ArticleScientific reports2021
A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 25 citations in OpenAlex.
- A Comparison of Mechanisms Driving Lesion Outcomes During Lung Tumor and Tuberculosis Granuloma Formation.Bulletin of mathematical biology · 2026Article
- Article
- The eight pillars of within-host tuberculosis modelling.Frontiers in immunology · 2026Review
- A novel machine-learning based optimization: identifying new treatment regimens for tuberculosis.Numerical algebra, control and optimization · 2025Article
- Rankings of tuberculosis antibiotic treatment regimens are sensitive to spatial scale, detection limit, and initial host bacterial burden.Journal of theoretical biology · 2025Article
- GEODE: anFrontiers in pharmacology · 2025Article
- A systematic efficacy analysis of tuberculosis treatment with BPaL-containing regimens using a multiscale modeling approach.CPT: pharmacometrics & systems pharmacology · 2024Article
- Integrative analysis of multimodal patient data identifies personalized predictors of tuberculosis treatment prognosis.iScience · 2024Article
- Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens.PLoS computational biology · 2023Article
- Advances in the design of combination therapies for the treatment of tuberculosis.Expert opinion on drug discovery · 2023Review
- Integrative analysis of clinical health records, imaging and pathogen genomics identifies personalized predictors of disease prognosis in tuberculosis.medRxiv : the preprint server for health sciences · 2022Article
- A flux-based machine learning model to simulate the impact of pathogen metabolic heterogeneity on drug interactions.PNAS nexus · 2022Article
- Tools to develop antibiotic combinations that target drug tolerance inFrontiers in cellular and infection microbiology · 2022Review
- Advancing therapies for viral infections using mechanistic computational models of the dynamic interplay between the virus and host immune response.Current opinion in virology · 2021Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Tuberculosis (TB) is the deadliest infectious disease worldwide. The design of new treatments for TB is hindered by the large number of candidate drugs, drug combinations, dosing choices, and complex pharmaco-kinetics/dynamics (PK/PD). Here we study the interplay of these factors in designing combination therapies by linking a machine-learning model, INDIGO-MTB, which predicts in vitro drug interactions using drug transcriptomics, with a multi-scale model of drug PK/PD and pathogen-immune interactions called GranSim. We calculate an in vivo drug interaction score (iDIS) from dynamics of drug diffusion, spatial distribution, and activity within lesions against various pathogen sub-populations. The iDIS of drug regimens evaluated against non-replicating bacteria significantly correlates with efficacy metrics from clinical trials. Our approach identifies mechanisms that can amplify synergistic or mitigate antagonistic drug interactions in vivo by modulating the relative distribution of drugs. Our mechanistic framework enables efficient evaluation of in vivo drug interactions and optimization of combination therapies.
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