Chun Wei Yap

Prediction of P-Glycoprotein Substrates by a Support Vector Machine Approach

P-glycoproteins (P-gp) actively transport a wide variety of chemicals out of cells and function as drug efflux pumps that mediate multidrug resistance and limit the efficacy of many drugs. Methods for facilitating early elimination of potential P-gp substrates are useful for facilitating new drug discovery. A computational ensemble pharmacophore model has recently been used for the prediction of P-gp substrates with a promising accuracy of 63%. It is desirable to extend the prediction range beyond compounds covered by the known pharmacophore models. For such a purpose, a machine learning method, support vector machine (SVM), was explored for the prediction of P-gp substrates. A set of 201 chemical compounds, including 116 substrates and 85 nonsubstrates of P-gp, was used to train and test a SVM classification system. This SVM system gave a prediction accuracy of at least 81.2% for P-gp substrates based on two different evaluation methods, which is substantially improved against that obtained from the multiple-pharmacophore model. The prediction accuracy for nonsubstrates of P-gp is 79.2% using 5-fold cross-validation. These accuracies are slightly better than those obtained from other statistical classification methods, including k-nearest neighbor (k-NN), probabilistic neural networks (PNN), and C4.5 decision tree, that use the same sets of data and molecular descriptors. Our study indicates the potential of SVM in facilitating the prediction of P-gp substrates.

Therapeutic Targets: Progress of Their Exploration and Investigation of Their Characteristics

Modern drug discovery is primarily based on the search and subsequent testing of drug candidates acting on a preselected therapeutic target. Progress in genomics, protein structure, proteomics, and disease mechanisms has led to a growing interest in and effort for finding new targets and more effective exploration of existing targets. The number of reported targets of marketed and investigational drugs has significantly increased in the past 8 years. There are 1535 targets collected in the therapeutic target database compared with ∼500 targets reported in a 1996 review. Knowledge of these targets is helpful for molecular dissection of the mechanism of action of drugs and for predicting features that guide new drug design and the search for new targets. This article summarizes the progress of target exploration and investigates the characteristics of the currently explored targets to analyze their sequence, structure, family representation, pathway association, tissue distribution, and genome location features for finding clues useful for searching for new targets. Possible “rules” to guide the search for druggable proteins and the feasibility of using a statistical learning method for predicting druggable proteins directly from their sequences are discussed.

Effect of Molecular Descriptor Feature Selection in Support Vector Machine Classification of Pharmacokinetic and Toxicological Properties of Chemical Agents

Statistical-learning methods have been developed for facilitating the prediction of pharmacokinetic and toxicological properties of chemical agents. These methods employ a variety of molecular descriptors to characterize structural and physicochemical properties of molecules. Some of these descriptors are specifically designed for the study of a particular type of properties or agents, and their use for other properties or agents might generate noise and affect the prediction accuracy of a statistical learning system. This work examines to what extent the reduction of this noise can improve the prediction accuracy of a statistical learning system. A feature selection method, recursive feature elimination (RFE), is used to automatically select molecular descriptors for support vector machines (SVM) prediction of P-glycoprotein substrates (P-gp), human intestinal absorption of molecules (HIA), and agents that cause torsades de pointes (TdP), a rare but serious side effect. RFE significantly reduces the number of descriptors for each of these properties thereby increasing the computational speed for their classification. The SVM prediction accuracies of P-gp and HIA are substantially increased and that of TdP remains unchanged by RFE. These prediction accuracies are comparable to those of earlier studies derived from a selective set of descriptors. Our study suggests that molecular feature selection is useful for improving the speed and, in some cases, the accuracy of statistical learning methods for the prediction of pharmacokinetic and toxicological properties of chemical agents.

