May 31, 2012
It has been almost 30 years since the French virologists Luc Montagnier and Françoise Barré-Sinoussi first described human immunodeficiency virus, or HIV. Nowadays, highly active anti-retroviral therapy (HAART) offers a way to extend the lives of those infected with the virus. But so far, it has been impossible to find either a vaccine or a cure-all to treat the virus, which causes AIDS. “It’s frustrating,” says Max von Kleist, but at the same time, this is what motivates his research. And yet, the 32-year-old researcher is neither a doctor nor a pharmacist. “I come from a family of doctors. Maybe that’s what sensitized me to medical topics,” says von Kleist, a Berlin native who first studied bioinformatics at Freie Universität Berlin before earning a doctorate in mathematics at the National University of Ireland. Since last October, von Kleist has been in charge of the Computational Pharmacometrics research group at the Department of Mathematics and Computer Science at Freie Universität Berlin.
“We are working on mathematical models to improve treatment of infectious diseases like AIDS and influenza,” von Kleist explains. Using pharmacometrics, researchers can create computer simulations showing how and when a medical active ingredient arrives at its target location in the body. At the same time, they can draw conclusions regarding issues such as what dosage of a medication an individual patient needs in order to achieve the desired effect.
“In dealing with HIV, what we are interested in is strategies we can use to prevent drug resistance,” von Kleist says. In fact, doctors all over the world are facing a major problem: More and more HIV-infected patients have stopped responding appropriately to their medications, so the immunodeficiency disease manifests itself sooner. Von Kleist and his team are trying to understand the development of drug resistance. “HIV can infect a great many different types of cells. That’s why we want to find out where, in which cells, specific types of resistance emerge,” the mathematician explains. Using “virtual patients” created in a computer based on data from clinical studies, the team was recently able to examine the dynamics of development of drug resistance. They discovered that the crucial point marking whether antiretroviral therapy will succeed or fail comes very early on, after only about three months. Failure can have serious consequences for the patient’s further treatment. The reason lies in the fact that information from mutated HIV viruses is “archived” in “latent” infected immune cells that seem healthy when viewed from the outside. These cells last an extremely long time, so they can nullify the effectiveness of certain active substances for a long period.
Pharmacometric modeling has now allowed the mathematicians to calculate a new treatment strategy on the computer, in which an early shift in therapy counteracts the development of drug resistance and the “archiving” effect. “Our treatment approach only works in silicio – in the computer model – right now,” von Kleist says. “But there’s a good chance that our findings will be reviewed in clinical studies as well in the near future.” If the studies’ findings are similar to those generated using the pharmacometric models, it would be a huge step forward in treating AIDS.
Along with treatment options, the experts in the Computational Pharmacometrics research group are also studying how to prevent new HIV infections. For example, the researchers have used a pharmacometric model to accurately predict the risk of mother-to-child transmission of the virus and the development of drug resistance in patients receiving the AIDS medication nevirapine during and after birth. “That means we now have a tool we can use to assess the prophylactic potential of other AIDS medications in a model,” von Kleist says. And that offers a ray of hope for hundreds of thousands of unborn children; according to the World Health Organization (WHO), more than 90 percent of all HIV-infected children have contracted the disease from their mothers.