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

Economic implications of novel regimens for tuberculosis treatment in three high-burden countries: a modelling analysis

BACKGROUND

With numerous trials investigating novel drug combinations to treat tuberculosis, we aimed to evaluate the extent to which future improvements in tuberculosis treatment regimens could offset potential increases in drug costs.

METHODS

In this modelling analysis, we used an ingredients-based approach to estimate prices at which novel regimens for rifampin-susceptible and rifampin-resistant tuberculosis treatment would be cost-neutral or cost-effective compared with standards of care in India, the Philippines, and South Africa. We modelled regimens meeting targets set in the WHO's 2023 Target Regimen Profiles (TRPs). Our decision-analytical model tracked cohorts of adults initiating rifampin-susceptible or rifampin-resistant tuberculosis treatment, simulating their health outcomes and costs accumulated during and following treatment under standard-of-care and novel regimen scenarios. Price thresholds included short-term cost-neutrality (considering only savings accrued during treatment), medium-term cost-neutrality (additionally considering savings from averted retreatments and secondary cases), and cost-effectiveness (incorporating willingness-to-pay for improved health outcomes).

FINDINGS

Total medium-term costs per person treated using standard-of-care regimens were estimated at US$450 (95% uncertainty interval 310-630) in India, $560 (350-860) in the Philippines, and $730 (530-1090) in South Africa for rifampin-susceptible tuberculosis (current drug costs $46) and $2100 (1590-2810) in India, $2610 (2090-3280) in the Philippines, and $3790 (3090-4630) in South Africa for rifampin-resistant tuberculosis (current drug costs $432). A rifampin-susceptible tuberculosis regimen meeting the optimal targets defined in the TRPs could be cost-neutral in the short term at drug costs of $140 (90-210) per full course in India, $230 (130-380) in the Philippines, and $280 (180-460) in South Africa. For rifampin-resistant tuberculosis, short-term cost-neutral thresholds were higher with $930 (720-1230) in India, $1180 (980-1430) in the Philippines, and $1480 (1230-1780) in South Africa. Medium-term cost-neutral prices were approximately $50-100 higher than short-term cost-neutral prices for rifampin-susceptible tuberculosis and $250-550 higher for rifampin-resistant tuberculosis. Health system cost-neutral prices that excluded patient-borne costs were 45-70% lower (rifampin-susceptible regimens) and 15-50% lower (rifampin-resistant regimens) than the cost-neutral prices that included patient costs. Cost-effective prices were substantially higher. Shorter duration was the most important driver of medium-term savings with novel regimens, followed by ease of adherence.

INTERPRETATION

Improved tuberculosis regimens, particularly shorter regimens or those that facilitate better adherence, could reduce overall costs, potentially offsetting higher prices.

FUNDING

WHO.

Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis

OBJECTIVES

Active case finding (ACF) is an important tuberculosis (TB) intervention in high-burden settings. However, empirical evidence garnered from field data has been equivocal about the long-term community-level impact, and more data at a finer geographic scale and data-informed methods to quantify their impact are necessary.

METHODS

Using village development committee (VDC)-level data on TB notification and demography between 2016 and 2017 in four southern districts of Nepal, where ACF activities were implemented as a part of the IMPACT-TB study between 2017 and 2019, we developed VDC-level transmission models of TB and ACF. Using these models and ACF yield data collected in the study, we estimated the potential epidemiological impact of IMPACT-TB ACF and compared its efficiency across VDCs in each district.

RESULTS

Cases were found in the majority of VDCs during IMPACT-TB ACF, but the number of cases detected within VDCs correlated weakly with historic case notification rates. We projected that this ACF intervention would reduce the TB incidence rate by 14% (12-16) in Chitwan, 8.6% (7.3-9.7) in Dhanusha, 8.3% (7.3-9.2) in Mahottari and 3% (2.5-3.2) in Makwanpur. Over the next 10 years, we projected that this intervention would avert 987 (746-1282), 422 (304-571), 598 (450-782) and 197 (172-240) cases in Chitwan, Dhanusha, Mahottari and Makwanpur, respectively. There was substantial variation in the efficiency of ACF across VDCs: there was up to twofold difference in the number of cases averted in the 10 years per case detected.

