Antimicrobial pharmacodynamics: critical interactions of 'bug and drug'

Key Points Antimicrobial pharmacodynamics attempts to link measures of drug exposure to the observed effect. This differs from other areas of pharmacodynamics because the main indicator of effect and the site of action of the drug is the organism that causes the pathological process. Understanding a...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Nature reviews. Microbiology 2004-04, Vol.2 (4), p.289-300
1. Verfasser: Drusano, George L
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page 300
container_issue 4
container_start_page 289
container_title Nature reviews. Microbiology
container_volume 2
creator Drusano, George L
description Key Points Antimicrobial pharmacodynamics attempts to link measures of drug exposure to the observed effect. This differs from other areas of pharmacodynamics because the main indicator of effect and the site of action of the drug is the organism that causes the pathological process. Understanding antimicrobial pharmacodynamics requires the acceptance of four important ideas. First, the drug exposure achieved with a fixed drug dose varies greatly in the infected population of interest. Second, the shape of the concentration–time curve can sometimes affect the outcome. Third, only non-protein-bound drug is microbiologically active. Finally, as the measure of potency increases, the effect any fixed drug dose will cause decreases. All these ideas can be integrated by use of the Monte Carlo simulation to determine the potential use of a drug and dose for the intended population and to estimate susceptibility breakpoints. These techniques can also be used to help suppress the amplification of resistant subpopulations by identifying the drug exposure that will cause this effect and then evaluating the use of different doses for attaining the exposure target in the population of interest. These ideas can be transferred to the clinical arena. The use of optimal sampling techniques allows informative times for blood sample acquisition to be identified. Population modelling followed by Bayesian estimation allows robust estimation of the exposure achieved in a specific patient. Exposure measures relative to the MIC of the pathogen (peak/MIC ratio, AUC/MIC ratio and time > MIC) can then be linked to the desired clinical or microbiological outcome through common statistical techniques, such as logistic regression analysis, classification and regression tree (CART) analysis and Cox proportional hazards modelling. Antimicrobial pharmacodynamics is the discipline that integrates microbiology and pharmacology, with the aim of linking a measure of drug exposure, relative to a measure of drug potency for the pathogen in question, to the microbiological or clinical effect achieved. The delineation of such relationships allows the drug dose to be chosen in a rational manner, so that the desired effect (for example, the maximal bactericidal effect) can be achieved in a large proportion of the intended patient population. Ultimately, the goal of any anti-infective therapy is to administer a dose of drug that has an acceptably high probability of achieving the desired therapeutic
doi_str_mv 10.1038/nrmicro862
format Article
fullrecord <record><control><sourceid>gale_proqu</sourceid><recordid>TN_cdi_proquest_miscellaneous_71756748</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><galeid>A188965879</galeid><sourcerecordid>A188965879</sourcerecordid><originalsourceid>FETCH-LOGICAL-c507t-c0585a4266398cc8924afdf8929e759f0d1e4f49e53a912bf351c218ad6da48f3</originalsourceid><addsrcrecordid>eNqFkclKBDEQhoMo7hcfQBoPCspoks7qbRA3ELzouclkGSPdyZh0H3x7oz04IoLkUKHq-4uq-gE4QPAcwVpchNR5naJgeA1sI07gBNGarH__MdsCOzm_Qogp5XgTbCEKa8Sx2Aa309D7L_nMq7ZavKjUKR3Ne1Almy8rnXzvdSn50NukdO9jyFV01clsmFcqmMqkYX6yBzacarPdX8Zd8Hxz_XR1N3l4vL2_mj5MNIW8n2hIBVUEM1ZLobWQmChnXInSciodNMgSR6SltZIIz1xNkcZIKMOMIsLVu-B47LtI8W2wuW86n7VtWxVsHHLDEaeME_EviLgUDGJSwKNf4GscUihLNBgTVgvMZYHOR2iuWtv44GJfblGeseVMMVjnS36KhJCMii_B6Sgol805Wdcsku9Uem8QbD5da1auFfhwOcIw66xZoUubCnA2ArmUwtym1Yx_tqtGOqh-SPa73Q_kA3z5rWg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>224638279</pqid></control><display><type>article</type><title>Antimicrobial pharmacodynamics: critical interactions of 'bug and drug'</title><source>MEDLINE</source><source>Nature