Sebastian G. Wicha

Unbound fraction of vancomycin in intensive care unit patients

Published data on the unbound fraction of vancomycin in patient samples exhibit high variability. In the present study, a robust ultrafiltration method was developed and applied to 102 clinical samples from 22 intensive care unit patients who were treated with continuous infusion of vancomycin. A validated HPLC method was used for determination of total and unbound concentrations. The mean unbound fraction was 67.2% (standard deviation 7.5%, range 47.2–92.1%) and independent of total concentration of vancomycin or of albumin. The unbound fraction was significantly correlated (r = +0.67, P = .0009) with the renally filtered fraction (drug clearance/creatinine clearance), providing functional evidence for the validity of the measurements. Ultrafiltration proved to be susceptible to variations in the experimental conditions such as pH, temperature and centrifugal force. The measured unbound fraction increased from 60% at pH 6 to 100% at pH 9, from 57% at 4°C to 80% at 37°C, and was 76% at 1,000 g compared with 45% at 10,000 g. Lack of standardization may therefore partly explain the variable results reported in the literature.

Population pharmacokinetics of meropenem during continuous infusion in surgical ICU patients

Continuous infusion of meropenem is a candidate strategy for optimization of its pharmacokinetic/pharmacodynamic profile. However, plasma concentrations are difficult to predict in critically ill patients. Steady‐state concentrations of meropenem were determined prospectively during continuous infusion in 32 surgical ICU patients (aged 21‐85 years, body weight 55‐125 kg, APACHE II 5‐29, measured creatinine clearance 22.7‐297 mL/min). Urine was collected for the quantification of renal clearance of meropenem and creatinine. Cystatin C was measured as an additional marker of renal function. Population pharmacokinetic models were developed using NONMEM®, which described total meropenem clearance and its relationship with several estimates of renal function (measured creatinine clearance CLCR, Cockcroft‐Gault formula CLCG, Hoek formula, 1/plasma creatinine, 1/plasma cystatin C) and other patient characteristics. Any estimate of renal function improved the model performance. The strongest association of clearance was found with CLCR (typical clearance = 11.3 L/h × [1 + 0.00932 × (CLCR – 80 mL/min)]), followed by 1/plasma cystatin C; CLCG was the least predictive covariate. Neither age, weight, nor sex was found to be significant. These models can be used to predict dosing requirements or meropenem concentrations during continuous infusion. The covariate CLCR offers the best predictive performance; if not available, cystatin C may provide a promising alternative to plasma creatinine.

Unbound fraction of ertapenem in intensive care unit patients

OBJECTIVES To determine unbound ertapenem concentrations in plasma and to describe the pharmacokinetics of unbound ertapenem in intensive care unit (ICU) patients. PATIENTS AND METHODS For assessing the influence of experimental conditions and for development of the ultrafiltration protocol, plasma from healthy volunteers was used. Concentrations of total and unbound ertapenem were determined by HPLC in 29 plasma samples from six ICU patients treated with 1 g of ertapenem once daily. The concentration-time courses were described by a one-compartment model. Ertapenem binding to albumin was assessed by Michaelis-Menten kinetics in solutions of human serum albumin, in plasma from healthy volunteers and in plasma from ICU patients. RESULTS The unbound fraction (fu) of ertapenem was highly susceptible to pH and temperature during ultrafiltration and was ∼20% in plasma from healthy volunteers at clinically relevant concentrations. In ICU patients, fu was substantially higher (range 30.9%-53.6%). The unbound concentrations of ertapenem exceeded 2 mg/L for 72% (median; range 39%-100%) of the 24 h dosing interval and 0.25 mg/L for 100% (range 79%-100%). The number of binding sites per albumin molecule was 1.22 (95% CI 1.07-1.38) in plasma from healthy volunteers and 0.404 (95% CI 0.158-0.650) in samples from ICU patients. CONCLUSIONS Determination of unbound ertapenem by ultrafiltration is susceptible to experimental conditions. When determined at physiological pH and temperature, fu of ertapenem is 2- to 4-fold higher than previously reported and even higher in ICU patients. Binding studies indicate that hypoalbuminaemia alone does not explain these differences. This issue should be further investigated for its clinical relevance.