CONCLUSION

ACF data confirm that TB is widely prevalent, including in VDCs with relatively low reporting rates. Although ACF is a highly efficient component of TB control, its impact can vary substantially at local levels and must be combined with other interventions to alter TB epidemiology significantly.

Highly transmitted M. tuberculosis strains are more likely to evolve MDR/XDR and cause outbreaks, but what makes them highly transmitted?

Multi-Drug-Resistant strains of Mycobacterium tuberculosis (MDR-TB) are a serious obstacle to global TB eradication. While most MDR-TB strains are infrequently transmitted, a few cause large transmission clusters that contribute substantially to local MDR-TB burdens. Here we examine whether the known mutations in these strains can explain their success. Drug resistance mutations differ in fitness costs and strains can also acquire compensatory mutations (CM) to restore fitness, but some highly transmitted MDR strains have no CM. The acquisition of resistance mutations that maintain high transmissibility seems to occur by chance and are more likely in strains that are intrinsically highly transmitted and cause many cases. Modern Beijing lineage strains have caused several large outbreaks, but MDR outbreaks are also caused by ancient Beijing and lineage 4 strains, suggesting the lineage is less important than the characteristics of the individual strain. The development of fluoroquinolone resistance appears to represent another level of selection, in which strains must surmount unknown fitness costs of gyrA mutations. The genetic determinants of high transmission are poorly defined but may involve genes encoding proteins involved in molybdenum acquisition and the Esx systems. In addition, strains eliciting lower cytokine responses and producing more caseating granulomas may have advantages for transmission. Successful MDR/XDR strains generally evolve from highly transmitted drug sensitive parent strains due to selection pressures from deficiencies in local TB control programs. Until TB incidence is considerably reduced, there will likely be highly transmitted strains that develop resistance to any new antibiotic.

The Importance of Heterogeneity to the Epidemiology of Tuberculosis

Although less well-recognized than for other infectious diseases, heterogeneity is a defining feature of tuberculosis (TB) epidemiology. To advance toward TB elimination, this heterogeneity must be better understood and addressed. Drivers of heterogeneity in TB epidemiology act at the level of the infectious host, organism, susceptible host, environment, and distal determinants. These effects may be amplified by social mixing patterns, while the variable latent period between infection and disease may mask heterogeneity in transmission. Reliance on notified cases may lead to misidentification of the most affected groups, as case detection is often poorest where prevalence is highest. Assuming that average rates apply across diverse groups and ignoring the effects of cohort selection may result in misunderstanding of the epidemic and the anticipated effects of control measures. Given this substantial heterogeneity, interventions targeting high-risk groups based on location, social determinants, or comorbidities could improve efficiency, but raise ethical and equity considerations.

Estimating the impact of a novel drug regimen for treatment of tuberculosis: a modeling analysis of projected patient outcomes and epidemiological considerations

BACKGROUND

Regimens that could treat both rifampin-resistant (RR) and rifampin-susceptible tuberculosis (TB) while shortening the treatment duration have reached late-stage clinical trials. Decisions about whether and how to implement such regimens will require an understanding of their likely clinical impact and how this impact depends on local epidemiology and implementation strategy.

METHODS

A Markov state-transition model of 100,000 representative South African adults with TB was used to simulate implementation of the regimen BPaMZ (bedaquiline, pretomanid, moxifloxacin, and pyrazinamide), either for RR-TB only or universally for all patients. Patient outcomes, including cure rates, time with active TB, and time on treatment, were compared to outcomes under current care. Sensitivity analyses varied the drug-resistance epidemiology, rifampin susceptibility testing practices, and regimen efficacy.