Anticancer activity of thymoquinone in breast cancer cells: Possible involvement of PPAR-γ pathway

Thymoquinone (TQ), an active ingredient of Nigella sativa, has been reported to exhibit anti-oxidant, anti-inflammatory and anti-tumor activities through mechanism(s) that is not fully understood. In this study, we report the anticancer effects of TQ on breast cancer cells, and its potential effect on the PPAR-γ activation pathway. We found that TQ exerted strong anti-proliferative effect in breast cancer cells and, when combined with doxorubicin and 5-fluorouracil, increased cytotoxicity. TQ was found to increase sub-G1 accumulation and annexin-V positive staining, indicating apoptotic induction. In addition, TQ activated caspases 8, 9 and 7 in a dose-dependent manner. Migration and invasive properties of MDA-MB-231 cells were also reduced in the presence of TQ. Interestingly, we report for the first time that TQ was able to increase PPAR-γ activity and down-regulate the expression of the genes for Bcl-2, Bcl-xL and survivin in breast cancer cells. More importantly, the increase in PPAR-γ activity was prevented in the presence of PPAR-γ specific inhibitor and PPAR-γ dominant negative plasmid, suggesting that TQ may act as a ligand of PPAR-γ. Also, we observed using molecular docking analysis that TQ indeed formed interactions with 7 polar residues and 6 non-polar residues within the ligand-binding pocket of PPAR-γ that are reported to be critical for its activity. Taken together, our novel observations suggest that TQ may have potential implication in breast cancer prevention and treatment, and show for the first time that the anti-tumor effect of TQ may also be mediated through modulation of the PPAR-γ activation pathway.

Effect of Selection of Molecular Descriptors on the Prediction of Blood−Brain Barrier Penetrating and Nonpenetrating Agents by Statistical Learning Methods

The ability or inability of a drug to penetrate into the brain is a key consideration in drug design. Drugs for treating central nervous system (CNS) disorders need to be able to penetrate the blood-brain barrier (BBB). BBB nonpenetration is desirable for non-CNS-targeting drugs to minimize potential CNS-related side effects. Computational methods have been employed for the prediction of BBB-penetrating (BBB+) and -nonpenetrating (BBB-) agents at impressive accuracies of 75-92% and 60-80%, respectively. However, the majority of these studies give a substantially lower BBB- accuracy, and thus overall accuracy, than the BBB+ accuracy. This work examined whether proper selection of molecular descriptors can improve both the BBB- and the overall accuracies of statistical learning methods. The methods tested include logistic regression, linear discriminate analysis, k nearest neighbor, C4.5 decision tree, probabilistic neural network, and support vector machine. Molecular descriptors were selected by using a feature selection method, recursive feature elimination (RFE). Results by using 415 BBB+ and BBB- agents show that RFE substantially improves both the BBB- and the overall accuracy for all of the methods studied. This suggests that statistical learning methods combined with proper feature selection is potentially useful for facilitating a more balanced and improved prediction of BBB+ and BBB- agents.

QSAR Modeling is not “Push a Button and Find a Correlation”: A Case Study of Toxicity of (Benzo-)triazoles on Algae

A case study of toxicity of (benzo)triazoles ((B)TAZs) to the algae Pseudokirchneriella subcapitata is used to discuss some problems and solutions in QSAR modeling, particularly in the environmental context. The relevance of data curation (not only of experimental data, but also of chemical structures and input formats for the calculation of molecular descriptors), the crucial points of QSAR model validation and the potential application for new chemicals (internal robustness, exclusion of chance correlation, external predictivity, applicability domain) are described, while developing MLR‐OLS models based on molecular descriptors, calculated by various QSAR software tools (commercial DRAGON, free PaDEL‐Descriptor and QSPR‐THESAURUS). Additionally, the utility of consensus models is highlighted. This work summarizes a methodology for a rigorous statistical approach to obtain reliable QSAR predictions, also for a large number of (B)TAZs in the ECHA preregistration list of REACH (even if starting from limited experimental data availability), and has evidenced some ambiguities and discrepancies related to SMILES notations from different databases; furthermore it highlighted some general problems related to QSAR model generation and was useful in the implementation of the PaDEL‐Descriptor software.

In silico prediction of pregnane X receptor activators by machine learning approaches

Pregnane X receptor (PXR) regulates drug metabolism and is involved in drug-drug interactions. Prediction of PXR activators is important for evaluating drug metabolism and toxicity. Computational pharmacophore and quantitative structure-activity relationship models have been developed for predicting PXR activators. Because of the structural diversity of PXR activators, more efforts are needed for exploring methods applicable to a broader spectrum of compounds. We explored three machine learning methods (MLMs) for predicting PXR activators, which were trained and tested by using significantly higher number of compounds, 128 PXR activators (98 human) and 77 PXR non-activators, than those of previous studies. The recursive feature-selection method was used to select molecular descriptors relevant to PXR activator prediction, which are consistent with conclusions from other computational and structural studies. In a 10-fold cross-validation test, our MLM systems correctly predicted 81.2 to 84.0% of PXR activators, 80.8 to 85.0% of hPXR activators, 61.2 to 70.3% of PXR nonactivators, and 67.7 to 73.6% of hPXR nonactivators. Our systems also correctly predicted 73.3 to 86.7% of 15 newly published hPXR activators. MLMs seem to be useful for predicting PXR activators and for providing clues to physicochemical features of PXR activation.

