For years, doctors treating those with HIV have recognized a relationship between how faithfully patients take the drugs they prescribe, and how likely the virus is to develop drug resistance. More recently, research has shown that the relationship between adherence to a drug regimen and resistance is different for each of the drugs that make up the "cocktail" used to control the disease.
New research conducted by Harvard scientists could help explain why those differences exist, and may help doctors quickly and cheaply design new combinations of drugs that are less likely to result in resistance.
As described in a September 2 paper in Nature Medicine, a team of researchers led by Martin Nowak, Professor of Mathematics and of Biology and Director of the Program for Evolutionary Dynamics, have developed a technique medical researchers can use to model the effects of various treatments, and predict whether they will cause the virus to develop resistance.
"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," Alison Hill, a PhD student in Biophysics and co-first author of the paper, said. "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."
The hope, said Nowak, is that the new technique will take some of the guesswork out of what is now largely a trial-and-error process.
"This is a mathematical tool that will help design clinical trials," he said. "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."
Creating a model that can make such predictions accurately, however, re
|Contact: Peter Reuell|