In silico identification of T-type calcium channel blockers: A ligand-based pharmacophore mapping approach.

Limited progress has been made in the quest to identify both selective and non-toxic T-type calcium channel blocking compounds. The present research work was directed toward slaking the same by identifying the selective three dimensional (3D) pharmacophore map for T-type calcium channel blockers (CCBs). Using HipHop module in the CATALYST 4.10 software, both selective and non-selective HipHop pharmacophore maps for T-type CCBs were developed to identify its important common pharmacophoric features. HipHop pharmacophore map of the selective T-type CCBs contained six different chemical features, namely ring aromatic (R), positive ionizable (P), two hydrophobic aromatic (Y), hydrophobic aliphatic (Z), hydrogen bond acceptor (H) and hydrogen bond donor (D). However, non-selective T-type CCBs contain all the above mentioned features except ring aromatic (R). The present ligand-based pharmacophore mapping approach could thus be utilized in classifying selective vs. non-selective T-type CCBs. Further, the model can be used for virtual screening of several small molecule databases.

Therapeutic potential of Voltage gated Calcium channels.

Voltage-gated Calcium channels (VGCCs) play important roles in neurotransmitter release, excitation-contraction coupling, hormone secretion, and a variety of other physiological processes. Currently, there exist ion channel therapeutics for anxiety, epilepsy, hypertension, insomnia and pain. There is limited amount of study in this area despite their relevance to human disease and VGCCs remain considerably underexploited. The present review mainly focuses on calcium channel blockers (CCBs), especially for L-type channels and T-type channels, and therein lie some of the opportunities and advantages associated with VGCCs as drug targets.

Exploring QSTR and toxicophore of hERG K+ channel blockers using GFA and HypoGen techniques.

Predictive quantitative structure-toxicity and toxicophore models were developed for a diverse series of hERG K+ channel blockers, acting as anti-arrhythmic agents using QSAR+ module in Cerius2 and HypoGen module in Catalyst software, respectively. The 2D-QSTR analysis has been performed on a dataset of 68 molecules carefully selected from literature for which IC50 values measured on hERG K+ channels expressed in mammalian cells lines using the voltage patch clamp assay technique were reported. Their biological data, expressed as IC50, spanned from 7.0nM to 1.4mM, with 7 orders difference. Several types of descriptors including electrotopological, thermodynamic, ADMET, graph theoretical (topological and information content) were used to derive a quantitative relationship between the channel blockers and its physico-chemical properties. Statistically significant QSTR model was obtained using genetic function approximation methodology, having seven descriptors, with a correlation coefficient (r2) of 0.837, cross-validated correlation coefficient (q2) of 0.776 and predictive correlation coefficient (r2 pred) of 0.701, indicating the robustness of the model. Toxicophore model generated using HypoGen module in Catalyst, on these datasets, showed three important features for hERG K+ channel blockers, (i) hydrophobic group (HP), (ii) ring aromatic group (RA) and (iii) hydrogen bond acceptor lipid group (HBAl). The most predictive hypothesis (Hypo 1), consisting of these three features had a best correlation coefficient of 0.820, a low rms deviation of 1.740, and a high cost difference of 113.50, which represents a true correlation and a good predictivity. The hypothesis, Hypo 1 was validated by a test set consisting of 12 molecules and by a cross-validation of 95% confidence level. Accordingly, our 2D-QSTR and toxicophore model has strong predictivity to identify structurally diverse hERG K+ channel blockers with desired biological activity. These models provide a useful framework for understanding binding, and gave structural insight into the specific protein-ligand interactions responsible for affinity, and how one might modify any given structure to mitigate binding.

Computer-assisted methods in chemical toxicity prediction.

In Silico predictive ADME/Tox screening of compounds is one of the hottest areas in drug discovery. To provide predictions of compound drug-like characteristics early in modern drug-discovery decision making, computational technologies have been widely accepted to develop rapid high throughput in silico ADMET analysis. It is widely perceived that the early screening of chemical entities can significantly reduce the expensive costs associated with late stage failures of drugs due to poor ADME/Tox properties. Drug toxic effects are broadly defined to include toxicity, mutagenicity, carcinogenicity, teratogenicity, neurotoxicity and immunotoxicity. Toxicity prediction techniques and structure-activity relationships relies on the accurate estimation and representation of physico-chemical and toxicological properties. This review highlights some of the freely and commercially available softwares for toxicity predictions. The information content can be utilized as a guide for the scientists involved in the drug discovery arena.