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 journal Nature 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.
Promising new approach to combat HIV