Ferdinand A. Weinelt (1,2), Lisa Ehmann (1,2), Robin Michelet (1), Wilhelm Huisinga (3), Johannes Zander (4), Michael Zoller (5), Charlotte Kloft (1)
Institution: (1) Dept. of Clinical Pharmacy & Biochemistry, Institute of Pharmacy, Freie Universität Berlin, Germany, (2) Graduate Research Training Program PharMetrX, Germany, (3) Institute of Mathematics, Universität Potsdam, Germany (4) Institute of Laboratory Medicine, University Hospital, LMU Munich, Germany, (5) Dept. of Anaesthesiology, University Hospital, LMU Munich, Germany
Objectives: Piperacillin (PIP) is a broad-spectrum β-lactam antibiotic used in combination with the β-lactamase inhibitor tazobactam (TAZ) for the treatment of severe infections in critically ill patients. The alarming spread of antimicrobial resistance motivates further research to optimise antibiotic treatment [1]. The large pharmacokinetic (PK) interindividual variability often observed in critically ill patients, increasing the risk of subtherapeutic plasma concentrations and therapeutic failure [2], further adds to this motivation. The efficacy of PIP is linked to the time the PIP concentration remains above the minimal inhibitory concentration (T>MIC) [3] and the efficacy of TAZ is linked to the time the TAZ concentration remains above a bacterial strain specific threshold [4]. Because of the high variability, therapeutic drug monitoring for PIP is advised [5] while TAZ is most often not quantified, assuming similar PK properties to PIP. Given the known drug-drug interaction between PIP and TAZ due to their shared tubular secretion via the organic anion transporters 1 (OAT1) and 3 (OAT3) [6,7] in the kidney, the assumption that the PK of both drugs changes similarly in critically ill patients might be questionable. The objective of the presented work was to quantitatively describe the PK and its variability of PIP and TAZ in a critically ill patient population, mechanistically including the drug-drug interaction in the tubular secretion process of the renal clearance.
Methods: A monocentric prospective observational study was conducted in intensive care units at the University Hospital of Munich in 60 critically ill patients with severe infections. According to clinical guidelines patients were treated with 4 g PIP and 0.5 g TAZ as intravenous 0.5 h infusions twice daily (impaired renal function) or thrice daily (normal renal function). Multiple serum samples were taken over four study days and different patient factors were determined. Both drugs where quantified in the same serum sample by a combined LC-MS/MS assay [8]. Using NONMEM 7.4.3, a nonlinear mixed-effects (NLME) PK model was developed. First-order conditional estimation with interaction was employed and model adequacy was assessed considering plausibility and precision of the parameter estimates and goodness-of-fit plots.
Results: For both, PIP and TAZ a 3-compartment disposition model with a total volume of distribution of 21.6 L for PIP (V1 4.7 L, V2 9.1 L, V3 7.8 L) and a total volume of distribution of 31.7 L for TAZ (V1 5.15 L, V2 10.4 L, V3 16.1 L) was established. The total clearance of both drugs was separated into nonrenal clearance and renal clearance, the later consisting of glomerular filtration and tubular secretion. The linear nonrenal clearance was estimated to be 1.25 L/h for PIP and 1.03 L/h for TAZ, whereas glomerular filtration was set to the glomerular filtration rate assumed to be equal to the creatinine clearance, calculated using 24-hour urine collection method (median of the population 2.8 L/h). Tubular secretion was estimated assuming non-linear Michaelis-Menten kinetics for PIP (VM 1460 mmol/h and KM 500 mM) and linearized Michaelis-Menten kinetics for TAZ (CLint 13.2 L/h) with a competitive inhibition of PIP on TAZ (Ki 22.3 mM).
Interindividual variability for PIP was estimated on KM (224 %CV), V1 (81.3 %CV), CL (36.0 %CV) and Q1 (24.6 %CV). For TAZ Interindividual variability was estimated on CLint (82.1 %CV), V1 (70.7 %CV), CL (34.9 %CV) and Q1 (54.5 %CV). An additional combined interoccasion variability on the linear CL (38.3 %CV) between a patient’s monitored dosing events was implemented.
Conclusions: A joint NLME PK model for PIP and TAZ, mechanistically implementing the drug-drug interaction at the tubular secretion process of the renal clearance, was successfully developed. High interindividual variability in the PK parameters of PIP and TAZ in critically ill patients was identified and quantified. Next, a covariate analysis will be performed to detect patient factors (e.g. demographics, clinical parameters) explaining the large PK variability. Ultimately, the NLME model including covariates will be used to assess whether critically ill patients and/or different subgroups of the study population would benefit from dosing adjustments, further paving the way towards optimal treatment of this fragile population.
References:
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Reference: PAGE 28 (2019) Abstr 9093 [www.page-meeting.org/?abstract=9093]
Poster: Drug/Disease Modelling - Infection