R&D performance varies widely… Fewer novel therapies seem to be coming from the drug industry as a whole. What is not generally recognized is how much firms differ in performance and (perhaps) why. This Lehman analysis shocked many but confirmed that some firms were either “lucky” or were somehow “better designed”. Later analyses suggest it is their “R&D Architecture”; what new capabilities are designed-in, that makes the difference. See this 2010 McKinsey article on where those “value levers” are.
This site examines what firms have employed to get even farther ahead. Some of these “sea-changes” are still very relevant to the future, other hot ideas are creating new value and advantages for the next five years. Let’s explore what leaders are doing to stay far ahead of median performance. More…
Key Advances in R&D Returns:
Long ago, many felt that discovering and launching drugs required insight, expertise and not a small bt of “serendipity”. Others felt that winners were somehow just lucky… Some firms decided to make their own luck. Some lessons and principles:
Firms are now looking for any way to improve R&D performance with 6σ black belts. It was not always so receptive. Why (and how) it took 10-15 years for BioPharma to find its way…
“One day sooner is worth $1M” (in 1990s’ dollars when it was worth something…(How we got there)
“What we do in the labs is an art.
If you are trying to compete with only 20% of the information your competitors can access, no wonder they are finding the choice in-licensing and disease targets. But many firms are confused by software and consultant hype. A more rational approach…
Some LS firms are “drowning in new information types (not just volumes) which are 10x over a decade ago. Semantics, text analytics and data federation – are all new tools to capture the value and are proving beyond the hype … (More)
Half of industry pipelines may require images to prove hypothesis
FDA submissions are now mostly for anti-cancer agents. Add in CNS and other areas that require images to prove localized efficacy and managing those huge files becomes a “pig in a python”. How to digest this and come out on top vs. the competition? … (Upcoming Topic)