Trainable structure–activity relationship model for virtual screening of CYP3A4 inhibition

A new structure–activity relationship model predicting the probability for a compound to inhibit human cytochrome P450 3A4 has been developed using data for >800 compounds from various literature sources and tested on PubChem screening data. Novel GALAS (Global, Adjusted Locally According to Similarity) modeling methodology has been used, which is a combination of baseline global QSAR model and local similarity based corrections. GALAS modeling method allows forecasting the reliability of prediction thus defining the model applicability domain. For compounds within this domain the statistical results of the final model approach the data consistency between experimental data from literature and PubChem datasets with the overall accuracy of 89%. However, the original model is applicable only for less than a half of PubChem database. Since the similarity correction procedure of GALAS modeling method allows straightforward model training, the possibility to expand the applicability domain has been investigated. Experimental data from PubChem dataset served as an example of in-house high-throughput screening data. The model successfully adapted itself to both data classified using the same and different IC₅₀ threshold compared with the training set. In addition, adjustment of the CYP3A4 inhibition model to compounds with a novel chemical scaffold has been demonstrated. The reported GALAS model is proposed as a useful tool for virtual screening of compounds for possible drug-drug interactions even prior to the actual synthesis.     

Development of a pharmacophore for cruzain using oxadiazoles as virtual molecular probes: quantitative structure–activity relationship studies

Chagas’s is a neglected tropical disease caused by the protozoan parasite Trypanosoma cruzi. According to the World Health Organization, 7 million people are infected worldwide leading to 7000 deaths per year. Drugs available, nifurtimox and benzimidazole, are limited due to low efficacy and high toxicity. As a validated target, cruzain represents a major front in drug discovery attempts for Chagas disease. Herein, we describe the development of 2D QSAR (\(r_{{{\text{pred}}}}^{2}\)?=?0.81) and a 3D-QSAR-based pharmacophore (\(r_{{{\text{pred}}}}^{2}\)?=?0.82) from a series of non-covalent cruzain inhibitors represented mostly by oxadiazoles (lead compound, IC₅₀?=?200 nM). Both models allowed us to map key intermolecular interactions in S1′, S2 and S3 cruzain sub-sites (including halogen bond and C?H/π). To probe the predictive capacity of obtained models, inhibitors available in the literature from different classes displaying a range of scaffolds were evaluate achieving mean absolute deviation of 0.33 and 0.51 for 2D and 3D models, respectively. CoMFA revealed an unexplored region where addition of bulky substituents to produce new compounds in the series could be beneficial to improve biological activity.     

Prospective virtual screening for novel p53–MDM2 inhibitors using ultrafast shape recognition

The p53 protein, known as the guardian of genome, is mutated or deleted in approximately 50 % of human tumors. In the rest of the cancers, p53 is expressed in its wild-type form, but its function is inhibited by direct binding with the murine double minute 2 (MDM2) protein. Therefore, inhibition of the p53–MDM2 interaction, leading to the activation of tumor suppressor p53 protein presents a fundamentally novel therapeutic strategy against several types of cancers. The present study utilized ultrafast shape recognition (USR), a virtual screening technique based on ligand–receptor 3D shape complementarity, to screen DrugBank database for novel p53–MDM2 inhibitors. Specifically, using 3D shape of one of the most potent crystal ligands of MDM2, MI-63, as the query molecule, six compounds were identified as potential p53–MDM2 inhibitors. These six USR hits were then subjected to molecular modeling investigations through flexible receptor docking followed by comparative binding energy analysis. These studies suggested a potential role of the USR-selected molecules as p53–MDM2 inhibitors. This was further supported by experimental tests showing that the treatment of human colon tumor cells with the top USR hit, telmisartan, led to a dose-dependent cell growth inhibition in a p53-dependent manner. It is noteworthy that telmisartan has a long history of safe human use as an approved anti-hypertension drug and thus may present an immediate clinical potential as a cancer therapeutic. Furthermore, it could also serve as a structurally-novel lead molecule for the development of more potent, small-molecule p53–MDM2 inhibitors against variety of cancers. Importantly, the present study demonstrates that the adopted USR-based virtual screening protocol is a useful tool for hit identification in the domain of small molecule p53–MDM2 inhibitors.     

Development of an information-intensive structure–activity relationship model and its application to human respiratory chemical sensitizers

Structure–activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.     

α-Glucosidase inhibitors screening from Cyclocarya paliurus based on spectrum–effect relationship and UPLC–MS/MS

Cyclocarya paliurus is an edible and medicinal plant exhibiting significant hypoglycemic effect. However, its active components are still unclear and need further elucidation. In this research, the active components of the leaves of C. paliurus responsible for the α-glucosidase inhibitory activity were screened and identified based on a spectrum–effect relationship study in combination with ultra-performance liquid chromatography tandem mass spectrometry (UPLC–MS/MS) analysis. The 70% ethanol eluate fraction of the leaves of C. paliurus with the strongest α-glucosidase inhibitory activity was obtained after extraction and purification with macroporous resin. Their chromatographic fingerprints (15 batches) were established by UPLC analysis and 32 common peaks were specified by similarity analysis. Their IC₅₀ values for α-glucosidase inhibition were measured by an enzymatic reaction. Several multivariate statistical analysis methods including hierarchical cluster analysis, principal component analysis, partial least square analysis and gray relational analysis were applied to explore the spectrum–effect relationship between common peaks and IC₅₀ values, and the chromatographic peaks making a large contribution to efficacy were screened out. To further elucidate the active components of leaves of C. paliurus, the 70% ethanol eluate fraction was characterized by UPLC–MS/MS analysis, and 10 compounds were identified. This study provides a valuable reference for further research and development of hypoglycemic active components of C. paliurus.     

