New Model Makes It Possible To Predict Emergence Of Antiviral Drug Resistance

Researchers led by a UC San Francisco scientist have developed a model for predicting the emergence of antiviral drug resistance and for identifying the key factors that generate drug resistance.

The model, reported in the June 1 issue of Nature Medicine and lauded in an accompanying review by a Yale School of Management professor of management sciences and medicine, looked specifically at the likelihood of drug resistance occurring with increased use of a drug to treat genital herpes. The findings predicted that while increased use would lead to elevated levels of drug resistant strains, the number of cases of the disease would significantly decrease.

However, the greater significance of the study, said the principal investigator, Sally Blower, PhD, an associate professor of microbiology, immunology and medicine at UCSF, is the potential use of the new methodology for predicting drug resistance in many infectious diseases prone to antibiotic or antiviral resistance, including HIV.

"The methodology we've developed and applied to genital herpes can be used as a health policy tool to predict the likelihood of future levels of antibiotic or antiviral resistance for numerous infectious diseases, including HIV," said Blower.

A tool to predict future drug resistance is sorely needed, according to Edward H. Kaplan, PhD, of Yale in his review of the study. "This new model," he said, "provides a way to challenge the inertia often seen in public health decisions in which the overwhelming cost and timing required by empirical studies renders such analyses unfeasible, leading by default to decisions not to promote drug treatment when there is a risk of drug resistance developing."

The UCSF mathematical model, which traced the dynamics of drug-sensitive and drug-resistant herpes simplex virus in the general population, was designed to predict how much drug resistance could be expected to emerge over the next 50 years if treatment rates increased, and to iden

Contact: Jennifer O'Brien
(415) 476-2557
University of California - San Francisco

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