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Model-informed precision dosing (MIPD) is a quantitative dosing framework that combines prior knowledge on the drug-disease-patient system with patient data from therapeutic drug/ biomarker monitoring (TDM) to support individualized dosing in ongoing treatment. Structural models and prior parameter distributions used in MIPD approaches typically build on prior clinical trials that involve only a limited number of patients selected according to some exclusion/inclusion criteria. Compared to the prior clinical trial population, the patient population in clinical practice can be expected to also include altered behavior and/or increased interindividual variability, the extent of which, however, is typically unknown. Here, we address the question of how to adapt and refine models on the level of the model parameters to better reflect this real-world diversity. We propose an approach for continued learning across patients during MIPD using a sequential hierarchical Bayesian framework. The approach builds on two stages to separate the update of the individual patient parameters from updating the population parameters. Consequently, it enables continued learning across hospitals or study centers, because only summary patient data (on the level of model parameters) need to be shared, but no individual TDM data. We illustrate this continued learning approach with neutrophil-guided dosing of paclitaxel. The present study constitutes an important step toward building confidence in MIPD and eventually establishing MIPD increasingly in everyday therapeutic use.
Background: Circulating infliximab (IFX) concentrations correlate with clinical outcomes, forming the basis of the IFX concentration monitoring in patients with Crohn's disease. This study aims to investigate and refine the exposure-response relationship by linking the disease activity markers "Crohn's disease activity index" (CDAI) and C-reactive protein (CRP) to IFX exposure. In addition, we aim to explore the correlations between different disease markers and exposure metrics.
Methods: Data from 47 Crohn's disease patients of a randomized controlled trial were analyzed post hoc. All patients had secondary treatment failure at inclusion and had received intensified IFX of 5 mg/kg every 4 weeks for up to 20 weeks. Graphical analyses were performed to explore exposure-response relationships. Metrics of exposure included area under the concentration-time curve (AUC) and trough concentrations (Cmin). Disease activity was measured by CDAI and CRP values, their change from baseline/last visit, and response/remission outcomes at week 12.
Results: Although trends toward lower Cmin and lower AUC in nonresponders were observed, neither CDAI nor CRP showed consistent trends of lower disease activity with higher IFX exposure across the 30 evaluated relationships. As can be expected, Cmin and AUC were strongly correlated with each other. Contrarily, the disease activity markers were only weakly correlated with each other.
Conclusions: No significant relationship between disease activity, as evaluated by CDAI or CRP, and IFX exposure was identified. AUC did not add benefit compared with Cmin. These findings support the continued use of Cmin and call for stringent objective disease activity (bio-)markers (eg, endoscopy) to form the basis of personalized IFX therapy for Crohn's disease patients with IFX treatment failure.
Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy
(2020)
An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.
Bayesian Data Assimilation to Support Informed Decision Making in Individualized Chemotherapy
(2020)
An essential component of therapeutic drug/biomarker monitoring (TDM) is to combine patient data with prior knowledge for model-based predictions of therapy outcomes. Current Bayesian forecasting tools typically rely only on the most probable model parameters (maximum a posteriori (MAP) estimate). This MAP-based approach, however, does neither necessarily predict the most probable outcome nor does it quantify the risks of treatment inefficacy or toxicity. Bayesian data assimilation (DA) methods overcome these limitations by providing a comprehensive uncertainty quantification. We compare DA methods with MAP-based approaches and show how probabilistic statements about key markers related to chemotherapy-induced neutropenia can be leveraged for more informative decision support in individualized chemotherapy. Sequential Bayesian DA proved to be most computationally efficient for handling interoccasion variability and integrating TDM data. For new digital monitoring devices enabling more frequent data collection, these features will be of critical importance to improve patient care decisions in various therapeutic areas.
Effective antibiotic dosing is vital for therapeutic success in critically ill patients. This work aimed to develop an algorithm to identify appropriate meropenem dosing in critically ill patients. Population pharma-cokinetic (PK) modelling was performed in NONMEM (R) 7.3 based on densely sampled meropenem serum samples (n(patients) = 48; n(samples) =1376) and included a systematic analysis of 27 pre-selected covariates to identify factors influencing meropenem exposure. Using Monte Carlo simulations newly considering the uncertainty of PK parameter estimates, standard meropenem dosing was evaluated with respect to attainment of the pharmacokinetic/pharmacodynamic (PK/PD) target and was compared with alternative infusion regimens (short-term, prolonged, continuous; daily dose, 2000-6000 mg). Subsequently, a dosing algorithm was developed to identify appropriate dosing regimens. The two-compartment population PK model included three factors influencing meropenem pharmacokinetics: the Cockcroft-Gault creatinine clearance (CLCRCG ) on meropenem clearance; and body weight and albumin on the central and peripheral volume of distribution, respectively; of these, only CLCRCG was identified as a vital influencing factor on PK/PD target attainment. A three-level dosing algorithm was developed (considering PK parameter uncertainty), suggesting dosing regimens depending on renal function and the level (L) of knowledge about the infecting pathogen (L1, pathogen unknown; L2, pathogen known; L3((-MIC)), pathogen and susceptibility known; L3((+MIC)), MIC known). Whereas patients with higher CLCRCG and lower pathogen susceptibility required mainly intensified dosing regimens, lower than standard doses appeared sufficient for highly susceptible pathogens. In conclusion, a versatile meropenem dosing algorithm for critically ill patients is proposed, indicating appropriate dosing regimens based on patient- and pathogen-specific information. (C) 2019 Published by Elsevier B.V.
