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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. Source

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. Source

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. Source

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. Source

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. Source

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