Rutgers Center for Computational and Integrative Biology

Camden, NJ, United States

Rutgers Center for Computational and Integrative Biology

Camden, NJ, United States
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Solimeo R.,Rutgers University | Zhang J.,Rutgers Center for Computational and Integrative Biology | Kim M.,Rutgers University | Kim M.,Rutgers Center for Computational and Integrative Biology | And 3 more authors.
Chemical Research in Toxicology | Year: 2012

Regulatory agencies require testing of chemicals and products to protect workers and consumers from potential eye injury hazards. Animal screening, such as the rabbit Draize test, for potential environmental toxicants is time-consuming and costly. Therefore, virtual screening using computational models to tag potential ocular toxicants is attractive to toxicologists and policy makers. We have developed quantitative structure-activity relationship (QSAR) models for a set of small molecules with animal ocular toxicity data compiled by the National Toxicology Program Interagency Center for the Evaluation of Alternative Toxicological Methods. The data set was initially curated by removing duplicates, mixtures, and inorganics. The remaining 75 compounds were used to develop QSAR models. We applied both k nearest neighbor and random forest statistical approaches in combination with Dragon and Molecular Operating Environment descriptors. Developed models were validated on an external set of 34 compounds collected from additional sources. The external correct classification rates (CCR) of all individual models were between 72 and 87%. Furthermore, the consensus model, based on the prediction average of individual models, showed additional improvement (CCR = 0.93). The validated models could be used to screen external chemical libraries and prioritize chemicals for in vivo screening as potential ocular toxicants. © 2012 American Chemical Society.

Xiang J.,Jilin University | Zhang Z.,Jilin University | Mu Y.,Shandong University | Xu X.,Jilin University | And 7 more authors.
ACS Combinatorial Science | Year: 2016

An efficient discovery strategy by combining diversity-oriented synthesis and converging cellular screening is described. By a three-round screening process, we identified novel tricyclic pyrido[2,3-b][1,4]benzothiazepines showing potent inhibitory activity against paclitaxel-resistant cell line H460TaxR (EC50 < 1.0 μM), which exhibits much less toxicity toward normal cells (EC50 > 100 μM against normal human fibroblasts). The most active hits also exhibited drug-like properties suitable for further preclinical research. This redeployment of antidepressing compounds for anticancer applications provides promising future prospects for treating drug-resistant tumors with fewer side effects. © 2016 American Chemical Society.

Wu L.,Shandong University | Zhang Y.,Shandong University | Zhang C.,Shandong University | Cui X.,Shandong University | And 8 more authors.
ACS Nano | Year: 2014

The induction of autophagy by nanoparticles causes nanotoxicity, but appropriate modulation of autophagy by nanoparticles may have therapeutic potential. Multiwalled carbon nanotubes (MWCNTs) interact with cell membranes and membrane-associated molecules before and after internalization. These interactions alter cellular signaling and impact major cell functions such as cell cycle, apoptosis, and autophagy. In this work, we demonstrated that MWCNT-cell interactions can be modulated by varying densely distributed surface ligands on MWCNTs. Using a fluorescent autophagy-reporting cell line, we evaluated the autophagy induction capability of 81 surface-modified MWCNTs. We identified strong and moderate autophagy-inducing MWCNTs as well as those that did not induce autophagy. Variation of the surface ligand structure of strong autophagy nanoinducers led to the induction of different autophagy-activating signaling pathways, presumably through their different interactions with cell surface receptors. © 2014 American Chemical Society.

