Invariant set theory for predicting potential failure of antibiotic cycling
Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fit...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-09-01
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| Series: | Infectious Disease Modelling |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2468042725000272 |
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| author | Alejandro Anderson Matthew W. Kinahan Alejandro H. Gonzalez Klas Udekwu Esteban A. Hernandez-Vargas |
| author_facet | Alejandro Anderson Matthew W. Kinahan Alejandro H. Gonzalez Klas Udekwu Esteban A. Hernandez-Vargas |
| author_sort | Alejandro Anderson |
| collection | DOAJ |
| description | Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fitness trade-offs and adaptive landscapes to predict the evolution of collateral sensitivity pathways, providing insights into optimizing sequential drug therapies.Our approach embeds collateral information into a network of switched systems, allowing us to abstract the effects of sequential antibiotic exposure on antimicrobial resistance. We analyze the system stability at disease-free equilibrium and employ set-control theory to tailor therapeutic windows. Consequently, we propose a computational algorithm to identify effective sequential therapies to counter antibiotic resistance. By leveraging our theory with data on collateral sensivity interactions, we predict scenarios that may prevent bacterial escape for chronic Pseudomonas aeruginosa infections. |
| format | Article |
| id | doaj-art-2c0dbe24c5ea4c8a8bd14cb07b1f6f84 |
| institution | DOAJ |
| issn | 2468-0427 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Infectious Disease Modelling |
| spelling | doaj-art-2c0dbe24c5ea4c8a8bd14cb07b1f6f842025-08-20T03:18:19ZengKeAi Communications Co., Ltd.Infectious Disease Modelling2468-04272025-09-0110389790810.1016/j.idm.2025.04.001Invariant set theory for predicting potential failure of antibiotic cyclingAlejandro Anderson0Matthew W. Kinahan1Alejandro H. Gonzalez2Klas Udekwu3Esteban A. Hernandez-Vargas4Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID, USADepartment of Biological Sciences, Bioinformatics and Computational Biology, University of Idaho, Moscow, ID, USAUniversity of Littoral (UNL), Institute of Technological Development for the Chemical Industry (INTEC) and National Scientific and Technical Research Council (CONICET), Santa Fe, ArgentinaDepartment of Biological Sciences, Bioinformatics and Computational Biology, University of Idaho, Moscow, ID, USADepartment of Mathematics and Statistical Science, University of Idaho, Moscow, ID, USA; Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, 83844–1103, Idaho, USA; Corresponding author. Department of Mathematics and Statistical Science, University of Idaho, Moscow, ID, USA.Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fitness trade-offs and adaptive landscapes to predict the evolution of collateral sensitivity pathways, providing insights into optimizing sequential drug therapies.Our approach embeds collateral information into a network of switched systems, allowing us to abstract the effects of sequential antibiotic exposure on antimicrobial resistance. We analyze the system stability at disease-free equilibrium and employ set-control theory to tailor therapeutic windows. Consequently, we propose a computational algorithm to identify effective sequential therapies to counter antibiotic resistance. By leveraging our theory with data on collateral sensivity interactions, we predict scenarios that may prevent bacterial escape for chronic Pseudomonas aeruginosa infections.http://www.sciencedirect.com/science/article/pii/S2468042725000272Switched systemsControl invariant setsAntibacterial resistanceCollateral sensitivity |
| spellingShingle | Alejandro Anderson Matthew W. Kinahan Alejandro H. Gonzalez Klas Udekwu Esteban A. Hernandez-Vargas Invariant set theory for predicting potential failure of antibiotic cycling Infectious Disease Modelling Switched systems Control invariant sets Antibacterial resistance Collateral sensitivity |
| title | Invariant set theory for predicting potential failure of antibiotic cycling |
| title_full | Invariant set theory for predicting potential failure of antibiotic cycling |
| title_fullStr | Invariant set theory for predicting potential failure of antibiotic cycling |
| title_full_unstemmed | Invariant set theory for predicting potential failure of antibiotic cycling |
| title_short | Invariant set theory for predicting potential failure of antibiotic cycling |
| title_sort | invariant set theory for predicting potential failure of antibiotic cycling |
| topic | Switched systems Control invariant sets Antibacterial resistance Collateral sensitivity |
| url | http://www.sciencedirect.com/science/article/pii/S2468042725000272 |
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