Assessment and modelling of antibacterial combination regimens.

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.

Population pharmacokinetics of colistin methanesulfonate and formed colistin in critically ill patients from a multicenter study provide dosing suggestions for various categories of patients.

With increasing clinical emergence of multidrug-resistant Gram-negative pathogens and the paucity of new agents to combat these infections, colistin (administered as its inactive prodrug colistin methanesulfonate [CMS]) has reemerged as a treatment option, especially for critically ill patients. There has been a dearth of pharmacokinetic (PK) data available to guide dosing in critically ill patients, including those on renal replacement therapy. In an ongoing study to develop a population PK model for CMS and colistin, 105 patients have been studied to date; these included 12 patients on hemodialysis and 4 on continuous renal replacement therapy. For patients not on renal replacement, there was a wide variance in creatinine clearance, ranging from 3 to 169 ml/min/1.73 m(2). Each patient was treated with a physician-selected CMS dosage regimen, and 8 blood samples for PK analysis were collected across a dosage interval on day 3 or 4 of therapy. A linear PK model with two compartments for CMS and one compartment for formed colistin best described the data. Covariates included creatinine clearance on the total clearance of CMS and colistin, as well as body weight on the central volume of CMS. Model-fitted parameter estimates were used to derive suggested loading and maintenance dosing regimens for various categories of patients, including those on hemodialysis and continuous renal replacement. Based on our current understanding of colistin PK and pharmacodynamic relationships, colistin may best be used as part of a highly active combination, especially for patients with moderate to good renal function and/or for organisms with MICs of ≥ 1.0 mg/liter.