RALEIGH, N.C. — Can mathematics and statistics be used to design better, individualized treatments for AIDS?

Over the next few years, a team of researchers at North Carolina State University will attempt to find that out.

More than 38 million people were living with the HIV virus in 2005, according to global statistics. The disease killed more than 2.8 million people. The death toll is more than 25 million dating back to 1981.

The four professors have landed a $3.5 million grant to use mathematical models to better predict best courses of treatment for HIV patients. The National Institute of Allergy and Infectious Disease awarded the grant, which is for five years.

The project’s goal is to create mathematical and statistical models for use in devising treatment plans of acutely infected HIV patients as well as patients who have been infected recently.

Drug firms such as Triangle-based Trimeris and international giant GlaxoSmithKline have spent untold millions of dollars seeking to develop and deliver effective AIDS drugs. Massive efforts are also underway to develop an AIDS vaccine. But neither a cure nor a vaccine has yet to make it to market.

The NCSU researchers include Tom Banks, a mathematics professor in NCSU’s College of Physical Mathematical Sciences who also is director of NCSU’s Center for Research and Scientific Computation.

Other team members are Marie Davidian, a professor of statistics, Eric Rosenberg, a clinician at Massachusetts General Hospital and professor at Harvard Medical School, and Hien Tran, a professor of mathematics and associate head of the Department of Mathematics. The three are also members of the NCSU Center for Research and Scientific Computation.

“Based on what we know about HIV, there is really no consensus on the best treatment for acutely infected individuals,” Davidian said in a statement. “The medical community needs to know how immediate drug therapy may affect the patient’s own ability to cope with the disease and the treatment itself down the line.”

While HIV drugs can bring down the so-called viral load of the HIV virus in the bloodstream, some researchers believe letting a patient’s body adapt to the virus might be a better strategy. HIV patients also often develop resistance to drugs.

The researchers will utilize more than five years of patient data gathered by Rosenberg.

“The first step is to use existing data to define a mathematical model that can show us what happens to acutely infected patients when they are treated or not treated,” Banks said. “Then we extrapolate from the existing data using statistical methods, to see what the model predicts will happen under no treatment or under a given treatment interval. Based on the results, we can design a clinical trial to see if the data from actual patients match the predictions.”

Variables include viral load and how long people have been infected.

“Once we’re convinced that this model is accurate, we can then simulate virtual patients by combining it with a statistical model for the variation in the patient population in order to test treatment theories, to determine the most promising treatment times and durations for optimum results” Davidian explained.

The researchers plan to eventually test their results on patients in clinical trials. The result could be tailored treatments for HIV patients that take into account personal variables.

“It’s not a cure, but maybe it can improve the quality of treatment these patients receive,” Davidian said. “And this work has implications for a number of other diseases that involve compromised immune systems. We hope that this mathematical-statistical modeling approach will be a step toward the current goal of modern medicine – personalized treatment of diseases.”