Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections
The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density d...
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description | The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments. |
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The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1005098</identifier><identifier>PMID: 27764095</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Anti-Bacterial Agents - administration & dosage ; Antibacterial agents ; Antibiotics ; Bacteria ; Bacterial infections ; Bacterial Load - drug effects ; Bacterial Load - physiology ; Biology and Life Sciences ; Biophysics ; Clinical outcomes ; Colleges & universities ; Computer Simulation ; Dosage and administration ; Dose-Response Relationship, Drug ; Drug dosages ; Drug Resistance, Bacterial - drug effects ; Drug Resistance, Bacterial - physiology ; Drug therapy ; Enterococcus faecalis ; Enterococcus faecalis - drug effects ; Enterococcus faecalis - physiology ; Enzymes ; Gram-Positive Bacterial Infections - drug therapy ; Gram-Positive Bacterial Infections - microbiology ; Growth rate ; Humans ; Infections ; Mathematical models ; Medicine and Health Sciences ; Microbial Sensitivity Tests - methods ; Models, Biological ; Parameter estimation ; Physical Sciences ; Population density</subject><ispartof>PLoS computational biology, 2016-10, Vol.12 (10), p.e1005098-e1005098</ispartof><rights>COPYRIGHT 2016 Public Library of Science</rights><rights>2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Karslake J, Maltas J, Brumm P, Wood KB (2016) Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections. PLoS Comput Biol 12(10): e1005098. doi:10.1371/journal.pcbi.1005098</rights><rights>2016 Karslake et al 2016 Karslake et al</rights><rights>2016 Public Library of Science. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited: Karslake J, Maltas J, Brumm P, Wood KB (2016) Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections. 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The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. 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drug therapy</subject><subject>Gram-Positive Bacterial Infections - microbiology</subject><subject>Growth rate</subject><subject>Humans</subject><subject>Infections</subject><subject>Mathematical models</subject><subject>Medicine and Health Sciences</subject><subject>Microbial Sensitivity Tests - methods</subject><subject>Models, Biological</subject><subject>Parameter estimation</subject><subject>Physical Sciences</subject><subject>Population density</subject><issn>1553-7358</issn><issn>1553-734X</issn><issn>1553-7358</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>DOA</sourceid><recordid>eNqVk01v1DAQhiMEomXhHyCIxAUOu9jxV3Kp1A8oKxValXK2HH9svUrixXYqeuaP4-ymVYN6APkQa-aZNzOvNVn2GoIFRAx-XLved6JZbGRtFxAAAqrySbYPCUFzhkj59MF9L3sRwhqAdK3o82yvYIxiUJH97PeF2_SNiNZ1-Ynugo23-VenhpAO-YnvV_myu7a13RKiU_mpvUmZSxt0Hl1-4aLuohVNfmRDFLVtBgVn8iuvRWxTLj_vo3RtqjHO50dCRu0HftkZLQfV8DJ7ZkQT9KvxO8t-fP50dfxlfnZ-ujw-PJvL1G2cU6lYATWhpqgQKAWuVJqIsqIwBsNaVlVpFDNSCpwSsFbGFAAwXFKkCdMGzbK3O91N4wIf_QsclskKjEpGErHcEcqJNd942wp_y52wfBtwfsWFj1Y2mgOMCqFqJIUyWElWVxSIAoGakpLWKTHLDsa_9XWrlUxWeNFMRKeZzl7zlbvhBLCCQZoE3o8C3v3sdYi8tUHqphGddv22b4oqWmD4DygiBJQMDCO--wt93IiRWok0q-2MSy3KQZQf4uR4URKEE7V4hEpH6dZK12ljU3xS8GFSkJiof8WV6EPgy--X_8F-m7J4x0rvQvDa3NsMAR-25W5IPmwLH7cllb15-ET3RXfrgf4A1X8Rgg</recordid><startdate>20161001</startdate><enddate>20161001</enddate><creator>Karslake, Jason</creator><creator>Maltas, Jeff</creator><creator>Brumm, Peter</creator><creator>Wood, Kevin B</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ISN</scope><scope>ISR</scope><scope>3V.