Computational methods for prediction of in vitro effects of new chemical structures

Background With a constant increase in the number of new chemicals synthesized every year, it becomes important to employ the most reliable and fast in silico screening methods to predict their safety and activity profiles. In recent years, in silico prediction methods received great attention in an attempt to reduce animal experiments for the evaluation of various toxicological endpoints, complementing the theme of replace, reduce and refine. Various computational approaches have been proposed for the prediction of compound toxicity ranging from quantitative structure activity relationship modeling to molecular similarity-based methods and machine learning. Within the “Toxicology in the 21st Century” screening initiative, a crowd-sourcing platform was established for the development and validation of computational models to predict the interference of chemical compounds with nuclear receptor and stress response pathways based on a training set containing more than 10,000 compounds tested in high-throughput screening assays. Results Here, we present the results of various molecular similarity-based and machine-learning based methods over an independent evaluation set containing 647 compounds as provided by the Tox21 Data Challenge 2014. It was observed that the Random Forest approach based on MACCS molecular fingerprints and a subset of 13 molecular descriptors selected based on statistical and literature analysis performed best in terms of the area under the receiver operating characteristic curve values. Further, we compared the individual and combined performance of different methods. In retrospect, we also discuss the reasons behind the superior performance of an ensemble approach, combining a similarity search method with the Random Forest algorithm, compared to individual methods while explaining the intrinsic limitations of the latter. Conclusions Our results suggest that, although prediction methods were optimized individually for each modelled target, an ensemble of similarity and machine-learning approaches provides promising performance indicating its broad applicability in toxicity prediction

Exploring DNA Topoisomerase I Ligand Space in Search of Novel Anticancer Agents

DNA topoisomerase I (Top1) is over-expressed in tumour cells and is an important target in cancer chemotherapy. It relaxes DNA torsional strain generated during DNA processing by introducing transient single-strand breaks and allowing the broken strand to rotate around the intermediate Top1 – DNA covalent complex. This complex can be trapped by a group of anticancer agents interacting with the DNA bases and the enzyme at the cleavage site, preventing further topoisomerase activity. Here we have identified novel Top1 inhibitors as potential anticancer agents by using a combination of structure- and ligand-based molecular modelling methods. Pharmacophore models have been developed based on the molecular characteristics of derivatives of the alkaloid camptothecin (CPT), which represent potent antitumour agents and the main group of Top1 inhibitors. The models generated were used for in silico screening of the National Cancer Institute (NCI, USA) compound database, leading to the identification of a set of structurally diverse molecules. The strategy is validated by the observation that amongst these molecules are several known Top1 inhibitors and agents cytotoxic against human tumour cell lines. The potential of the untested hits to inhibit Top1 activity was further evaluated by docking into the binding site of a Top1 – DNA complex, resulting in a selection of 10 compounds for biological testing. Limited by the compound availability, 7 compounds have been tested in vitro for their Top1 inhibitory activity, 5 of which display mild to moderate Top1 inhibition. A further compound, found by similarity search to the active compounds, also shows mild activity. Although the tested compounds display only low in vitro antitumour activity, our approach has been successful in the identification of structurally novel Top1 inhibitors worthy of further investigation as potential anticancer agents

Design of novel inhibitors of DNA topoisomerases using computer-aided methods

Humans possess two main types of topoisomerase (Top) enzymes, Top1 and Top2, which have different structures and mechanisms of action. Both Top1 and Top2 are over-expressed in tumour cells and therefore important targets in anticancer therapy. Several topoisomerase inhibitors are used clinically. However, their use is limited due to side effects and drug resistance. Hence, the search for novel Top inhibitors is ongoing. In particular, the development of dual Top1/Top2 inhibitors is believed to be associated with advantageous drugs displaying improved anticancer activity and reduced drug resistance problems.This study reports the use of computer-aided drug design methods to discover novel Top1, Top2 and dual inhibitors. In particular, available ligand and crystal structure information was analysed and used to develop ligand- and complex-based pharmacophores which were applied in database screening of the National Cancer Institute (NCI, USA) database. To filter the hits obtained, docking, 2D-similarity methods, druglikeness filters and expert selection were used. Structurally novel potential Top1 and Top2 inhibitors were identified by the study and, in case of Top1, the activity of the hits was confirmed in in vitro enzyme inhibition and cytotoxicity assays performed at the National Cancer Institute (USA). Furthermore, the performance of the developed pharmacophores and the docking protocols used was evaluated in a retrospective analysis based on selected test set compounds. As an alternative approach, pharmacophore models for Top1 were also generated in a purely structure-based manner, based on binding pocket interaction maps. Different approaches to cluster and select pharmacophore features were investigated, including hierarchical clustering methods, energy calculations and the use of pharmacophore subsets. In addition, the performance of structure-based pharmacophores was investigated in prospective and retrospective analyses. In the final part of the study, all methods developed for Top1 and Top2 were combined to suggest novel dual topoisomerase inhibitors.In conclusion, this study presents the application of computer-aided drug design techniques which led to the identification of structurally novel Top1, Top2 and dual inhibitors worthy of further investigation as potential anticancer agents

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Possible residues to be targeted by Top2α versus Top2β-selective molecules.

<p>Superimposition of Top2α homology model (dark cyan) and Top2β crystal structure (light blue) <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114904#pone.0114904-Xiao2" target="_blank">[34]</a>, shown in cartoon representation. Residues in the etoposide binding pocket which differ between the Top2α and Top2β isoform are indicated as sticks (residue names: Top2α/Top2β). Etoposide pose from the Top2β crystal structure (stick representation, carbons white) shown for clarity.</p

Compounds suggested from Top2α docking studies.

1<p>score of best-scoring pose of largest cluster;</p>2<p>mean score of largest cluster.</p><p>Compounds suggested from Top2α docking studies.</p

Docking pose of etoposide in Top2α and development of complex-based pharmacophore.

<p>A) Docking pose of etoposide relative to Top2α. Protein backbone and surface shown in cyan. Two residues interacting with etoposide are shown as sticks. B) Docking pose of etoposide relative to DNA. DNA residues shown as green sticks. Hydrogen bonds indicated as green, hydrophobic interactions as blue and stacking interactions as orange lines. C) and D) 2D- and 3D-representation of complex-based pharmacophore developed from the etoposide docking pose. Colour code of pharmacophore features: hydrogen bond acceptor (HBA), green; hydrogen bond donor (HBD), pink; hydrophobic group, blue; cyclic π-interaction (CYPI), orange; excluded volume, grey.</p

Compounds selected from screening with etoposide pharmacophores – biological results.

1<p>Chemical Abstracts Registration Number;</p>2<p>Cytotoxic activity measured in the US National Cancer Institute (NCI) 60 human tumour cell line anticancer drug screen <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0114904#pone.0114904-Shoemaker1" target="_blank">[55]</a>, GI₅₀ corresponds to the concentration of the drug resulting in a 50% growth inhibition.</p><p>Compounds selected from screening with etoposide pharmacophores – biological results.</p