Ask any Italian how they like their pasta cooked and they’ll say ‘al dente’. Most trained chefs rely on taste and many throw it against the wall to see if it sticks as a measure of ‘cooked’. This method doesn’t seem that accurate and sounds like a bit of a mess. So why are many of today’s drug developers taking the same approach of throw it at the wall and see what sticks? The complexities of human physiology are not as well understood as simple physics, but firms must focus their efforts on bettering abilities to model and predict.
A recent study analyzed the reasons for clinical failures for 410 drugs that entered testing between 2000 and 2009. The results show that commercial viability and efficacy are key reasons for failure in phases one and two of clinical trials. However, it seems crazy to make such huge investments into the development of a new drug without having any idea if it will ‘stick to the wall’ when it comes to clinical trials.
Commercial viability is linked to proper analysis up front of the potential market for the drug i.e. the total number of patients, combined with all the other cost factors of getting to market, manufacturing, sales and marketing, as well as speed to market. Given that commercial viability is the biggest cause of failure in phase one, should these projects perhaps be shelved before trials begin? Knowing whether the new drug will make money before running it through testing makes sense but it does require more work up front around social factors and patient stratification, combined with deep disease knowledge, such as mechanism of action and disease pathways.
In phase two trials you can kind of understand efficacy being a big problem. Challenges include uncertainty around whether a drug will work in humans, dosing, and the unpredictable nature of PK and ADME when comparing humans with other species in earlier development testing. Mapping animal to human models poses a translational problem. Often the issue is around available data and data integration. Researchers need all preclinical data aggregated with available clinical trial and post-licence data to be able to really delve into the complex relationships that obviously exist.
So a paradigm shift is due. Organizations must be able to stratify data and leverage corporate knowledge to validate target populations properly and to better estimate commercial viability. Understanding the market size and patient demographic from the outset is vital. Access to all patient, research and development (R&D), and external data about diseases and how drugs work in animals and humans can provide the big picture, and help drive the translational knowledge of the science to better predict pharmacological behaviors.
The solution? Bringing all the data together in a meaningful manner at each stage of the development-research, preclinical development and clinical stages will lead to better clinical outcomes down the line with less risk of failure. No noodle throwing involved.