Wolfgang Gentzsch, UberCloud, December 14, 2018
Extended article appeared in Diagnostics World
Personalized healthcare has existed for more than a decade, because of great advances in science and technology which have made diagnostics smarter, more targeted, and more accessible. For example, based on an intricate understanding of the biology of a disease, scientists and physicians are able to identify biomarkers: biological molecules found in body fluids or tissues that provide insight into the state of the disease, such as gene mutations or protein expressions. Employing diagnostic tests like liquid biopsy helps to find specific genetic defects in the patient’s tissue to better understand the molecular root cause of the disease. Scientists and physicians are now able to work with leading diagnostic partners to develop tests and making informed treatment decisions for the individual patients.
Major factors contributing to the acceleration of personalized healthcare in recent years come from advances in high-performance computing (HPC), data analytics, machine learning, and artificial intelligence, enabling scientists now to perform the most sophisticated simulations, in genomics, proteomics, and many other fields, using methods like genome analysis, molecular dynamics, and more general computer aided analysis methods widely applied and proven in other areas of scientific and engineering modeling.
In this Blog, we demonstrate the impact of computer simulations on personalized health care and present two recent research projects managed by UberCloud, aiming at living heart and brain simulations which have recently been rewarded with several prestigious international awards.
Studying Personalized Drug-induced Arrhythmias of a Human Heart
This Stanford LHP project dealt with simulating cardiac arrhythmia which can be an undesirable and potentially lethal side effect of drugs. During this condition, the electrical activity of the heart turns chaotic, decimating its pumping function, thus diminishing the circulation of blood through the body. Some kind of cardiac arrhythmia, if not treated with a defibrillator, will cause death within minutes.
Before a new drug reaches the market, pharmaceutical companies need to check for the risk of inducing arrhythmias. Currently, this process takes years and involves costly animal and human studies. In this project, the Living Matter Laboratory of Stanford University developed a new software tool enabling drug developers to quickly assess the viability of a new compound. This means better and safer drugs reaching the market to improve patients’ lives.
A computational model that is able to assess the response of new drug compounds rapidly and inexpensively is of great interest for pharmaceutical companies, doctors, and patients. Such a tool will increase the number of successful drugs that reach the market, while decreasing cost and time to develop them, and thus help hundreds of thousands of patients in the future. However, the creation of a suitable model requires taking a multi-scale approach that is computationally expensive: the electrical activity of cells is modeled in high detail and resolved simultaneously in the entire heart. Due to the fast dynamics that occur in this problem, the spatial and temporal resolutions are highly demanding.
Electrical activity for the no-drug case and after application of the drug Quinidine. The electrical propagation turns chaotic after the drug is applied, showing the high risk of Quinidine to produce arrhythmias.
The project team consisted of researchers from the Living Matter Laboratory at Stanford University, Hewlett Packard Enterprise and Intel (the sponsors), Dassault Systemes SIMULIA (for Abaqus 2017), Advania (providing HPC Cloud resources), and the UberCloud tech team for developing the Abaqus HPC container integrating all software and hardware components into one seamless solution stack.
Personalized Non-invasive Clinical Treatment of Schizophrenia
This NIMHANS project represents a breakthrough in demonstrating the high value of computational modeling and simulation in improving the clinical application of non-invasive electro-stimulation of the human brain in schizophrenia and the potential to apply this technology to the treatment of other neuropsychiatric disorders such as depression and Parkinson’s disease. With the addition of HPC, clinicians can now precisely and non-invasively target regions of the brain without disrupting nearby healthy brain regions!
International 10/10 convention (left) showing anode AF3 (red) and cathode CP5 (blue); subject-specific model (right).
The project team consisted of the National Institute of Mental Health & Neuro Sciences NIMHANS in Bangalore, Dassault Systemes SIMULIA, Advania Data Centers, Hewlett Packard Enterprise and Intel, and UberCloud.
26 different electrical simulations were performed using UberCloud’s SIMULIA Abaqus container – each representing a different electrode configuration based on clinical guidance – on the Advania/UberCloud HPC cluster of HPE ProLiant servers. On a system with 16 cores at SIMULIA’s office in India, a single run took about 75 minutes, whereas on the UberCloud/Advania cluster in Iceland a single run took about 28 minutes on 24 cores.
Now, an even bigger advantage comes from performing all 26 different electrical simulations in parallel, with a speedup of 26, reducing simulation time for all 26 simulations from 33 hours to 28 minutes, a speed-up factor of 70 just by using HPC cloud computing resources, and an even higher speed-up the more HPC cloud servers are used.
With this achievement, the patient can now wait for the doctor’s simulations resulting in the optimal electrode configuration. Compared to the traditional hospital treatment which comes along with a painful, risky, and expensive operation, this novel ambulant treatment enabled by HPC Cloud is non-intrusive, safe, and affordable for everybody, ready for a wider research and commercial adoption!
Interested readers can download the two extended case studies HERE.