preDiCT

Many drugs fail to reach the market because of side effects on the heart. The principal objective of this proposal is to create an advanced computational technology for in silico assessment of the efficacy and safety of specific drugs [ICT-2007.5.3(c) (3)], i.e. an open environment comprising validated computational models, tools and numerical methods that will enable simulations of drug actions on the electrophysiology of the human heart.

Such simulations will involve modelling of drug interactions at the molecular and cellular level, will extend current technology to enable prediction of the effects of those interactions on the dynamics of the whole heart, and will lead to an understanding of how genetic factors can be used to assess patient-specific risk profiles. This requires a multi-level systems approach, based on multi-scale, multi-physics methods, including computations on adaptive spatial grids and multi-grid time integration. Computations on realistic models at appropriate spatial and temporal scales are currently not feasible, so we will investigate new algorithms and their implementation on high-performance platforms, including a new generation of petaflop computers, to achieve 'faster than real-time' simulation.

These tools form part of the infrastructure required to simulate the physiology of major organ systems, thereby contributing to the goal of creating the Virtual Physiological Human (VPH) [ICT-2007.5.3]. The balanced team in this project, including founders of the Human Physiome Project, has decades of experience in the experimental study and modelling of the electrophysiology and mechanics of the heart, while pharmaceutical industry partners bring deep understanding of the mechanisms of drug actions. The results will demonstrate the value of the VPH initiative to fundamental scientific understanding of the heart, with major economic and clinical impacts through accelerated drug development, approval and use.

For further information, please visit:
http://www.vph-predict.eu

Project co-ordinator:
The Chancellor, Master and Scholars of the University of Oxford

Partners:

  • F. Hoffmann-La Roche AG
  • Szegedi Tudományegyetem
  • Fujitsu Laboratories of Europe Limited
  • Glaxo Smithkline Research and Development
  • Universidad Politécnica de Valencia
  • Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna
  • Novartis Pharma AG
  • Aureus Pharma SA

Timetable: from 06/2008 – to 05/2011

Total cost: € 5.545.692

EC funding: € 4.100.000

Programme Acronym: FP7-ICT

Subprogramme Area: Virtual physiological human

Contract type: Collaborative project (generic)


Related news article:

Most Popular Now

AI Catches One-Third of Interval Breast …

An AI algorithm for breast cancer screening has potential to enhance the performance of digital breast tomosynthesis (DBT), reducing interval cancers by up to one-third, according to a study published...

Great plan: Now We need to Get Real abou…

The government's big plan for the 10 Year Health Plan for the NHS laid out a big role for delivery. However, the Highland Marketing advisory board felt the missing implementation...

Researchers Create 'Virtual Scienti…

There may be a new artificial intelligence-driven tool to turbocharge scientific discovery: virtual labs. Modeled after a well-established Stanford School of Medicine research group, the virtual lab is complete with an...

From WebMD to AI Chatbots: How Innovatio…

A new research article published in the Journal of Participatory Medicine unveils how successive waves of digital technology innovation have empowered patients, fostering a more collaborative and responsive health care...

New AI Tool Accelerates mRNA-Based Treat…

A new artificial intelligence (AI) model can improve the process of drug and vaccine discovery by predicting how efficiently specific mRNA sequences will produce proteins, both generally and in various...

AI also Assesses Dutch Mammograms Better…

AI is detecting tumors more often and earlier in the Dutch breast cancer screening program. Those tumors can then be treated at an earlier stage. This has been demonstrated by...

RSNA AI Challenge Models can Independent…

Algorithms submitted for an AI Challenge hosted by the Radiological Society of North America (RSNA) have shown excellent performance for detecting breast cancers on mammography images, increasing screening sensitivity while...

AI could Help Emergency Rooms Predict Ad…

Artificial intelligence (AI) can help emergency department (ED) teams better anticipate which patients will need hospital admission, hours earlier than is currently possible, according to a multi-hospital study by the...

Head-to-Head Against AI, Pharmacy Studen…

Students pursuing a Doctor of Pharmacy degree routinely take - and pass - rigorous exams to prove competency in several areas. Can ChatGPT accurately answer the same questions? A new...

NHS Active 10 Walking Tracker Users are …

Users of the NHS Active 10 app, designed to encourage people to become more active, immediately increased their amount of brisk and non-brisk walking upon using the app, according to...

New AI Tool Illuminates "Dark Side…

Proteins sustain life as we know it, serving many important structural and functional roles throughout the body. But these large molecules have cast a long shadow over a smaller subclass...

Deep Learning-Based Model Enables Fast a…

Stroke is the second leading cause of death globally. Ischemic stroke, strongly linked to atherosclerotic plaques, requires accurate plaque and vessel wall segmentation and quantification for definitive diagnosis. However, conventional...