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

Stanford Medicine Study Suggests Physici…

Artificial intelligence-powered chatbots are getting pretty good at diagnosing some diseases, even when they are complex. But how do chatbots do when guiding treatment and care after the diagnosis? For...

OmicsFootPrint: Mayo Clinic's AI To…

Mayo Clinic researchers have pioneered an artificial intelligence (AI) tool, called OmicsFootPrint, that helps convert vast amounts of complex biological data into two-dimensional circular images. The details of the tool...

Adults don't Trust Health Care to U…

A study finds that 65.8% of adults surveyed had low trust in their health care system to use artificial intelligence responsibly and 57.7% had low trust in their health care...

Testing AI with AI: Ensuring Effective A…

Using a pioneering artificial intelligence platform, Flinders University researchers have assessed whether a cardiac AI tool recently trialled in South Australian hospitals actually has the potential to assist doctors and...

AI Unlocks Genetic Clues to Personalize …

A groundbreaking study led by USC Assistant Professor of Computer Science Ruishan Liu has uncovered how specific genetic mutations influence cancer treatment outcomes - insights that could help doctors tailor...

The 10 Year Health Plan: What do We Need…

Opinion Article by Piyush Mahapatra, Consultant Orthopaedic Surgeon and Chief Innovation Officer at Open Medical. There is a new ten-year plan for the NHS. It will "focus efforts on preventing, as...

Deep Learning to Increase Accessibility…

Coronary artery disease is the leading cause of death globally. One of the most common tools used to diagnose and monitor heart disease, myocardial perfusion imaging (MPI) by single photon...

People's Trust in AI Systems to Mak…

Psychologists warn that AI's perceived lack of human experience and genuine understanding may limit its acceptance to make higher-stakes moral decisions. Artificial moral advisors (AMAs) are systems based on artificial...

Relationship Between Sleep and Nutrition…

Diet and sleep, which are essential for human survival, are interrelated. However, recently, various services and mobile applications have been introduced for the self-management of health, allowing users to record...

AI Model can Read ECGs to Identify Femal…

A new AI model can flag female patients who are at higher risk of heart disease based on an electrocardiogram (ECG). The researchers say the algorithm, designed specifically for female patients...

New AI Tool Mimics Radiologist Gaze to R…

Artificial intelligence (AI) can scan a chest X-ray and diagnose if an abnormality is fluid in the lungs, an enlarged heart or cancer. But being right is not enough, said...

DMEA 2025 - Innovations, Insights and Ne…

8 - 10 April 2025, Berlin, Germany. Less than 50 days to go before DMEA 2025 opens its doors: Europe's leading event for digital health will once again bring together experts...