The relationship between how accurately HIV patients take the drugs prescribed by their doctors and the chance of the virus developing drug resistance has been well known for quite some time. However, according to a new study by Harvard scientists, the relationship between faithfulness to a drug plan and resistance is different for each of the drugs that make up the “cocktail” used to fight against the disease.
In order to understand the reason those differences exist, and to help doctors quickly and cost-effectively design new combinations of drugs that have a lower chance of resulting in resistance, a team led by Martin Nowak, Professor of Mathematics and of Biology and Director of the Program for Evolutionary Dynamics, invented a technique that medical researchers can use to understand the effects of different drugs, and be able to predict if certain treatments will cause the virus to become resistant.
Alison Hill, a PhD student in Biophysics and co-first author of the paper published in Nature Medicine, said:
“What we demonstrate in this paper is a prototype for predicting, through modeling, whether a patient at a given adherence level is likely to develop resistance to treatment. Compared to the time and expense of a clinical trial, this method offers a relatively easy way to make these predictions. And, as we show in the paper, our results match with what doctors are seeing in clinical settings.”
According to Nowak, the new technique will help scientists figure out exactly they need to do so they don’t have to keep struggling with a trial-and-error process.
“This is a mathematical tool that will help design clinical trials,” he explained. “Right now, researchers are using trial and error to develop these combination therapies. Our approach uses the mathematical understanding of evolution to make the process more akin to engineering.”
However, large amounts of data are needed to make a model than can make these predictions accurately.
Hill and Daniel Scholes Rosenbloom, a Ph.D. student in Organismic and Evolutionary Biology and the paper’s other first author, wanted to find that data. In order to do so, they turned to Johns Hopkins University Medical School, where Professor of Medicine and of Molecular Biology and Genetics Robert F. Siliciano was working with Ph.D. student Alireza Rabi (also co-first author) to learn how the HIV virus reacted to different drug dosages.
Since the level of the drug in patients, even in those that adhere to their treatment perfectly, naturally varies, the data was essential to the model that Hill, Rabi, and Rosenblood eventually created.
The virus is able to replicate and grow when drug levels are low (like they are between doses, or if the person misses a dose). Although higher drug levels keep the virus in check, they also heighten the chance of mutant strains of the virus emerging, which causes drug resistance.
Hill, Rabi, and Rosenbloom created a computer model with help from the data from John Hopkins. The design could predict whether and how much the virus, or a drug-resistant strain, was growing based on whether or not the patient took the right dosage of the medicine, and at each hour they needed to.
Rosenbloom said:
“Our model is essentially a simulation of what goes on during treatment. We created a number of simulated patients, each of whom had different characteristics, and then we said, ‘Let’s imagine these patients have 60 percent adherence to their treatment- they take 60 percent of the pills they’re supposed to.’ Our model can tell us what their drug concentration is over time, and based on that, we can say whether the virus is growing or shrinking, and whether they’re likely to develop resistance.”
As the researchers work to invent new drug cocktails to fight HIV, they can use the model’s predictions as their guide.
Hill and Rosenbloom hope to make the design even better in order for it to take additional factors, including multiple mutant-resistant strains of the virus and varying drug concentrations in other parts of the body, into effect.
“The prototype we have so far looks at concentrations of drugs in blood plasma,” Rosenbloom explained. “But a number of drugs don’t penetrate other parts of the body, like the brains or the gut, with the same efficiency, so it’s important to model these other areas where the concentrations of drugs might not be as high.”
Most importantly, both say that their design can help doctors create better, less expensive, and more efficient treatments that will give patients a new sense of hope.
“Over the past 10 years, the number of HIV-infected people receiving drug treatment has increased immensely,” Hill pointed out. “Figuring out what the best ways are to treat people in terms of cost effectiveness, adherence and the chance of developing resistance is going to become even more important.”
Written by Sarah Glynn