</scope><scope>7QO</scope><scope>7QP</scope><scope>7TK</scope><scope>7TM</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0002-1596-6866</orcidid></search><sort><creationdate>20161001</creationdate><title>Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections</title><author>Karslake, Jason ; Maltas, Jeff ; Brumm, Peter ; Wood, Kevin B</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c764t-6cd721e56f29308a49d0036722ff41bc998fd7fcca4d001bdff20074863e57ef3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Anti-Bacterial Agents - 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methods</topic><topic>Models, Biological</topic><topic>Parameter estimation</topic><topic>Physical Sciences</topic><topic>Population density</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Karslake, Jason</creatorcontrib><creatorcontrib>Maltas, Jeff</creatorcontrib><creatorcontrib>Brumm, Peter</creatorcontrib><creatorcontrib>Wood, Kevin B</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Calcium & Calcified Tissue Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest One Sustainability</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Karslake, Jason</au><au>Maltas, Jeff</au><au>Brumm, Peter</au><au>Wood, Kevin B</au><au>Gore, Jeff</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections</atitle><jtitle>PLoS computational biology</jtitle><addtitle>PLoS Comput Biol</addtitle><date>2016-10-01</date><risdate>2016</risdate><volume>12</volume><issue>10</issue><spage>e1005098</spage><epage>e1005098</epage><pages>e1005098-e1005098</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><abstract>The inoculum effect (IE) is an increase in the minimum inhibitory concentration (MIC) of an antibiotic as a function of the initial size of a microbial population. The IE has been observed in a wide range of bacteria, implying that antibiotic efficacy may depend on population density. Such density dependence could have dramatic effects on bacterial population dynamics and potential treatment strategies, but explicit measures of per capita growth as a function of density are generally not available. Instead, the IE measures MIC as a function of initial population size, and population density changes by many orders of magnitude on the timescale of the experiment. Therefore, the functional relationship between population density and antibiotic inhibition is generally not known, leaving many questions about the impact of the IE on different treatment strategies unanswered. To address these questions, here we directly measured real-time per capita growth of Enterococcus faecalis populations exposed to antibiotic at fixed population densities using multiplexed computer-automated culture devices. We show that density-dependent growth inhibition is pervasive for commonly used antibiotics, with some drugs showing increased inhibition and others decreased inhibition at high densities. For several drugs, the density dependence is mediated by changes in extracellular pH, a community-level phenomenon not previously linked with the IE. Using a simple mathematical model, we demonstrate how this density dependence can modulate population dynamics in constant drug environments. Then, we illustrate how time-dependent dosing strategies can mitigate the negative effects of density-dependence. Finally, we show that these density effects lead to bistable treatment outcomes for a wide range of antibiotic concentrations in a pharmacological model of antibiotic treatment. As a result, infections exceeding a critical density often survive otherwise effective treatments.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>27764095</pmid><doi>10.1371/journal.pcbi.1005098</doi><orcidid>https://orcid.org/0000-0002-1596-6866</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Anti-Bacterial Agents - administration & dosage Antibacterial agents Antibiotics Bacteria Bacterial infections Bacterial Load - drug effects Bacterial Load - physiology Biology and Life Sciences Biophysics Clinical outcomes Colleges & universities Computer Simulation Dosage and administration Dose-Response Relationship, Drug Drug dosages Drug Resistance, Bacterial - drug effects Drug Resistance, Bacterial - physiology Drug therapy Enterococcus faecalis Enterococcus faecalis - drug effects Enterococcus faecalis - physiology Enzymes Gram-Positive Bacterial Infections - drug therapy Gram-Positive Bacterial Infections - microbiology Growth rate Humans Infections Mathematical models Medicine and Health Sciences Microbial Sensitivity Tests - methods Models, Biological Parameter estimation Physical Sciences Population density |
title | Population Density Modulates Drug Inhibition and Gives Rise to Potential Bistability of Treatment Outcomes for Bacterial Infections |
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