Math model can help fight HIV

A new mathematical model may help scientists develop effective drugs to not only treat, but actually cure HIV. HIV is Human Immunodeficiency Virus, and it causes AIDS—Acquired Immune Deficiency Syndrome.

Current antiretroviral medicines attack cells that act as active virus factories to mass produce and spread the virus so it can infect more cells. However, any cure for HIV must deal with latently-infected cells.

AIDS orphans from Cambodia. By Cambodia4kids.org, Beth Kanter from Massachusetts, USA (HIV Kids) [CC-BY-2.0 (http://creativecommons.org/licenses/by/2.0)], via Wikimedia Commons.

AIDS orphans from Cambodia. By Cambodia4kids.org, Beth Kanter from Massachusetts, USA (HIV Kids) [CC-BY-2.0 (http://creativecommons.org/licenses/by/2.0)%5D, via Wikimedia Commons.

Instead of actively reproducing, the virus in these long-lived CD4+T cells is basically resting. And virus in those cells can survive, even after patients have been taking HIV medicines for years. Think of the latently-infected cells as sitting on the bench and waiting at some point to get into the game, as it were.

Unfortunately, HIV and AIDS are no game. Roughly 35 million people are living with HIV, reports the Joint United Nations Programme on HIV/AIDS (UNAIDS). AIDS-related causes killed about 1.5 million people last year, and the total death toll since the 1980s is 39 million.

Research is underway on latency-reversing agents, or LRAs. Until now, though, it’s been hard to say how strong those drugs would have to be to have a real cure.

“For HIV, a cure means being able to stop taking all drugs, without a risk of the virus growing back to high levels,” says Alison Hill, a researcher in evolutionary dynamics at Harvard University.

Now Hill and other researchers have developed a computer model to gauge how effective a medicine must be to really tackle those latently-infected cells. The team includes scientists from Harvard, Columbia, and Johns Hopkins universities in the United States, the Howard Hughes Medical Center, and the Institute of Integrative Biology in Zurich, Switzerland. PNAS, Proceedings of the National Academy of Sciences, is publishing the research.

As with any computer model, the researchers use math to see what might happen under different scenarios. Programming includes detailed algorithms—sets of instructions that tell the computer what to do with data.

The team found that a latency-reversing agent would have to reduce the number of infected cells by about 2,000-fold in order to let a majority of patients skip antiretroviral therapy, or ART, for one year. After that time, however, rebound could occur suddenly.

“We were able to determine that a 90%, 99%, or even 99.9% reduction in the latent pool is unlikely to lead to a cure,” notes Hill. “We predicted that if patients stop their drug cocktails after this type of reduction, they may appear to be cured for many months, but the virus is very likely to eventually reappear.”

“This means we likely need drugs much stronger than anything tested so far in the lab,” Hill continues. In order to prevent rebound altogether, the research team concluded, treatments would have to reduce infected cells by more than 10,000-fold, the team found.

That sounds like a daunting task, and it is. Moreover, the team’s results predict there would be large variation in when patients would rebound.

Nonetheless, the model can ultimately help speed up the search for a cure.

“This study was important because using math, we could help answer an important medical question that cannot yet be answered experimentally,” Hill says. “It may hopefully save researchers and patients from clinical trials that are unlikely to be successful.”

The research also helps explain why some apparent cures, such as treatment immediately after birth or bone marrow transplants, were not in fact permanent, Hill notes.

The more scientists can understand about HIV and what the challenges in fighting it are, the closer we all are to winning the battle against AIDS.

 

 

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