But proponents say the deliberate pace underscores the complexity of the relationship between medicine and disease and, indeed, argues for more funding. Thus far, these competing narratives have been based mostly on anecdotes. Medical doctor Ramy Arnaout from Beth Israel Deaconess Medical Center (BIDMC) turned to quantitative modeling, a numerical forecasting approach used to predict everything from weather events to the outcomes of political elections, and an extremely useful way to both set expectations and assist in decision-making.
Arnaout and colleagues knew that drug-related adverse outcomes cost the health-care system upwards of 80 billion dollars a year, and 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. Their findings provide one of the first examples of data-driven estimates being applied to genomic medicine and offer a template for the use of quantitative modeling in this field.
After analyzing their model for a range of situations, the research team found that the cost can be expected to be less than 10 billion dollars, 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 has been hard to know how it all adds up in the big picture," explains Arnaout. "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."
Arnaout and Vikas Sukhatme, Chief Academic Officer of BIDMC, decided to try and answer this question by applying forecasting methods to a big clinical problem – drug-related adverse outcomes. "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."
Using an approach called Monte Carlo modeling, 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. For statistical confidence, they ran their simulations thousands of times and explored a wide range of assumptions. "The results were surprising," says Arnaout. "Before we did this work, I could not 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 have got a basis for thinking that we are 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."
MEDICA.de; Source: Beth Israel Deaconess Medical Center