Journals Online</source><source>SpringerLink Journals - AutoHoldings</source><creator>Drusano, George L</creator><creatorcontrib>Drusano, George L</creatorcontrib><description>Key Points Antimicrobial pharmacodynamics attempts to link measures of drug exposure to the observed effect. This differs from other areas of pharmacodynamics because the main indicator of effect and the site of action of the drug is the organism that causes the pathological process. Understanding antimicrobial pharmacodynamics requires the acceptance of four important ideas. First, the drug exposure achieved with a fixed drug dose varies greatly in the infected population of interest. Second, the shape of the concentration–time curve can sometimes affect the outcome. Third, only non-protein-bound drug is microbiologically active. Finally, as the measure of potency increases, the effect any fixed drug dose will cause decreases. All these ideas can be integrated by use of the Monte Carlo simulation to determine the potential use of a drug and dose for the intended population and to estimate susceptibility breakpoints. These techniques can also be used to help suppress the amplification of resistant subpopulations by identifying the drug exposure that will cause this effect and then evaluating the use of different doses for attaining the exposure target in the population of interest. These ideas can be transferred to the clinical arena. The use of optimal sampling techniques allows informative times for blood sample acquisition to be identified. Population modelling followed by Bayesian estimation allows robust estimation of the exposure achieved in a specific patient. Exposure measures relative to the MIC of the pathogen (peak/MIC ratio, AUC/MIC ratio and time &gt; MIC) can then be linked to the desired clinical or microbiological outcome through common statistical techniques, such as logistic regression analysis, classification and regression tree (CART) analysis and Cox proportional hazards modelling. Antimicrobial pharmacodynamics is the discipline that integrates microbiology and pharmacology, with the aim of linking a measure of drug exposure, relative to a measure of drug potency for the pathogen in question, to the microbiological or clinical effect achieved. The delineation of such relationships allows the drug dose to be chosen in a rational manner, so that the desired effect (for example, the maximal bactericidal effect) can be achieved in a large proportion of the intended patient population. Ultimately, the goal of any anti-infective therapy is to administer a dose of drug that has an acceptably high probability of achieving the desired therapeutic effect balanced with an acceptably low probability of toxicity. Appropriate use of the latest pharmacodynamic modelling approaches can minimize the emergence of resistance and optimize the outcome for patients.</description><identifier>ISSN: 1740-1526</identifier><identifier>EISSN: 1740-1534</identifier><identifier>DOI: 10.1038/nrmicro862</identifier><identifier>PMID: 15031728</identifier><language>eng</language><publisher>London: Nature Publishing Group UK</publisher><subject>Anti-Bacterial Agents - pharmacokinetics ; Anti-Bacterial Agents - pharmacology ; Area Under Curve ; Bacterial Infections - drug therapy ; Bacterial Infections - metabolism ; Biological Availability ; Biomedical and Life Sciences ; Dose-Response Relationship, Drug ; Drug dosages ; Drug Resistance, Bacterial ; Humans ; Infections ; Infectious Diseases ; Life Sciences ; Medical Microbiology ; Microbial Sensitivity Tests ; Microbiology ; Parasitology ; Pathogens ; Patients ; Penicillin ; Pharmacodynamics ; Pharmacology ; Physiology ; review-article ; Virology</subject><ispartof>Nature reviews. Microbiology, 2004-04, Vol.2 (4), p.289-300</ispartof><rights>Springer Nature Limited 2004</rights><rights>COPYRIGHT 2004 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Apr 2004</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c507t-c0585a4266398cc8924afdf8929e759f0d1e4f49e53a912bf351c218ad6da48f3</citedby><cites>FETCH-LOGICAL-c507t-c0585a4266398cc8924afdf8929e759f0d1e4f49e53a912bf351c218ad6da48f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/nrmicro862$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/nrmicro862$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,2727,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/15031728$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Drusano, George