ANAHEIM, CaliforniaEven as the use of combination regimens including protease inhibitors is becoming more routine among individuals infected with HIV, research is starting to highlight the growing problem of drug resistance,
Sally Blower, PhD, said at the American Association for the Advancement of Science annual meeting. Her research also forecasts a grim upsurge of drug-resistant strains of tuberculosis (TB).
Dr. Blower, associate professor of microbiology, immunology and medicine, University of California, San Francisco (UCSF), and her colleagues developed new mathematical models to predict the course of both HIV and TB.
Although safe sex practices have increased over the last decade, compliance with protease inhibitor drug regimens has been decreasing, she said. Thus, drug resistance is growing. This problem will only increase as the number of people being treated increases, Dr. Blower said. The mathematical model predicts a 20% increase in the number of cases of HIV in San Francisco.
Although the new combination regimens have made HIV a chronic disease, not a fatal one, for many infected individuals, the treatment regimens are not failsafe. Physicians are now becoming increasingly aware of the need to help patients who develop resistance, whether through noncompliance or another cause, or who cannot take the drugs because of side effects.
Dr. Blower believes that as protease inhibitor use increases, both patients and clinicians may become more lax about keeping to a strict protocol. And as people begin to believe that AIDS is not a fatal disease, there may be an increase in high-risk sex in the gay community.
The model we developed helps us see that community clinics must adhere very closely to a strict clinical-trial-like paradigm, and that high-risk sexual behavior must not return, Dr. Blower said. Otherwise, the promise of controlling AIDS will vanish.
The UCSF mathematical models have also been used to predict the course of tuberculosis in developing countries and genital herpes in the United States. Theres some good news, at least, with genital herpes, the research revealed.
About 22% of people in the United States are affected by genital herpes, and about 5% seek treatment. Yet certain traits of the herpesvirus, such as its slow mutation rate, allow treatment, even aggressive treatment, without generating a great number of drug-resistant cases.
So even in the inner cities, where the infection rate for herpes in some cases stands at above 40%, drug resistance should not be a problem, she said.
Tuberculosis a Different Story
Tuberculosis is a far different story. Three million people die every year around the world from the disease. The World Health Organization has recently made it a goal to rid the developing world of TB. The organizations strategy is to treat drug-sensitive TB cases, but not the more difficult drug-resistant ones.
Yet the mathematical model found that as you rid the population of drug-sensitive TB, there will also be an increase in the number of drug-resistant cases. Why? Because drug-resistant bacteria act differently than their drug-sensitive counterparts, and treating only drug-sensitive microbes can actually create an epidemic of drug-resistant strains.
Its what we call a perverse effect. The result of a strategy such as that of the World Health Organization will be a new, even more intractable epidemic, she said.
In the report on AIDS, TB, and herpes, published in Nature Medicine in June 1998, Dr. Blower and her colleagues urged the World Health Organization to adopt a new strategy to treat both drug-sensitive and drug-resistant TB aggressively. Otherwise, current control strategies could eventually result in a threefold increase in the death rate, and TB may become an even greater killer than it is today, she said.
Dr. Blower believes that mathematical models such as those for herpes, TB, and AIDS are sorely needed in the world of medicine. The study of infectious disease has traditionally been the practice of recording what has happened, she said. But we must and can turn epidemiology into a predictive science.