How Distributed R&D Could Spark Entrepreneurship in Biopharma

This piece originally appeared in the Timmerman Report.

Remember the patent cliff and the general lack of new and innovative medicines in the industry pipeline? That was the big story of the past decade in biopharma. It caused a lot of searching for the next best way to organize R&D to improve productivity. One doesn’t hear that quite as often today. There are more innovative drugs both recently approved and moving forward through the pipelines of several biopharma.

The conversation these days has shifted toward drug pricing, and how the public is going to pay for some of these new, exciting drugs (the answer, in some cases, is maybe it can’t).

I don’t think the industry out of the woods yet. One of the main reasons drug prices have become such an issue is because even though there are new, innovative drugs, there aren’t enough of them. At the same time many of the drugs being approved are incrementally better but nevertheless being priced at a premium. And good reporting has made the public more aware of how many of our existing drugs are rising in price on a yearly basis. Especially in a time of little inflation, prices of most goods have not been going up at nearly the rate of pharmaceuticals.

Biopharma sits in a tough place. Analyses suggest the cost of developing a new drug has generally been doubling every nine years, which may be a by-product of some combination of the complexity of biology, our inability to predict which drugs will work, and the “better than the Beatles” problem. The question then is how to overcome these issues and increase the efficiency of developing new, innovative drugs. Without some kind of change, the industry is looking at a very difficult future in which price hikes run headlong into the wall of payers who finally say enough. Then what? Continue reading

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Big data and baseball efficiency: the traveling salesman had nothing on a baseball scout

All opinions are my own and do not necessarily reflect those of Novo Nordisk

The MLB draft is coming up and with any luck I’ll get this posted by Thursday and take advantage of web traffic. I can hope! Anyway, Tuesday in Fangraphs I read a fascinating portrayal of the draft process, laying out the nuts and bolts of how organizations scout for the draft. The piece, written by Tony Blengino (whose essays are rapidly becoming one of my favorite parts of this overall terrific baseball site), describes all the behind the scenes work that happens to prepare a major league organization for the Rule 4 draft. Blengino described the dedication scouts show in following up on all kinds of prospects at the college and high school levels, what they do, how much they need to travel, and especially how much ground they often need to cover to try and lay eyes on every kid in their area.

One neat insight for me was Blengino’s one-word description of most scouts as entrepreneurs. You could think of them almost as founders of a startup, with the kids they scout as the product the scouts are trying to sell to upper layers of management in the organization. As such, everything they can do to get a better handle on a kid’s potential can feed into the pitch to the scouting director.

I respect and envy scouts’ drive to keep looking for the next big thing, the next Jason Heyward or Mike Trout. As Blengino puts it, scouts play “one of the most vital, underrated, and underpaid roles in the game.” While one might make the argument that in MLB, unlike the NFL or NBA, draft picks typically are years away from making a contribution and therefore how important can draft picks be?, numerous studies have shown that the draft presents an incredible opportunity for teams in building and sustaining success. In fact, given that so much of an organization’s success hinges on figuring out which raw kids will be able to translate tools and potential into talent, one could (and others have)  made the argument that scouting is a huge potential market inefficiency for teams to exploit. Although I’ll have a caveat later. But in any case, for a minor league system every team wants to optimize their incoming quality because, like we say in genomic data analysis, “garbage in, garbage out.”

As I was reading this piece, I started thinking about ways to try and create more efficiencies. And I started thinking about Big Data.  Continue reading