Academic journal article Communications of the IIMA

Cloud Computing in Emerging Biotech and Pharmaceutical Companies

Academic journal article Communications of the IIMA

Cloud Computing in Emerging Biotech and Pharmaceutical Companies

Article excerpt


In today's global economic climate, start-up and emerging biotech and pharmaceutical organizations are seeking greater cost-saving measures, increased agility, and the type of scalability that responds to the rapid changes in both technology and business. Cloud computing, with its low cost pay-as-you-grow business model, could potentially help these companies manage similar changes while transforming Information Technology (IT) into an engine that drives business (Roehrig, 2010). The immediate benefits from on-demand clouds seem to provide users with enhanced portability and the capacity to have secure access to information from virtually anywhere, with almost any mobile device, regardless of location or time of day, whether it be from a lab, a client location, when traveling, or while in a meeting at the office. Additionally, small and medium business (SMB) life science companies represent a unique market that could potentially benefit from this new computing paradigm. These organizations could then economically scale their businesses as needed while rapidly completing complex research-to-market tasks they simply could not accomplish on their own (Bowers, 2011).

Cloud Computing Model

The idea of cloud computing mystifies many organizations, especially those dealing with a deluge of data being generated by life science companies. Similar terms are often used to describe cloud computing, such as grid, distributed, on-demand, cluster, utility, virtualization, and software-as-a-service. More directly, cloud computing refers to endusers connecting with applications or services running on sets of shared servers, often hosted and virtualized, instead of traditional dedicated servers. For over 30 years, client-server computing has provided applications that were assigned to specific hardware, often residing in on-premise data centers. On-demand cloud computing empowers its end-users by allowing them to use their choice of Internet-connected devices, on any day or at any time (Knorr & Gruman, 2009).


Prior research in this area focused on the viability and outcomes of using cloud computing in life sciences, both internally and outsourced to cloud service providers (CSPs). This literature review helps establish a theoretical framework for the research topic. Independent authors used a variety of qualitative and quantitative methods to arrive at their results, although none included the distinct qualitative mixed methods used by this researcher.

Reduced Cost at Greater Speed

According to Proffitt (2009), early adopters of cloud computing such as Pfizer, Johnson & Johnson, and Eli Lily all used Amazon Web Services (AWS) and Amazon EC2 (Elastic Compute Cloud). These pharmaceutical companies were able to perform R&D using the cloud, and process proteomics, bioinformatics, statistics and adaptive trial design more rapidly with predictable time and costs. Davies (2009) illustrated the relative low cost of cloud computing for early adopters by describing reactions of members at the inaugural Bio-IT World European Conference in 2009. BioTeam cofounder and technology director Chris Dagdigian argued Amazon impressed users by starting at 10 cents/hr. Dagdigian later pronounced a traditional 100 CPU-hour research problem could be solved using EC2 in 1 hour for $40, and what he called the "Aha" moment (Davies, 2009).

Better Connectivity

Effective connectivity is a major factor in life science research and development. Kubick (2011) argued cloud computing, using a solitary Internet connection, could reduce the effort of individually integrating each research system at various locations yet provide availability to all. Bowers (2011) suggested CSPs could provide SMB life science organizations with best practices they generally could not afford. These cloud CSPs utilize multiple connections to massive networks of interconnected servers that include comprehensive data protection, 24x7 disaster recovery, multi-site replication, real-time monitoring, and state-of-the-art emergency response systems, all from user-friendly, front-end interfaces (Bowers, 2011). …

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