Moving the bottleneck

As many of you are wandering the aisles of Drug Discovery Technology World Congress in Boston, I am sitting in wonderful air conditioning, ducking the discomfort of the autoclave summer of 2006. You see, while my DDN colleagues are pressing the flesh in Boston, I already put in my summer conference duty by visiting the recent meeting of the American Association of Clinical Chemistry in Chicago. And I'll let you in on a little secret: The clinical environment is about to be turned on its head, just like the basic research lab was two decades ago.

The commoditization of basic diagnostic tests and the movement of these processes into automated, high-throughput platforms will likely bring next-generation medicine to a crawl or possibly a screeching halt as the healthcare industry is presented with an onslaught of data, the likes of which it has never seen. But the drug discovery industry knows what I'm talking about. A similar move in medicinal chemistry, target identification, and biological screening platforms over the last 20 or so years has produced a sea of data from which we are still trying to surface.

And while the discovery industry continues to struggle with its myriad IT issues, the instrumentation and platform companies have expanded out to their next logical target: the clinic. The Beckman Coulters, the PerkinElmers, and the Thermo Electrons seem to be taking what they learned on the basic research side of the equation and applying it to large-scale clinical assays, targeting and/or working with companies like Abbott, Roche, and Siemens.

These companies are working to transition current-generation instruments and tests that are in many cases low-throughput and personnel-intensive into laboratory workhorses that will allow hospitals and regional health centers to push more patients through the system faster and less expensively.

But whereas most of these companies talk a good game about developing their informatics capabilities in parallel with the instrumentation platforms (for which they are better known), I truly wonder how prepared anyone is for the amount of data that can be generated in a clinic. Although the platform may monitor changes in proteins or nucleic acids, the clinical data points revolve around people.

Unlike one molecule of albumin that looks and behaves like most other molecules of albumin within an individual or across large populations, two identical twins can have very different responses to their environments or therapeutic regimens because of lifestyles or behavioral differences. As I've asked in previous editorials, how complicated will be the data matrices that not only include SNP markers but also drinking and smoking habits, home geography, work and social environments, and preference for hot or cold morning cereals to exaggerate the point.