Almost every other pharma RD conferences these days would invite a keynote speaker who would use statistic data to show that the productivity and efficiency of pharma R&D declines while cost increases.
People claiming that the pharma RD productivity are on the decline often calculate the productivityas the Outcome (# of new drugs)/Investment (R&D cost). Although, that seems an ultimate way to estimate the pharma RD productivity, it does not consider the fact that many drug targets under research today are novel as the results from the newly revealed human genome. These targets hold the key to future drugs but may not give the quick return as analysts expected. Just to make the situation even worse, study on these new targets often requires newer but more expensive technology with higher sensitivities. However, the question is: does that mean our productivity and efficiency are on the decline?
Many researchers would disagree with the conclusion, simply because they have witnessed the great increase of productivity and efficiency in the lab research from last 10-20 years. Works used to be completed in weeks, now can be done in days or hours. Large scale work, such as chemical library screening, that was not imaginable many years ago now can be carried out in a lab with just a few robots and readers. Our information and knowledge of human diseases are much richer and deeper. All these speak for an industry-wide increase of productivity and efficiency. Although there is no statistic data, it is clear that the number of “products” as mesured by the number of compounds synthesized, number of assay datas, etc are continousely on the rise and the cost of generating these materials and information are on the decline. In another word, many scientists see the opposit picture thar the pharma R&D productivity and efficiency are indeed going up.
So, which would be the better way to estimate pharma R&D productivity and efficiency? In my 0pinion, the latter is a bette formula since it ties the investment with outcome that, if does not produce immediate drugs, will product future potential drugs. With my many years industrial experience, I don’t see much varition from lab to lab in terms of productivity and efficiency as technologies and information are spreading fast in the industry. However, there are huge differences in productivity and efficiency from company to company simply due to different infrastructures and managment styles. Companies with unnecessary management layers and fragmented departments clearly suffer greatly in their productivity and efficiency.
For people who are interested to know how your company’s productivity and efficiency is doing, I’d like to offer a simple and empirical method that I would name the “Bench Index” (BI) with a value between 0-1. BI calculates the fraction of FTE (from CEO to research assistant) time spends on bench work where the “products”, chemicals and assay data, are eventually produced. Based on this index, companies with more “Chiefs” than “Indians” will have lower BI (i.e. lower productivity). Companies spend more time on “integration” and “communications (i.e. meetings)” will have lower BI (i.e. lower efficiency). On the other hand, based on this theory, higher cost is not a factor of concern if it also produces more “products” and will eventually leads to new drugs.
If you are interested, give it a try on your department or your company, it your company’s BI is between 0.3-0.5, you are probably doing fine regardless what others think.
Don’t ask me where I got the magic number.
