Amylin’s R&D day and thoughts on the emerging firm pipeline
Amylin was good enough to provide a PDF of supporting materials from its R&D day yesterday (see Amylin - Investor Relations - Event Details). I wish more companies were so kind to the interested public.
As expected, though, juicy details of Exenatide LAR–this company’s best chance of making the big time soon–were hidden from view. Nevertheless the single data slide on LAR suggests that proof-of-concept in humans has been demonstrated. Now, it’s a matter of awaiting Phase 2 to complete, making enough of the stuff to do Phase 3 and sell it. Amylin hints at their time line for this by stating that they expect to finalize the commercial manufacturing process by 2H08. Typically, a company won’t ship Phase 3 drug supplies until the commercial process is established or nearly so. Expect a minimum of 18 months before filing for approval after Phase 3 starts, pushing a filing to 1H10 by my reckoning. I don’t know what Amylin has previously told investors about this.
The only other reason I mention this research day is that I like Amylin’s approach to research. They’re sticking with what they know best–neurohormonal regulation of metabolism–instead of diversifying into all sorts of therapeutic areas (their foray into CHF with GLP-1 the single exception).
It’s an axiom by now that diversity in the pipeline of an emerging company is a good thing, and it likely is, to the extent that technology diversity does not unduly impinge on a small-firm’s experience (aka incumbent) effects. Such effects are essentially learning opportunities that come only through experience. Experience effects contribute greatly to productivity, in this case R&D productivity.
So there’s a tradeoff a small firm must make between diversity of the pipeline, which increases the chances of A product succesfully entering the market by diminishing the risk that ANY ONE technology will fail in development, and gaining experience that allows it to compete with larger firms, without requiring extraordinary sacrifice by investors or risking insolvency. It’s a difficult tradeoff to get your brain around, given the multiplicity of factors that interact nonlinearly, which is why system modeling of the type I’ve discussed previously is advisable.
