Drivers and trajectories of resistance to new first-line drug regimens for tuberculosis.
BACKGROUND
New first-line drug regimens for treatment of tuberculosis (TB) are in clinical trials: emergence of resistance is a key concern. Because population-level data on resistance cannot be collected in advance, epidemiological models are important tools for understanding the drivers and dynamics of resistance before novel drug regimens are launched.


METHODS
We developed a transmission model of TB after launch of a new drug regimen, defining drug-resistant TB (DR-TB) as resistance to the new regimen. The model is characterized by (1) the probability of acquiring resistance during treatment, (2) the transmission fitness of DR-TB relative to drug-susceptible TB (DS-TB), and (3) the probability of treatment success for DR-TB versus DS-TB. We evaluate the effect of each factor on future DR-TB prevalence, defined as the proportion of incident TB that is drug-resistant.


RESULTS
Probability of acquired resistance was the strongest predictor of the DR-TB proportion in the first 5 years after the launch of a new drug regimen. Over a longer term, however, the DR-TB proportion was driven by the resistant population's transmission fitness and treatment success rates. Regardless of uncertainty in acquisition probability and transmission fitness, high levels (>10%) of drug resistance were unlikely to emerge within 50 years if, among all cases of TB that were detected, 85% of those with DR-TB could be appropriately diagnosed as such and then successfully treated.


CONCLUSIONS
Short-term surveillance cannot predict long-term drug resistance trends after launch of novel first-line TB regimens. Ensuring high treatment success of drug-resistant TB through early diagnosis and appropriate second-line therapy can mitigate many epidemiological uncertainties and may substantially slow the emergence of drug-resistant TB.
Department of Epidemiology , Johns Hopkins School of Public Health , Baltimore, Maryland.
Sourya
S
Shrestha
TB Modelling Group, Department of Infectious Disease Epidemiology , London School of Hygiene and Tropical Medicine , United Kingdom.
Gwenan M
GM
Knight
Department of Epidemiology , Johns Hopkins School of Public Health , Baltimore, Maryland.
Mariam
M
Fofana
Division of Global Health Equity , Brigham and Women's Hospital , Boston, Massachusetts.
Ted
T
Cohen
TB Modelling Group, Department of Infectious Disease Epidemiology , London School of Hygiene and Tropical Medicine , United Kingdom.
Richard G
RG
White
Amsterdam Institute for Global Health and Development , The Netherlands.
Frank
F
Cobelens
Department of Epidemiology , Johns Hopkins School of Public Health , Baltimore, Maryland.
David W
DW
Dowdy
Mycobacterium tuberculosis; TB drug regimens; TB drug resistance; TB mathematical model