The numbers are down
Two undeniable facts about today’s pharmaceutical industry: New drug output is not keeping up with investment, and bringing new medicines to market is getting more and more difficult. In fact, more than 95% of experimental medicines studied in humans fail to prove both effective and safe.
The costs are rising
At the same time the number of new drugs is decreasing, the cost of developing new drugs is skyrocketing. In part because such a high percentage of drugs fail, large pharmaceutical companies now spend an average of $1 to $2 billion to bring each new drug to market. Eroom’s Law of pharmaceutical R&D (in pharma lingo, “Moore’s Law spelled backwards”) pointedly highlights the fact that the number of new drugs approved by the FDA per $1 billion (inflation-adjusted) spent on research and development (R&D) has halved roughly every nine years.
To put it another way: Every nine years, drug development costs double.
Pharmacogenomics and complex data analysis
Pharmacogenomics is the emerging study of why individuals respond differently to drugs. One piece of the personalized medicine approach, it aims to replace current “one-size-fits-all” therapeutics with personalized pharmacogenomic tests to predict drug response. The challenge: To identify predictive pharmacogenomics patterns, in large part by sifting through massive amounts of complex genome sequencing and biometric data and identifying new drug routes though the body. Using traditional pharmaceutical research methods, however, this process requires a near-impossible level of computation analysis and reporting. In this unique landscape, traditional drug makers are seeing pressure from nontraditional competitors that are making drug and device development inroads using technology-driven approaches to R&D.
Not only is research getting more complicated, but the reimbursement process is changing. In the “blockbuster era” of the late 20th century, once a drug cleared regulatory hurdles and had been approved, drug companies could forecast a relatively predictable revenue stream. But the recent shift to outcome-based reimbursement means that to be reimbursed, drug companies must assure payors, such as insurers and governments, that each new drug works as intended, delivers the desired result in individual cases, and brings down overall healthcare costs for the entire patient population.
Smaller target populations
As data sources expand and target populations shrink, it will be hard to keep R&D costs contained. Gone are the days of large clinical trials and blockbuster drugs marketed to the masses, with development based on adequate results across large populations. Personalized medicine requires finding the few people who will respond well to a certain drug, based on multiple complex factors, or developing drugs for smaller and smaller segments of the population based on biomarkers such as disease subtype, other drugs being taken, and how a patient responds to them.
How tech can help
Information technology can help advance personalized medicine in several ways. For example, it can be used to automate population pre-selection based on any combination of biomarkers and past medical history. SAP can provide the IT infrastructure, big data analysis and reporting, and process automation required to revolutionize how drug companies find patients, deliver drugs, and even pay investigators – ultimately improving diagnosis and treatment for a wide range of patients and populations.
SAP Foundation for Health includes a sophisticated platform and advanced analytic solutions that can help unlock the value of biomedical data – from genomes to electronic medical records to clinical trials. Supporting deeper insights and enabling collaboration, SAP Foundation for Health helps connect data silos and bring together mission-critical biomedical data, advancing personalized medicine to new levels.
 Herper, Matthew, “The Cost Of Creating A New Drug Now $5 Billion, Pushing Big Pharma To Change,” Forbes, August 2013.