Koordiniert durch:Prof. Dr. med. S. Stein
Förderungszeitraum:von Feb 2009 bis Jan 2012

MoPS-Modellierung von peripheren Schmerzschaltern

A valid mechanistic model for the signalling in chronic pain will fundamentally change pain therapy. This project applies established mathematical models of signalling switches involved in pain sensitisation, optimises and expands them by reflection on molecular, cellular as well as animal experiments, to finally apply their predictive power to humans enabling a “mechanism-based” pain therapy.
In clinical practice pain is among the most prevalent symptoms with often insufficient treatment. The common approach of “disease-based” therapy shows highly variable success, which indicates the urgent need to shift toward an emerging “mechanismbased” paradigm, targeting the underlying mechanisms instead of the symptoms. This strategy is pioneered by the BMBF-financed network “Deutscher Forschungsverbund Neuropathischer Schmerz (DFNS)”. DFNS profiles sensory modalities of patients and healthy control subjects by standardized quantitative sensory testing (QST), rendering these profiles accessible for complex evaluation. Complementation of diagnostic parameters with a systematic evaluation of the underlying molecular signalling networks is largely missing.
The proposed project is aiming to fill this gap. A number of intracellular signalling cascades have been documented to sensitise peripheral nociceptive neurons that transduce noxious stimuli into electrical impulses travelling towards the central nervous system. Much information of signalling events lies in the kinetics as well as the interplay of different pathways. The current lack of models and data makes a judgement about one of the most fundamental descriptions of a signalling network so far impossible, namely whether nociceptive cascades converge onto one or more “nociceptive modules”. Our project will combine mathematical modelling of pre-existing data of established signalling systems with integrative data collection of a comprehensive number of signalling components including quantitative kinetic data to feedback into the mathematical models. Thus, we will be able to identify differential nociceptive modules. These data in turn will allow us to test the derived mathematical models electrophysiologically on primary sensory neurons and in vivo animal models. Ultimately testing on human patients with suitable sensory modality profiles from the DFNS QST-database is anticipated.
The recursive interplay between data modelling, data creation, theory testing in model systems and transfer to humans, will provide a roadmap towards a mechanism-based therapy as well as a description of novel therapeutical targets.