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Systems biology of liver toxicity predictions (SysBioToP)

Projectomschrijving

Ronde 2014 Internationaal (Innovative Systems Toxicology for Alternatives to Animal Testing): There is a strong need to develop new toxicological approaches, incorporating mechanistic knowledge of toxicant action, using human cells as biological basis and leveraging the recent progress of systems biology. The SysBioToP project will address the exemplary question of liver toxicity by using this new strategy. The main question explored is whether high-throughput dynamic imaging data of cell stress responses can predict potential hepatotoxicity. For this purpose, imaging data sets from liver cells are connected by a systems biology model to toxic responses of the same cells. These two levels of information will be linked by mathematical equations and extensive data sets on toxicant-induced changes of gene transcription, cellular metabolism, and of the role of some of these genes in the stress response. The single cell information will be fed into a computer model of the three-dimensional liver structure. Thus, a network model predicting liver toxicity will be delivered.

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Samenvatting van de aanvraag

There is a strong need to develop new toxicological approaches, incorporating mechanistic knowledge of toxicant action, using human cells as biological basis and leveraging the recent progress of systems biology. The project will address the exemplary question of liver toxicity by using this new strategy. The main question explored is whether high-throughput dynamic imaging data of cell stress responses can predict potential hepatotoxicity. For this purpose, imaging data sets from liver cells are connected by a systems biology model to toxic responses of the same cells. These two levels of information will be linked by mathematical equations and extensive data sets on toxicant-induced changes of gene transcription, cellular metabolism, and of the role of some of these genes in the stress response. The single cell information will be fed into a computer model of the three-dimensional liver structure. Thus, a network model predicting liver toxicity will be delivered. The originality of SysBioToP lies in the fact that it will establish a completely new integration of quantitative dynamic cell biological cellular stress response signalling information with molecular systems biology and biokinetics modelling to come to a fit-for-purpose systems toxicology approach for human chemical safety prediction. We will follow up on our previous work and expertise and focus on liver toxicity. To establish such a systems toxicology-based chemical safety prediction approach, SysBioToP has designed five unique highly interrelated objectives to establish: (1) a quantitative dynamic cellular stress response data repository for hepatotoxicants; (2) the dynamics of toxicity pathway activation by a novel transcriptome analysis technology; (3) apical endpoints of toxicity and altered cellular metabolic states as anchoring points for altered signaling and transcriptome responses; (4) computational pathway models that can relate cellular stress response pathways to liver cell toxicity; (5) a translation of dynamic stress pathway models to organ/organism levels for quantitative hazard prediction. The public and private partners provide the various and complementary types of expertise and infrastructure required for this interdisciplinary project. All partners already have joint publications. SysBioToP will also engage the field of safety assessment sciences as important stakeholders, and will thus reach out to the regulatory community for feedback and interaction right from the start of the project. Specialists from Europe and the US have agreed to actively contribute to SysBioToP activities. A transdisciplinary education of a new generation of scientists will be promoted by regular project meetings involving all contributing young researchers and by mandatory research visits/exchange of PhD students in partner laboratories for at least three months. The behaviour of the stress signalling network determines to a large extent whether the outcome of xenobiotic exposure is homeostatic (adaptive) or adverse (maladaptive). The interplay between the various pathways and their effect on toxicity is likely to be nonlinear. This explains why previous qualitative descriptions of signal transduction and metabolic pathways could not deliver a quantitative understanding of toxic outcome. We have the vision that dynamic measurements of relevant stress responses in combination with computational models can deliver such a quantitative understanding of the relation between stress pathway activation and toxicity. This vision closely relates to the recently developed unifying concept based on “adverse outcome pathways” (AOPs) supported by e.g., OECD. We believe that this systems biology approach provides a way forward to prediction of human hazard on the basis of cellular models and novel high content data generation technologies. We anticipate that the models can be linked to the organ level invivo and ultimately to risk assessment for humans. The main tangible outcomes of SysBioToP will be: a) a validated in-vitro high-content-imaging reporter cell platform for high-throughput quantitative analysis of liver cell stress responses; b) a computable model predicting apical outcomes of toxicity based on quantitative information on toxicity pathway activation in liver cells; c) a quantitative in-vitro/in-vivo extrapolation model to predict human liver toxicity based on in-vitro data; d) an overall scientifically underpinned strategy to replace in-vivo repeated-dose toxicity studies by targeted quantitative in-vitro high-throughput assays that integrate measurements of critical key events of toxicity pathways. The 3Rs will be impacted by SysBioToP in the following ways: elimination of hepatotoxicants during early drug discovery phases; contribution to an ITS to predict repeat dose systemic/target organ toxicity; reduction of the number of animals and the duration of studies to identify hepatotoxicants by use of biomarkers correlating with hepatotoxicity.

Onderwerpen

Kenmerken

Projectnummer:
114027002
Looptijd: 100%
Looptijd: 100 %
2015
2023
Onderdeel van programma:
Gerelateerde subsidieronde:
Projectleider en penvoerder:
Prof. dr. B. van de Water PhD
Verantwoordelijke organisatie:
Leiden Academic Centre for Drug Research