Design Help for Drug Cocktails for HIV Patients: Mathematical Model Helps Design Efficient Multi-Drug Therapies (Science Daily)

ScienceDaily (Sep. 2, 2012) — 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.

HIV is shown attaching to and infecting a T4 cell. The virus then inserts its own genetic material into the T4 cell’s host DNA. The infected host cell then manufactures copies of the HIV. (Credit: iStockphoto/Medical Art Inc.)

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, requires huge amounts of data.

To get that data, Hill and Daniel Scholes Rosenbloom, a PhD student in Organismic and Evolutionary Biology and the paper’s other first author, turned to Johns Hopkins University Medical School, where Professor of Medicine and of Molecular Biology and Genetics Robert F. Siliciano was working with PhD student Alireza Rabi (also co-first author) to study how the HIV virus reacted to varying drug dosages.

Such data proved critical to the model that Hill, Rabi and Rosenbloom eventually designed, because the level of the drug in patients — even those that adhere to their treatment perfectly — naturally varies. When drug levels are low — as they are between doses, or if a dose is missed — the virus is better able to replicate and grow. Higher drug levels, by contrast, may keep the virus in check, but they also increase the risk of mutant strains of the virus emerging, leading to drug resistance.

Armed with the data from Johns Hopkins, Hill, Rabi and Rosenbloom created a computer model that could predict whether and how much the virus, or a drug-resistant strain, was growing based on how strictly patients stuck to their drug regimen.

“Our model is essentially a simulation of what goes on during treatment,” Rosenbloom said. “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.”

The model’s predictions, Rosenbloom explained, can then serve as a guide to researchers as they work to design new drug cocktails to combat HIV.

While their model does hold out hope for simplifying the process of designing drug “cocktails,” Hill and Rosenbloom said they plan to continue to refine the model to take additional factors — such as 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.”

Ultimately, though, both say their model can offer new hope to patients by helping doctors design better, cheaper and more efficient treatments.

“Over the past 10 years, the number of HIV-infected people receiving drug treatment has increased immensely,” Hill said. “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.”

Journal Reference:

  1. Daniel I S Rosenbloom, Alison L Hill, S Alireza Rabi, Robert F Siliciano, Martin A Nowak. Antiretroviral dynamics determines HIV evolution and predicts therapy outcomeNature Medicine, 2012; DOI: 10.1038/nm.2892

*   *   *

Anti-HIV Drug Simulation Offers ‘Realistic’ Tool to Predict Drug Resistance and Viral Mutation

ScienceDaily (Sep. 2, 2012) — Pooling data from thousands of tests of the antiviral activity of more than 20 commonly used anti-HIV drugs, AIDS experts at Johns Hopkins and Harvard universities have developed what they say is the first accurate computer simulation to explain drug effects. Already, the model clarifies how and why some treatment regimens fail in some patients who lack evidence of drug resistance. Researchers say their model is based on specific drugs, precise doses prescribed, and on “real-world variation” in how well patients follow prescribing instructions.

Johns Hopkins co-senior study investigator and infectious disease specialist Robert Siliciano, M.D., Ph.D., says the mathematical model can also be used to predict how well a patient is likely to do on a specific regimen, based on their prescription adherence. In addition, the model factors in each drug’s ability to suppress viral replication and the likelihood that such suppression will spur development of drug-resistant, mutant HIV strains.

“With the help of our simulation, we can now tell with a fair degree of certainty what level of viral suppression is being achieved — how hard it is for the virus to grow and replicate — for a particular drug combination, at a specific dosage and drug concentration in the blood, even when a dose is missed,” says Siliciano, a professor at the Johns Hopkins University School of Medicine and a Howard Hughes Medical Institute investigator. This information, he predicts, will remove “a lot of the current trial and error, or guesswork, involved in testing new drug combination therapies.”

Siliciano says the study findings, to be reported in the journalNature Medicine online Sept. 2, should help scientists streamline development and clinical trials of future combination therapies, by ruling out combinations unlikely to work.

One application of the model could be further development of drug combinations that can be contained in a single pill taken once a day. That could lower the chance of resistance, even if adherence is not perfect. Such future drug regimens, he says, will ideally strike a balance between optimizing viral suppression and minimizing risk of drug resistance.

Researchers next plan to expand their modeling beyond blood levels of virus to other parts of the body, such as the brain, where antiretroviral drug concentrations can be different from those measured in the blood. They also plan to expand their analysis to include multiple-drug-resistant strains of HIV.

Besides Siliciano, Johns Hopkins joint medical-doctoral student Alireza Rabi was a co-investigator in this study. Other study investigators included doctoral candidates Daniel Rosenbloom, M.S.; Alison Hill, M.S.; and co-senior study investigator Martin Nowak, Ph.D. — all at Harvard University.

Funding support for this study, which took two years to complete, was provided by the National Institutes of Health, with corresponding grant numbers R01-MH54907, R01-AI081600, R01-GM078986; the Bill and Melinda Gates Foundation; the Cancer Research Institute; the National Science Foundation; the Howard Hughes Medical Institute; Natural Sciences and Engineering Research Council of Canada; the John Templeton Foundation; and J. Epstein.

Currently, an estimated 8 million of the more than 34 million people in the world living with HIV are taking antiretroviral therapy to keep their disease in check. An estimated 1,178,000 in the United States are infected, including 23,000 in the state of Maryland.

Journal Reference:

  1. Daniel I S Rosenbloom, Alison L Hill, S Alireza Rabi, Robert F Siliciano, Martin A Nowak. Antiretroviral dynamics determines HIV evolution and predicts therapy outcomeNature Medicine, 2012; DOI: 10.1038/nm.2892

Deixe um comentário

Preencha os seus dados abaixo ou clique em um ícone para log in:

Logotipo do WordPress.com

Você está comentando utilizando sua conta WordPress.com. Sair /  Alterar )

Foto do Google

Você está comentando utilizando sua conta Google. Sair /  Alterar )

Imagem do Twitter

Você está comentando utilizando sua conta Twitter. Sair /  Alterar )

Foto do Facebook

Você está comentando utilizando sua conta Facebook. Sair /  Alterar )

Conectando a %s