Abstract
Abstract With looming patent cliffs, resulting in no patent protections for several block buster drugs, several Life sciences organizations are looking at ways to reduce the costs of drug discovery. They are looking to change business models from having all drug discovery activities being done in-house to a more economical collaborative innovation model by forming ecosystems through consortiums and alliances with several other partners to collaborate especially in the pre-competitive areas of drug discovery. They are considering leveraging cloud computing platforms to create the collaborative drug discovery platforms needed to support these new drug discovery models. Another area of focus is to improve the success rate of drug discovery by creating more complex computer models and performing more data intensive simulations. Next generation sequence sequencers are also providing unprecedented amounts of data to work with. Cloud computing has proven to be scalable and capable of meeting the computation needs in life sciences domain but a key inhibitor has been security concerns. This paper is an extension of an earlier paper we had written that describes how to leverage a public cloud to build a scalable genome sequence search platform to enable secure collaboration among multiple partners. This paper describes a few additional techniques and open source solutions that can be leveraged to address security concerns while leveraging public cloud platforms for collaborative drug discovery activities.
Highlights
Several block buster drugs will go off patent protection by 2015 [1]
Given this background of Amazon Amazon Web Service (AWS) Cloud, OpenAM, Truecrypt, Hadoop Security, Access Control List (ACL)-based security Model and OpenVPN we describe the security techniques
The experience report along with the earlier reports [5,7] described how to use open source tools and solutions to create a secure drug discovery collaboration platform in a public cloud which provides several features like protection against possible Denial of Service attacks, security of data in transit, security of data at rest, implementation of Federated Identity and creation of secure Circle-Of-Trusts for collaboration, parallelization of processing using Hadoop and securing data stored in Hadoop, enabling secured access to Amazon AWS instances through virtual private network (VPN), correlating public and private data sets and providing access controls
Summary
Several block buster drugs will go off patent protection by 2015 [1]. This means several life sciences companies will have cost pressures and so will be looking at ways to reduce costs. Our earlier paper [6] explained how to implement a Secure Generation Sequence Services business cloud platform that is highly scalable and can be shared by multiple life sciences companies securely. The earlier paper described a few techniques for securing web applications and data hosted on a public cloud such as Amazon AWS leveraging open source security components and how they have been leveraged to secure the Ensembl solution. In the paper - Implementation of a Scalable Generation Sequencing Business Cloud Platform [7], the authors describe how to address the scalability of a Generation Sequencing solution and a strategy to port a preconfigured Sequence Search application such as BLAST [4] onto a scalable storage and processing framework like Hadoop framework to address scalability and performance concerns. A malicious user can impersonate as the superuser or any valid user of Hadoop Cluster and can access the HDFS cluster from any machine
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