Isorhamnetin inhibits proliferation and invasion and induces apoptosis through the modulation of peroxisome proliferator-activated receptor γ activation pathway in gastric cancer.

Background: PPAR-γ, a nuclear transcription factor, plays a critical role in the development of gastric cancer (GC). Hence, novel agents that can modulate PPAR-γ cascade have a great potential for the treatment of GC. Results: Isorhamnetin (IH) modulates PPAR-γ pathway in GC. Conclusion: IH induces apoptosis through the activation of the PPAR-γ pathway. Significance: The study proposes a novel agent for GC treatment. Gastric cancer (GC) is a lethal malignancy and the second most common cause of cancer-related deaths. Although treatment options such as chemotherapy, radiotherapy, and surgery have led to a decline in the mortality rate due to GC, chemoresistance remains as one of the major causes for poor prognosis and high recurrence rate. In this study, we investigated the potential effects of isorhamnetin (IH), a 3′-O-methylated metabolite of quercetin on the peroxisome proliferator-activated receptor γ (PPAR-γ) signaling cascade using proteomics technology platform, GC cell lines, and xenograft mice model. We observed that IH exerted a strong antiproliferative effect and increased cytotoxicity in combination with chemotherapeutic drugs. IH also inhibited the migratory/invasive properties of GC cells, which could be reversed in the presence of PPAR-γ inhibitor. We found that IH increased PPAR-γ activity and modulated the expression of PPAR-γ regulated genes in GC cells. Also, the increase in PPAR-γ activity was reversed in the presence of PPAR-γ-specific inhibitor and a mutated PPAR-γ dominant negative plasmid, supporting our hypothesis that IH can act as a ligand of PPAR-γ. Using molecular docking analysis, we demonstrate that IH formed interactions with seven polar residues and six nonpolar residues within the ligand-binding pocket of PPAR-γ that are reported to be critical for its activity and could competitively bind to PPAR-γ. IH significantly increased the expression of PPAR-γ in tumor tissues obtained from xenograft model of GC. Overall, our findings clearly indicate that antitumor effects of IH may be mediated through modulation of the PPAR-γ activation pathway in GC.

Regression Methods for Developing QSAR and QSPR Models to Predict Compounds of Specific Pharmacodynamic, Pharmacokinetic and Toxicological Properties

Quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) models have been extensively used for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property from structure-derived physicochemical and structural features. These models can be developed by using various regression methods including conventional approaches (multiple linear regression and partial least squares) and more recently explored genetic (genetic function approximation) and machine learning (k-nearest neighbour, neural networks, and support vector regression) approaches. This article describes the algorithms of these methods, evaluates their advantages and disadvantages, and discusses the application potential of the recently explored methods. Freely available online and commercial software for these regression methods and the areas of their applications are also presented.

SVM model for virtual screening of Lck inhibitors.

Lymphocyte-specific protein tyrosine kinase (Lck) inhibitors have treatment potential for autoimmune diseases and transplant rejection. A support vector machine (SVM) model trained with 820 positive compounds (Lck inhibitors) and 70 negative compounds (Lck noninhibitors) combined with 65 142 generated putative negatives was developed for predicting compounds with a Lck inhibitory activity of IC(50) < or = 10 microM. The SVM model, with an estimated sensitivity of greater than 83% and specificity of greater than 99%, was used to screen 168 014 compounds in the MDDR and was found to have a yield of 45.8% and a false positive rate of 0.52%. The model was also able to identify novel Lck inhibitors and distinguish inhibitors from structurally similar noninhibitors at a false positive rate of 0.27%. To the best of our knowledge, the SVM model developed in this work is the first model with a broad applicability domain and low false positive rate, which makes it very suitable for the virtual screening of chemical libraries for Lck inhibitors.