Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design.
Antibiotic resistance is an important public health problem. One potential solution is the development of synergistic antibiotic combinations, in which the combination is more effective than the component drugs. However, experimental progress in this direction is severely limited by the number of sa...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-07-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1010311 |
| _version_ | 1798039543684792320 |
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| author | Jennifer Brennan Lalit Jain Sofia Garman Ann E Donnelly Erik Scott Wright Kevin Jamieson |
| author_facet | Jennifer Brennan Lalit Jain Sofia Garman Ann E Donnelly Erik Scott Wright Kevin Jamieson |
| author_sort | Jennifer Brennan |
| collection | DOAJ |
| description | Antibiotic resistance is an important public health problem. One potential solution is the development of synergistic antibiotic combinations, in which the combination is more effective than the component drugs. However, experimental progress in this direction is severely limited by the number of samples required to exhaustively test for synergy, which grows exponentially with the number of drugs combined. We introduce a new metric for antibiotic synergy, motivated by the popular Fractional Inhibitory Concentration Index and the Highest Single Agent model. We also propose a new experimental design that samples along all appropriately normalized diagonals in concentration space, and prove that this design identifies all synergies among a set of drugs while only sampling a small fraction of the possible combinations. We applied our method to screen two- through eight-way combinations of eight antibiotics at 10 concentrations each, which requires sampling only 2,560 unique combinations of antibiotic concentrations. |
| first_indexed | 2024-04-11T21:55:20Z |
| format | Article |
| id | doaj.art-ea7137edadf649d484822793f5c4a527 |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-11T21:55:20Z |
| publishDate | 2022-07-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-ea7137edadf649d484822793f5c4a5272022-12-22T04:01:07ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582022-07-01187e101031110.1371/journal.pcbi.1010311Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design.Jennifer BrennanLalit JainSofia GarmanAnn E DonnellyErik Scott WrightKevin JamiesonAntibiotic resistance is an important public health problem. One potential solution is the development of synergistic antibiotic combinations, in which the combination is more effective than the component drugs. However, experimental progress in this direction is severely limited by the number of samples required to exhaustively test for synergy, which grows exponentially with the number of drugs combined. We introduce a new metric for antibiotic synergy, motivated by the popular Fractional Inhibitory Concentration Index and the Highest Single Agent model. We also propose a new experimental design that samples along all appropriately normalized diagonals in concentration space, and prove that this design identifies all synergies among a set of drugs while only sampling a small fraction of the possible combinations. We applied our method to screen two- through eight-way combinations of eight antibiotics at 10 concentrations each, which requires sampling only 2,560 unique combinations of antibiotic concentrations.https://doi.org/10.1371/journal.pcbi.1010311 |
| spellingShingle | Jennifer Brennan Lalit Jain Sofia Garman Ann E Donnelly Erik Scott Wright Kevin Jamieson Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. PLoS Computational Biology |
| title | Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. |
| title_full | Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. |
| title_fullStr | Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. |
| title_full_unstemmed | Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. |
| title_short | Sample-efficient identification of high-dimensional antibiotic synergy with a normalized diagonal sampling design. |
| title_sort | sample efficient identification of high dimensional antibiotic synergy with a normalized diagonal sampling design |
| url | https://doi.org/10.1371/journal.pcbi.1010311 |
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