Today, predictive modeling is commonplace for understanding everything from presidential elections to the weather.29-32 Predictive modeling in pharmacogenomics suggests that cutting adverse outcomes by as much as half will require a research investment of about $6 billion and take 20 years.33 Numbers like these allow comparisons. For example, $6 billion annualized over 20 years represents 4% of the U.S. NIH’s 2011 budget,34 2% of the pharmaceutical industry’s 2011 research budget,35 and 0.2% of 2009 payments made by U.S. private health insurers.36 These numbers are also comparable to the price-tag and timeline for developing just four to five new drugs from scratch.37
Numbers also help set expectations. For example, a 20-year timeline is consistent with forecasts by the National Human Genome Research Institute that genomics will produce some advances in the science of medicine before 2020 but much more thereafter.6-8 But for the specific goal of developing pharmacogenomic guidelines that can halve drug-related adverse outcomes, forecasting predicts a specific completion date (2032) and a requirement that most of the investment—around $3 billion at $400–500 million per year—will have to come over the next 5–6 years, before most guidelines appear. Thus the evidence supports patience, for now.
The availability of numbers shifts the debate to practical issues. These include deciding whether the clinical benefit of developing guidelines is worth $6 billion, and if so what party or parties—for example government (via taxation), pharmaceutical companies, or health insurers—should pay; finding measures of progress during the pump-priming years; and ensuring that downstream processes for implementing guidelines be ready by the time the bulk of the guidelines start appearing.
Predictive modeling can inform policy choices by pointing out bottlenecks, for example discovering and confirming candidate associations between genetic variants and adverse outcomes. Modeling suggests that this process could be sped up by comprehensively mapping genomic variation vs. incidence of adverse outcomes for ~1,500 people of representative ethnicities taking each of the 40–50 most-used prescription drugs.33 Such a “50,000 Pharmacogenomes Project” would be a nontrivial undertaking. But in the spirit of the 1,000 Genomes Project, UK10K, and the Million Veteran Program, it would represent a disruptive improvement that could save time and money. Finally, modeling helps set research priorities—in this case better understanding the extent to which, and the ways in which, genomic variation influences adverse outcomes.33
Genomics is maturing. As the fruits of research come within reach of clinical medicine, charting the road ahead will become easier. The numbers in this article are not the last word, but they do start a conversation and illustrate what is possible. Similar analyses covering cancer, infectious disease, and heritable conditions will soon be possible (Figure).
Being specific about goals, costs, and benefits helps advance the debate about future directions and helps focus attention on making choices and meeting the challenges of translating research into better patient care. In this way, it helps keep the promise of genomic medicine bright, free from the tarnish of false expectations.