I like this post from Forbes Science Business blog. The author, David Shaywitz, cites a new book called Little Bets by Peter Sims, that talks about innovation as resulting from a series of small changes. Shaywitz notes that it is difficult to apply to drug development due to the long time cycles for approval -- i.e the ultimate measure of "successful innovation" is whether the drug is commercially successful or not. But how do you know if you are making correct changes if the cycles are so damned long.
He notes you can look at intermediary steps like moving from one drug phase to another (1 to 2, 2 to 3, etc) but this doesn't really say the drug will be successful. That's only known if it makes it to commercialization and does well there. More troublng, if you look to measuring say cost improvements or decreases in cycle times -- you may get gains in these parameters but no more success. And, you may in fact stifle innovation in the process.
I find the whole discussion interesting as I've long struggled with this problem. I resorted to some of the same things mentioned -- and it's true that you can save money by reducing cost or cutting cycle time but without achieving the commercial results that you want. In those instances, you delude yourself into thinking this is success (it's success in saving money but not in commercialization).
It's missing the forest for the trees or the operation is a success but the patient has died. Bascially, we're measuring what we can but don't have a good surrogate endpoint.
Shaywith concludes:
We urgently need a better way to figure out how we’re doing, and reliably assess whether our pipeline is generating successful molecules, so we can make the sort of iterative improvements modern drug development desperately requires. While I doubt anyone is going to stumble upon a magic crystal ball, it’s possible that refinements in the approaches used to select products and identify patients might help. If not, the industry appears destined to remain where we are now: developing products of uncertain utility — in an increasingly efficient, quantitatively monitored fashion.
Anybody out there got any ideas on this one?
Posted by Bruce Lehr April 22nd 2011.
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