L</creatorcontrib><title>Antimicrobial pharmacodynamics: critical interactions of 'bug and drug'</title><title>Nature reviews. Microbiology</title><addtitle>Nat Rev Microbiol</addtitle><addtitle>Nat Rev Microbiol</addtitle><description>Key Points Antimicrobial pharmacodynamics attempts to link measures of drug exposure to the observed effect. This differs from other areas of pharmacodynamics because the main indicator of effect and the site of action of the drug is the organism that causes the pathological process. Understanding antimicrobial pharmacodynamics requires the acceptance of four important ideas. First, the drug exposure achieved with a fixed drug dose varies greatly in the infected population of interest. Second, the shape of the concentration–time curve can sometimes affect the outcome. Third, only non-protein-bound drug is microbiologically active. Finally, as the measure of potency increases, the effect any fixed drug dose will cause decreases. All these ideas can be integrated by use of the Monte Carlo simulation to determine the potential use of a drug and dose for the intended population and to estimate susceptibility breakpoints. These techniques can also be used to help suppress the amplification of resistant subpopulations by identifying the drug exposure that will cause this effect and then evaluating the use of different doses for attaining the exposure target in the population of interest. These ideas can be transferred to the clinical arena. The use of optimal sampling techniques allows informative times for blood sample acquisition to be identified. Population modelling followed by Bayesian estimation allows robust estimation of the exposure achieved in a specific patient. Exposure measures relative to the MIC of the pathogen (peak/MIC ratio, AUC/MIC ratio and time &gt; MIC) can then be linked to the desired clinical or microbiological outcome through common statistical techniques, such as logistic regression analysis, classification and regression tree (CART) analysis and Cox proportional hazards modelling. Antimicrobial pharmacodynamics is the discipline that integrates microbiology and pharmacology, with the aim of linking a measure of drug exposure, relative to a measure of drug potency for the pathogen in question, to the microbiological or clinical effect achieved. The delineation of such relationships allows the drug dose to be chosen in a rational manner, so that the desired effect (for example, the maximal bactericidal effect) can be achieved in a large proportion of the intended patient population. Ultimately, the goal of any anti-infective therapy is to administer a dose of drug that has an acceptably high probability of achieving the desired therapeutic effect balanced with an acceptably low probability of toxicity. Appropriate use of the latest pharmacodynamic modelling approaches can minimize the emergence of resistance and optimize the outcome for patients.</description><subject>Anti-Bacterial Agents - pharmacokinetics</subject><subject>Anti-Bacterial Agents - pharmacology</subject><subject>Area Under Curve</subject><subject>Bacterial Infections - drug therapy</subject><subject>Bacterial Infections - metabolism</subject><subject>Biological Availability</subject><subject>Biomedical and Life Sciences</subject><subject>Dose-Response Relationship, Drug</subject><subject>Drug dosages</subject><subject>Drug Resistance, Bacterial</subject><subject>Humans</subject><subject>Infections</subject><subject>Infectious Diseases</subject><subject>Life Sciences</subject><subject>Medical Microbiology</subject><subject>Microbial Sensitivity Tests</subject><subject>Microbiology</subject><subject>Parasitology</subject><subject>Pathogens</subject><subject>Patients</subject><subject>Penicillin</subject><subject>Pharmacodynamics</subject><subject>Pharmacology</subject><subject>Physiology</subject><subject>review-article</subject><subject>Virology</subject><issn>1740-1526</issn><issn>1740-1534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2004</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><recordid>eNqFkclKBDEQhoMo7hcfQBoPCspoks7qbRA3ELzouclkGSPdyZh0H3x7oz04IoLkUKHq-4uq-gE4QPAcwVpchNR5naJgeA1sI07gBNGarH__MdsCOzm_Qogp5XgTbCEKa8Sx2Aa309D7L_nMq7ZavKjUKR3Ne1Almy8rnXzvdSn50NukdO9jyFV01clsmFcqmMqkYX6yBzacarPdX8Zd8Hxz_XR1N3l4vL2_mj5MNIW8n2hIBVUEM1ZLobWQmChnXInSciodNMgSR6SltZIIz1xNkcZIKMOMIsLVu-B47LtI8W2wuW86n7VtWxVsHHLDEaeME_EviLgUDGJSwKNf4GscUihLNBgTVgvMZYHOR2iuWtv44GJfblGeseVMMVjnS36KhJCMii_B6Sgol805Wdcsku9Uem8QbD5da1auFfhwOcIw66xZoUubCnA2ArmUwtym1Yx_tqtGOqh-SPa73Q_kA3z5rWg</recordid><startdate>200404</startdate><enddate>200404</enddate><creator>Drusano, George L</creator><general>Nature Publishing Group UK</general><general>Nature Publishing Group</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>3V.