{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T06:07:39Z","timestamp":1791266859752,"version":"4.1.0"},"reference-count":64,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2020,4,17]],"date-time":"2020-04-17T00:00:00Z","timestamp":1587081600000},"content-version":"vor","delay-in-days":87,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R15GM126503"],"award-info":[{"award-number":["R15GM126503"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Antimicrobial treatment regimens against bacterial pathogens are designed using the drug's minimum inhibitory concentration (MIC) measured at a bacterial density of 5.7 log10(colony-forming units (CFU)\/mL) in vitro. However, MIC changes with pathogen density, which varies among infectious diseases and during treatment. Incorporating this into treatment design requires realistic mathematical models of the relationships. We compared the MIC\u2013density relationships for Gram-negative Escherichia coli and non-typhoidal Salmonella enterica subsp. enterica and Gram-positive Staphylococcus aureus and Streptococcus pneumonia (for n\u00a0=\u00a04 drug-susceptible strains per (sub)species and 1\u20138 log10(CFU\/mL) densities), for antimicrobial classes with bactericidal activity against the (sub)species: \u03b2-lactams (ceftriaxone and oxacillin), fluoroquinolones (ciprofloxacin), aminoglycosides (gentamicin), glycopeptides (vancomycin) and oxazolidinones (linezolid). Fitting six candidate mathematical models to the log2(MIC) vs. log10(CFU\/mL) curves did not identify one model best capturing the relationships across the pathogen\u2013antimicrobial combinations. Gompertz and logistic models (rather than a previously proposed Michaelis\u2013Menten model) fitted best most often. Importantly, the bacterial density after which the MIC sharply increases (an MIC advancement-point density) and that density's intra-(sub)species range evidently depended on the antimicrobial mechanism of action. Capturing these dependencies for the disease\u2013pathogen\u2013antimicrobial combination could help determine the MICs for which bacterial densities are most informative for treatment regimen design.<\/jats:p>","DOI":"10.1093\/femsle\/fnaa012","type":"journal-article","created":{"date-parts":[[2020,1,17]],"date-time":"2020-01-17T20:10:04Z","timestamp":1579291804000},"source":"Crossref","is-referenced-by-count":23,"title":["Mathematical modeling of the \u2018inoculum effect': six applicable models and the MIC advancement point concept"],"prefix":"10.1093","volume":"367","author":[{"given":"Jessica R","family":"Salas","sequence":"first","affiliation":[{"name":"Department of Diagnostic Medicine\/Pathobiology, Kansas State University, Manhattan, KS 66506, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Majid","family":"Jaberi-Douraki","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Kansas State University, Manhattan, KS 66506, 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