Identification of Michael acceptor-centric pharmacophores with substituents that yield strong thioredoxin reductase inhibitory character correlated to antiproliferative activity.

AIMS: The role of thioredoxin reductase (TrxR) in tumorigenesis has made it an attractive anticancer target. A systematic approach for development of novel compounds as TrxR inhibitors is currently lacking. Structurally diversified TrxR inhibitors share in common electrophilic propensities for the sulfhydryl groups, among which include the Michael reaction acceptors containing an α,ß-unsaturated carbonyl moiety. We aimed to identify features among structurally diversified Michael acceptor-based compounds that would yield a strong TrxR inhibitory character. RESULTS: Structurally dissimilar Michael acceptor-based natural compounds such as isobutylamides, zerumbone, and shogaols (SGs) were found to possess a poor TrxR inhibitory activity, indicating that a sole Michael acceptor moiety was insufficient to produce TrxR inhibition. The 1,7-diphenyl-hept-3-en-5-one pharmacophore in 3-phenyl-3-SG, a novel SG analog that possessed comparable TrxR inhibitory and antiproliferative potencies as 6-SG, was modified to yield 1,5-diphenyl-pent-1-en-3-one (DPPen) and 1,3-diphenyl-pro-1-en-3-one (DPPro, also known as chalcone) pharmacophores. These Michael acceptor-centric pharmacophores, when substituted with the hydroxyl and fluorine groups, gave rise to analogs displaying a TrxR inhibitory character positively correlated to their antiproliferative potencies. Lead analogs 2,2'-diOH-5,5'-diF-DPPen and 2-OH-5-F-DPPro yielded a half-maximal TrxR inhibitory concentration of 9.1 and 10.5 µM, respectively, after 1-h incubation with recombinant rat TrxR, with the C-terminal selenocysteine residue found to be targeted. INNOVATION: Identification of Michael acceptor-centric pharmacophores among diversified compounds demonstrates that a systematic approach to discover and develop Michael acceptor-based TrxR inhibitors is feasible. CONCLUSION: A strong TrxR inhibitory character correlated to the antiproliferative potency is attributed to structural features that include an α,ß-unsaturated carbonyl moiety centered in a DPPen or DPPro pharmacophore bearing hydroxyl and fluorine substitutions.

PaDEL-DDPredictor: open-source software for PD-PK-T prediction.

ADMET (absorption, distribution, metabolism, excretion, and toxicity)-related failure of drug candidates is a major issue for the pharmaceutical industry today. Prediction of PD-PK-T properties using in silico tools has become very important in pharmaceutical research to reduce cost and enhance efficiency. PaDEL-DDPredictor is an in silico tool for rapid prediction of PD-PK-T properties of compounds from their chemical structures. It is free and open-source software that, has both graphical user interface and command line interface, can work on all major platforms (Windows, Linux, and MacOS) and supports more than 90 different molecular file formats. The software can be downloaded from http://padel.nus.edu.sg/software/padelddpredictor.

QSAR and Predictors of Eye and Skin Effects.

In this study, the ensemble of features and training samples was examined with a collection of support vector machines. The effects of data sampling methods, ratio of positive to negative compounds, and types of base models combiner to produce ensemble models were explored. The ensemble method was applied to produce four separate in silico models to classify the labels for eye/skin corrosion (H314), skin irritation (H315), serious eye damage (H318), and eye irritation (H319), which are defined in the "Globally Harmonized System of Classification and Labelling of Chemicals". To the best of our knowledge, the training set used in this work is one of the largest (made of publicly available data) with acceptable prediction performances. These models were distributed via PaDEL-DDPredictor (http://padel.nus.edu.sg/software/padelddpredictor) that can be downloaded freely for public use.

QSAR classification of metabolic activation of chemicals into covalently reactive species.

Metabolic activation of chemicals into covalently reactive species might lead to toxicological consequences such as tissue necrosis, carcinogenicity, teratogenicity, or immune-mediated toxicities. Early prediction of this undesirable outcome can help in selecting candidates with increased chance of success, thus, reducing attrition at all stages of drug development. The ensemble modelling of mixed features was used for the development of a model to classify the metabolic activation of chemicals into covalently reactive species. The effects of the quality of base classifiers and performance measure for sorting were examined. An ensemble model of 13 naive Bayes classifiers was built from a diverse set of 1,479 compounds. The ensemble model was validated internally with five-fold cross validation and it has achieved sensitivity of 67.4% and specificity of 93.4% when tested on the training set. The final ensemble model was made available for public use.

Mixed learning algorithms and features ensemble in hepatotoxicity prediction.

Drug-induced liver injury, although infrequent, is an important safety concern that can lead to fatality in patients and failure in drug developments. In this study, we have used an ensemble of mixed learning algorithms and mixed features for the development of a model to predict hepatic effects. This robust method is based on the premise that no single learning algorithm is optimum for all modelling problems. An ensemble model of 617 base classifiers was built from a diverse set of 1,087 compounds. The ensemble model was validated internally with five-fold cross-validation and 25 rounds of y-randomization. In the external validation of 120 compounds, the ensemble model had achieved an accuracy of 75.0%, sensitivity of 81.9% and specificity of 64.6%. The model was also able to identify 22 of 23 withdrawn drugs or drugs with black box warning against hepatotoxicity. Dronedarone which is associated with severe liver injuries, announced in a recent FDA drug safety communication, was predicted as hepatotoxic by the ensemble model. It was found that the ensemble model was capable of classifying positive compounds (with hepatic effects) well, but less so on negatives compounds when they were structurally similar. The ensemble model built in this study is made available for public use.

Consensus model for identification of novel PI3K inhibitors in large chemical library.

Phosphoinositide 3-kinases (PI3Ks) inhibitors have treatment potential for cancer, diabetes, cardiovascular disease, chronic inflammation and asthma. A consensus model consisting of three base classifiers (AODE, kNN, and SVM) trained with 1,283 positive compounds (PI3K inhibitors), 16 negative compounds (PI3K non-inhibitors) and 64,078 generated putative negatives was developed for predicting compounds with PI3K inhibitory activity of IC(50) < or = 10 microM. The consensus model has an estimated false positive rate of 0.75%. Nine novel potential inhibitors were identified using the consensus model and several of these contain structural features that are consistent with those found to be important for PI3K inhibitory activities. An advantage of the current model is that it does not require knowledge of 3D structural information of the various PI3K isoforms, which is not readily available for all isoforms.