The open-access dataset for insilico cardiotoxicity prediction system

Drug cardiotoxicity is one of the main reasons of fatal drug related problem events and the subsequent withdrawals. Therefore, its early assessment is a crucial element of the drug development process. For the drug driven hERG inhibition assessment, which is assumed to be the main reason for toxicity, in vitro techniques are used. Gold standards are based on the Patch Clamp method with the use of various cell models but due to its low throughput, insilico models have become more appreciated. To develop a reliable empirical QSAR model, wide dataset containing a variety of cases has to be available. In this article, a freely available for download, set of data is described. It is based on literature peer-reviewed reports and contains hERG inhibition information expressed as IC50 value for 263 molecules described in 642 records. All studies were done with the use of three cell models (XO, CHO, HEK) and other elements describe the electrophysiological settings of the in vitro study. The above mentioned set was used for the successful development of the predictive models.     

CoMFA and HQSAR studies on 6,7-dimethoxy-4-pyrrolidylquinazoline derivatives as phosphodiesterase10A inhibitors

Phosphodiesterase10A (PDE10A) is an important enzyme with diverse biological actions in intracellular signaling systems, making it an emerging target for diseases such as schizophrenia, Huntington's disease, and diabetes mellitus. The objective of the current 3D QSAR study is to uncover some of the structural parameters which govern PDE10A inhibitory activity over PDE3A/B. Thus, comparative molecular field analysis (CoMFA) and hologram quantitative structure-activity relationship (HQSAR) studies were carried out on recently reported 6,7-dimethoxy-4-pyrrolidylquinazoline derivatives as PDE10A inhibitors. The best CoMFA model using atom-fit alignment approach with the bound conformation of compound 21 as the template yielded the steric parameter as a major contributor (nearly 70%) to the observed variations in biological activity. The best CoMFA model produced statistically significant results, with the cross-validated (r(cv)(2)) and conventional correlation (r(ncv)(2)) coefficients being 0.557 and 0.991, respectively, for the 21 training set compounds. Validation of the model by external set of six compounds yielded a high (0.919) predictive value. The CoMFA models of PDE10A and PDE3A/B activity were compared in order to address the selectivity issue of these inhibitors. The best HQSAR model for PDE10A was obtained with an r(cv)(2) of 0.704 and r(ncv)(2) of 0.902 using atoms, bonds, connections, chirality, donor, and acceptor as fragment distinction and default fragment size of 4-7 with three components for the 21 compounds. The HQSAR model predicted the external test-set of compounds well since a good agreement between the experimental and predicted values was verified. Taken together, the present QSAR models were found to accurately predict the PDE10A inhibitory activity of the test-set compounds and to yield reliable clues for further optimization of the quinazoline derivatives in the dataset.     

Two- and three-dimensional quantitative structure-activity relationships for a series of purine nucleoside phosphorylase inhibitors

Comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis, and hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of 52 training set inhibitors of calf spleen purine nucleoside phosphorylase (PNP). Significant cross-validated correlation coefficients (CoMFA, q(2)=0.68; CoMSIA, q(2)=0.66; and HQSAR, q(2)=0.70) were obtained, indicating the potential of the models for untested compounds. The models were then used to predict the inhibitory potency of 16 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results. The final QSAR models along with the information gathered from 3D contour and 2D contribution maps should be useful for the design of novel inhibitors of PNP having improved potency.     

Quantitative structure-activity relationship of phenoxyphenyl-methanamine compounds with 5HT2A, SERT, and hERG activities

QSAR models have been used to evaluate activities for compounds in the phenoxyphenyl-methanamine (PPMA) class of compounds. These models utilize Hammett-type donating-withdrawing substituent values as well as simple parameters to describe substituent size and elucidate the SAR of the 'A' and 'B' rings. Using this methodology, intuitive QSAR relationships were found for the three biological activities with R(2) values of 0.73, 0.45, and 0.58 for 5HT(2A), SerT, and hERG activities.     

QSAR studies on piperazinylalkylisoxazole analogues selectively acting on dopamine D3 receptor by HQSAR and CoMFA

QSAR studies for piperazinylalkylisoxazole analogues were conducted by hologram QSAR (HQSAR) and comparative molecular field analysis (CoMFA) to explain the binding affinities of 264 ligands acting on dopamine D(3) receptor. The HQSAR was assessed by r(2) value of 0.917 and cross validated q(2) value of 0.841. In the CoMFA, r(2) is 0.919 and cross validated q(2) is 0.727. The results provide the tools for predicting the affinity of related compounds and guiding the design of new ligands.     

Synthesis and biological evaluation of 4-piperidinecarboxylate and 4-piperidinecyanide derivatives for T-type calcium channel blockers

To obtain selective and potent inhibitor for T-type calcium channel by ligand based drug design, 4-piperidinecarboxylate and 4-piperidinecyanide derivatives were prepared and evaluated for in vitro and in vivo activity against α(1G) calcium channel. Among them, several compounds showed good T-type calcium channel inhibitory activity and minimal off-target activity over hERG channel (% inhibition at 10 μM=61.85-71.99, hERG channel IC(50)=1.57 ± 0.14-4.98 ± 0.36 μM). Selected compound 31a was evaluated on SNL model of neuropathic pain and showed inhibitory effect on mechanical allodynia.     

