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Drivers and trajectories of resistance to new first-line drug regimens for tuberculosis.

Shrestha S, Knight GM, Fofana M, Cohen T, White RG, Cobelens F, Dowdy DW - Open Forum Infect Dis (2014)

Bottom Line: We evaluate the effect of each factor on future DR-TB prevalence, defined as the proportion of incident TB that is drug-resistant.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology , Johns Hopkins School of Public Health , Baltimore, Maryland.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus

An epidemiological model of drug-susceptible (DS) and drug-resistant (DR) tuberculosis (TB) to investigate the future prevalence of drug resistance following a roll-out of novel first-line regimens. Tuberculosis subpopulations are broadly classified as either susceptible to the new regimen (DS-TB) or resistant (DR-TB). After successful infection with either of these subpopulations, individuals can develop active TB disease or latent TB infection (LTBI), with probabilities p and 1−p, respectively. Individuals with LTBI can subsequently progress to active TB, at per capita rate ϕ. Individuals with active TB are diagnosed and treatment is initiated after an average composite duration of 1/ω. Successful treatment is modeled as individuals returning to latent class, the probability of which is 1-ks and 1-kr for the susceptible and resistant strain, respectively; unsuccessful treatment is modeled as remaining in the active infectious state. Resistance can be acquired through spontaneous mutation, but we assume that such resistance can expand in an individual to the point of becoming transmissible only when selective pressure is applied during treatment, at a probability of ε per treatment. Individuals with LTBI may be reinfected with either the same or the opposite strain, but previous infections impart partial immunity, where the degree of protection is given by 1−ξ. Demographic turnovers are present in the model, but they are omitted here for simplicity.
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OFU073F1: An epidemiological model of drug-susceptible (DS) and drug-resistant (DR) tuberculosis (TB) to investigate the future prevalence of drug resistance following a roll-out of novel first-line regimens. Tuberculosis subpopulations are broadly classified as either susceptible to the new regimen (DS-TB) or resistant (DR-TB). After successful infection with either of these subpopulations, individuals can develop active TB disease or latent TB infection (LTBI), with probabilities p and 1−p, respectively. Individuals with LTBI can subsequently progress to active TB, at per capita rate ϕ. Individuals with active TB are diagnosed and treatment is initiated after an average composite duration of 1/ω. Successful treatment is modeled as individuals returning to latent class, the probability of which is 1-ks and 1-kr for the susceptible and resistant strain, respectively; unsuccessful treatment is modeled as remaining in the active infectious state. Resistance can be acquired through spontaneous mutation, but we assume that such resistance can expand in an individual to the point of becoming transmissible only when selective pressure is applied during treatment, at a probability of ε per treatment. Individuals with LTBI may be reinfected with either the same or the opposite strain, but previous infections impart partial immunity, where the degree of protection is given by 1−ξ. Demographic turnovers are present in the model, but they are omitted here for simplicity.

Mentions: Using classic models of DR-TB as a guide [7, 8], we constructed a compartmental model of TB to simulate the introduction of a novel first-line drug regimen for TB treatment (Figure 1). In this model, we consider 2 bacterial populations of TB, one that is susceptible to the novel regimen (DS-TB) and one that is resistant (DR-TB). We do not consider resistance levels to regimens other than the novel regimen; thus, the “DS-TB” strain may include strains that are resistant to existing regimens but susceptible to the new regimen. We make this simplification for purposes of evaluating key principles and providing generalizable insight in the absence of data on the true spectrum of drug resistance after the launch of any given novel regimen. We consider that the 2 forms of TB differ in 3 important ways: (1) DS-TB may convert to DR-TB during treatment—drug resistance is acquired through spontaneous mutation, but bacterial populations with such acquired resistance only expand within hosts to the point of becoming transmissible when subjected to selective pressure during treatment with the novel regimen; (2) DS-TB is successfully transmitted at a higher per-person rate than DR-TB (differential transmission fitness); and (3) probability of success upon treatment with the novel regimen is higher for DS-TB than DR-TB (differential treatment success).Figure 1.


Drivers and trajectories of resistance to new first-line drug regimens for tuberculosis.

Shrestha S, Knight GM, Fofana M, Cohen T, White RG, Cobelens F, Dowdy DW - Open Forum Infect Dis (2014)

An epidemiological model of drug-susceptible (DS) and drug-resistant (DR) tuberculosis (TB) to investigate the future prevalence of drug resistance following a roll-out of novel first-line regimens. Tuberculosis subpopulations are broadly classified as either susceptible to the new regimen (DS-TB) or resistant (DR-TB). After successful infection with either of these subpopulations, individuals can develop active TB disease or latent TB infection (LTBI), with probabilities p and 1−p, respectively. Individuals with LTBI can subsequently progress to active TB, at per capita rate ϕ. Individuals with active TB are diagnosed and treatment is initiated after an average composite duration of 1/ω. Successful treatment is modeled as individuals returning to latent class, the probability of which is 1-ks and 1-kr for the susceptible and resistant strain, respectively; unsuccessful treatment is modeled as remaining in the active infectious state. Resistance can be acquired through spontaneous mutation, but we assume that such resistance can expand in an individual to the point of becoming transmissible only when selective pressure is applied during treatment, at a probability of ε per treatment. Individuals with LTBI may be reinfected with either the same or the opposite strain, but previous infections impart partial immunity, where the degree of protection is given by 1−ξ. Demographic turnovers are present in the model, but they are omitted here for simplicity.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4281792&req=5

OFU073F1: An epidemiological model of drug-susceptible (DS) and drug-resistant (DR) tuberculosis (TB) to investigate the future prevalence of drug resistance following a roll-out of novel first-line regimens. Tuberculosis subpopulations are broadly classified as either susceptible to the new regimen (DS-TB) or resistant (DR-TB). After successful infection with either of these subpopulations, individuals can develop active TB disease or latent TB infection (LTBI), with probabilities p and 1−p, respectively. Individuals with LTBI can subsequently progress to active TB, at per capita rate ϕ. Individuals with active TB are diagnosed and treatment is initiated after an average composite duration of 1/ω. Successful treatment is modeled as individuals returning to latent class, the probability of which is 1-ks and 1-kr for the susceptible and resistant strain, respectively; unsuccessful treatment is modeled as remaining in the active infectious state. Resistance can be acquired through spontaneous mutation, but we assume that such resistance can expand in an individual to the point of becoming transmissible only when selective pressure is applied during treatment, at a probability of ε per treatment. Individuals with LTBI may be reinfected with either the same or the opposite strain, but previous infections impart partial immunity, where the degree of protection is given by 1−ξ. Demographic turnovers are present in the model, but they are omitted here for simplicity.
Mentions: Using classic models of DR-TB as a guide [7, 8], we constructed a compartmental model of TB to simulate the introduction of a novel first-line drug regimen for TB treatment (Figure 1). In this model, we consider 2 bacterial populations of TB, one that is susceptible to the novel regimen (DS-TB) and one that is resistant (DR-TB). We do not consider resistance levels to regimens other than the novel regimen; thus, the “DS-TB” strain may include strains that are resistant to existing regimens but susceptible to the new regimen. We make this simplification for purposes of evaluating key principles and providing generalizable insight in the absence of data on the true spectrum of drug resistance after the launch of any given novel regimen. We consider that the 2 forms of TB differ in 3 important ways: (1) DS-TB may convert to DR-TB during treatment—drug resistance is acquired through spontaneous mutation, but bacterial populations with such acquired resistance only expand within hosts to the point of becoming transmissible when subjected to selective pressure during treatment with the novel regimen; (2) DS-TB is successfully transmitted at a higher per-person rate than DR-TB (differential transmission fitness); and (3) probability of success upon treatment with the novel regimen is higher for DS-TB than DR-TB (differential treatment success).Figure 1.

Bottom Line: We evaluate the effect of each factor on future DR-TB prevalence, defined as the proportion of incident TB that is drug-resistant.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology , Johns Hopkins School of Public Health , Baltimore, Maryland.

ABSTRACT

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.

No MeSH data available.


Related in: MedlinePlus