1.2. Statistical support to non-clinical activities

In an eloquent viewpoint article, Dennis Lendrem (2002) discussed non-clinical statistical support. Traditionally, statistical thinking and approaches are more embraced in areas where regulators have issued guidelines. Examples are pre-clinical testing of cardiac liability, carcinogenicity, and stability testing. Recently, Good Manufacturing Practice has also become a subject of great regulatory interest. The latter captured public attention when manufacturing problems created a shortage of the flu vaccines for the 2004–2005 season. By comparison, statistical input in areas such as high-throughput screening, chemical development, formulation development, drug delivery, and assay development is being sought only when the scientists feel that statisticians could truly add value. This mentality could limit statistician's contribution since researchers will not knowhow statisticians could help unless they have previously worked with statisticians or have been referred to statisticians by their grateful colleagues. For example, scientists who are used to experimenting with one factor at a time won't know the value of factorial experiments. Similarly, even though statisticians well versed in Six Sigma and Design for Six Sigma are well aware of the many applications of the Six Sigma principles, they need to actively sell the applications to potential clients.

The non-clinical support model differs from that in the clinical area because of the usually large client to statistician ratio. As a result, after a statistician completes a particular job, he/she often looks for opportunity to consolidate the techniques and institutionalize the tools for the client to use on a routine basis. The automation allows statisticians to focus on opportunities for new collaboration and developing new methodologies for applications.

Non-clinical statisticians often work individually with their clients. Lendrem (2002) described them as "pioneers" because of the frequent needs to venture into unknown areas of new technology. Quantifying gene expression via the microarray technology is onesuch example. Another is industry's (and government alike) investment in identifying biomarkers for testing mechanism of action of new molecular or biologic entities. In both cases, the findings will have great clinical implications, but the work starts in the research laboratories and our non-clinical statisticians are the first to deal with the need to measure, to quantify, and to validate the measurements from the technical perspective.

Because of the small number of non-clinical statisticians in many pharmaceutical companies, it is useful for non-clinical statisticians to form inter-company network to benefit mutual learning. Some of this networking has been in existence for some time. In the U.S., a CMC (Chemistry, Manufacturing, and Control) Statistical Expert Team was formed in the late 60's to focus on the chemistry and control issues related to the manufacturing of pharmaceutical products. Another example is the Pharmacogenomics Statistical Expert Team that was formed in the fall of 2003. Both teams are sanctioned by PhRMA and consist of statisticians from major pharmaceutical companies.