Dataset Information

Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model.

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.

SUBMITTER: Kendall EA

PROVIDER: S-EPMC5207633 | biostudies-literature | 2017 Jan

REPOSITORIES: biostudies-literature

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Publications

Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model.

Kendall Emily A EA Shrestha Sourya S Cohen Ted T Nuermberger Eric E Dooley Kelly E KE Gonzalez-Angulo Lice L Churchyard Gavin J GJ Nahid Payam P Rich Michael L ML Bansbach Cathy C Forissier Thomas T Lienhardt Christian C Dowdy David W DW

PLoS medicine 20170103 1

<h4>Background</h4>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.<h4>Methods and findings</h4>We developed a dynamic transmission mode ...[more]

PMID: 28045934

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Dataset Information

Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model.

Background

Methods and findings

Conclusions

Publications

Priority-Setting for Novel Drug Regimens to Treat Tuberculosis: An Epidemiologic Model.

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