Journal: Journal of Theoretical Biology

Article Title: In vitro transcriptomic prediction of hepatotoxicity for early drug discovery

doi: 10.1016/j.jtbi.2011.08.009

Figure Lengend Snippet: Rat liver ( in vivo ) and human liver cell ( in vitro ) microarray datasets were used for computational model derivation and evaluation Six previously-published microarray sets from four in vivo and two in vitro hepatocellular toxicity experiments were used to construct and validate our prediction model. Four microarray data sets of hepatology Rat1, Human1, and Human2 were from the NCBI GEO database ( http://www.ncbi.nlm.nih.gov/geo ). Two additional in vivo microarray data sets, Rat2 and Rat3, were obtained from the public web portal of the National Institute of Environmental Health Science (NIEHS, http://cebs.niehs.nih.gov ). Rat1 set was chosen and used for our multi-gene model development. Human1 set was used to identify our COXEN genes which showed concordant gene expression networks between in vitro human liver cells and in vivo rat liver cells. The other four sets---Rat2, Rat3, Rat4, and Human2 were used only for our independent evaluation of the hepatocellular toxicity prediction model.

Article Snippet: The ALT data of the Rat3 set were also provided in the recent publication ( Chou and Bushel, 2009 ). table ft1 table-wrap mode="anchored" t5 caption a7 Data Set Sample Species Study usage Source a ID Array Platform Samples Drug Rat1 Rat liver ( in vivo ) Training GEO GSE5509 Affymetrix Rat 230 2.0 39 6 Human1 Human HepG2 ( in vitro) COXEN & Test GEO GSE6907 Affymetrix Human HG-Focus 30 9 Rat2 Rat Liver ( in vivo ) Test NIEHS 839186700 Affymetrix Rat 230 2.0 34 1 Rat3 Rat Liver ( in vivo ) Test NIEHS 839173713 Affymetrix Rat 230 2.0 48 1 Rat4 Rat Liver ( in vivo ) Test GEO GSE8251 GE 990 147 Healthcare/AmershamCodeLi nkUniSet Rat I Human2 Human primary Test GEO GSE10410 Affymetrix HG U133 Plus 2.0 37 3 hepatocyte ( in vitro ) Open in a separate window a GEO web address: http://www.ncbi.nlm.nih.gov/geo/ NIEHS web address: http://cebs.niehs.nih.gov/cebs-browser/cebsHome.do?enter=home caption a8 Rat liver ( in vivo ) and human liver cell ( in vitro ) microarray datasets were used for computational model derivation and evaluation Six previously-published microarray sets from four in vivo and two in vitro hepatocellular toxicity experiments were used to construct and validate our prediction model. Four microarray data sets of hepatology Rat1, Human1, and Human2 were from the NCBI GEO database ( http://www.ncbi.nlm.nih.gov/geo ).

Techniques: In Vivo, In Vitro, Microarray, Construct, Expressing

Journal: Journal of Theoretical Biology

Article Title: In vitro transcriptomic prediction of hepatotoxicity for early drug discovery

doi: 10.1016/j.jtbi.2011.08.009

Figure Lengend Snippet: Prediction results for 5 independent test sets Rat2, Rat3, Rat4, Human1, and Human2 were used independently to evaluate the classification performance of our hepatocellular toxicity prediction model. We wanted to see if the predicted scores of hepatocellular toxicity could statistically discriminate cells and animals treated with toxic compounds from the ones treated with non-toxic compounds (or untreated controls) in these sets. The statistical significance of the difference in scores was first evaluated using the twosample t-test between the toxic and non-toxic groups. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were then calculated. Sensitivity, specificity, PPV, NPV were defined as Sensitivity = number of true positives number of true positives + number of false negatives Sensitivity = number of true negatives number of true negatives + number of false positives PPV = number of true positives number of true positives + number of false positives NPV = number of true negatives number of true negatives + number of false negatives

