Establishing a person’s exposome requires capturing the dynamics and variety of exposure levels, including both external exposure profiles and biological responses, over prolonged periods of time. This generates enormous data flows. Consequently, data infrastructures, data-processing and advanced data-analysis techniques for combining and analysing exposome data are required. The data infrastructure has to bring together data from different sources, after which, data processing selects and combines the appropriate data. Whereas data-analysis techniques enable data interpretation into relevant information on which to base conclusions, that can then be translated in actionable feedback. The tools that combine and analyse exposome data throw light onto which exposures occur when, where, why and for whom. And it will provide insight in under which internal and external circumstances disease develops.
• Sensor-model integration for external exposures:
There is a clear need for technology that can accurately assess personal exposure profiles, in terms of high resolution in time and space, in both a general and working environment with the possibility to integrate both. To achieve this, TNO is collaborating with the Utrecht Exposome Hub on developing methodology for the enrichment of exposure models with sensor data and thereby obtaining personal exposure profiles in the general environment.
• Integrated modelling of benzene:
We are developing methodologies for integrated modelling from external exposure towards internal biological responses and health effects. This includes the development/adoption of PBTK models, the prioritisation of modes of action and adverse outcome pathways of relevance to the effect (in this case Acute Myeloid Leukemia), the semi-quantitative definition of these pathways and, finally, the full integration of the data within a computational model towards the prediction of health. For benzene, this development is being done in collaboration with the Utrecht Exposome Hub