Simultaneous determination and stability studies of linezolid, meropenem and vancomycin in bacterial growth medium by high-performance liquid chromatography

For pharmacokinetic/pharmacodynamic (PK/PD) assessment of antibiotics combinations in in vitro infection models, accurate and precise quantification of drug concentrations in bacterial growth medium is crucial for derivation of valid PK/PD relationships. We aimed to (i) develop a high-performance liquid chromatography (HPLC) assay to simultaneously quantify linezolid (LZD), vancomycin (VAN) and meropenem (MER), as typical components of broad-spectrum antibiotic combination therapy, in bacterial growth medium cation-adjusted Mueller-Hinton broth (CaMHB) and (ii) determine the stability profiles of LZD, VAN and MER under conditions in in vitro infection models. To separate sample matrix components, the final method comprised the pretreatment of 100μL sample with 400μL methanol, the evaporation of supernatant and its reconstitution in water. A low sample volume of 2μL processed sample was injected onto an Accucore C-18 column (2.6μm, 100×2.1mm) coupled to a Dionex Ultimate 3000 HPLC+ system. UV detection at 251, 240 and 302nm allowed quantification limits of 0.5, 2 and 0.5μg/mL for LZD, VAN and MER, respectively. The assay was successfully validated according to the relevant EMA guideline. The rapid method (14min) was successfully applied to quantify significant degradation of LZD, VAN and MER in in vitro infection models: LZD was stable, VAN degraded to 90.6% and MER to 62.9% within 24h compared to t=0 in CaMHB at 37°C, which should be considered when deriving PK/PD relationships in in vitro infection models. Inclusion of further antibiotics into the flexible gradient-based HPLC assay seems promising.

Individualized Dosing Algorithms and Therapeutic Monitoring for Antiepileptic Drugs

Pharmacokinetic (PK) models exist for most antiepileptic drugs (AEDs). Yet their use in clinical practice to assess interindividual differences and derive individualized doses has been limited. Here we show how model‐based dosing algorithms can be used to ensure attainment of target exposure and improve treatment response in patients. Using simulations, different treatment scenarios were explored for 11 commonly used AEDs. For each drug, five scenarios were considered: 1) all patients receive the same dose. 2) Individual clearance (CL), as predicted by population PK models, is used to personalize treatment. 3–5) Individual CL, obtained by therapeutic drug monitoring (TDM) according to different sampling schemes, is used to personalize treatment. Attainment of steady‐state target exposure was used as the performance criterion to rank each scenario. In contrast to current clinical guidelines, our results show that patient demographic and clinical characteristics should be used in conjunction with TDM to personalize the treatment of seizures.

A model-informed preclinical approach for prediction of clinical pharmacodynamic interactions of anti-TB drug combinations

Abstract Background Identification of pharmacodynamic interactions is not reasonable to carry out in a clinical setting for many reasons. The aim of this work was to develop a model-informed preclinical approach for prediction of clinical pharmacodynamic drug interactions in order to inform early anti-TB drug development. Methods In vitro time–kill experiments were performed with Mycobacterium tuberculosis using rifampicin, isoniazid or ethambutol alone as well as in different combinations at clinically relevant concentrations. The multistate TB pharmacometric (MTP) model was used to characterize the natural growth and exposure–response relationships of each drug after mono exposure. Pharmacodynamic interactions during combination exposure were characterized by linking the MTP model to the general pharmacodynamic interaction (GPDI) model with successful separation of the potential effect on each drug’s potency (EC50) by the combining drug(s). Results All combinations showed pharmacodynamic interactions at cfu level, where all combinations, except isoniazid plus ethambutol, showed more effect (synergy) than any of the drugs alone. Using preclinical information, the MTP-GPDI modelling approach was shown to correctly predict clinically observed pharmacodynamic interactions, as deviations from expected additivity. Conclusions With the ability to predict clinical pharmacodynamic interactions, using preclinical information, the MTP-GPDI model approach outlined in this study constitutes groundwork for model-informed input to the development of new and enhancement of existing anti-TB combination regimens.

A general pharmacodynamic interaction model identifies perpetrators and victims in drug interactions

Assessment of pharmacodynamic (PD) drug interactions is a cornerstone of the development of combination drug therapies. To guide this venture, we derive a general pharmacodynamic interaction (GPDI) model for ≥2 interacting drugs that is compatible with common additivity criteria. We propose a PD interaction to be quantifiable as multidirectional shifts in drug efficacy or potency and explicate the drugs’ role as victim, perpetrator or even both at the same time. We evaluate the GPDI model against conventional approaches in a data set of 200 combination experiments in Saccharomyces cerevisiae: 22% interact additively, a minority of the interactions (11%) are bidirectional antagonistic or synergistic, whereas the majority (67%) are monodirectional, i.e., asymmetric with distinct perpetrators and victims, which is not classifiable by conventional methods. The GPDI model excellently reflects the observed interaction data, and hence represents an attractive approach for quantitative assessment of novel combination therapies along the drug development process.Assessment of pharmacodynamic interactions is at the heart of combination therapy development. Here the authors introduce a general drug interaction scoring model that enables quantification of synergistic and antagonistic interactions and determination of the directionality of the interactions.

