Genomic research will transform medicine but progress has been slower than expected, leading critics to charge that the promise of genomics was hyperbole to get funding mandates and that while research should continue, the bulk of the money earmarked because of its applied science potential might instead be better spent elsewhere.
Proponents argue that the slowness shows the complexity of the relationship between medicine and disease and argue for more funding.
Both sides are arguing with anecdote so Ramy Arnaout, MD, DPhil, a founding member of the Genomic Medicine Initiative at Beth Israel Deaconess Medical Center (BIDMC), has tried to show benefits versus cost using quantitative modeling, the numerical forecasting approach used to try and predict everything from weather events to the outcomes of political elections.
Drug-related adverse outcomes cost the health-care system upwards of $80 billion a year and they contend that many such cases should be avoidable by choosing and dosing drug prescriptions according to a person's genome, so they developed a quantitative model to estimate how much time and money would be required to use genomics, specifically pharmacogenomics, to cut these adverse outcomes in half. They believe their findings offer a template for the use of quantitative modeling in this field.
How do the numbers in their estimates shake out? Using their parameters, the research team found that the cost could be less than $10 billion spread out over approximately 20 years.
"If you look across medicine, you can see specific places here and there where genomics is really starting to change things, but it's been hard to know how it all adds up in the big picture," Arnaout, also an Assistant Professor of Pathology at Harvard Medical School. "Quantitative modeling is a standard approach for forecasting and setting expectations in many fields as we all remember from the recent presidential election and from the hurricane season. Genomics is so important and is so often on the minds of our patients, students and staff, that it seemed like a good idea to use modeling to get some hard numbers on where we're headed."
The authors created the model to answer the question many people ask about biology that got a lot of attention last decade, like human embryonic stem cell research and genomics; when will it help people? They decided to try and answer this question by applying forecasting methods to a big clinical problem – drug-related adverse outcomes that are not preventable, like patients who take it wrongly or medical error.
"We know that preventable causes of these adverse outcomes -- patients' non-adherence, interactions between multiple drugs, and medical error, for example -- account for only a fraction of the millions of adverse outcomes that patients experience each year," explains Arnaout. "This leaves a significant number that are currently considered non-preventable and are thought to be caused by genomic variation."
Arnaout says 30 million Americans currently use the blood-thinning drug warfarin but in some cases, patients' genomes contain variants that make the standard dose of warfarin too high for them and those individuals are likely to experience bleeding, an extremely dangerous side effect. He says three-quarters of the variability in warfarin dosing requirement is due to genomic variants, and that scientists have already identified a set of variants in six specific genes that explain two-thirds of the variability.
"This kind of progress suggested an interesting thought experiment," says Arnaout. "What if we took existing examples in which there appears to be a carefully vetted, clinically useful connection between a specific adverse outcome and a specific genetic variant, found out how much it cost and how long it took to discover, and applied that model to all drugs? How much would it cost and how long would it take to cut adverse outcomes by 25 percent? How about by half?"
As data for the model, the authors selected eight associations involving six prescription drugs (clopidogrel, warfarin, escitalopram, carbamazepine, the nicotine-replacement patch and abacavir) and one drug class, the statin class of anticholesterol drugs.
Using Monte Carlo modeling, an assumption- and parameter-based modeling method, the team ran simulations to forecast the research investment required to learn how to cut adverse outcomes by meaningful amounts, and how long that research work would be expected to take. To increase their statistical confidence, they ran the simulations thousands of times and explored a wide range of assumptions. "The results were surprising," says Arnaout. "Before we did this work, I couldn't have told you whether it would take a million dollars or a trillion dollars or whether it would take five years or a hundred years. But now, we've got a basis for thinking that we're looking at single-digit billions of dollars and a couple of decades. That may sound like a lot or a little, depending on your point of view. But with these numbers, we can now have a more informed conversation about planning for the future of genomic medicine."
The most important determinant of the numbers is the extent to which the examples used in the model will turn out to be representative of drugs as a whole. "It's a broad set of drugs that were used, but we know how the genome can surprise us," says senior author Dr. Vikas P. Sukhatme. "For example, you won't be able to use genomics to cut adverse outcomes in half if genomics turns out to explain less than half of the adverse outcomes. But even in that case, we found that pharmacogenomics will be able to make a significant dent in adverse outcomes – cutting them by a quarter – for multi-billion-dollar investments."
Also surprising, say the authors, was the timing. "As a rule, the fruits of research come only after research dollars have already been spent," points out Arnaout. This means that, in this case, hundreds of millions of dollars will be spent for "pump-priming" long before the public can expect to see any meaningful clinical impact. "It's one thing to say, 'Be patient,' based on just faith," he adds. "It's another to be able to say so based on data and a model. We now have that. This enables the conversation to shift to which indicators of progress to look for, over the five or so years of pump-priming, to make sure we're on track."
Can results be sped up? Researchers always argue more money now will speed things up but it may be achievable here. "If we could enroll an ethnically diverse set of patients who are taking each of the 40 or 50 most commonly prescribed drugs, get their blood samples, and keep track of the adverse outcomes that some of them are bound to experience, we should be able to move faster, for less money," adds Arnaout, who describes this idea as a "50,000 Pharmacogenomes Project," a pursuit along the lines of the 1,000 Genomes Project, the UK10K or the Veteran's Association Million Veteran Program.
Published in Clinical Chemistry.