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QSPainRelief

Financiado

Cerrado

Effective combinational treatment of chronic pain in individual patients by an...

Effective combinational treatment of chronic pain in individual patients by an innovative quantitative systems pharmacology pain relief approach. Chronic pain is a complex disease suffered by about 20% of Europeans. Up to 60% of these patients do not experience adequate pain relief from currently available analgesic combinational therapies and/or suffer confounding adverse... ver más

30/06/2025

ULEI

6M€

Presupuesto del proyecto: 6M€

Líder del proyecto

UNIVERSITEIT LEIDEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

Ver 11 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2019-12-13

SC1-BHC-02-2019: Systems approaches for the discover...

I+D

Cerrada hace 6 años

0% 100% 100%

Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

11 Participantes

ULEI

1.40M€ | Lider

CHDR

677.64K€ | Participante

UNIBO

390.88K€ | Participante

UAB

340.88K€ | Participante

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Duración del proyecto: 66 meses Fecha Inicio: 2019-12-13
Fecha Fin: 2025-06-30

Líder del proyecto

UNIVERSITEIT LEIDEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 6M€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Chronic pain is a complex disease suffered by about 20% of Europeans. Up to 60% of these patients do not experience adequate pain relief from currently available analgesic combinational therapies and/or suffer confounding adverse effects. Of the many conceivable combinations only a few have been studied in formal clinical trials. Thus, physicians have to rely on clinical experience when treating chronic pain patients. The vision of the QSPainRelief consortium is that alternative novel drug combinations with improved analgesic and reduced adverse effects can be identified and assessed by mechanism-based Quantitative Systems Pharmacology in silico modelling. This is far cheaper and less time-consuming than clinical trials. We will develop an in silico QSPainRelief platform which integrates recently developed 1) physiologically based pharmacokinetic model to quantitate and adequately predict drug pharmacokinetics in human CNS, 2) target-binding kinetic models; 3) cellular signalling models and 4) a proprietary neural circuit model to quantitate the drug effects on the activity of relevant brain neuronal networks, that also adequately predicts clinical outcome. This platform will include patient characteristics such as age, sex, disease status and genotypes, and will predict efficacy and tolerability of a wide range of analgesic and other centrally active drug combinations, and rank these. The best combinations will then be validated in a suitable animal model, in two clinical studies in healthy volunteers, as well as in real world clinical practice. Quantitative insights and confirmed effective combinational treatments will result in a game-changer by improving the management of pain in individuals and stratified sub-populations of chronic pain patients, and reduce the large burden on health-care providers greatly. It would also increase the understanding of chronic pain in general, and trigger the development of even better combination therapies in the future.

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