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July 10, 2007
Travels In Numerica Deserta
There's a problem in the drug industry that people have recognized for some years, but we're not that much closer to dealing with it than we were then. We keep coming up with these technologies and techniques which seem as if they might be able to help us with some of our nastiest problems - I'm talking about genomics in all its guises, and metabolic profiling, and naturally the various high-throughput screening platforms, and others. But whether these are helping or not (and opinions sure do vary), one thing that they all have in common is that they generate enormous heaps of data.
We're not the only field to wish that the speed of collating and understanding all these results would start to catch up with the speed with which they're being generated. But some days I feel as if the two curves don't even have the same exponent in their equations. High-throughput screening data are fairly manageable, as these things go, and it's a good thing. When you can rip through a million compounds screening a new target, generating multiple-point binding curves along the way, you have a good-sized brick of numbers. But you're looking for just the ones with tight binding and reasonable curves, which is a relatively simple operation, and by the time you're done there may only be a couple of dozen compounds worth looking at. (More often than you'd think, there may be none at all).
But genomics/metabolomics/buzzwordomics platforms are tougher. In these cases, we don't actually know what we're looking for much of the time. I mean, we don't understand what the huge majority of the genes on a gene-chip assay really do, not in any useful detail, anyway. So the results of a given assay aren't the horserace leader board of a binding assay; they're more like a huge, complicated fingerprint or an abstract painting. We can say that yes, this compound seems to be different from that one, which is certainly different from this one over here but maybe similar to these on the left - but sometimes that's about all we can say.
Of course, the story isn't supposed to stop there, and everyone's hoping it won't. The idea is that we'll learn to interpret these things as we see more and more compounds and their ultimate effects. Correlations, trends, and useful conclusions are out there (surely?) and if we persevere we'll uncover them. The problem is, finding these things looks like requiring the generation of still more endless terabytes of data. It takes nerve to go on, but we seem to have no other choice.
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