TY - JOUR
T1 - A transcriptomics data-driven gene space accurately predicts liver cytopathology and drug-induced liver injury
AU - Kohonen, Pekka
AU - Parkkinen, Juuso A.
AU - Willighagen, Egon L.
AU - Ceder, Rebecca
AU - Wennerberg, Krister
AU - Kaski, Samuel
AU - Grafström, Roland C.
PY - 2017/7/3
Y1 - 2017/7/3
N2 - Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion' - concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.
AB - Predicting unanticipated harmful effects of chemicals and drug molecules is a difficult and costly task. Here we utilize a 'big data compacting and data fusion' - concept to capture diverse adverse outcomes on cellular and organismal levels. The approach generates from transcriptomics data set a 'predictive toxicogenomics space' (PTGS) tool composed of 1,331 genes distributed over 14 overlapping cytotoxicity-related gene space components. Involving ∼2.5 × 108 data points and 1,300 compounds to construct and validate the PTGS, the tool serves to: explain dose-dependent cytotoxicity effects, provide a virtual cytotoxicity probability estimate intrinsic to omics data, predict chemically-induced pathological states in liver resulting from repeated dosing of rats, and furthermore, predict human drug-induced liver injury (DILI) from hepatocyte experiments. Analysing 68 DILI-annotated drugs, the PTGS tool outperforms and complements existing tests, leading to a hereto-unseen level of DILI prediction accuracy.
UR - http://www.scopus.com/inward/record.url?scp=85021759690&partnerID=8YFLogxK
U2 - 10.1038/ncomms15932
DO - 10.1038/ncomms15932
M3 - Article
AN - SCOPUS:85021759690
SN - 2041-1723
VL - 8
SP - 1
EP - 15
JO - Nature Communications
JF - Nature Communications
M1 - 15932
ER -