178
Z. Liu et al.
22. Uehara T et al (2010) The Japanese toxicogenomics project: application of toxicogenomics.
Mol Nutr Food Res 54(2):218–227
23. Igarashi Y et al (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids
Res 43:D921–D927 (Database issue)
24. Ganter B et al (2005) Development of a large-scale chemogenomics database to improve drug
candidate selection and to understand mechanisms of chemical toxicity and action. J Biotechnol
119(3):219–244
25. Suter L et al (2011) EU framework 6 project: predictive toxicology (PredTox)-overview and
outcome. Toxicol Appl Pharmacol 252(2):73–84
26. Rowan AN (2015) Ending the use of animals in toxicity testing and risk evaluation. Camb Q
Healthc Ethics 24(4):448–458
27. Kirkland D et al (2005) Evaluation of the ability of a battery of three in vitro genotoxicity tests
to discriminate rodent carcinogens and non-carcinogens—I. Sensitivity, specificity and relative
predictivity. Mutat Res Genet Toxicol Environ Mutagen 584(1–2):1–256
28. Nel A et al (2013) Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening. Acc Chem Res 46(3):607–621
29. Sonneveld E et al (2006) Comparison of in vitro and in vivo screening models for androgenic
and estrogenic activities. Toxicol Sci 89(1):173–187
30. Lamb J et al (2006) The connectivity map: using gene-expression signatures to connect small
molecules, genes, and disease. Science 313(5795):1929–1935
31. Igarashi Y et al (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids
Res 43(D1):D921–D927
32. Ganter B et al (2006) Toxicogenomics in drug discovery and development: mechanistic analysis
of compound/class-dependent effects using the DrugMatrix ® database. Pharmacogenomics
7(7):1025–1044
33. Sugden LA et al (2013) Assessing the validity and reproducibility of genome-scale predictions.
Bioinformatics 29(22):2844–2851
34. Chen M et al (2011) FDA-approved drug labeling for the study of drug-induced liver injury.
Drug Discov Today 16(15):697–703
35. Chen M et al (2016) DILIrank: the largest reference drug list ranked by the risk for developing
drug-induced liver injury in humans. Drug Discov Today 21(4):648–653
36. Wen Z et al (2018) Drug-induced rhabdomyolysis atlas (DIRA) for idiosyncratic adverse drug
reaction management. Drug Discov Today 24(1):9–15
37. Liu ZC et al (2017) In vitro to in vivo extrapolation for drug-induced liver injury using a pair
ranking method. ALTEX-Altern Anim Experimentation 34(3):399–408
38. Liu Z et al (2018) Transcriptional responses reveal similarities between preclinical rat liver
testing systems. Front Genet 9:74
39. Consortium M (2006) The microarray quality control (MAQC) project shows interand intraplatform reproducibility of gene expression measurements. Nat Biotechnol
24(9):1151–1161
40. Davis AP et al (2017) The comparative toxicogenomics database: update 2017. Nucleic Acids
Res 45:D972–D978 (Database issue)
