ArticleDrug discovery today. Disease models2015
Article in Drug discovery today. Disease models, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Experimental dissection of tuberculosis protective immunity: a human perspective.Frontiers in cellular and infection microbiology · 2025Review
- Characterizing the immune response toFrontiers in immunology · 2024Review
- A computational model tracks whole-lung Mycobacterium tuberculosis infection and predicts factors that inhibit dissemination.PLoS computational biology · 2020Article
- Advanced model systems and tools for basic and translational human immunology.Genome medicine · 2018Review
- Integrating Lung Physiology, Immunology, and Tuberculosis.Trends in microbiology · 2017Review
- A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment.Current opinion in systems biology · 2017Article
- In-host modeling.Infectious Disease Modelling · 2017Article
- A multi-scale approach to designing therapeutics for tuberculosis.Integrative biology : quantitative biosciences from nano to macro · 2015Article
- Strategies for efficient numerical implementation of hybrid multi-scale agent-based models to describe biological systems.Cellular and molecular bioengineering · 2015Article
- A computational tool integrating host immunity with antibiotic dynamics to study tuberculosis treatment.Journal of theoretical biology · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Tuberculosis (TB) is a global health problem responsible for ~2 million deaths per year. Current antibiotic treatments are lengthy and fraught with compliance and resistance issues. There is a crucial need for additional approaches to provide a cost-effective means of exploring the design space for potential therapies. We discuss the use of mathematical and computational models in virtual experiments and virtual clinical trials both to develop new hypotheses regarding the disease and to provide a cost-effective means of discovering new treatment strategies.
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.