Adoption of SaaS in the Life Sciences

By Bruce Maches

February 28, 2011

Pharmaceutical research has always been a time consuming and expensive endeavor with each new therapeutic advance requiring millions of dollars in expenditures and years of effort. As I have mentioned in prior blogs all sectors of the life science community are looking at ways to reduce costs, IT complexity, and speed time to market. As the types of research being done become more complex and the amount of data generated grows these companies simply cannot afford to keep expanding their IT infrastructure and application environments to meet all of the demands being placed upon them.

The advent of genomic sequencing alone has had a dramatic impact on the amount of data being created. Overall it is estimated that data storage demands by life science companies are doubling about every 3 months. Companies with hard pressed research budgets simply cannot afford to keep throwing more and more money at the problem along with the associated issues of storage management, backup/recovery, and disaster recovery planning.

The same holds true with the types and complexity of the applications being used. The need for systems to perform complex data modeling, gene sequencing, target identification, and computational chemistry places an ever increasing support burden on the IT department. Many of these applications require significant hardware investments and experienced support staff to maintain in house. Not to mention the expense and effort required to validate and implement these systems to meet FDA regulatory requirements.

In a prior blog I discussed how IaaS is helping many life science organizations deal with some of the infrastructure demand and support issues. In this entry we will take a high level look at SaaS and how that can be leveraged by these organizations to help them meet their research objectives in a more cost effective and timely manner. 

Many life science organizations are taking a harder look at utilizing SaaS as a means of provisioning the complex applications required for their research. There are many vendors offering specialized applications for life science research via the SaaS model and more industry vendors are moving in this direction. Life science companies can take advantage of systems in functional areas such as:

– genomic sequencing

– molecular modeling

– toxicology studies

– drug stability

– clinical trials

– document management

– regulatory submissions

What advantages can these companies realize from utilizing SaaS based applications?

– There  is no hardware required as the storage and compute resources will be provisioned at the vendors data center

– Many of these applications are either validated or since a portion of the application environment is already validated that process can be completed much quicker

– Reducing time spent on acquiring and  implementing applications can allow a life science company to reduce time to market for new products

– Professional data centers can usually provide much better security than in-house environments

– The vendor takes care of all support requirements, patching, and administrative duties

– The primary disaster recovery responsibilities are also handled by the vendor

Of course nothing is perfect, so what are some of the downsides to following a SaaS based strategy?

– Some companies are concerned with the security of their data in a shared environment

– Depending on a SaaS based application that is critical for your business requires extra care when negotiating contracts and service level agreements

– Using off premise applications may cause latency issues

– You never own the application so you are continually paying

– There may be regulatory issues with storing patient data from clinical trials or other private information in a 3rd party data center

While all of these factors require consideration, with careful planning and foresight life science companies can utilize SaaS based systems to reduce costs and complexity while enhancing the R&D process and speeding time to market for new therapies.
 

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