RESULTS

Using BPaMZ exclusively for RR-TB increased the proportion of all RR-TB that was cured by initial treatment from 60 ± 1% to 67 ± 1%. Expanding use of BPaMZ to all patients increased cure of RR-TB to 89 ± 1% and cure of all TB from 87.3 ± 0.1% to 89.5 ± 0.1%, while shortening treatment by 1.9 months/person. In sensitivity analyses, reducing the coverage of rifampin susceptibility testing resulted in lower projected proportions of patients cured under all regimen scenarios (current care, RR-only BPaMZ, and universal BPaMZ), compared to the proportions projected using South Africa's high coverage; however, this reduced coverage resulted in greater expected incremental benefits of universal BPaMZ implementation, both when compared to RR-only BPaMZ implementation and when compared to to current care under the same low rifampin susceptibility testing coverage. In settings with higher RR-TB prevalence, the benefits of BPaMZ were magnified both for RR-specific and universal BPaMZ implementation.

CONCLUSIONS

Novel regimens such as BPaMZ could improve RR-TB outcomes and shorten treatment for all patients, particularly with universal use. Decision-makers weighing early options for implementing such regimens at scale will want to consider the expected impact on patient outcomes and on the burden of treatment in their local context.

Population-level mathematical modeling of antimicrobial resistance: a systematic review

BACKGROUND

Mathematical transmission models are increasingly used to guide public health interventions for infectious diseases, particularly in the context of emerging pathogens; however, the contribution of modeling to the growing issue of antimicrobial resistance (AMR) remains unclear. Here, we systematically evaluate publications on population-level transmission models of AMR over a recent period (2006-2016) to gauge the state of research and identify gaps warranting further work.

METHODS

We performed a systematic literature search of relevant databases to identify transmission studies of AMR in viral, bacterial, and parasitic disease systems. We analyzed the temporal, geographic, and subject matter trends, described the predominant medical and behavioral interventions studied, and identified central findings relating to key pathogens.

RESULTS

We identified 273 modeling studies; the majority of which (> 70%) focused on 5 infectious diseases (human immunodeficiency virus (HIV), influenza virus, Plasmodium falciparum (malaria), Mycobacterium tuberculosis (TB), and methicillin-resistant Staphylococcus aureus (MRSA)). AMR studies of influenza and nosocomial pathogens were mainly set in industrialized nations, while HIV, TB, and malaria studies were heavily skewed towards developing countries. The majority of articles focused on AMR exclusively in humans (89%), either in community (58%) or healthcare (27%) settings. Model systems were largely compartmental (76%) and deterministic (66%). Only 43% of models were calibrated against epidemiological data, and few were validated against out-of-sample datasets (14%). The interventions considered were primarily the impact of different drug regimens, hygiene and infection control measures, screening, and diagnostics, while few studies addressed de novo resistance, vaccination strategies, economic, or behavioral changes to reduce antibiotic use in humans and animals.

CONCLUSIONS

The AMR modeling literature concentrates on disease systems where resistance has been long-established, while few studies pro-actively address recent rise in resistance in new pathogens or explore upstream strategies to reduce overall antibiotic consumption. Notable gaps include research on emerging resistance in Enterobacteriaceae and Neisseria gonorrhoeae; AMR transmission at the animal-human interface, particularly in agricultural and veterinary settings; transmission between hospitals and the community; the role of environmental factors in AMR transmission; and the potential of vaccines to combat AMR.

MDR-TB treatment as prevention: The projected population-level impact of expanded treatment for multidrug-resistant tuberculosis

Background

In 2013, approximately 480,000 people developed active multidrug-resistant tuberculosis (MDR-TB), while only 97,000 started MDR-TB treatment. We sought to estimate the impact of improving access to MDR-TB diagnosis and treatment, under multiple diagnostic algorithm and treatment regimen scenarios, on ten-year projections of MDR-TB incidence and mortality.