Wang W.,Rutgers Center for Computational and Integrative Biology | Kim M.T.,Rutgers Center for Computational and Integrative Biology | Kim M.T.,Rutgers University | Sedykh A.,Rutgers University | And 3 more authors.
Pharmaceutical Research | Year: 2015

ABSTRACT Purpose: Experimental Blood-Brain Barrier (BBB) permeability models for drug molecules are expensive and time-consuming. As alternative methods, several traditional Quantitative Structure-Activity Relationship (QSAR) models have been developed previously. In this study, we aimed to improve the predictivity of traditional QSAR BBB permeability models by employing relevant public bio-assay data in the modeling process. Methods: We compiled a BBB permeability database consisting of 439 unique compounds from various resources. The database was split into a modeling set of 341 compounds and a validation set of 98 compounds. Consensus QSAR modeling workflow was employed on the modeling set to develop various QSAR models. A five-fold cross-validation approach was used to validate the developed models, and the resulting models were used to predict the external validation set compounds. Furthermore, we used previously published membrane transporter models to generate relevant transporter profiles for target compounds. The transporter profiles were used as additional biological descriptors to develop hybrid QSAR BBB models. Results: The consensus QSAR models have R2 = 0.638 for five-fold cross-validation and R2 = 0.504 for external validation. The consensus model developed by pooling chemical and transporter descriptors showed better predictivity (R2 = 0.646 for five-fold cross-validation and R2 = 0.526 for external validation). Moreover, several external bio-assays that correlate with BBB permeability were identified using our automatic profiling tool. Conclusions: The BBB permeability models developed in this study can be useful for early evaluation of new compounds (e.g., new drug candidates). The combination of chemical and biological descriptors shows a promising direction to improve the current traditional QSAR models. © 2015 Springer Science+Business Media New York.

Sprague B.,Rutgers University | Shi Q.,University of North Carolina at Chapel Hill | Kim M.T.,Rutgers University | Kim M.T.,Rutgers Center for Computational and Integrative Biology | And 8 more authors.
Journal of Computer-Aided Molecular Design | Year: 2014

Compared to the current knowledge on cancer chemotherapeutic agents, only limited information is available on the ability of organic compounds, such as drugs and/or natural products, to prevent or delay the onset of cancer. In order to evaluate chemical chemopreventive potentials and design novel chemopreventive agents with low to no toxicity, we developed predictive computational models for chemopreventive agents in this study. First, we curated a database containing over 400 organic compounds with known chemoprevention activities. Based on this database, various random forest and support vector machine binary classifiers were developed. All of the resulting models were validated by cross validation procedures. Then, the validated models were applied to virtually screen a chemical library containing around 23,000 natural products and derivatives. We selected a list of 148 novel chemopreventive compounds based on the consensus prediction of all validated models. We further analyzed the predicted active compounds by their ease of organic synthesis. Finally, 18 compounds were synthesized and experimentally validated for their chemopreventive activity. The experimental validation results paralleled the cross validation results, demonstrating the utility of the developed models. The predictive models developed in this study can be applied to virtually screen other chemical libraries to identify novel lead compounds for the chemoprevention of cancers. © 2014 Springer International Publishing.

Kim M.T.,Rutgers University | Kim M.T.,Rutgers Center for Computational and Integrative Biology | Sedykh A.,University of North Carolina at Chapel Hill | Chakravarti S.K.,Multicase Inc | And 3 more authors.
Pharmaceutical Research | Year: 2014

Purpose: Oral bioavailability (%F) is a key factor that determines the fate of a new drug in clinical trials. Traditionally, %F is measured using costly and time-consuming experimental tests. Developing computational models to evaluate the %F of new drugs before they are synthesized would be beneficial in the drug discovery process. Methods: We employed Combinatorial Quantitative Structure-Activity Relationship approach to develop several computational %F models. We compiled a %F dataset of 995 drugs from public sources. After generating chemical descriptors for each compound, we used random forest, support vector machine, k nearest neighbor, and CASE Ultra to develop the relevant QSAR models. The resulting models were validated using five-fold cross-validation. Results: The external predictivity of %F values was poor (R2 = 0.28, n = 995, MAE = 24), but was improved (R2 = 0.40, n = 362, MAE = 21) by filtering unreliable predictions that had a high probability of interacting with MDR1 and MRP2 transporters. Furthermore, classifying the compounds according to the %F values (%F < 50% as "low", %F ≥ 50% as 'high") and developing category QSAR models resulted in an external accuracy of 76%. Conclusions: In this study, we developed predictive %F QSAR models that could be used to evaluate new drug compounds, and integrating drug-transporter interactions data greatly benefits the resulting models. © 2013 Springer Science+Business Media New York.