Assessing Pharmacodynamic Interactions in Mice using the Multistate Tuberculosis Pharmacometric and General Pharmacodynamic Interaction Models

The aim of this study was to investigate pharmacodynamic (PD) interactions in mice infected with Mycobacterium tuberculosis using population pharmacokinetics (PKs), the Multistate Tuberculosis Pharmacometric (MTP) model, and the General Pharmacodynamic Interaction (GPDI) model. Rifampicin, isoniazid, ethambutol, or pyrazinamide were administered in monotherapy for 4 weeks. Rifampicin and isoniazid showed effects in monotherapy, whereas the animals became moribund after 7 days with ethambutol or pyrazinamide alone. No PD interactions were observed against fast‐multiplying bacteria. Interactions between rifampicin and isoniazid on killing slow and non‐multiplying bacteria were identified, which led to an increase of 0.86 log10 colony‐forming unit (CFU)/lungs at 28 days after treatment compared to expected additivity (i.e., antagonism). An interaction between rifampicin and ethambutol on killing non‐multiplying bacteria was quantified, which led to a decrease of 2.84 log10 CFU/lungs at 28 days after treatment (i.e., synergism). These results show the value of pharmacometrics to quantitatively assess PD interactions in preclinical tuberculosis drug development.

Population pharmacokinetics and target attainment analysis of moxifloxacin in patients with diabetic foot infections.

The objective of this study was to provide a pharmacokinetic/pharmacodynamic (PK/PD) analysis of moxifloxacin in patients with diabetic foot infections (DFI). The plasma concentration‐time courses were determined in 50 DFI patients on day 1 and 3 after intravenous moxifloxacin 400 mg once‐daily. A two‐compartment population pharmacokinetic model was developed, identifying as covariates total body weight on central and peripheral volume of distribution (V1, V2) and ideal body weight on clearance (CL), respectively. For a 70 kg patient V1 was 68.1 L (interindividual variability, CV: 27.4%), V2 44.6 L, and CL 12.1 L/h (25.6%). Simulations were performed to calculate the probability of target attainment (PTA) for Gram‐positive and Gram‐negative pathogens with fAUC/MIC targets of ≥30 and ≥100, respectively. PTA was 0.68–1 for susceptible (MIC ≤0.5 mg/L according to EUCAST) Gram‐positive, but <0.25 for Gram‐negative pathogens with MIC ≥0.25 mg/L. With the exception of the first 24 hours of therapy, obesity affected PTA only marginally. Pharmacokinetic parameters in DFI patients were similar to those reported for healthy volunteers, indicating the appropriateness of the standard dose of moxifloxacin. Overall clinical efficacy has been shown previously, but PTA is limited in a subpopulation infected with formally susceptible Gram‐negative pathogens close to the EUCAST breakpoint.

Linezolid in liver failure: exploring the value of the maximal liver function capacity (LiMAx) test in a pharmacokinetic pilot study

Patients in the intensive care unit frequently require antibiotic treatment. Liver impairment poses substantial challenges for dose selection in these patients. The aim of the present pilot study was to assess the novel maximal liver function capacity (LiMAx test) in comparison with conventional liver function markers as covariates of drug clearance in liver failure using linezolid as a model drug. A total of 28 patients with different degrees of liver failure were recruited. LiMAx test as well as plasma, dialysate and urine sampling were performed under linezolid steady-state therapy (600 mg twice daily). NONMEM® was used for a pharmacometric analysis in which the different clearance routes of linezolid were elucidated. Linezolid pharmacokinetics was highly variable in patients with liver failure. The LiMAx score displayed the strongest association with non-renal clearance (CLnon-renal) [ = 4.46∙(body weight/57.9) 0.75∙(LiMAx/221.5)0.388 L/h], which reduced interindividual variability in CLnon-renal from 46.6% to 33.6%, thereby being superior to other common markers of liver function (international normalised ratio, gamma-glutaryl transferase, bilirubin, thrombocytes, alanine aminotransferase, aspartate aminotransferase). For LiMAx < 100 µg/kg/h, 64% of linezolid trough concentrations were above the recommended trough concentration of 8 mg/L, indicating the necessity of therapeutic drug monitoring in these patients. This is the first pilot application of the LiMAx test in a pharmacokinetic (PK) study demonstrating its potential to explain PK variability in linezolid clearance. Further studies with a larger patient collective and further drugs are highly warranted to guide dosing in patients with severe liver impairment.