Article Snippet: The ALT data of the Rat3 set were also provided in the recent publication ( Chou and Bushel, 2009 ). table ft1 table-wrap mode="anchored" t5 caption a7 Data Set Sample Species Study usage Source a ID Array Platform Samples Drug Rat1 Rat liver ( in vivo ) Training GEO GSE5509 Affymetrix Rat 230 2.0 39 6 Human1 Human HepG2 ( in vitro) COXEN & Test GEO GSE6907 Affymetrix Human HG-Focus 30 9 Rat2 Rat Liver ( in vivo ) Test NIEHS 839186700 Affymetrix Rat 230 2.0 34 1 Rat3 Rat Liver ( in vivo ) Test NIEHS 839173713 Affymetrix Rat 230 2.0 48 1 Rat4 Rat Liver ( in vivo ) Test GEO GSE8251 GE 990 147 Healthcare/AmershamCodeLi nkUniSet Rat I Human2 Human primary Test GEO GSE10410 Affymetrix HG U133 Plus 2.0 37 3 hepatocyte ( in vitro ) Open in a separate window a GEO web address: http://www.ncbi.nlm.nih.gov/geo/ NIEHS web address: http://cebs.niehs.nih.gov/cebs-browser/cebsHome.do?enter=home caption a8 Rat liver ( in vivo ) and human liver cell ( in vitro ) microarray datasets were used for computational model derivation and evaluation Six previously-published microarray sets from four in vivo and two in vitro hepatocellular toxicity experiments were used to construct and validate our prediction model. Four microarray data sets of hepatology Rat1, Human1, and Human2 were from the NCBI GEO database ( http://www.ncbi.nlm.nih.gov/geo ).

Techniques: In Vivo, In Vitro

Journal: Journal of Theoretical Biology

Article Title: In vitro transcriptomic prediction of hepatotoxicity for early drug discovery

doi: 10.1016/j.jtbi.2011.08.009

Figure Lengend Snippet: (A) Comparison of COXEN scores of rats in set Rat2 treated with low dose and high sublethal-dose of acetaminophen. (B) Comparison of COXEN scores of rats in set Rat3 treated with low dose and a high sublethal-dose of 1,4-dichlorobenzene. Predicted scores of these two compounds were plotted in each of two sub-panels: 6 hrs and ≥24hrs after drug treatments. In these applications, animals treated with high sublethal-doses for 24 hrs could be perfectly discriminated from those with low doses by our prediction model. (C) Comparison of COXEN predictive scores of control rats in set Rat4 (without any treatment), rats treated with low-toxic and toxic compounds. Toxic and low toxic compounds were defined by the Micromedex database (www.micromedex.com). Predicted scores of these compounds were plotted in three sub-panels: ≤24 hrs, 3 days, and 5 days after drug treatments. Statistical significance (p value) of the set of predictions was assessed by a two sample t-test.

Article Snippet: The ALT data of the Rat3 set were also provided in the recent publication ( Chou and Bushel, 2009 ). table ft1 table-wrap mode="anchored" t5 caption a7 Data Set Sample Species Study usage Source a ID Array Platform Samples Drug Rat1 Rat liver ( in vivo ) Training GEO GSE5509 Affymetrix Rat 230 2.0 39 6 Human1 Human HepG2 ( in vitro) COXEN & Test GEO GSE6907 Affymetrix Human HG-Focus 30 9 Rat2 Rat Liver ( in vivo ) Test NIEHS 839186700 Affymetrix Rat 230 2.0 34 1 Rat3 Rat Liver ( in vivo ) Test NIEHS 839173713 Affymetrix Rat 230 2.0 48 1 Rat4 Rat Liver ( in vivo ) Test GEO GSE8251 GE 990 147 Healthcare/AmershamCodeLi nkUniSet Rat I Human2 Human primary Test GEO GSE10410 Affymetrix HG U133 Plus 2.0 37 3 hepatocyte ( in vitro ) Open in a separate window a GEO web address: http://www.ncbi.nlm.nih.gov/geo/ NIEHS web address: http://cebs.niehs.nih.gov/cebs-browser/cebsHome.do?enter=home caption a8 Rat liver ( in vivo ) and human liver cell ( in vitro ) microarray datasets were used for computational model derivation and evaluation Six previously-published microarray sets from four in vivo and two in vitro hepatocellular toxicity experiments were used to construct and validate our prediction model. Four microarray data sets of hepatology Rat1, Human1, and Human2 were from the NCBI GEO database ( http://www.ncbi.nlm.nih.gov/geo ).

Techniques: