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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

The effective reproductive ratio and its association with the 3 potential drivers of drug resistance. The effective reproductive ratio (REFF) compares the ability of drug-resistant tuberculosis (TB) to propagate through populations (ie, “composite fitness”) relative to drug-susceptible TB. The derived expression of the effective reproductive ratio is independent of the probability of acquiring drug resistance during treatment, ε (as can be seen from the mathematical expression provided in the text), and hence has no effect on REFF (A); REFF remains fixed for the entire range of ε. In contrast, transmission fitness (f) has a strong linear relationship (B); REFF increases in a linear fashion with increase in f; and treatment success differential Δk has an even stronger effect (C); REFF increases in supralinear fashion with increase in Δk. Dashed vertical red lines show the baseline values of each of the parameters, as provided in Table 1. The shaded gray region indicates the parameter values that lead to an effective reproductive ratio of greater than 1. Abbreviations: DR-TB, drug-resistant tuberculosis; DS-TB, drug-susceptible TB.
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OFU073F2: The effective reproductive ratio and its association with the 3 potential drivers of drug resistance. The effective reproductive ratio (REFF) compares the ability of drug-resistant tuberculosis (TB) to propagate through populations (ie, “composite fitness”) relative to drug-susceptible TB. The derived expression of the effective reproductive ratio is independent of the probability of acquiring drug resistance during treatment, ε (as can be seen from the mathematical expression provided in the text), and hence has no effect on REFF (A); REFF remains fixed for the entire range of ε. In contrast, transmission fitness (f) has a strong linear relationship (B); REFF increases in a linear fashion with increase in f; and treatment success differential Δk has an even stronger effect (C); REFF increases in supralinear fashion with increase in Δk. Dashed vertical red lines show the baseline values of each of the parameters, as provided in Table 1. The shaded gray region indicates the parameter values that lead to an effective reproductive ratio of greater than 1. Abbreviations: DR-TB, drug-resistant tuberculosis; DS-TB, drug-susceptible TB.

Mentions: By examining how each of the 3 drivers affects the effective reproductive ratio, we can understand their individual roles in the ability of DR-TB to proliferate after launch of a new drug regimen. The 3 drivers of drug resistance had markedly different effects on REFF (Figure 2). The acquisition rate had no effect on the effective reproductive ratio, because REFF is independent of the acquisition rate (ε) (Figure 2A). In contrast, the relative transmission fitness of DR-TB (f) had a linear relationship with REFF, as seen by its presence as a single term in the numerator of the expression of REFF (and graphically in Figure 2B). Likewise, the treatment success differential had a positive relationship with REFF. Furthermore, the effect of Δk on REFF increased as Δk increased (Figure 2C). Hence, the DR-TB proportion at any time not only grows, but does so progressively faster, as the differential in treatment success increases.Figure 2.


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)

The effective reproductive ratio and its association with the 3 potential drivers of drug resistance. The effective reproductive ratio (REFF) compares the ability of drug-resistant tuberculosis (TB) to propagate through populations (ie, “composite fitness”) relative to drug-susceptible TB. The derived expression of the effective reproductive ratio is independent of the probability of acquiring drug resistance during treatment, ε (as can be seen from the mathematical expression provided in the text), and hence has no effect on REFF (A); REFF remains fixed for the entire range of ε. In contrast, transmission fitness (f) has a strong linear relationship (B); REFF increases in a linear fashion with increase in f; and treatment success differential Δk has an even stronger effect (C); REFF increases in supralinear fashion with increase in Δk. Dashed vertical red lines show the baseline values of each of the parameters, as provided in Table 1. The shaded gray region indicates the parameter values that lead to an effective reproductive ratio of greater than 1. Abbreviations: DR-TB, drug-resistant tuberculosis; DS-TB, drug-susceptible TB.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

OFU073F2: The effective reproductive ratio and its association with the 3 potential drivers of drug resistance. The effective reproductive ratio (REFF) compares the ability of drug-resistant tuberculosis (TB) to propagate through populations (ie, “composite fitness”) relative to drug-susceptible TB. The derived expression of the effective reproductive ratio is independent of the probability of acquiring drug resistance during treatment, ε (as can be seen from the mathematical expression provided in the text), and hence has no effect on REFF (A); REFF remains fixed for the entire range of ε. In contrast, transmission fitness (f) has a strong linear relationship (B); REFF increases in a linear fashion with increase in f; and treatment success differential Δk has an even stronger effect (C); REFF increases in supralinear fashion with increase in Δk. Dashed vertical red lines show the baseline values of each of the parameters, as provided in Table 1. The shaded gray region indicates the parameter values that lead to an effective reproductive ratio of greater than 1. Abbreviations: DR-TB, drug-resistant tuberculosis; DS-TB, drug-susceptible TB.
Mentions: By examining how each of the 3 drivers affects the effective reproductive ratio, we can understand their individual roles in the ability of DR-TB to proliferate after launch of a new drug regimen. The 3 drivers of drug resistance had markedly different effects on REFF (Figure 2). The acquisition rate had no effect on the effective reproductive ratio, because REFF is independent of the acquisition rate (ε) (Figure 2A). In contrast, the relative transmission fitness of DR-TB (f) had a linear relationship with REFF, as seen by its presence as a single term in the numerator of the expression of REFF (and graphically in Figure 2B). Likewise, the treatment success differential had a positive relationship with REFF. Furthermore, the effect of Δk on REFF increased as Δk increased (Figure 2C). Hence, the DR-TB proportion at any time not only grows, but does so progressively faster, as the differential in treatment success increases.Figure 2.

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