ArticlePloS one2026
Identifying optimal combination regimens for therapy of Mycobacterium tuberculosis with an algorithmic approach: prospective predictions and validations.
Article in PloS one, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMycobacterium tuberculosis resistance to standard-of-care agents is increasing. It is imperative to identify new combinations that increase the rate and depth of bacterial kill, shorten therapy and also suppress resistance. There has been little prior effort to identify combination regimens that employ new or repurposed drugs in a rational way. METHODS AND
findingsOur group developed a pathway to combine agents to achieve this end. This pathway starts with standard baseline evaluations (e.g., MIC), leverages information from in vitro assessments (hollow fiber infection model), then analyzes 2-agent combinations in a 96 well quantitative culture checkerboard format (Greco URSA model with simulation). Finally, development of a high dimensional mathematical model allowed evaluation of 2- and 3-drug regimens in multiple metabolic states to draw inferences regarding combination therapies. We prospectively evaluated these regimens in animal models. We showed that a prospectively chosen regimen of pretomanid, moxifloxacin plus bedaquiline performed as predicted. In the BALB/c murine model, this regimen produced sterilization in a cohort that was held for 12 weeks after therapy cessation, as it did in the C3HeB/FeJ ("Kramnik") murine model. Finally, this and other regimens were evaluated in a cynomolgus macaque model. The decrement of the 18F-deoxyglucose signal in Positron emission tomography (PET)- computed tomography (CT) evaluations was best with this regimen. Other endpoints such as necropsy score and colony counts in lung and lymph nodes also demonstrated that this regimen behaved as predicted from our pathway/algorithm.
conclusionsWe conclude that this provides a way forward for the future to identify the most promising regimens to shorten therapy for tuberculosis and suppress emergence of resistance.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.