How to improve R&D productivity: the pharmaceutical industry's grand challenge

that is a great point. for those not familiar with where the money really goes in R&D, and where the biggest opportunities for improvement are, check out these charts [1] and [2] from the seminal paper on calculating the cost of getting a drug approved [3]. the biggest areas to improve R&D productivity lie in 1) picking more validated targets to reduce Phase 2 and 3 failure rate and 2) reducing the cost of lead optimization (basically the process of turning a compound that has the desired impact on the target (target is a molecule implicated in disease that you want to effect with a drug) into a molecule with drug like properties (ie gets where you need it in the body,…

Yeah, $5Bn may be a bit of an exaggeration, but it's not off by an order of magnitude; this review, based on data from Eli Lilly, put the fully capitalized R&D number at $1.8Bn in 2010: https://www.nature.com/articles/nrd3078 (one can certainly quibble over the "cost of capital" assumptions these days, but the model isn't wildly wrong.) > That billion dollar figure is risk adjusted per product meaning that's the cost of all R&D and clinical trials against the number of products that hit the market. Yes, but as you know, you can't simply separate the successful products from the ones that fail. The former pay for the latter. > A lucky startup can pull off a successful…

Could you expand a bit on that? I'm having a hard time making the connection between declining efficiency and there necessarily being something broken about the science. To expand just a bit: Scannell seems to be primarily focused on the disease model as the root issue here. This is curious to me. My understanding is that drugs fail primarily in two ways: 1) It's ineffective - fails to treat the disease. Which is partially covered by model validity, but also impacted by pharmacokinetics and distribution. Essentially your molecule can work, it just can't get where it needs to go in a high enough concentration to make a difference. 2) It's unsafe - your molecule is toxic…

This is a nice article but doesn't discuss the most widely used and impactful application of "personalized medicine" thus far: selecting patients for inclusion in clinical trials based on their genetics. When you talk to people in the biotech and pharma world this is often what they mean by personalized medicine, although outside of biotech and pharma this definition is not what you think of when you hear "personalized medicine" Studying homogeneous, genetically / biomarker-defined patient populations, rather than more heterogenous populations, meaningfully increases the success rates of clinical trials [0]. This is important because clinical trial failure, mostly Phase 2…

This is the seminal and oft-cited source of those estimates for cost of drug development. The data comes from internal financial records at pharma companies. This is a 2010 paper and costs have since increased: https://www.nature.com/articles/nrd3078 Bottom line is that it is expensive to develop drugs because most of them fail. You need to spend millions of dollars on dozens of drugs that don't work in order to get one drug approved. The single biggest cost driver is cost of Phase 2 failure. Basically when a drug that works in animals doesn't work in people. It costs $40-50M+ and takes 5-7 years to get through phase 2 and over 60% of phase 2 studies fail This is because…