Abstract
Background
Novel drug regimens are needed for tuberculosis (TB) treatment. New regimens aim to improve on characteristics such as duration, efficacy, and safety profile, but no single regimen is likely to be ideal in all respects. By linking these regimen characteristics to a novel regimen’s ability to reduce TB incidence and mortality, we sought to prioritize regimen characteristics from a population-level perspective.
Methods and Findings
We developed a dynamic transmission model of multi-strain TB epidemics in hypothetical populations reflective of the epidemiological situations in India (primary analysis), South Africa, the Philippines, and Brazil. We modeled the introduction of various novel rifampicin-susceptible (RS) or rifampicin-resistant (RR) TB regimens that differed on six characteristics, identified in consultation with a team of global experts: (1) efficacy, (2) duration, (3) ease of adherence, (4) medical contraindications, (5) barrier to resistance, and (6) baseline prevalence of resistance to the novel regimen. We compared scale-up of these regimens to a baseline reflective of continued standard of care.
For our primary analysis situated in India, our model generated baseline TB incidence and mortality of 157 (95% uncertainty range [UR]: 113–187) and 16 (95% UR: 9–23) per 100,000 per year at the time of novel regimen introduction and RR TB incidence and mortality of 6 (95% UR: 4–10) and 0.6 (95% UR: 0.3–1.1) per 100,000 per year. An optimal RS TB regimen was projected to reduce 10-y TB incidence and mortality in the India-like scenario by 12% (95% UR: 6%–20%) and 11% (95% UR: 6%–20%), respectively, compared to current-care projections. An optimal RR TB regimen reduced RR TB incidence by an estimated 32% (95% UR: 18%–46%) and RR TB mortality by 30% (95% UR: 18%–44%). Efficacy was the greatest determinant of impact; compared to a novel regimen meeting all minimal targets only, increasing RS TB treatment efficacy from 94% to 99% reduced TB mortality by 6% (95% UR: 1%–13%, half the impact of a fully optimized regimen), and increasing the efficacy against RR TB from 76% to 94% lowered RR TB mortality by 13% (95% UR: 6%–23%). Reducing treatment duration or improving ease of adherence had smaller but still substantial impact: shortening RS TB treatment duration from 6 to 2 mo lowered TB mortality by 3% (95% UR: 1%–6%), and shortening RR TB treatment from 20 to 6 mo reduced RR TB mortality by 8% (95% UR: 4%–13%), while reducing nonadherence to the corresponding regimens by 50% reduced TB and RR TB mortality by 2% (95% UR: 1%–4%) and 6% (95% UR: 3%–10%), respectively. Limitations include sparse data on key model parameters and necessary simplifications to model structure and outcomes.
Conclusions
In designing clinical trials of novel TB regimens, investigators should consider that even small changes in treatment efficacy may have considerable impact on TB-related incidence and mortality. Other regimen improvements may still have important benefits for resource allocation and outcomes such as patient quality of life.
Author Summary
Why Was This Study Done?
• Improvements in tuberculosis (TB) treatment are expected to play an important role in reaching the WHO goal of reducing TB deaths by 95%—that is, by more than 1.3 million deaths per year—between 2015 and 2035.
• Multiple aspects of existing treatment regimens have room for improvement, but it is unclear which types of improvement are most important.
• This study sought to prioritize different features of TB treatment regimens based on their potential to prevent TB deaths and new TB cases.
What Did the Researchers Do and Find?
• The researchers developed a mathematical model to simulate the introduction of a novel regimen for the treatment of either drug-susceptible or multidrug-resistant (MDR) TB.
• They then compared regimens with different characteristics in terms of their ability to reduce TB deaths and new TB cases.
• Of the six characteristics modeled, regimen efficacy was the characteristic with the greatest potential to reduce TB cases and deaths, but other characteristics also had important effects (for example, shorter regimens could save health care resources).
What Do These Findings Mean?
• It is important for developers of novel regimens to at least maintain the efficacy of existing regimens, and improvements in efficacy could prevent a large number of deaths.
• Other improvements, such as shorter duration and increased ease of adherence, may still have important effects by enabling more people with TB to receive appropriate and timely treatment.
Citation: Kendall EA, Shrestha S, Cohen T, Nuermberger E, Dooley KE, Gonzalez-Angulo L, et al. (2017) Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model. PLoS Med 14(1): e1002202. doi:10.1371/journal.pmed.1002202
Academic Editor: Katharina Kranzer, London School of Hygiene and Tropical Medicine, UNITED KINGDOM
Received: June 1, 2016; Accepted: November 16, 2016; Published: January 3, 2017
Copyright: © 2017 Kendall et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All data used are available from the published sources cited in this manuscript.
Funding: This work was supported by the National Institutes of Health 5T32-AI007291-25 (www.nih.gov) and by the Bill and Melinda Gates Foundation work order 10 (www.gatesfoundation.org). The NIH had no role in study design, data collection and analysis, or decision to publish. TF and CB are employees of the Bill and Melinda Gates Foundation and contributed as co-authors to framing of the study and review of the manuscript prior to publication; TF and CB did not have any role in the decision to fund this grant.
Competing interests: This work was supported by the National Institutes of Health 5T32-AI007291-25 (www.nih.gov) and by the Bill and Melinda Gates Foundation work order 10 (www.gatesfoundation.org). The NIH had no role in study design, data collection and analysis, or decision to publish. TF and CB are employees of the Bill and Melinda Gates Foundation and contributed as coauthors to framing of the study and review of the manuscript prior to publication; TF and CB did not have any role in the decision to fund this grant.
Abbreviations: DST, drug-susceptibility testing; LHS, Latin Hypercube Sampling; MDR, multidrug-resistant; PRCCs, partial rank correlation coefficients; RR, rifampicin-resistant; RS, rifampicin-susceptible; TB, tuberculosis; UR, uncertainty range
