Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones.
Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) t...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-08-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1008106 |
| _version_ | 1818588145055170560 |
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| author | Fabrizio Clarelli Adam Palmer Bhupender Singh Merete Storflor Silje Lauksund Ted Cohen Sören Abel Pia Abel Zur Wiesch |
| author_facet | Fabrizio Clarelli Adam Palmer Bhupender Singh Merete Storflor Silje Lauksund Ted Cohen Sören Abel Pia Abel Zur Wiesch |
| author_sort | Fabrizio Clarelli |
| collection | DOAJ |
| description | Antibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R2>0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92-94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants. |
| first_indexed | 2024-12-16T09:20:06Z |
| format | Article |
| id | doaj.art-cedf55b060ce4ede83872a81b49ec7a9 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-12-16T09:20:06Z |
| publishDate | 2020-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-cedf55b060ce4ede83872a81b49ec7a92022-12-21T22:36:47ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582020-08-01168e100810610.1371/journal.pcbi.1008106Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones.Fabrizio ClarelliAdam PalmerBhupender SinghMerete StorflorSilje LauksundTed CohenSören AbelPia Abel Zur WieschAntibiotic resistance is rising and we urgently need to gain a better quantitative understanding of how antibiotics act, which in turn would also speed up the development of new antibiotics. Here, we describe a computational model (COMBAT-COmputational Model of Bacterial Antibiotic Target-binding) that can quantitatively predict antibiotic dose-response relationships. Our goal is dual: We address a fundamental biological question and investigate how drug-target binding shapes antibiotic action. We also create a tool that can predict antibiotic efficacy a priori. COMBAT requires measurable biochemical parameters of drug-target interaction and can be directly fitted to time-kill curves. As a proof-of-concept, we first investigate the utility of COMBAT with antibiotics belonging to the widely used quinolone class. COMBAT can predict antibiotic efficacy in clinical isolates for quinolones from drug affinity (R2>0.9). To further challenge our approach, we also do the reverse: estimate the magnitude of changes in drug-target binding based on antibiotic dose-response curves. We overexpress target molecules to infer changes in antibiotic-target binding from changes in antimicrobial efficacy of ciprofloxacin with 92-94% accuracy. To test the generality of our approach, we use the beta-lactam ampicillin to predict target molecule occupancy at MIC from antimicrobial action with 90% accuracy. Finally, we apply COMBAT to predict antibiotic concentrations that can select for resistance due to novel resistance mutations. Using ciprofloxacin and ampicillin as well defined test cases, our work demonstrates that drug-target binding is a major predictor of bacterial responses to antibiotics. This is surprising because antibiotic action involves many additional effects downstream of drug-target binding. In addition, COMBAT provides a framework to inform optimal antibiotic dose levels that maximize efficacy and minimize the rise of resistant mutants.https://doi.org/10.1371/journal.pcbi.1008106 |
| spellingShingle | Fabrizio Clarelli Adam Palmer Bhupender Singh Merete Storflor Silje Lauksund Ted Cohen Sören Abel Pia Abel Zur Wiesch Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. PLoS Computational Biology |
| title | Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. |
| title_full | Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. |
| title_fullStr | Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. |
| title_full_unstemmed | Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. |
| title_short | Drug-target binding quantitatively predicts optimal antibiotic dose levels in quinolones. |
| title_sort | drug target binding quantitatively predicts optimal antibiotic dose levels in quinolones |
| url | https://doi.org/10.1371/journal.pcbi.1008106 |
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