Despite their size, small pharmaceutical companies must adhere to the same regulations that govern large ones. Production of statistical computing environments must be validated and compliant; no exceptions. With limited resources, minimal QA, and an IT staff having little to no understanding of SAS, it is difficult to know how to proceed.
Small pharmaceutical companies can become paralyzed by the risk of various failure scenarios such as:
• Creating a system that they can’t afford to validate
• Creating a system that doesn’t grow as the company grows
• Creating a system that is too costly to support.
They fear the big bad audit from the FDA; however, they don’t have the time to implement technology and processes to alleviate that fear. Some companies buy a costly package labeled ‘statistical computing environment’ only to find that they cannot envision doing enough trials to recoup the capital expenditure. Buyer’s remorse really sets in when they realize that they still have to spend time and money to validate the system and define work processes. So what is a small band of statisticians and programmers holding the next blockbuster compound to do? Build it. But before setting out to build it, the team needs a plan, a template, if you will, to keep the project scope focused and ensure that the project achieves its end goal: a validated computing environment with a controlled, documented work process.
The case study presented here describes such a project. Technical consultants from d-Wise teamed with a cutting edge biotechnology company to develop a project approach and system design. We compressed our initial costs by keeping the design simple and using free, open source technologies, where possible, for several system components. We employed virtualization technology to enable a return on investment and minimize validation overhead..