Methods

We constructed a dynamic transmission model of an MDR-TB epidemic in an illustrative East/Southeast Asian setting. Using approximate Bayesian computation, we investigated a wide array of potential epidemic trajectories consistent with current notification data and known TB epidemiology.

Results

Despite an overall projected decline in TB incidence, data-consistent simulations suggested that MDR-TB incidence is likely to rise between 2015 and 2025 under continued 2013 treatment practices, although with considerable uncertainty (median 17% increase, 95% Uncertainty Range [UR] -38% to +137%). But if, by 2017, all identified active TB patients with previously-treated TB could be tested for drug susceptibility, and 85% of those with MDR-TB could initiate MDR-appropriate treatment, then MDR-TB incidence in 2025 could be reduced by 26% (95% UR 4–52%) relative to projections under continued current practice. Also expanding this drug-susceptibility testing and appropriate MDR-TB treatment to treatment-naïve as well as previously-treated TB cases, by 2020, could reduce MDR-TB incidence in 2025 by 29% (95% UR 6–55%) compared to continued current practice. If this diagnosis and treatment of all MDR-TB in known active TB cases by 2020 could be implemented via a novel second-line regimen with similar effectiveness and tolerability as current first-line therapy, a 54% (95% UR 20–74%) reduction in MDR-TB incidence compared to current-practice projections could be achieved by 2025.

Conclusions

Expansion of diagnosis and treatment of MDR-TB, even using current sub-optimal second-line regimens, is expected to significantly decrease MDR-TB incidence at the population level. Focusing MDR diagnostic efforts on previously-treated cases is an efficient first-step approach.

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

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.

Cost and cost-effectiveness of tuberculosis treatment shortening: a model-based analysis

Background

Despite improvements in treatment success rates for tuberculosis (TB), current six-month regimen duration remains a challenge for many National TB Programmes, health systems, and patients. There is increasing investment in the development of shortened regimens with a number of candidates in phase 3 trials.

Methods

We developed an individual-based decision analytic model to assess the cost-effectiveness of a hypothetical four-month regimen for first-line treatment of TB, assuming non-inferiority to current regimens of six-month duration. The model was populated using extensive, empirically-collected data to estimate the economic impact on both health systems and patients of regimen shortening for first-line TB treatment in South Africa, Brazil, Bangladesh, and Tanzania. We explicitly considered ‘real world’ constraints such as sub-optimal guideline adherence.

Results

From a societal perspective, a shortened regimen, priced at USD1 per day, could be a cost-saving option in South Africa, Brazil, and Tanzania, but would not be cost-effective in Bangladesh when compared to one gross domestic product (GDP) per capita. Incorporating ‘real world’ constraints reduces cost-effectiveness. Patient-incurred costs could be reduced in all settings. From a health service perspective, increased drug costs need to be balanced against decreased delivery costs. The new regimen would remain a cost-effective option, when compared to each countries’ GDP per capita, even if new drugs cost up to USD7.5 and USD53.8 per day in South Africa and Brazil; this threshold was above USD1 in Tanzania and under USD1 in Bangladesh.

Conclusion

Reducing the duration of first-line TB treatment has the potential for substantial economic gains from a patient perspective. The potential economic gains for health services may also be important, but will be context-specific and dependent on the appropriate pricing of any new regimen.

Electronic supplementary material

The online version of this article (doi:10.1186/s12879-016-2064-3) contains supplementary material, which is available to authorized users.

The Distribution of Fitness Costs of Resistance-Conferring Mutations Is a Key Determinant for the Future Burden of Drug-Resistant Tuberculosis: A Model-Based Analysis

BACKGROUND

Drug resistance poses a serious challenge for the control of tuberculosis in many settings. It is well established that the expected future trend in resistance depends on the reproductive fitness of drug-resistant Mycobacterium tuberculosis. However, the variability in fitness between strains with different resistance-conferring mutations has been largely ignored when making these predictions.

