The potential rewards of investing in CNS Research and Development are enormous - The global market for CNS drugs for 2007 was $77.3billion (an increase of 8.0% from 2006) and is one of the largest and fastest growing therapeutic categories in the pharmaceutical industry. High levels of investment have contributed to a rich product pipeline, with an estimated 600 clinical candidates currently under development for CNS indications.(1)
However, the costs of drug development continue to spiral and the average cost of development of a new drug is estimated to be over $1.7 Billion(2)
. Furthermore, the chance of success of a novel compound in Phase 1 reaching the market is 11% for all therapeutic categories and only 8% for CNS disorders(3)
. To date the main approach of the pharmaceutical industry to this problem has been to increase compound throughput using new technologies. Although there has been little improvement in the overall success rate of compounds in development during the past ten years, there have been significant changes in the causes of attrition. In 1991, the main reason for failure of a drug in clinical development was poor or unpredictable bioavailability and pharmacokinetics which accounted for 40% of all attrition. This issue has been successfully addressed by the introduction of comprehensive in vitro
and in vivo
drug metabolism and pharmacokinetic screening at an early stage of the drug discovery process.
Today the principal cause of failure in clinical development is lack of efficacy and safety (each accounting for 30% of all failures) and this is a particular problem in CNS drug development which has a lower than average chance of success due to the poor predictive validity of pre-clinical models. It is increasingly recognised by the pharmaceutical industry that the introduction of Experimental Medicine models at the interface between Phase 1 and Phase 2 clinical trials is the way forward in CNS drug development. Such studies provide a faster route through clinical trials by providing a rapid Go/No-Go signal which conserves resources while allowing more informed decision making during the development process. The studies can use volunteers or small patient groups but importantly employ experimental design in a laboratory setting to introduce rigour and harder endpoints for measurement.