Regular monitoring of drug regulatory agency web sites and similar resources for information on new drug approvals and changes to legal status of marketed drugs is impractical. It requires navigation through several resources to find complete information about a drug as none of the publicly accessible drug databases provide all features essential to complement in silico drug discovery. Here, we propose SuperDRUG2 (http://cheminfo.charite.de/superdrug2) as a comprehensive knowledge-base of approved and marketed drugs. We provide the largest collection of drugs (containing 4587 active pharmaceutical ingredients) which include small molecules, biological products and other drugs. The database is intended to serve as a one-stop resource providing data on: chemical structures, regulatory details, indications, drug targets, side-effects, physicochemical properties, pharmacokinetics and drug–drug interactions. We provide a 3D-superposition feature that facilitates estimation of the fit of a drug in the active site of a target with a known ligand bound to it. Apart from multiple other search options, we introduced pharmacokinetics simulation as a unique feature that allows users to visualise the ‘plasma concentration versus time’ profile for a given dose of drug with few other adjustable parameters to simulate the kinetics in a healthy individual and poor or extensive metabolisers.

Drug-induced inhibition of the human ether-à-go-go-related gene (hERG)-encoded potassium ion channels can lead to fatal cardiotoxicity. Several marketed drugs and promising drug candidates were recalled because of this concern. Diverse modeling methods ranging from molecular similarity assessment to quantitative structure-activity relationship analysis employing machine learning techniques have been applied to data sets of varying size and composition (number of blockers and nonblockers). In this study, we highlight the challenges involved in the development of a robust classifier for predicting the hERG end point using bioactivity data extracted from the public domain. To this end, three different modeling methods, nearest neighbors, random forests, and support vector machines, were employed to develop predictive models using different molecular descriptors, activity thresholds, and training set compositions. Our models demonstrated superior performance in external validations in comparison with those reported in the previous studies from which the data sets were extracted. The choice of descriptors had little influence on the model performance, with minor exceptions. The criteria used to filter bioactivity data, the activity threshold settings used to separate blockers from nonblockers, and the structural diversity of blockers in training data set were found to be the crucial indicators of model performance. Training sets based on a binary threshold of 1 μM/10 μM to separate blockers (IC/ K ≤ 1 μM) from nonblockers (IC/ K > 10 μM) provided superior performance in comparison with those defined using a single threshold (1 μM or 10 μM). A major limitation in using the public domain hERG activity data is the abundance of blockers in comparison with nonblockers at usual activity thresholds, since not many studies report the latter.

Short-chain chlorinated paraffins (SCCPs) are new group of persistent organic pollutants (POPs) listed in the Stockholm Convention. The Yangtze River Delta is among the industrially most developed areas in China, supporting a large production and consumption of chlorinated paraffins (CPs). Despite this, there is very limited data on the environmental exposure of SCCPs from the region. This study analyzed SCCPs in 14 wild aquatic organisms from Dianshan Lake, Shanghai, China. The concentrations of total SCCPs ranged from 10 to 1300μgg lipid weight, with significantly higher levels (p<0.05) in benthic (benthic fish and invertebrates) than in non-benthic species (pelagic and mesopelagic fish). The abundance of C congeners was much higher in the benthic species compared to in the non-benthic species. The calculated trophic magnification factors (TMFs) of SCCP congeners varied from 1.19 (CHCl) to 1.57 (CHCl). The TMFs were significantly correlated (p<0.01) with carbon-chain length in a positive linear relationship and with Log K in a parabolic curve relationship. Considering the high concentrations of SCCPs in wild aquatic organisms and the trophic magnification observed in the freshwater food web, further studies should be undertaken to assess the environmental fate of SCCPs and the public health risk in the Yangtze River Delta.