Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

[PK/PD Modeling as a Tool for Predicting Bacterial Resistance to Antibiotics: Alternative Analyses of Experimental Data].

Antibiotiki i khimioterapiia = Antibiotics and chemoterapy [sic] - 1 Jan 2015

Golikova M V, Strukova E N, Portnoy Y A, Firsov A A

Abstract excerpt

Postexposure number of mutants (NM) is a conventional endpoint in bacterial resistance studies using in vitro dynamic models that simulate antibiotic pharmacokinetics. To compare NM with a recently introduced integral parameter AUBC(M), the area under the time course of resistance mutants, the enrichment of resistant Staphylococcus aureus was studied in vitro by simulation of mono(daptomycin, doxycycline) and...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Anti-Bacterial Agents
Share to Drug Resistance, Multiple, Bacterial

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Drug Synergism

Share to Models, Biological

Share to Mutation

Share to Staphylococcus aureus

Source

Identifiers

PMID: 27141633

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

[PK/PD Modeling as a Tool for Predicting Bacterial Resistance to Antibiotics: Alternative Analyses of Experimental Data].PMID 27141633Read ↗

Testing the mutant selection window hypothesis with Staphylococcus aureus exposed to daptomycin and vancomycin in an in vitro dynamic model.Semantic proximity Read ↗

Testing the mutant selection window hypothesis with Staphylococcus aureus exposed to linezolid in an in vitro dynamic model.Semantic proximity Read ↗

Bacterial resistance studies using in vitro dynamic models: the predictive power of the mutant prevention and minimum inhibitory antibiotic concentrations.Semantic proximity Read ↗

Pharmacokinetically-based prediction of the effects of antibiotic combinations on resistant Staphylococcus aureus mutants: in vitro model studies with linezolid and rifampicin.Semantic proximity Read ↗

Bacterial antibiotic resistance studies using in vitro dynamic models: Population analysis vs. susceptibility testing as endpoints of mutant enrichment.Semantic proximity Read ↗

Dynamic interaction of colistin and meropenem on a WT and a resistant strain ofPseudomonas aeruginosaas quantified in a PK/PD modelSemantic proximity Read ↗

Time inside the mutant selection window as a predictor of staphylococcal resistance to linezolid.Semantic proximity Read ↗

Pharmacokinetic/pharmacodynamic model-based combination therapy approach to target antibiotic resistant populations emerged from ciprofloxacin exposure.Semantic proximity Read ↗

[PK/PD of antimicrobials].Semantic proximity Read ↗

Mathematical Modeling of Escherichia coli Kinetics to Predict Growth and Rifampicin-dependent Killing ProfileSemantic proximity Read ↗

Daptomycin Dose-Effect Relationship against Resistant Gram-Positive OrganismsSemantic proximity Read ↗

Predictors of bacterial resistance using in vitro dynamic models: area under the concentration-time curve related to either the minimum inhibitory or mutant prevention antibiotic concentration.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.