March 2008
Urgent Computing: Exploring Supercomputing's New Role
Steven Manos
Stefan Zasada
Peter V. Coveney, Centre for Computational Science, Chemistry Department, University College London

4.3 Treating cancer with patient-specific chemotherapeutic drug targeting

The identification and treatment of cancer exists on various levels, from the large-scale view of tumour growth down to individual molecular interactions. The treatment of cancer often takes two directions, using targeted radiotherapy to kill malignant cells, while also using tumour-growth inhibitors in an attempt to selectively target and kill tumourous cells. The effectiveness of particular chemotheraputic treatment differs from patient to patient, with some courses of treatment not being effective at all.

A new generation of anticancer drugs are part of an approved scheme called ‘targeted therapy,’ in which anticancer drugs are directed against cancer-specific molecules and signalling pathways. These are designed to interfere with a specific molecular target, usually a protein that plays a crucial role in tumour cell growth and proliferation. Receptor tyrosine kinases (RTKs) are an example; they are cell surface proteins that can be used as targets to control tumour growth in various preclinical treatment models. Tyrosine kinase inhibitors (TKI) interfere with the related cell signalling pathways and thus allow target-specific therapy for selected malignancies. In fact, some TKIs have been approved for use in cancer therapy, and others are in various stages of clinical trials.

RTKs have been found to be over-expressed or mutated in tumour cells, and these mutations allow cancer cells to develop drug resistance. Clinical studies have shown a strong correlation between a reduction in the response to treatment with TKIs and the presence of these mutations, where the resistance is introduced by preventing or weakening the binding of the receptor to the targeted TKI.

The binding of the tumour-growth inhibitors to cell receptors is identical to small molecule-protein or protein-protein interactions. Molecular dynamics techniques can be used to study these interactions in atomistic detail, and to predict the effect of different receptors and mutations on inhibitor binding affinities. Using patient-specific data, such as the RTK mutation, which is expressed on tumourous cells, MD techniques can be used to rank the binding affinities, and therefore the effectiveness of various treatments against a patient-specific case.

Using a grid-infrastructure, turnaround times can be dramatically accelerated. MD simulations, particularly for the case of various inhibitors and possibly various targets, can be independently run by being farmed off to various grid resources. Providing turnaround times of five days will ensure that the findings are clinically relevant and become part of the clinical decision making process. One of the aims within this project is to develop a work-flow tool, which will use the AHE to permit the automated running of such patient-specific simulations, hiding the unnecessary grid details from clinicians.

5. Discussion

Patient-specific medical simulation holds the promise of revolutionising the diagnosis and treatment of many different medical conditions, by making use of advanced simulation techniques and high performance compute resources. For computational medicine to be of use in modern clinical settings, the timeliness with which results are delivered is of primary concern. Results need to be generated in a timeframe that is useful to the clinician initiating the simulation results; that is, they must be generated in time to inform the treatment regime or procedure under consideration. In the case of neurosurgical treatments, this is in the order of 15 to 20 minutes. In the case of HIV or cancer pathology reports, this is in the order of 24 to 48 hours.

Due to the urgent requirements of patient-specific simulations, the current standard model of high performance compute provision, the batch queue model, is of no use. Simulations have to fit into existing clinical processes; clinical processes cannot be altered to adapt to a batch compute model, as very often a simulation will be used to inform an urgent life or death decision. Because of this, technologies that enable and facilitate urgent computing are of great relevance to the emerging field of patient-specific simulation. Advance reservation tools such as HARC and urgent computing systems such as SPRUCE are essential for making patient-specific medical simulation a reality when using general purpose, high performance compute resources that typically run a wide range of different tasks.

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Reference this article
Manos, S., Zasada, S., Coveney, P. V. "Life or Death Decision-making: The Medical Case for Large-scale, On-demand Grid Computing," CTWatch Quarterly, Volume 4, Number 1, March 2008. http://www.ctwatch.org/quarterly/articles/2008/03/life-or-death-decision-making-the-medical-case-for-large-scale-on-demand-grid-computing/

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