(Computational Prediction of Drug Cardiac Toxicity)
Predicting Drug Interactions

The preDiCT project will model and ultimately predict the impact of pharmaceutical compounds on the heart’s
rhythm using computer simulation. Using this information, the project hopes to identify new biomarkers which
will provide more reliable indication of harmful drug side effects.
Problem or Context
In the first 18 months of the project the currently available mathematical models of cells have been checked, their limitations identified and strategies Current best practice in pharmaceutical development relies on the Q-T inter- for refinement developed. In order to test these models, they had to be built val (the spacing of two points on an electrocardiogram) as a proxy for poten- from and validated against experimental data from the scientific literature tial y dangerous side effects. However, it is known that some drugs which fail and provided by preDiCT academic and pharmaceutical partners. Signifi- this test do not lead to arrhythmia (e.g. Ranolazine, whose safety was dem- cant work has been required to ‘normalise’ these data with respect to the onstrated by researchers at the University of Oxford). We hope to be able to experimental protocols used to acquire them, in order to make them directly develop more accurate gauges of potential cardiotoxicity.
comparable. A database for publications, protocols and tested compounds has been developed and populated with an initial list of drugs and selected A significant and growing number of drug candidates fail to reach market due to adverse effects on heart rhythm which only show up during clinical trials. We hope to achieve a better understanding of the underlying mecha- Sensitivity Analysis using software developed by preDiCT has determined nisms, which may lead to refinement of the drug development process to that most of the existing models must be refined in order to be predictive. Effort is now being concentrated on improving the models, to enable fast, reliable simulation of normal and altered cardiac electrophysiology.
A better understanding of the factors determining species-dependent drug The preDiCT project will model and ultimately predict the impact of phar- interactions, combined with analysis of ECG signals and the assessment of maceutical compounds on the heart’s rhythm using computer simulation.
arrhythmogenic factors, enables the investigation of new and better biomar-kers to complement current drug-safety metrics. This will require advances beyond the current state-of-the-art in: As of January 2010, we have developed models of the action of two drugs, • Mathematical models of individual ion channels, which control the elec- lidocaine and dofetilide, on ionic channels, and computationally efficient models of rabbit and human cardiac electrophysiology. We have also evalu- • Tissue models, which encapsulate chemical processes and physical rela- ated new biomarkers of arrhythmic risk and reviewed ionic and cellular tionships between millions of individual muscle cells in the heart; and • The computer code, which must compute these relationships as a series of complex equations, to enable faster than real-time simulation of a beat- In order to compute drug effects on human ventricles, the project has begun to develop highly efficient numerical algorithms and implement them on SCenario
Given that most of the costs of bringing a new drug to market are incurred during the clinical trial phases, there would be a huge economic and clinical impact for being able more accurately to predict which
drugs are likely to cause arrhythmias. Even when drugs do make it through to clinical trials, the statistical power of those trials is often insufficient to predict adverse effects which may (recently in the cases
of Vioxx and Celebrex) appear only when the drug is given to large numbers of patients over long periods of time.
A more predictive approach utilising advanced mathematical and computational model ing holds out hope of being able to spot and pre-empt these very low-probability effects.
massively parallel computers. The consortium is making use of extensive high-performance com-puting facilities in the UK (DEISA), Italy, Germany and Japan.
All the tools will be integrated into a Virtual Research Environment: an integrative portal to the complex set of tools and information needed to conduct in silico experiments. The VRE will be designed for everyday use by academic researchers and pharmaceutical industry scientists, to facil-itate more extensive use of in silico methods in the drug discovery and development process.
The traceability of the complete project will be ensured by storing all the results and simulations, Computational Prediction of
including the metadata describing the in silico experiments, and the models developed will be pro- Drug Cardiac Toxicity
vided to the wider community via the CellML repository.
Project co-ordinator:
The key to the success of the project is the cooperation with the pharmaceutical industry. Our ini- tial consortium involved key pharmaceutical companies to assist with steering the project. Over the first 18 months, we have deepened these relationships and extended the number of pharma- Contact person:
ceutical companies involved. At our pharma workshop in October 2009, this engagement extended to defining projects to work on with each of these companies.
Tel: +44 (0)1865 272 501Fax: +44 (0)1865 272 554 Email: katherine.fletcher@dpag.ox.ac.ukWebsite: www.vph-predict.eu The preDiCT project aims to improve our understanding of the mechanisms of negative drug actions Partners:
• Aureus Pharma Ltd (France)• Centro di Ricerca, Sviluppo e Studi Superiori in Sardegna • Improve safety testing for new drugs; • Help speed up and streamline the drug discovery process by identifying likely profiles of ‘good’ • Fujitsu Laboratories of Europe (United Kingdom) and ‘risky’ compounds (the pharmaceutical industry currently spends nearly €3bn per new • Help pave the way to patient-specific healthcare through simulation; and • Push the boundaries of simulation and high-performance computing, enabling progress in Furthermore, by extending the frontiers of in silico experimentation, our project will enable future • Universidad Politécnica de Valencia (Spain) researchers to refine, replace and ultimately reduce the use of animals in pharmaceutical and other • University of Oxford (United Kingdom) Timetable: from June 2008 to May 2011
Total cost: € 5,500,000
eC funding: € 4,100,000
instrument: STREP
Project identifier: FP7-2008-IST-224381
Cardiac, Physiological model ing, Drug safety, Ventricle,

Source: http://vph-portal.eu/documents/10180/35174/predict-factsheet.pdf


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