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Assessment and modelling of antibacterial combination regimens.
- Source :
-
Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases [Clin Microbiol Infect] 2018 Jul; Vol. 24 (7), pp. 689-696. Date of Electronic Publication: 2017 Dec 18. - Publication Year :
- 2018
-
Abstract
- Background: The increasing global prevalence of multidrug-resistant bacteria is forcing clinicians to prescribe combination antibiotic regimens to treat serious infections. Currently, the joint activity of a combination is quantified by comparing the observed and expected effects using a reference model. These reference models make different assumptions and interpretations of synergy. They fail to: (i) account for multiple bacterial subpopulations with differing susceptibilities; (ii) quantify or interpret the explicit interaction (synergy/antagonism) mechanisms; and (iii) accommodate spontaneous mutations.<br />Aims: To develop better study designs, mathematical models, metrics and pharmacodynamic analyses to assist with the identification of highly active combinations that are translatable to the clinical context to address the mounting antibiotic resistance threat.<br />Sources: PubMed, references of identified studies and reviews, and personal experience when evidence was lacking.<br />Content: We reviewed metrics and approaches for quantifying the joint activity of the combination. The first example is using experimental data from an in vitro checkerboard synergy panel to develop and illustrate a less model-dependent method for assessing combination regimens. In the second example a pharmacokinetic/pharmacodynamic model was developed using mechanism-based mathematical modelling and monotherapy and combination therapy data obtained from an in vitro hollow fibre infection model evaluating linezolid and rifampin regimens against Mycobacterium tuberculosis.<br />Implications: Mechanism-based mathematical approach provides an excellent platform for describing the time course of effect while taking into account the mechanisms of different antibiotics and differing pathogen susceptibilities. This approach allows for the future integration of 'omics' data describing host-pathogen interactions, that will provide a systems-level understanding of the underlying infectious process, and enable the design of effective combination therapies.<br /> (Copyright © 2017. Published by Elsevier Ltd.)
- Subjects :
- Drug Therapy, Combination standards
Drug Therapy, Combination trends
Humans
Linezolid pharmacokinetics
Linezolid pharmacology
Microbial Sensitivity Tests
Mycobacterium tuberculosis drug effects
Mycobacterium tuberculosis metabolism
Rifampin pharmacokinetics
Rifampin pharmacology
Anti-Bacterial Agents pharmacokinetics
Anti-Bacterial Agents pharmacology
Drug Synergism
Models, Biological
Subjects
Details
- Language :
- English
- ISSN :
- 1469-0691
- Volume :
- 24
- Issue :
- 7
- Database :
- MEDLINE
- Journal :
- Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases
- Publication Type :
- Academic Journal
- Accession number :
- 29269090
- Full Text :
- https://doi.org/10.1016/j.cmi.2017.12.004