A Multistrain Mathematical Model To Investigate the Role of Pyrazinamide in the Emergence of Extensively Drug-Resistant Tuberculosis

Abstract

Several infectious diseases of global importance-e.g., HIV infection and tuberculosis (TB)-require prolonged treatment with combination antimicrobial regimens typically involving high-potency core agents coupled with additional companion drugs that protect against the de novo emergence of mutations conferring resistance to the core agents. Often, the most effective (or least toxic) companion agents are reused in sequential (first-line, second-line, etc.) regimens. We used a multistrain model of Mycobacterium tuberculosis transmission in Southeast Asia to investigate how this practice might facilitate the emergence of extensive drug resistance, i.e., resistance to multiple core agents. We calibrated this model to regional TB and drug resistance data using an approximate Bayesian computational approach. We report the proportion of data-consistent simulations in which the prevalence of pre-extensively drug-resistant (pre-XDR) TB-defined as resistance to both first-line and second-line core agents (rifampin and fluoroquinolones)-exceeds predefined acceptability thresholds (1 to 2 cases per 100,000 population by 2035). The use of pyrazinamide (the most effective companion agent) in both first-line and second-line regimens increased the proportion of simulations in which the prevalence exceeded the pre-XDR acceptability threshold by 7-fold compared to a scenario in which patients with pyrazinamide-resistant TB received an alternative drug. Model parameters related to the emergence and transmission of pyrazinamide-resistant TB and resistance amplification were among those that were the most strongly correlated with the projected pre-XDR prevalence, indicating that pyrazinamide resistance acquired during first-line treatment subsequently promotes amplification to pre-XDR TB under pyrazinamide-containing second-line treatment. These findings suggest that the appropriate use of companion drugs may be critical to preventing the emergence of strains resistant to multiple core agents.

Keywords: antimicrobial combinations; mathematical modeling; multidrug resistance; pyrazinamide; tuberculosis.

FIG 1

Model structure diagram. (A) The model features separate compartments for individuals who are uninfected with TB, latently infected with TB, or experiencing active disease. Individuals with TB are further distinguished on the basis of their prior treatment experience. A separate compartment exists for patients who are receiving ineffective treatment; these individuals remain ill with TB and are then initiated on a repeat course of treatment. All five TB compartments (with the exception of the uninfected compartment) are replicated for each of eight drug resistance states for a total of 41 unique compartments. Births and deaths are not shown here for simplicity. (B) Progression between drug resistance states is assumed to result only in increasing resistance. In addition to the transitions shown here, resistance to multiple drugs can be acquired within a single course of treatment. The primary mode of acquiring pre-XDR TB (defined as concomitant resistance to at least RIF and FQ) is highlighted in red and includes acquisition of resistance to PZA, a companion drug that is routinely used in both first- and second-line treatment.

FIG 2

Experimental approach. Shown here is the step-by-step approach of selecting simulations that are consistent with existing epidemiologic data and projecting outcomes under those simulations, for purposes of elucidating the dynamics between strains with different patterns of resistance to multiple antimicrobial agents. ρ, distance function.

FIG 3

Reuse of PZA increases the projected prevalence of pre-XDR TB. Projected prevalence of RIF-resistant (RIFr), FQ-resistant (FQr), and pre-XDR (RIF- and FQ-resistant [RIF/FQr] or RIF-, FQ-, and PZA-resistant [RIF/FQ/PZAr]) TB with and without additional resistance to PZA in 2035 under the baseline (A) and PZA replacement (B) scenarios. Box plots show the median, 25th, and 75th percentile values across all data-consistent simulations. Outlier simulations with a projected pre-XDR TB prevalence of greater than 20 per 100,000 population are not shown; the numbers of such outliers, if applicable, are indicated in parentheses at the top of each box plot. (C) Proportion of data-consistent simulations in which the projected pre-XDR TB prevalence in 2035 exceeds three predefined acceptability thresholds. Replacing PZA with an alternative drug of equal efficacy among patients with PZA-resistant TB greatly reduces the proportion of trajectories in which the prevalence exceeds the pre-XDR TB acceptability threshold in 2035.

FIG 4

Parameters associated with a high future prevalence of pre-XDR TB. Leading drivers of future pre-XDR TB prevalence, as assessed by logistic regression on the odds of the primary outcome, namely, exceeding a predefined acceptability threshold of 1 case per 100,000 population in 2035, comparing baseline conditions (blue and black squares) to the alternative scenario in which PZA is replaced (gray diamonds). Odds ratios reflect the change in the primary outcome associated with an increase of 1/10 of a standard deviation in the independent variable. Parameters related to strains resistant to PZA only (PZAr) or resistant to both RIF and PZA (RIF/PZAr) are highlighted in blue. As an example of scale, 1/10 of a standard deviation corresponds to absolute changes of 0.5% in the probability of acquiring RIF resistance in a single course of treatment, 6% in the transmission fitness of RIF- and PZA-resistant strains, or 5% in the probability of cure for RIF- and PZA-resistant strains on the first-line regimen.

FIG 5

Sequential acquisition of resistance and emergence of pre-XDR TB. (A) Pathways from RIF and FQ resistance, with and without additional PZA resistance. We demonstrate that, when PZA prevents the development of resistance to RIF and FQs, the primary pathway to developing pre-XDR TB goes through an intermediate step that includes resistance to both RIF and PZA (RIF/PZAr, arrow 4), rather than directly from RIF or FQ resistance (arrows 1 and 2). (B) Proportion of data-consistent simulations in which the projected pre-XDR TB prevalence in 2035 exceeds various acceptability thresholds, after blocking specific pathways of resistance acquisition. Blocking the progression from combined RIF and PZA resistance to RIF, FQ, and PZA resistance (corresponding to arrow 4 in panel A) greatly reduces the proportion of trajectories in which the prevalence exceeds the pre-XDR TB acceptability threshold in 2035, as shown in the rightmost bars. In contrast, blocking resistance amplification directly from strains that are RIF or FQ monoresistant results in a minimal change from the baseline scenario.