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Predicting Cardiotoxicity Induced by Kinase Inhibitors From Systems Biology to...

Predicting Cardiotoxicity Induced by Kinase Inhibitors From Systems Biology to Systems Pharmacology Kinase inhibitors (KIs) are a major class of highly effective anti-cancer drugs. Unfortunately, therapeutic use of KIs is often associated with cardiotoxicity (CT), a serious adverse condition which limits their use. This fellowsh... ver más

05/12/2018

ULEI

243K€

Presupuesto del proyecto: 243K€

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.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2018-12-05

MSCA-IF-2014-GF: Marie Skłodowska-Curie Individual F...

I+D

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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

3 Participantes

ULEI

242.93K€ | Lider

ICAHN SCHOOL OF MEDICINE AT...

160.13K€ | Participante

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Duración del proyecto: 44 meses Fecha Inicio: 2015-03-16
Fecha Fin: 2018-12-05

Líder del proyecto

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

TRL 4-5

Presupuesto del proyecto 243K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Kinase inhibitors (KIs) are a major class of highly effective anti-cancer drugs. Unfortunately, therapeutic use of KIs is often associated with cardiotoxicity (CT), a serious adverse condition which limits their use. This fellowship aims to develop mathematical systems pharmacology models for KI-induced CT. These models will be used to identify predictive CT signatures that will allow to decrease CT risk of new KIs. This innovative multi-disciplinary approach consists of integrating mathematical systems pharmacology modelling, with state-of-the-art experimental data generation. To this aim, KIs with different magnitudes of CT will be selected based on clinical adverse event databases. Human cardiomyocytes derived from pluripotent stem cells will then be treated with the selected KIs and in combination with CT modifying drugs. The effect of these treatments on changes on untargeted mRNA and protein expression will be measured and then analyzed using network modelling. This approach allows identification of key regulatory proteins. The selected proteins will then be quantified over time along with cardiomyocyte health markers. With this data, dynamical models will be developed to capture the relationship between exposure to KIs and the effects on protein expression and cardiomyocyte health over time. Ultimately these models will allow generation of predictive network-based dynamically-weighted signatures for CT. The fellow aims to establish himself as independent researcher in systems pharmacology. Training in state-of-the-art computational and experimental technologies at the leading systems pharmacology group at Mount Sinai in New York will fundamentally strengthen and broaden the experience of the fellow. This project will significantly contribute consolidate the career track of the fellow, foster future collaboration between Mount Sinai and Leiden University, and disseminate training in Europe.

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