In this review, we provide an updated summary on colistin pharmacokinetics and pharmacodynamics. Colistin is an old molecule that is frequently used as last-line treatment for infections caused by multidrug-resistant Gram-negative bacteria. Colistin is a decapeptide administered either as a prodrug, colistin methanesulfonate (CMS), when used intravenously, or as colistin sulfate when used orally. Because colistin binds to laboratory materials, many experimental issues are raised and studies on colistin can be tricky. Due to its large molecular weight and its cationic properties at physiological pH, colistin passes through physiological membranes poorly and is mainly distributed within the extracellular space. Renal clearance of colistin is very low, but the dosing regimen should be adapted to the renal function of the patient because CMS is partly eliminated by the kidney. Therapeutic drug monitoring of colistin is warranted because the pharmacokinetics of colistin are very variable, and because its therapeutic window is narrow. Resistance of bacteria to colistin is increasing worldwide in parallel to its clinical and veterinary uses and a plasmid-mediated resistance mechanism (MCR-1) was recently described in animals and humans. In vitro, bacteria develop various resistance mechanisms rapidly when exposed to colistin. The use of a loading dose might reduce the emergence of resistance but the use of colistin in combination also seems necessary.

PurposePredicting target site drug concentration in the brain is of key importance for the successful development of drugs acting on the central nervous system. We propose a generic mathematical model to describe the pharmacokinetics in brain compartments, and apply this model to predict human brain disposition.MethodsA mathematical model consisting of several physiological brain compartments in the rat was developed using rich concentration-time profiles from nine structurally diverse drugs in plasma, brain extracellular fluid, and two cerebrospinal fluid compartments. The effect of active drug transporters was also accounted for. Subsequently, the model was translated to predict human concentration-time profiles for acetaminophen and morphine, by scaling or replacing system- and drug-specific parameters in the model.ResultsA common model structure was identified that adequately described the rat pharmacokinetic profiles for each of the nine drugs across brain compartments, with good precision of structural model parameters (relative standard error <37.5%). The model predicted the human concentration-time profiles in different brain compartments well (symmetric mean absolute percentage error <90%).ConclusionsA multi-compartmental brain pharmacokinetic model was developed and its structure could adequately describe data across nine different drugs. The model could be successfully translated to predict human brain concentrations.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-016-2065-3) contains supplementary material, which is available to authorized users.

No abstract

Objectives: To expand on previous reports of synergy between polymyxin B (PMB) and minocycline (MIN) against Acinetobacter baumannii; and to gain insight into the qualitative and quantitative determinants of their synergy. Methods: A semi-mechanistic pharmacokinetic/pharmacodynamic (PK/PD) model was developed on the basis of data from in vitro time-kill experiments with determination of resistant bacterial count to describe the effects of PMB and MIN alone and in combination. The model was enriched by complementary experiments providing information on the characteristics of the resistant subpopulation. Results: The model successfully described the data and made possible quantification of the strength of interaction between the two drugs and formulation of hypotheses about the mechanisms of the observed interaction. The effect of the combination was driven by MIN, with PMB acting as an helper drug; simulations at clinically achievable concentrations showed that 1.5 mg/L MIN þ0.2 mg/L PMB is expected to produce sustained killing over 30 hours, while 0.3 mg/L MIN þ1 mg/L PMB is met by bacterial regrowth. Interaction equations showed that maximal synergy is reached for PMB concentrations !0.1 mg/L and MIN concentrations !1 mg/L. Conclusions: Semi-mechanistic PK/PD modelling was used to investigate the quantitative determinants of synergy between PMB and MIN on a PMB-resistant A. baumannii strain. The developed model, improving on usual study techniques, showed asymmetry in the drug interaction, as PMB acted mostly as a helper to MIN, and provided simulations as a tool for future studies.