Assessment and modelling of antibacterial combination regimens. (July 2018)

• Record Type: Journal Article
• Authors: Rao, G.G.; Li, J.; Garonzik, S.M.; Nation, R.L.; Forrest, A.
• Is Part Of: Clinical microbiology and infection. Volume 24:Number 7(2018)
• Page Start: 689
• Page End: 696
• Publication Date: 2018-07
• Subjects: Additivity -- Combination regimens -- Interaction -- Joint activity -- Pharmacokinetic/pharmacodynamic Modelling -- Synergy; Medical microbiology -- Periodicals; Diagnostic microbiology -- Periodicals; Communicable diseases -- Periodicals; Infection -- Periodicals; 616.01
• DOI: 10.1016/j.cmi.2017.12.004
• Languages: English
• ISSNs: 1198-743X

Abstract

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. 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. Sources: PubMed, references of identified studies and reviews, and personal experience when evidence was lacking. 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 . 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.