Qualitative and quantitative structure–activity relationship modelling for predicting blood-brain barrier permeability of structurally diverse chemicals

In this study, structure–activity relationship (SAR) models have been established for qualitative and quantitative prediction of the blood–brain barrier (BBB) permeability of chemicals. The structural diversity of the chemicals and nonlinear structure in the data were tested. The predictive and generalization ability of the developed SAR models were tested through internal and external validation procedures. In complete data, the QSAR models rendered ternary classification accuracy of >98.15%, while the quantitative SAR models yielded correlation (r²) of >0.926 between the measured and the predicted BBB permeability values with the mean squared error (MSE) <0.045. The proposed models were also applied to an external new in vitro data and yielded classification accuracy of >82.7% and r² > 0.905 (MSE < 0.019). The sensitivity analysis revealed that topological polar surface area (TPSA) has the highest effect in qualitative and quantitative models for predicting the BBB permeability of chemicals. Moreover, these models showed predictive performance superior to those reported earlier in the literature. This demonstrates the appropriateness of the developed SAR models to reliably predict the BBB permeability of new chemicals, which can be used for initial screening of the molecules in the drug development process.     

Virtual screening of dipeptidyl peptidase-4 inhibitors using quantitative structure–activity relationship-based artificial intelligence and molecular docking of hit compounds

Dipeptidyl peptidase-4 (DPP-4) inhibitors are becoming an essential drug in the treatment of type 2 diabetes mellitus; however, some classes of these drugs exert side effects, including joint pain and pancreatitis. Studies suggest that these side effects might be related to secondary inhibition of DPP-8 and DPP-9. In this study, we identified DPP-4-inhibitor hit compounds selective against DPP-8 and DPP-9. We built a virtual screening workflow using a quantitative structure–activity relationship (QSAR) strategy based on artificial intelligence to allow faster screening of millions of molecules for the DPP-4 target relative to other screening methods. Five regression machine learning algorithms and four classification machine learning algorithms were applied to build virtual screening workflows, with the QSAR model applied using support vector regression (R²_pred 0.78) and the classification QSAR model using the random forest algorithm with 92.2% accuracy. Virtual screening results of > 10 million molecules obtained 2 716 hits compounds with a pIC₅₀ value of > 7.5. Additionally, molecular docking results of several potential hit compounds for DPP-4, DPP-8, and DPP-9 identified CH0002 as showing high inhibitory potential against DPP-4 and low inhibitory potential for DPP-8 and DPP-9 enzymes. These results demonstrated the effectiveness of this technique for identifying DPP-4-inhibitor hit compounds selective for DPP-4 and against DPP-8 and DPP-9 and suggest its potential efficacy for applications to discover hit compounds of other targets.     

Facile one-pot synthesis,butyrylcholinesterase and α-glucosidase inhibitory activities,structure–activity relationship,molecular docking and DNA–drug binding analysis of Meldrum’s acid derivatives

Research on Chemical Intermediates - Meldrum’s acid derivatives were facile synthesized by one-pot condensation process and characterized by NMR (1H, 13C, DEPT-90 and DEPT-135) and EI-MS. The...     

Efficient and rapid method for extraction of intact phospholipids from sediments combined with molecular structure elucidation using LC–ESI-MS–MS analysis

This paper presents the application of an efficient method for extraction and fractionation of intact phospholipids (PLs) from complex sediment matrices and elucidation of their molecular structure by normal-phase HPLC–ESI-MS–MS. Flow-blending extraction was tested with different solvent mixtures and the best recovery of all PLs classes from the sediment matrix was achieved by using methanol–dichloromethane–buffer, 2:1:0.8. The applied LC–ESI-MS system has linearity of R²=0.98 and a detection limit of 0.5 ng/PL, sufficient for reliable identification of complex mixtures of PLs. MS–MS analyses using a triple-quadrupole mass spectrometer enables detection of individual PL side-chain composition and, hence, characterization of the living organisms contributing to the sedimentary organic material. Parallel GC–MS analysis of the hydrolysed phospholipid fatty acids supports the characterized fatty acid patterns determined from intact PLs. The PL inventory of different investigated lacustrine surface sediments shows predominantly high abundance of phosphatidylglycerols and phosphatidylethanolamines and phosphatidyl-mono- and dimethyl-ethanolamines with fatty acyl side-chains typically known from bacteria. In a sample from Lake Baikal intense signals of bacterial 14:0-acyl-PGs were also identified, for the first time in sediments as far as we are aware.