Managing Drug Development Risks through Better Foresight
The Industry Challenge for Drug Discovery: Rising Costs and Risks
The cost of bringing new drugs to market is rising extremely fast. The average cost to bring a drug to market was $100 million in 1975, and rose to an inflation-adjusted $1.3 billion by 2005. Forbes recently cited the average cost has now approached $5 billion.
A key driver of this increase is the rapid growth of costs for human clinical trials, which constitute roughly 2/3 of pharmaceutical R&D budgets. From 1999 to 2005, the average length of a clinical trial increased by 70%; the average number of routine procedures per trial increased by 65%; and the average clinical trial staff work burden increased by 67%.
These rising costs increase risks for the entire industry.
Early stage researchers are pressured to show results sooner. Investors are retreating from the early discovery space, and those that remain are demanding greater confidence and better early results. Pharmaceutical companies are forced to increase their focus on drugs that either require only incremental development or have huge market potential, and thus must exclude many “small-market” diseases from their development efforts.
To overcome these challenges, drug discovery companies must:
- Work smarter
- Fail faster
- Allocate resources carefully
- Develop cost-efficiently
- Reduce failure risks
- Get to market faster
Better Foresight and Individual Patient Dynamics
Origent is dedicated to managing and reducing drug development risks through better foresight. We are experts in the development and application of predictive models that are designed to anticipate and predict the dynamics of individual patients.
By modeling patient-level dynamics (rather than modeling the characteristics of a population), our models uncover a deeper level of insight, one that allows biostatisticians and researchers to gain clearer understanding and greater knowledge from their data. Rather than considering similar patients to act “the same” as one another, we treat and model each individual patient separately, and predict their behavior individually. We then use this information to conduct in-depth comparisons and analyses of observed clinical data for each patient.
Models like these allow drug discovery companies to:
- Conduct smaller, faster clinical trials, thus reducing costs and risks
- Generate quantifiable efficacy data from trials that lack control arms
- Spot failing projects sooner and prioritize resources for the most promising therapies
- Identify patients and patient subgroups that responded to therapies in comparison to how their symptoms likely would have progressed
- Demonstrate earlier progress to investors