June 18, 2011
Life sciences companies are increasingly looking to the cloud for particular elements of their daily research and development efforts, including the use of hosted software solutions for drug discovery. Between these demands and the increasing need for enhanced collaboration capabilities, a new ecosystem is cropping up to support larger life sciences data sets and the dispersed researchers making use of them.
A recent article noted that that collaboration and R&D platforms in the life sciences are beneficial to the industry since many have smaller IT departments and would prefer to focus their attention on their core competency when it comes to technology versus haggle with collaboration and research technology challenges.
The author pointed to one of North America’s largest life sciences companies, stating:
"It was exchanging procurement and R&D messages across hundreds of external organizations, some of whom had basic back-end systems, while others were connecting via marketplaces, such as GHX, and some had procurement applications like SciQuest. They tried to manage all these connections themselves, but found it challenging, resource intensive and slow. This company implemented a Cloud-based integration solution, and also outsourced management of the solution to the Cloud provider, which minimized the impact on its fairly modest IT department."
A number of companies are springing up to meet the demand by offering SaaS platforms that provide rich information that can be used by research scientists seeking to make connections among chemical elements that could lead to tomorrow’s cures. These solutions must provide a means to make R&D outcomes easily shared, worked on from distributed locations and integreated.
One such company is a startup based in North Carolina’s Research Triangle Park (RTP) called SCYNEXIS. The scientists here provide support to medicinal chemists, biologists, computational chemists and others as they seek to identify potential candidates for pharmaceuticals. Much of their internal work is supported by their cloud-based discovery and collaboration platforms, HEOS (which manages vast amounts of drug discovery data) and their MEDCHEM-FACTORY platform, which integrates the needs of biochemists and medicinal chemists.
HEOS is “a SaaS, secure, cloud-based drug research information software platform that supports geographically dispersed project scientists who may be in different organizations.” They go on to describe that the cloud-based package “facilitates researchers’ efforts to consolidate, manage, share and analyze complex drug discovery information on a global basis.”These services feed into their “drug outsourcing” plan which provides the above services in line with FDA guidelines.
This is but one example that fits within a growing trend; life sciences companies are finding larger amounts of data coming in, especially in the genomics and personalized medicine arenas, but without growing their IT force significantly to handle all applications in-house they could miss out on important findings.
SCYNEXIS is certainly not the only company finding value in providing secure cloud-based drug discovery and R&D services. One of the first to provide a hosted solution, CDD (which stands for Collaborative Drug Discovery) has also found that this industry is in need of tailored solutions to manage growing complexity.
CDD counts a number of major drug companies in its customer ranks, including AstraZeneca, Pfizer, Novartis and others. As Jim Wikel from Apex Therapetuics says, solutions such as these “allow lab members across the country to access data in real time, and provides graphics/structure searching for chemists without burdening biologists."
If there is any prediction for the coming year that invokes life sciences and cloud computing, it is that IT service providers who can put forth fine-tuned platforms for this industry (with compliance and regulations at the fore) via a subscription-based or usage-based model will find an eager audience among established and new drug companies that have outstretched their current hardware resources.
Posted by Nicole Hemsoth - June 18, 2011 @ 3:12 AM, Pacific Daylight Time
Nicole Hemsoth is the managing editor of HPC in the Cloud and will discuss a range of overarching issues related to HPC-specific cloud topics in posts.
No Recent Blog Comments
The ever-growing complexity of scientific and engineering problems continues to pose new computational challenges. Thus, we present a novel federation model that enables end-users with the ability to aggregate heterogeneous resource scale problems. The feasibility of this federation model has been proven, in the context of the UberCloud HPC Experiment, by gathering the most comprehensive information to date on the effects of pillars on microfluid channel flow.
Large-scale, worldwide scientific initiatives rely on some cloud-based system to both coordinate efforts and manage computational efforts at peak times that cannot be contained within the combined in-house HPC resources. Last week at Google I/O, Brookhaven National Lab’s Sergey Panitkin discussed the role of the Google Compute Engine in providing computational support to ATLAS, a detector of high-energy particles at the Large Hadron Collider (LHC).
Frank Ding, engineering analysis & technical computing manager at Simpson Strong-Tie, discussed the advantages of utilizing the cloud for occasional scientific computing, identified the obstacles to doing so, and proposed workarounds to some of those obstacles.
May 23, 2013 |
The study of climate change is one of those scientific problems where it is almost essential to model the entire Earth to attain accurate results and make worthwhile predictions. In an attempt to make climate science more accessible to smaller research facilities, NASA introduced what they call ‘Climate in a Box,’ a system they note acts as a desktop supercomputer.
May 16, 2013 |
When it comes to cloud, long distances mean unacceptably high latencies. Researchers from the University of Bonn in Germany examined those latency issues of doing CFD modeling in the cloud by utilizing a common CFD and its utilization in HPC instance types including both CPU and GPU cores of Amazon EC2.
05/10/2013 | Cleversafe, Cray, DDN, NetApp, & Panasas | From Wall Street to Hollywood, drug discovery to homeland security, companies and organizations of all sizes and stripes are coming face to face with the challenges – and opportunities – afforded by Big Data. Before anyone can utilize these extraordinary data repositories, however, they must first harness and manage their data stores, and do so utilizing technologies that underscore affordability, security, and scalability.
04/02/2012 | AMD | Developers today are just beginning to explore the potential of heterogeneous computing, but the potential for this new paradigm is huge. This brief article reviews how the technology might impact a range of application development areas, including client experiences and cloud-based data management. As platforms like OpenCL continue to evolve, the benefits of heterogeneous computing will become even more accessible. Use this quick article to jump-start your own thinking on heterogeneous computing.