Zhu H.,Rutgers University | Zhu H.,Rutgers Center for Computational and Integrative Biology | Zhang J.,Rutgers University | Zhang J.,Rutgers Center for Computational and Integrative Biology | And 5 more authors.
Chemical Research in Toxicology | Year: 2014

High-throughput screening (HTS) assays that measure the in vitro toxicity of environmental compounds have been widely applied as an alternative to in vivo animal tests of chemical toxicity. Current HTS studies provide the community with rich toxicology information that has the potential to be integrated into toxicity research. The available in vitro toxicity data is updated daily in structured formats (e.g., deposited into PubChem and other data-sharing web portals) or in an unstructured way (papers, laboratory reports, toxicity Web site updates, etc.). The information derived from the current toxicity data is so large and complex that it becomes difficult to process using available database management tools or traditional data processing applications. For this reason, it is necessary to develop a big data approach when conducting modern chemical toxicity research. In vitro data for a compound, obtained from meaningful bioassays, can be viewed as a response profile that gives detailed information about the compound's ability to affect relevant biological proteins/receptors. This information is critical for the evaluation of complex bioactivities (e.g., animal toxicities) and grows rapidly as big data in toxicology communities. This review focuses mainly on the existing structured in vitro data (e.g., PubChem data sets) as response profiles for compounds of environmental interest (e.g., potential human/animal toxicants). Potential modeling and mining tools to use the current big data pool in chemical toxicity research are also described. (Chemical Equation Presented). © 2014 American Chemical Society.

Zhang Y.,Shandong University | Zhang Y.,University of Chinese Academy of Sciences | Wang Y.,Shandong University | Liu A.,Shandong University | And 8 more authors.
Advanced Functional Materials | Year: 2016

The liver plays an important role in metabolizing foreign materials, such as drugs. The high accumulation of carbon nanotubes and other hydrophobic nanoparticles in the liver has raised concerns that nanoparticles may interfere with liver metabolic function. We report here that carbon nanotubes enter hepatic cells after intravenous introduction and interact with CYP enzymes, including CYP3A4. Surface chemical modifications alter the carbon nanotubes' interactions with CYP450 enzymes in human liver microsomes. They enhance, inhibit, or have no effect on the enzymatic function of CYP3A4. Using a cheminformatics analysis, certain chemical structures are identified on the surface of the carbon nanotubes that induce an enzyme inhibitory effect or prevent disruption of CYP3A4 enzymes. © 2015 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Zhang J.,Rutgers University | Zhang J.,Rutgers Center for Computational and Integrative Biology | Hsieh J.-H.,National Health Research Institute | Zhu H.,Rutgers University | Zhu H.,Rutgers Center for Computational and Integrative Biology
PLoS ONE | Year: 2014

In vitro bioassays have been developed and are currently being evaluated as potential alternatives to traditional animal toxicity models. Already, the progress of high throughput screening techniques has resulted in an enormous amount of publicly available bioassay data having been generated for a large collection of compounds. When a compound is tested using a collection of various bioassays, all the testing results can be considered as providing a unique bio-profile for this compound, which records the responses induced when the compound interacts with different cellular systems or biological targets. Profiling compounds of environmental or pharmaceutical interest using useful toxicity bioassay data is a promising method to study complex animal toxicity. In this study, we developed an automatic virtual profiling tool to evaluate potential animal toxicants. First, we automatically acquired all PubChem bioassay data for a set of 4,841 compounds with publicly available rat acute toxicity results. Next, we developed a scoring system to evaluate the relevance between these extracted bioassays and animal acute toxicity. Finally, the top ranked bioassays were selected to profile the compounds of interest. The resulting response profiles proved to be useful to prioritize untested compounds for their animal toxicity potentials and form a potential in vitro toxicity testing panel. The protocol developed in this study could be combined with structure-activity approaches and used to explore additional publicly available bioassay datasets for modeling a broader range of animal toxicities.

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