ArticleCell systems2021
Systematic measurement of combination-drug landscapes to predict in vivo treatment outcomes for tuberculosis.
Article in Cell systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers.
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Who cites it
53 citing papers in PubMed.
- TACTIC: A transfer learning framework to predict drug interactions in emerging pathogens.Cell reports methods · 2026Article
- Systems biology framework for the rational design of operational conditions for in vitro/in vivo translation of tissue models.Science advances · 2026Article
- Strain diversity drives heterogeneous responses to tuberculosis combination therapy.Antimicrobial agents and chemotherapy · 2026Article
- The mycobacterial ATP burst is a lysis artifact and serves as an assay for drug-induced cell wall damage.Cell reports · 2026Article
- Article
- Antibiotic dose-response curves can measure antibiotic activity againstAntimicrobial agents and chemotherapy · 2026Article
- Polypharmacology is an enduring and nearly universal property of kinase inhibitors.bioRxiv : the preprint server for biology · 2026Article
- Large-scale testing of antimicrobial lethality at single-cell resolution predicts mycobacterial infection outcomes.Nature microbiology · 2026Article
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- A Metabolism-Informed Neural Network Identifies Pathways Influencing the Potency and Toxicity of Antimicrobial Combinations.npj drug discovery · 2026Article
- Methodology for a freshly engineered or cryo-preserved 3D tuberculoma bioplatform for studying tuberculosis biology and high-content screening of therapeutics.Frontiers in immunology · 2026Article
- Article
- Antibiotic dose-response curves can measure antibiotic activity againstbioRxiv : the preprint server for biology · 2025Article
- RNA vaccines: The dawn of a new age for tuberculosis?Human vaccines & immunotherapeutics · 2025Review
- Strain diversity drives heterogeneous responses to tuberculosis combination therapy.bioRxiv : the preprint server for biology · 2025Article
- A novel machine-learning based optimization: identifying new treatment regimens for tuberculosis.Numerical algebra, control and optimization · 2025Article
- A transcriptional plasticity-aware framework for RNA-seq differential expression analysis.Briefings in bioinformatics · 2025Article
- Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden.Journal of theoretical biology · 2025Article
- Integration of multi-modal measurements identifies critical mechanisms of tuberculosis drug action.Cell systems · 2025Article
- Article
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7 authors.
Funding
Abstract
Lengthy multidrug chemotherapy is required to achieve a durable cure in tuberculosis. However, we lack well-validated, high-throughput in vitro models that predict animal outcomes. Here, we provide an extensible approach to rationally prioritize combination therapies for testing in in vivo mouse models of tuberculosis. We systematically measured Mycobacterium tuberculosis response to all two- and three-drug combinations among ten antibiotics in eight conditions that reproduce lesion microenvironments, resulting in >500,000 measurements. Using these in vitro data, we developed classifiers predictive of multidrug treatment outcome in a mouse model of disease relapse and identified ensembles of in vitro models that best describe in vivo treatment outcomes. We identified signatures of potencies and drug interactions in specific in vitro models that distinguish whether drug combinations are better than the standard of care in two important preclinical mouse models. Our framework is generalizable to other difficult-to-treat diseases requiring combination therapies. A record of this paper's transparent peer review process is included in the supplemental information.
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