41. Hochreiter S et al (2006) A new summarization method for affymetrix probe level data. Bioinformatics 22(8):943–949
42. Dai M et al (2005) Evolving gene/transcript definitions significantly alter the interpretation of
GeneChip data. Nucleic Acids Res 33(20):e175–e175
43. Lee WM (2003) Drug-induced hepatotoxicity. N Engl J Med 349(5):474–485
44. Xu JJ et al (2008) Cellular imaging predictions of clinical drug-induced liver injury. Toxicol
Sci 105(1):97–105
45. Sakatis MZ et al (2012) Preclinical strategy to reduce clinical hepatotoxicity using in vitro
bioactivation data for >200 compounds. Chem Res Toxicol 25(10):2067–2082
46. Chen M et al (2014) Toward predictive models for drug-induced liver injury in humans: are we
there yet? Biomark Med 8(2):201–213
Z. Liu et al.
22. Uehara T et al (2010) The Japanese toxicogenomics project: application of toxicogenomics.
Mol Nutr Food Res 54(2):218–227
23. Igarashi Y et al (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids
Res 43:D921–D927 (Database issue)
24. Ganter B et al (2005) Development of a large-scale chemogenomics database to improve drug
candidate selection and to understand mechanisms of chemical toxicity and action. J Biotechnol
119(3):219–244
25. Suter L et al (2011) EU framework 6 project: predictive toxicology (PredTox)-overview and
outcome. Toxicol Appl Pharmacol 252(2):73–84
26. Rowan AN (2015) Ending the use of animals in toxicity testing and risk evaluation. Camb Q
Healthc Ethics 24(4):448–458
27. Kirkland D et al (2005) Evaluation of the ability of a battery of three in vitro genotoxicity tests
to discriminate rodent carcinogens and non-carcinogens—I. Sensitivity, specificity and relative
predictivity. Mutat Res Genet Toxicol Environ Mutagen 584(1–2):1–256
28. Nel A et al (2013) Nanomaterial toxicity testing in the 21st century: use of a predictive toxicological approach and high-throughput screening. Acc Chem Res 46(3):607–621
29. Sonneveld E et al (2006) Comparison of in vitro and in vivo screening models for androgenic
and estrogenic activities. Toxicol Sci 89(1):173–187
30. Lamb J et al (2006) The connectivity map: using gene-expression signatures to connect small
molecules, genes, and disease. Science 313(5795):1929–1935
31. Igarashi Y et al (2015) Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids
Res 43(D1):D921–D927
32. Ganter B et al (2006) Toxicogenomics in drug discovery and development: mechanistic analysis
of compound/class-dependent effects using the DrugMatrix ® database. Pharmacogenomics
7(7):1025–1044
33. Sugden LA et al (2013) Assessing the validity and reproducibility of genome-scale predictions.
Bioinformatics 29(22):2844–2851
34. Chen M et al (2011) FDA-approved drug labeling for the study of drug-induced liver injury.
Drug Discov Today 16(15):697–703
35. Chen M et al (2016) DILIrank: the largest reference drug list ranked by the risk for developing
drug-induced liver injury in humans. Drug Discov Today 21(4):648–653
36. Wen Z et al (2018) Drug-induced rhabdomyolysis atlas (DIRA) for idiosyncratic adverse drug
reaction management. Drug Discov Today 24(1):9–15
37. Liu ZC et al (2017) In vitro to in vivo extrapolation for drug-induced liver injury using a pair
ranking method. ALTEX-Altern Anim Experimentation 34(3):399–408
38. Liu Z et al (2018) Transcriptional responses reveal similarities between preclinical rat liver
testing systems. Front Genet 9:74
39. Consortium M (2006) The microarray quality control (MAQC) project shows interand intraplatform reproducibility of gene expression measurements. Nat Biotechnol
24(9):1151–1161
40. Davis AP et al (2017) The comparative toxicogenomics database: update 2017. Nucleic Acids
Res 45:D972–D978 (Database issue)
41. Hochreiter S et al (2006) A new summarization method for affymetrix probe level data. Bioinformatics 22(8):943–949
42. Dai M et al (2005) Evolving gene/transcript definitions significantly alter the interpretation of
GeneChip data. Nucleic Acids Res 33(20):e175–e175
43. Lee WM (2003) Drug-induced hepatotoxicity. N Engl J Med 349(5):474–485
44. Xu JJ et al (2008) Cellular imaging predictions of clinical drug-induced liver injury. Toxicol
Sci 105(1):97–105
45. Sakatis MZ et al (2012) Preclinical strategy to reduce clinical hepatotoxicity using in vitro
bioactivation data for >200 compounds. Chem Res Toxicol 25(10):2067–2082
46. Chen M et al (2014) Toward predictive models for drug-induced liver injury in humans: are we
there yet? Biomark Med 8(2):201–213