Loose coupling and biopharma

A few days ago, via the typical following of links that is typical of a good search and browse section on the interwebs, I chanced upon a discussion about a presentation given by Justin Gehtland at RailsConf. The talk was entitled Small Things, Loosely Joined, Written Fast and that title has been stuck in my head ever since. Funnily enough, what was in my head was not software, and web architectures, cause today, I consider that particular approach almost essential to building good applications and scalable infrastructures, and most people in the community seem to understand that (not sure about scientific programmers though). What I started thinking about was if that particular philosophy could be extended to the biopharma industry.

Without making direct analogies, but without suspending too much disbelief, one can imagine a world where drug development is not done in today’s model, but via a system consisting of a number of loosely coupled components that come together to combine cutting edge research and products (drugs) in a model that scales better and does a better, more efficient job of building and sustaining those products. One of the tenets of the loose coupling approach to scalable software and hardware is minimizing the risk of failure that is often a problem with more tightly coupled systems and in many ways the current blockbuster model is very much one where risk is not minimized and one failure along the path can result in the loss of millions of dollars. I have said in the past that by placing multiple smart bets, distributed collaborations and novel mechanisms (like a knowledge and technology exchange), we can reboot the biopharma industry, reducing costs and developer better drugs more efficiently. I don’t want to trivialize the challenge, the numerous ways in which the process can go wrong, and the vagaries of biology, but resiliency is a key design goal of high scale systems, and is one we need to build into the drug development process, one where the system chooses new paths when the original ones are blocked.

How could we build such a network model? I know folks like Stephen Friend have their ideas. Mine are ill formed, but data commons, distributed collaborations, and IP exchanges are a key component especially in an age where developing a drug is going to be a complex mix of disciplines, complex data sets and continuous pharmacavigilance. I can’t help but point to Matt Wood’s Into the Wonderful which does point to some of those concepts albeit from a computational perspective

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