topoisomerase I inhibitors.

METHODS

Pharmacokinetic parameters (total and lactone clearance, total steady-state volume of distribution, and lactone:total area under the curve ratio) and IC(50) values of cytotoxicity in both BCRP over-expressing and sensitive cell lines were extracted from the literature. Molecular descriptors were generated for both the lactone and carboxylic acid forms of the drugs using SYBYL and ACD/Labs software. A partial least squares algorithm in SAS was used to construct QSPR models for each of the properties of interest, and final models were validated using leave-one-out cross-validation.

RESULTS

The molecular descriptors calculated for the lactone forms were better correlated with the selected properties than that of the carboxylate forms. Reasonable correlations (R(2) range 0.63-0.99) and good predictive performances (Q(2) range 0.45-0.88) were obtained for all seven QSPR models. Molecular descriptors that contribute to each pharmacokinetic property and susceptibility to BCRP mediated efflux were identified.

CONCLUSIONS

QSPR models were successfully constructed for the pharmacokinetics and the susceptibility to BCRP mediated efflux of the camptothecin analogs. The identified molecular parameters may help guide the synthesis of new camptothecin analogs with improved pharmacokinetic properties and reduced potential for clinical resistance.