ArticleCellular and molecular bioengineering2017
Applying optimization algorithms to tuberculosis antibiotic treatment regimens.
Article in Cellular and molecular bioengineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- 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
- Incorporating temporal information during feature engineering bolsters emulation of spatio-temporal emergence.Bioinformatics (Oxford, England) · 2024Article
- Mathematical model of oxygen, nutrient, and drug transport in tuberculosis granulomas.PLoS computational biology · 2024Article
- Optimizing tuberculosis treatment efficacy: Comparing the standard regimen with Moxifloxacin-containing regimens.PLoS computational biology · 2023Article
- A framework for multi-scale intervention modeling: virtual cohorts, virtual clinical trials, and model-to-model comparisons.Frontiers in systems biology · 2023Article
- A multi-scale pipeline linking drug transcriptomics with pharmacokinetics predicts in vivo interactions of tuberculosis drugs.Scientific reports · 2021Article
- The Use of Artificial Neural Networks to Forecast the Behavior of Agent-Based Models of Pathophysiology: An Example Utilizing an Agent-Based Model of Sepsis.Frontiers in physiology · 2021Article
- Mathematical model and tool to explore shorter multi-drug therapy options for active pulmonary tuberculosis.PLoS computational biology · 2020Article
- Optimising efficacy of antibiotics against systemic infection by varying dosage quantities and times.PLoS computational biology · 2020Article
- Both Pharmacokinetic Variability and Granuloma Heterogeneity Impact the Ability of the First-Line Antibiotics to Sterilize Tuberculosis Granulomas.Frontiers in pharmacology · 2020Article
- Agent-based models of inflammation in translational systems biology: A decade later.Wiley interdisciplinary reviews. Systems biology and medicine · 2019Review
- Global sensitivity analysis of biological multi-scale models.Current opinion in biomedical engineering · 2019Article
- Emergence and selection of isoniazid and rifampin resistance in tuberculosis granulomas.PloS one · 2018Article
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Authors and funding
4 authors.
Funding
Abstract
introductionTuberculosis (TB), one of the most common infectious diseases, requires treatment with multiple antibiotics taken over at least 6 months. This long treatment often results in poor patient-adherence, which can lead to the emergence of multi-drug resistant TB. New antibiotic treatment strategies are sorely needed. New antibiotics are being developed or repurposed to treat TB, but as there are numerous potential antibiotics, dosing sizes and potential schedules, the regimen design space for new treatments is too large to search exhaustively. Here we propose a method that combines an agent-based multi-scale model capturing TB granuloma formation with algorithms for mathematical optimization to identify optimal TB treatment regimens.
methodsWe define two different single-antibiotic treatments to compare the efficiency and accuracy in predicting optimal treatment regimens of two optimization algorithms: genetic algorithms (GA) and surrogate-assisted optimization through radial basis function (RBF) networks. We also illustrate the use of RBF networks to optimize double-antibiotic treatments.
resultsWe found that while GAs can locate optimal treatment regimens more accurately, RBF networks provide a more practical strategy to TB treatment optimization with fewer simulations, and successfully estimated optimal double-antibiotic treatment regimens.
conclusionsOur results indicate surrogate-assisted optimization can locate optimal TB treatment regimens from a larger set of antibiotics, doses and schedules, and could be applied to solve optimization problems in other areas of research using systems biology approaches. Our findings have important implications for the treatment of diseases like TB that have lengthy protocols or for any disease that requires multiple drugs.
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