Hologram quantitative structure-activity relationships for a series of farnesoid X receptor activators

The farnesoid X receptor (FXR) is an attractive drug target for the development of novel therapeutic agents for the treatment of dyslipidemia and cholestasis. Hologram quantitative structure-activity relationship (HQSAR) studies were conducted on a series of potent FXR activators originated from natural product-like libraries. A training set containing 82 compounds served to establish the models. The best HQSAR model was generated using atoms, bonds, connections, chirality, and donor and acceptor as fragment distinction and fragment size default (4-7) with six components. The model was used to predict the potency of 20 test set compounds that were not included in the training set, and the predicted values were in good agreement with the experimental results. The final HQSAR model and the information obtained from HQSAR 2D contribution maps should be useful for the design of novel FXR ligands having improved potency.     

Structure-activity relationships for a class of selective inhibitors of the major cysteine protease from Trypanosoma cruzi

Chagas' disease is a parasitic infection widely distributed throughout Latin America, with devastating consequences in terms of human morbidity and mortality. Cruzain, the major cysteine protease from Trypanosoma cruzi, is an attractive target for antitrypanosomal chemotherapy. In the present work, classical two-dimensional quantitative structure-activity relationships (2D QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 45 thiosemicarbazone and semicarbazone derivatives as inhibitors of T. cruzi cruzain. Significant statistical models (HQSAR, q² = 0.75 and r² = 0.96; classical QSAR, q² = 0.72 and r² = 0.83) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 10 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, = 0.95; classical QSAR, = 0.91), indicating the existence of complementary between the two ligand-based drug design techniques.     

CoMFA and HQSAR of acylhydrazide cruzain inhibitors

An approach combining CoMFA and HQSAR methods was used to describe QSAR models for a series of cruzain inhibitors having the acylhydrazide framework. A CoMFA study using two alignment orientations (I and II), three different probe atoms and changes of the lattice spacing (1 and 2 A) was performed. Alignment II and an sp3 probe carbon atom yielded good cross-validation (q2=0.688) employing lattice spacing of 1 A. The best HQSAR model was generated using atoms, bond, and connectivity as fragment distinction and fragment size default (4-5) showing similar cross-validated value of CoMFA (q2=0.689). Based upon the information derived from CoMFA and HQSAR, we have identified some key features that may be used to design new acylhydrazide derivatives that may be more potent cruzain inhibitors.     

Comparative QSAR studies on peptide deformylase inhibitors

Comparative quantitative structure–activity relationship (QSAR) analyses of peptide deformylase (PDF) inhibitors were performed with a series of previously published (British Biotech Pharmaceuticals, Oxford, UK) reverse hydroxamate derivatives having antibacterial activity against Escherichia coli PDF, using 2D and 3D QSAR methods, comparative molecular field analysis (CoMFA), comparative molecular similarity indices analysis (CoMSIA), and hologram QSAR (HQSAR). Statistically reliable models with good predictive power were generated from all three methods (CoMFA r ² = 0.957, q ² = 0.569; CoMSIA r ² = 0.924, q ² = 0.520; HQSAR r ² = 0.860, q ² = 0.578). The predictive capability of these models was validated by a set of compounds that were not included in the training set. The models based on CoMFA and CoMSIA gave satisfactory predictive r ² values of 0.687 and 0.505, respectively. The model derived from the HQSAR method showed a low predictability of 0.178 for the test set. In this study, 3D prediction models showed better predictive power than 2D models for the test set. This might be because 3D information is more important in the case of datasets containing compounds with similar skeletons. Superimposition of CoMFA contour maps in the active site of the PDF crystal structure showed a meaningful correlation between receptor–ligand binding and biological activity. The final QSAR models, along with information gathered from 3D contour and 2D contribution maps, could be useful for the design of novel active inhibitors of PDF. Figure Superimposition of comparative molecular field analysis (CoMFA) contour plot in the active site of peptide deformylase (PDF)     

Hologram quantitative structure activity relationship studies on 5-HT6 antagonists

Predictive hologram quantitative structure activity relationship (HQSAR) models were developed for a series of arylsulfonamide compounds acting as specific 5-HT6 antagonists. A training set containing 48 compounds served to establish the model. The best HQSAR model was generated using atoms, bond, and connectivity as fragment distinction and 4-7 as fragment size showing cross-validated r2(q2) value of 0.702 and conventional r2 value of 0.971. The predictive ability of the model was validated by an external test set of 20 compounds giving satisfactory predictive r2 value of 0.678. The efficiency of HQSAR approach was further evidenced by the generation of predictive models for a training set containing 30 highly diverse, both specific and nonspecific 5-HT6 antagonists. The best HQSAR model for this training set was generated using atoms, bond, and connectivity as fragment distinction and 4-7 as fragment size showing cross-validated r2(q2) value of 0.693 and conventional r2 value of 0.923. This model was also validated by using an external test set of 10 compounds giving satisfactory predictive r2 value of 0.692. The contribution maps obtained from these models were used to explain the individual atomic contributions to the overall activity.