Sort by: Order: Results:

Now showing items 1-4 of 4
  • Kohonen, Pekka; Parkkinen, Juuso A.; Willighagen, Egon L.; Ceder, Rebecca; Wennerberg, Krister; Kaski, Samuel; Grafstrom, Roland C. (2017)
    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 similar to 2.5 x 10(8) 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.
  • Scheinin, Ilari; Ferreira, Jose A.; Knuutila, Sakari; Meijer, Gerrit A.; van de Wiel, Mark A.; Ylstra, Bauke (2010)
  • Kallio, Aleksi; Vuokko, Niko; Ojala, Markus; Haiminen, Niina; Mannila, Heikki (2011)
  • Kinaret, Pia Anneli Sofia; Serra, Angela; Federico, Antonio; Kohonen, Pekka; Nymark, Penny; Liampa, Irene; Ha, My Kieu; Choi, Jang-Sik; Jagiello, Karolina; Sanabria, Natasha; Melagraki, Georgia; Cattelani, Luca; Fratello, Michele; Sarimveis, Haralambos; Afantitis, Antreas; Yoon, Tae-Hyun; Gulumian, Mary; Grafström, Roland; Puzyn, Tomasz; Greco, Dario (2020)
    The starting point of successful hazard assessment is the generation of unbiased and trustworthy data. Conventional toxicity testing deals with extensive observations of phenotypic endpoints in vivo and complementing in vitro models. The increasing development of novel materials and chemical compounds dictates the need for a better understanding of the molecular changes occurring in exposed biological systems. Transcriptomics enables the exploration of organisms' responses to environmental, chemical, and physical agents by observing the molecular alterations in more detail. Toxicogenomics integrates classical toxicology with omics assays, thus allowing the characterization of the mechanism of action (MOA) of chemical compounds, novel small molecules, and engineered nanomaterials (ENMs). Lack of standardization in data generation and analysis currently hampers the full exploitation of toxicogenomics-based evidence in risk assessment. To fill this gap, TGx methods need to take into account appropriate experimental design and possible pitfalls in the transcriptomic analyses as well as data generation and sharing that adhere to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. In this review, we summarize the recent advancements in the design and analysis of DNA microarray, RNA sequencing (RNA-Seq), and single-cell RNA-Seq (scRNA-Seq) data. We provide guidelines on exposure time, dose and complex endpoint selection, sample quality considerations and sample randomization. Furthermore, we summarize publicly available data resources and highlight applications of TGx data to understand and predict chemical toxicity potential. Additionally, we discuss the efforts to implement TGx into regulatory decision making to promote alternative methods for risk assessment and to support the 3R (reduction, refinement, and replacement) concept. This review is the first part of a three-article series on Transcriptomics in Toxicogenomics. These initial considerations on Experimental Design, Technologies, Publicly Available Data, Regulatory Aspects, are the starting point for further rigorous and reliable data preprocessing and modeling, described in the second and third part of the review series.