</scope><scope>7QL</scope><scope>7RV</scope><scope>7U9</scope><scope>7X7</scope><scope>7XB</scope><scope>88A</scope><scope>88E</scope><scope>88I</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AEUYN</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>BKSAR</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0S</scope><scope>M1P</scope><scope>M2P</scope><scope>M7N</scope><scope>M7P</scope><scope>NAPCQ</scope><scope>P64</scope><scope>PCBAR</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>RC3</scope><scope>7T7</scope><scope>7X8</scope></search><sort><creationdate>200404</creationdate><title>Antimicrobial pharmacodynamics: critical interactions of 'bug and drug'</title><author>Drusano, George L</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c507t-c0585a4266398cc8924afdf8929e759f0d1e4f49e53a912bf351c218ad6da48f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2004</creationdate><topic>Anti-Bacterial Agents - pharmacokinetics</topic><topic>Anti-Bacterial Agents - pharmacology</topic><topic>Area Under Curve</topic><topic>Bacterial Infections - drug therapy</topic><topic>Bacterial Infections - metabolism</topic><topic>Biological Availability</topic><topic>Biomedical and Life Sciences</topic><topic>Dose-Response Relationship, Drug</topic><topic>Drug dosages</topic><topic>Drug Resistance, Bacterial</topic><topic>Humans</topic><topic>Infections</topic><topic>Infectious Diseases</topic><topic>Life Sciences</topic><topic>Medical Microbiology</topic><topic>Microbial Sensitivity Tests</topic><topic>Microbiology</topic><topic>Parasitology</topic><topic>Pathogens</topic><topic>Patients</topic><topic>Penicillin</topic><topic>Pharmacodynamics</topic><topic>Pharmacology</topic><topic>Physiology</topic><topic>review-article</topic><topic>Virology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Drusano, George L</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Nursing &amp; Allied Health Database</collection><collection>Virology and AIDS Abstracts</collection><collection>Health &amp; Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Biology Database (Alumni Edition)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech 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>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Earth, Atmospheric &amp; Aquatic Science Collection</collection><collection>Environmental Sciences and Pollution Management</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>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health &amp; Medical Complete (Alumni)</collection><collection>Nursing &amp; Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Health &amp; Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Science Database</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biological Science Database</collection><collection>Nursing &amp; Allied Health Premium</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Earth, Atmospheric &amp; Aquatic Science 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 Basic</collection><collection>Genetics Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>MEDLINE - Academic</collection><jtitle>Nature reviews. Microbiology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Drusano, George L</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Antimicrobial pharmacodynamics: critical interactions of 'bug and drug'</atitle><jtitle>Nature reviews. Microbiology</jtitle><stitle>Nat Rev Microbiol</stitle><addtitle>Nat Rev Microbiol</addtitle><date>2004-04</date><risdate>2004</risdate><volume>2</volume><issue>4</issue><spage>289</spage><epage>300</epage><pages>289-300</pages><issn>1740-1526</issn><eissn>1740-1534</eissn><abstract>Key Points Antimicrobial pharmacodynamics attempts to link