METHODS

We developed a novel approach for incorporating the variable fitness costs of drug resistance-conferring mutations and for tracking this distribution of fitness costs over time within a transmission model. We used this approach to describe the effects of realistic fitness cost distributions on the future prevalence of drug-resistant tuberculosis.

RESULTS

The shape of the distribution of fitness costs was a strong predictor of the long-term prevalence of resistance. While, as expected, lower average fitness costs of drug resistance-conferring mutations were associated with more severe epidemics of drug-resistant tuberculosis, fitness distributions with greater variance also led to higher levels of drug resistance. For example, compared to simulations in which the fitness cost of resistance was fixed, introducing a realistic amount of variance resulted in a 40% increase in prevalence of drug-resistant tuberculosis after 20 years.

CONCLUSIONS

The differences in the fitness costs associated with drug resistance-conferring mutations are a key determinant of the future burden of drug-resistant tuberculosis. Future studies that can better establish the range of fitness costs associated with drug resistance-conferring mutations will improve projections and thus facilitate better public health planning efforts.

Burden of transmitted multidrug resistance in epidemics of tuberculosis: a transmission modelling analysis

Background

Multidrug-resistant tuberculosis (MDR-TB) can be acquired through de novo mutation during TB treatment or through transmission from other individuals with active MDR-TB. Understanding the balance between these two mechanisms is essential when allocating resources for MDR-TB.

Methods

We constructed a dynamic transmission model of an MDR-TB epidemic, allowing for both treatment-related acquisition and person-to-person transmission of resistance. We used national TB notification data to inform Bayesian estimates of the fraction of each country’s 2013 MDR-TB incidence that resulted from MDR transmission rather than treatment-related MDR acquisition.

Findings

Global estimates of 3·5% MDR-TB prevalence among new TB notifications and 20·5% among retreatment notifications translate into an estimate that resistance transmission rather than acquisition accounts for a median 96% (95% UR: 68–100%) of all incident MDR-TB, and 61% (16–95%) of incident MDR-TB in previously-treated individuals. The estimated percentage of MDR-TB resulting from transmission varied substantially with different countries’ notification data; for example, we estimated this percentage at 48% (30–75%) of MDR-TB in Bangladesh, versus 99% (91–100%) in Uzbekistan. Estimates were most sensitive to estimates of the transmissibility of MDR strains, the probability of acquiring MDR during tuberculosis treatment, and the responsiveness of MDR TB to first-line treatment.

Interpretation

Notifications of MDR prevalence from most high-burden settings are most consistent with the vast majority of incident MDR-TB resulting from transmission rather than new treatment-related acquisition of resistance. Merely improving the treatment of drug-susceptible TB is unlikely to greatly reduce future MDR-TB incidence. Improved diagnosis and treatment of MDR-TB – including new tests and drug regimens – should be highly prioritized.

Mathematical Modelling and Tuberculosis: Advances in Diagnostics and Novel Therapies

As novel diagnostics, therapies, and algorithms are developed to improve case finding, diagnosis, and clinical management of patients with TB, policymakers must make difficult decisions and choose among multiple new technologies while operating under heavy resource constrained settings. Mathematical modelling can provide helpful insight by describing the types of interventions likely to maximize impact on the population level and highlighting those gaps in our current knowledge that are most important for making such assessments. This review discusses the major contributions of TB transmission models in general, namely, the ability to improve our understanding of the epidemiology of TB. We focus particularly on those elements that are important to appropriately understand the role of TB diagnosis and treatment (i.e., what elements of better diagnosis or treatment are likely to have greatest population-level impact) and yet remain poorly understood at present. It is essential for modellers, decision-makers, and epidemiologists alike to recognize these outstanding gaps in knowledge and understand their potential influence on model projections that may guide critical policy choices (e.g., investment and scale-up decisions).