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.
Genital Herpes
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.