Background: Optimal antibiotic exposure is a vital but challenging prerequisite for achieving clinical success in ICU patients. Objectives: To develop and externally validate a population pharmacokinetic model for continuous-infusion meropenem in critically ill patients and to establish a nomogram based on a routinely available marker of renal function. Methods: A population pharmacokinetic model was developed in NONMEM (R) 7.3 based on steady-state meropenem concentrations (C-ss) collected during therapeutic drug monitoring. Different serum creatinine-based markers of renal function were compared for their influence on meropenem clearance (the Cockcroft-Gault creatinine clearance CLCRcG, the CLCR bedside estimate according to Jelliffe, the Chronic Kidney Disease Epidemiology Collaboration equation and the four-variable Modification of Diet in Renal Disease equation). After validation of the pharmacokinetic model with independent data, a dosing nomogram was developed, relating renal function to the daily doses required to achieve selected target concentrations (4/8/16 mg/L) in 90% of the patients. Probability of target attainment was determined for efficacy (C-ss >= 8 mg/L) and potentially increased likelihood of adverse drug reactions (C-ss >32 mg/L). Results: In total, 433 plasma concentrations (3.20-48.0 mg/L) from 195 patients (median/P-0.05 - P-0.95 at baseline: weight 77.0/55.0-114 kg, CLCRCG 63.0/19.6-168 mL/min) were used for model building. We found that CLCRCG best described meropenem clearance (CL = 7.71 L/h, CLCRCG = 80 mL/min). The developed model was successfully validated with external data (n = 171, 73 patients). According to the nomogram, daily doses of 910/1480/2050/2800/ 3940 mg were required to reach a target C-ss = 8 mg/L in 90% of patients with CLCRCG = 20/50/80/120/180 mL/min, respectively. A low probability of adverse drug reactions (<0.5%) was associated with these doses. Conclusions: A dosing nomogram was developed for continuous-infusion meropenem based on renal function in a critically ill population.
Development of a tool to identify intensive care patients at risk of meropenem therapy failure
(2018)
Background:
Hemadsorption of cytokines is used in critically ill patients with sepsis or septic shock. Concerns have been raised that the cytokine adsorber CytoSorb (R) unintentionally adsorbs vancomycin. This study aimed to quantify vancomycin elimination by CytoSorb (R) .
Methods:
Critically ill patients with sepsis or septic shock receiving continuous renal replacement therapy and CytoSorb (R) treatment during a prospective observational study were included in the analysis. Vancomycin pharmacokinetics was characterized using population pharmacokinetic modeling. Adsorption of vancomycin by the CytoSorb (R) was investigated as linear or saturable process. The final model was used to derive dosing recommendations based on stochastic simulations.
Results:
20 CytoSorb (R) treatments in 7 patients (160 serum samples/24 during CytoSorb (R)-treatment, all continuous infusion) were included in the study. A classical one-compartment model, including effluent flow rate of the continuous hemodialysis as linear covariate on clearance, best described the measured concentrations (without CytoSorb (R)). Significant adsorption with a linear decrease during CytoSorb (R) treatment was identified (p <0.0001) and revealed a maximum increase in vancomycin clearance of 291% (initially after CytoSorb (R) installation) and a maximum adsorption capacity of 572 mg. For a representative patient of our cohort a reduction of the area under the curve (AUC) by 93 mg/L*24 h during CytoSorb (R) treatment was observed. The additional administration of 500 mg vancomycin over 2 h during CytoSorb (R) attenuated the effect and revealed a negligible reduction of the AUC by 4 mg/L*24h.
Conclusion:
We recommend the infusion of 500 mg vancomycin over 2 h during CytoSorb (R) treatment to avoid subtherapeutic concentrations.
Immunodeficient mice are crucial models to evaluate the efficacy of monoclonal antibodies (mAbs). When studying mAb pharmacokinetics (PK), protection from elimination by binding to the neonatal Fc receptor (FcRn) is known to be a major process influencing the unspecific clearance of endogenous and therapeutic IgG. The concentration of endogenous IgG in immunodeficient mice, however is reduced, and this effect on the FcRn protection mechanism and subsequently on unspecific mAb clearance is unknown, yet of great importance for the interpretation of mAb PK data. We used a PBPK modelling approach to elucidate the influence of altered endogenous IgG concentrations on unspecific mAb clearance. To this end, we used PK data in immunodeficient mice, i.e. nude and severe combined immunodeficiency mice. To avoid impact of target-mediated clearance processes, we focussed on mAbs without affinity to a target antigen in these mice. In addition, intravenous immunoglobulin (IVIG) data of immunocompetent mice was used to study the impact of increased total IgG concentrations on unspecific therapeutic antibody clearance. The unspecific clearance is linear, whenever therapeutic IgG concentrations, i.e. mAb and IVIG concentrations are lower than FcRn; it can be non-linear if therapeutic IgG concentrations are larger than FcRn and endogenous IgG concentrations (e.g., under IVIG therapy). Unspecific mAb clearance of immunodeficient mice is effectively linear (under mAb doses as typically used in human). Studying the impact of reduced endogenous IgG concentrations on unspecific mAb clearance is of great relevance for the extrapolation to clinical species, e.g., when predicting mAb PK in immunosuppressed cancer patients.