Patient-specific simulation as a basis for clinical decision-making

Abstract

Patient-specific medical simulation holds the promise of determining tailored medical treatment based on the characteristics of an individual patient (for example, using a genotypic assay of a sequence of DNA). Decision-support systems based on patient-specific simulation can potentially revolutionize the way that clinicians plan courses of treatment for various conditions, ranging from viral infections to arterial abnormalities. Basing medical decisions on the results of simulations that use models derived from data specific to the patient in question means that the effectiveness of a range of potential treatments can be assessed before they are actually administered, preventing the patient from experiencing unnecessary or ineffective treatments. We illustrate the potential for patient-specific simulation by first discussing the scale of the evolving international grid infrastructure that is now available to underpin such applications. We then consider two case studies, one concerned with the treatment of patients with HIV/AIDS and the other addressing neuropathologies associated with the intracranial vasculature. Such patient-specific medical simulations require access to both appropriate patient data and the computational resources on which to perform potentially very large simulations. Computational infrastructure providers need to furnish access to a wide range of different types of resource, typically made available through heterogeneous computational grids, and to institute policies that facilitate the performance of patient-specific simulations on those resources. To support these kinds of simulations, where life and death decisions are being made, computational resource providers must give urgent priority to such jobs, for example by allowing them to pre-empt the queue on a machine and run straight away. We describe systems that enable such priority computing.

Rapid and accurate ranking of binding affinities of epidermal growth factor receptor sequences with selected lung cancer drugs

“…We have previously reported a series of ensemble simulations for a range of HIV-1 protease inhibitors and associated mutations [24 -26] using BAC [13], with ensembles of 50 individual simulations and relatively short simulation time (up to 4 ns for each separate simulation). A remarkable level of correlation is obtained between computed binding free energies and experimental data [24][25][26]. A comparison study [14,25] of ensemble simulations and single long simulations has demonstrated the effectiveness of the ensemble approach, which exhibits significantly enhanced thermodynamic sampling.…”

“…2TS conf is the contribution of configurational entropy S conf at temperature T. Because AEE788 and Gefitinib are 'reversible' inhibitors that bind non-covalently to the receptor, the bonded terms cancel exactly in the binding free energy calculation (equation (2.1)). Hence, the binding free energy is The MM/PBSA method has been applied to rank affinities for inhibitors bound to WT and resistant variants of HIV-1 protease [24][25][26], and TKIs to kinases [16 -20]; here ensemble simulations are performed to calculate the binding free energies of the inhibitors AEE788 and Gefitinib to WT and four mutant EGFRs in a similar manner to that described by Sadiq et al [25].…”

Real science at the petascale

“…An emerging concept in the biomedical domain is that of the use of high-end computational power to confer clinical decision support for the treatment of a range of illnesses (Sadiq et al 2008b;Sloot et al 2008). Applied to HIV therapy, a new approach that uses this paradigm aims to compute the binding affinity of any given inhibitor to any sequence of the corresponding viral protein target with sufficient accuracy to discriminate resistant mutations on a patient-specific basis and with enough rapidity to return on clinically relevant time scales.…”

Audited credential delegation: a usable security solution for the virtual physiological human toolkit

“…Scientists and clinicians need to use such resources to perform patient-specific modelling and simulation that draws on the medical characteristics of an individual patient. Decision-support systems based on patient-specific computer simulation hold the potential to revolutionize the way clinicians plan courses of treatment for patients [1]. This leads immediately to the question of how to address information security within the VPH initiative.…”