Chemists at UC San Diego and statisticians at Harvard University have developed a novel way to trace mutations in HIV that lead to drug resistance. Their findings, once expanded to the full range of drugs available to treat the infection, would allow doctors to tailor drug cocktails to the particular strains of the virus found in individual patients.
"We want to crack the code of resistance," said Wei Wang, associate professor chemistry and biochemistry at UC San Diego who led the collaboration along with Jun Liu of Harvard. The team reports their work in this week's early online edition of the Proceedings of the National Academy of Sciences.
HIV replicates quickly, but the copies are imprecise. The constant mutation has made HIV infection difficult to treat, much less cure, because drugs designed to interrupt the cycle of infection fail when their targets change.
To better understand which mutations matter for drug resistance, the researchers compared sequences of HIV taken from patients treated with specific drugs to those from untreated patients. Using a novel statistical method, they identified clusters of mutations that seemed to be working together to help the virus escape treatment.
One drug, indinavir, targets a protein called protease, which the virus needs to assemble the capsule it uses to invade new cells. Substitutions in ten different places on protease occurred in patients who were taking the drug, but what combination of mutations would hinder the action of the drug wasn't clear before this analysis.
Chemists can determine how a drug fits to a particular protein using computer modeling, but those computations take considerable time. Evaluating all possible combinations of those 10 substitutions is impractical. The statistical screen narrowed down the possibilities.
"People never looked at this, because they didn't know which mutation or which combination of mutations to study," Wang said. "That's the advantage of using the statistical method first to find the patterns. After the statisticians discovered the connections between mutations, then we focused on those combinations. We built structural models to understand the molecular basis of drug resistance."