measures of drug exposure to the observed effect. This differs from other areas of pharmacodynamics because the main indicator of effect and the site of action of the drug is the organism that causes the pathological process. Understanding antimicrobial pharmacodynamics requires the acceptance of four important ideas. First, the drug exposure achieved with a fixed drug dose varies greatly in the infected population of interest. Second, the shape of the concentration–time curve can sometimes affect the outcome. Third, only non-protein-bound drug is microbiologically active. Finally, as the measure of potency increases, the effect any fixed drug dose will cause decreases. All these ideas can be integrated by use of the Monte Carlo simulation to determine the potential use of a drug and dose for the intended population and to estimate susceptibility breakpoints. These techniques can also be used to help suppress the amplification of resistant subpopulations by identifying the drug exposure that will cause this effect and then evaluating the use of different doses for attaining the exposure target in the population of interest. These ideas can be transferred to the clinical arena. The use of optimal sampling techniques allows informative times for blood sample acquisition to be identified. Population modelling followed by Bayesian estimation allows robust estimation of the exposure achieved in a specific patient. Exposure measures relative to the MIC of the pathogen (peak/MIC ratio, AUC/MIC ratio and time &gt; MIC) can then be linked to the desired clinical or microbiological outcome through common statistical techniques, such as logistic regression analysis, classification and regression tree (CART) analysis and Cox proportional hazards modelling. Antimicrobial pharmacodynamics is the discipline that integrates microbiology and pharmacology, with the aim of linking a measure of drug exposure, relative to a measure of drug potency for the pathogen in question, to the microbiological or clinical effect achieved. The delineation of such relationships allows the drug dose to be chosen in a rational manner, so that the desired effect (for example, the maximal bactericidal effect) can be achieved in a large proportion of the intended patient population. Ultimately, the goal of any anti-infective therapy is to administer a dose of drug that has an acceptably high probability of achieving the desired therapeutic effect balanced with an acceptably low probability of toxicity. Appropriate use of the latest pharmacodynamic modelling approaches can minimize the emergence of resistance and optimize the outcome for patients.</abstract><cop>London</cop><pub>Nature Publishing Group UK</pub><pmid>15031728</pmid><doi>10.1038/nrmicro862</doi><tpages>12</tpages></addata></record>
fulltext fulltext
identifier ISSN: 1740-1526
ispartof Nature reviews. Microbiology, 2004-04, Vol.2 (4), p.289-300
issn 1740-1526
1740-1534
language eng
recordid cdi_proquest_miscellaneous_71756748
source MEDLINE; Nature Journals Online; SpringerLink Journals - AutoHoldings
subjects Anti-Bacterial Agents - pharmacokinetics
Anti-Bacterial Agents - pharmacology
Area Under Curve
Bacterial Infections - drug therapy
Bacterial Infections - metabolism
Biological Availability
Biomedical and Life Sciences
Dose-Response Relationship, Drug
Drug dosages
Drug Resistance, Bacterial
Humans
Infections
Infectious Diseases
Life Sciences
Medical Microbiology
Microbial Sensitivity Tests
Microbiology
Parasitology
Pathogens
Patients
Penicillin
Pharmacodynamics
Pharmacology
Physiology
review-article
Virology
title Antimicrobial pharmacodynamics: critical interactions of 'bug and drug'
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T17%3A42%3A18IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_proqu&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Antimicrobial%20pharmacodynamics:%20critical%20interactions%20of%20'bug%20and%20drug'&rft.jtitle=Nature%20reviews.%20Microbiology&rft.au=Drusano,%20George%20L&rft.date=2004-04&rft.volume=2&rft.issue=4&rft.spage=289&rft.epage=300&rft.pages=289-300&rft.issn=1740-1526&rft.eissn=1740-1534&rft_id=info:doi/10.1038/nrmicro862&rft_dat=%3Cgale_proqu%3EA188965879%3C/gale_proqu%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=224638279&rft_id=info:pmid/15031728&rft_galeid=A188965879&rfr_iscdi=true