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Items: 1 to 20 of 159

1.

Predicting drug-induced hepatotoxicity using QSAR and toxicogenomics approaches.

Low Y, Uehara T, Minowa Y, Yamada H, Ohno Y, Urushidani T, Sedykh A, Muratov E, Kuz'min V, Fourches D, Zhu H, Rusyn I, Tropsha A.

Chem Res Toxicol. 2011 Aug 15;24(8):1251-62. doi: 10.1021/tx200148a. Epub 2011 Jul 21.

2.

Hybrid in silico models for drug-induced liver injury using chemical descriptors and in vitro cell-imaging information.

Zhu XW, Sedykh A, Liu SS.

J Appl Toxicol. 2014 Mar;34(3):281-8. doi: 10.1002/jat.2879. Epub 2013 May 3.

3.

Comparative analysis of predictive models for nongenotoxic hepatocarcinogenicity using both toxicogenomics and quantitative structure-activity relationships.

Liu Z, Kelly R, Fang H, Ding D, Tong W.

Chem Res Toxicol. 2011 Jul 18;24(7):1062-70. doi: 10.1021/tx2000637. Epub 2011 Jun 20.

PMID:
21627106
4.

Modeling liver-related adverse effects of drugs using knearest neighbor quantitative structure-activity relationship method.

Rodgers AD, Zhu H, Fourches D, Rusyn I, Tropsha A.

Chem Res Toxicol. 2010 Apr 19;23(4):724-32. doi: 10.1021/tx900451r.

5.

Use of in vitro HTS-derived concentration-response data as biological descriptors improves the accuracy of QSAR models of in vivo toxicity.

Sedykh A, Zhu H, Tang H, Zhang L, Richard A, Rusyn I, Tropsha A.

Environ Health Perspect. 2011 Mar;119(3):364-70. doi: 10.1289/ehp.1002476. Epub 2010 Oct 27.

6.

Species-specific differences in coumarin-induced hepatotoxicity as an example toxicogenomics-based approach to assessing risk of toxicity to humans.

Uehara T, Kiyosawa N, Shimizu T, Omura K, Hirode M, Imazawa T, Mizukawa Y, Ono A, Miyagishima T, Nagao T, Urushidani T.

Hum Exp Toxicol. 2008 Jan;27(1):23-35. doi: 10.1177/0960327107087910.

PMID:
18480146
7.

Is toxicogenomics a more reliable and sensitive biomarker than conventional indicators from rats to predict drug-induced liver injury in humans?

Zhang M, Chen M, Tong W.

Chem Res Toxicol. 2012 Jan 13;25(1):122-9. doi: 10.1021/tx200320e. Epub 2011 Dec 13.

PMID:
22122743
8.

Toxicogenomics and metabolomics of pentamethylchromanol (PMCol)-induced hepatotoxicity.

Parman T, Bunin DI, Ng HH, McDunn JE, Wulff JE, Wang A, Swezey R, Rasay L, Fairchild DG, Kapetanovic IM, Green CE.

Toxicol Sci. 2011 Dec;124(2):487-501. doi: 10.1093/toxsci/kfr238. Epub 2011 Sep 13.

9.
10.

Comparative gene and protein expression analyses of a panel of cytokines in acute and chronic drug-induced liver injury in rats.

Hanafusa H, Morikawa Y, Uehara T, Kaneto M, Ono A, Yamada H, Ohno Y, Urushidani T.

Toxicology. 2014 Oct 3;324:43-54. doi: 10.1016/j.tox.2014.07.005. Epub 2014 Jul 19.

PMID:
25051504
11.

Open TG-GATEs: a large-scale toxicogenomics database.

Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H.

Nucleic Acids Res. 2015 Jan;43(Database issue):D921-7. doi: 10.1093/nar/gku955. Epub 2014 Oct 13.

12.

Use of toxicogenomics to understand mechanisms of drug-induced hepatotoxicity during drug discovery and development.

Blomme EA, Yang Y, Waring JF.

Toxicol Lett. 2009 Apr 10;186(1):22-31. doi: 10.1016/j.toxlet.2008.09.017. Epub 2008 Oct 17. Review.

PMID:
18996174
13.

Differentiation of AmpC beta-lactamase binders vs. decoys using classification kNN QSAR modeling and application of the QSAR classifier to virtual screening.

Hsieh JH, Wang XS, Teotico D, Golbraikh A, Tropsha A.

J Comput Aided Mol Des. 2008 Sep;22(9):593-609. doi: 10.1007/s10822-008-9199-2. Epub 2008 Mar 13.

PMID:
18338225
14.

Predictive modeling of chemical hazard by integrating numerical descriptors of chemical structures and short-term toxicity assay data.

Rusyn I, Sedykh A, Low Y, Guyton KZ, Tropsha A.

Toxicol Sci. 2012 May;127(1):1-9. doi: 10.1093/toxsci/kfs095. Epub 2012 Mar 2.

15.

Prediction of drug induced liver injury using molecular and biological descriptors.

Muller C, Pekthong D, Alexandre E, Marcou G, Horvath D, Richert L, Varnek A.

Comb Chem High Throughput Screen. 2015;18(3):315-22.

PMID:
25747442
16.

Quantitative structure-activity relationship models for ready biodegradability of chemicals.

Mansouri K, Ringsted T, Ballabio D, Todeschini R, Consonni V.

J Chem Inf Model. 2013 Apr 22;53(4):867-78. doi: 10.1021/ci4000213. Epub 2013 Mar 27.

PMID:
23469921
17.

A novel two-step hierarchical quantitative structure-activity relationship modeling work flow for predicting acute toxicity of chemicals in rodents.

Zhu H, Ye L, Richard A, Golbraikh A, Wright FA, Rusyn I, Tropsha A.

Environ Health Perspect. 2009 Aug;117(8):1257-64. doi: 10.1289/ehp.0800471. Epub 2009 Apr 3.

18.

Systems toxicology used in nanotoxicology: mechanistic insights into the hepatotoxicity of nano-copper particles from toxicogenomics.

Yang B, Wang Q, Lei R, Wu C, Shi C, Wang Q, Yuan Y, Wang Y, Luo Y, Hu Z, Ma H, Liao M.

J Nanosci Nanotechnol. 2010 Dec;10(12):8527-37.

PMID:
21121362
19.

Combinatorial QSAR of ambergris fragrance compounds.

Kovatcheva A, Golbraikh A, Oloff S, Xiao YD, Zheng W, Wolschann P, Buchbauer G, Tropsha A.

J Chem Inf Comput Sci. 2004 Mar-Apr;44(2):582-95.

PMID:
15032539
20.

Developing Enhanced Blood-Brain Barrier Permeability Models: Integrating External Bio-Assay Data in QSAR Modeling.

Wang W, Kim MT, Sedykh A, Zhu H.

Pharm Res. 2015 Sep;32(9):3055-65. doi: 10.1007/s11095-015-1687-1. Epub 2015 Apr 11.

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