Attainment of Target Antibiotic Levels by Oral Treatment of Left-Sided Infective Endocarditis: A POET Substudy.

BACKGROUND: In the POET (Partial Oral Endocarditis Treatment) trial, oral step-down therapy was noninferior to full-length intravenous antibiotic administration. The aim of the present study was to perform pharmacokinetic/pharmacodynamic analyses for oral treatments of infective endocarditis to assess the probabilities of target attainment (PTAs). METHODS: Plasma concentrations of oral antibiotics were measured at day 1 and 5. Minimal inhibitory concentrations (MICs) were determined for the bacteria causing infective endocarditis (streptococci, staphylococci, or enterococci). Pharmacokinetic/pharmacodynamic targets were predefined according to literature using time above MIC or the ratio of area under the curve to MIC. Population pharmacokinetic modeling and pharmacokinetic/pharmacodynamic analyses were done for amoxicillin, dicloxacillin, linezolid, moxifloxacin, and rifampicin, and PTAs were calculated. RESULTS: A total of 236 patients participated in this POET substudy. For amoxicillin and linezolid, the PTAs were 88%-100%. For moxifloxacin and rifampicin, the PTAs were 71%-100%. Using a clinical breakpoint for staphylococci, the PTAs for dicloxacillin were 9%-17%.Seventy-four patients at day 1 and 65 patients at day 5 had available pharmacokinetic and MIC data for 2 oral antibiotics. Of those, 13 patients at day 1 and 14 patients at day 5 did only reach the target for 1 antibiotic. One patient did not reach target for any of the 2 antibiotics. CONCLUSIONS: For the individual orally administered antibiotic, the majority reached the target level. Patients with sub-target levels were compensated by the administration of 2 different antibiotics. The findings support the efficacy of oral step-down antibiotic treatment in patients with infective endocarditis.

Rifampicin reduces plasma concentration of linezolid in patients with infective endocarditis.

BACKGROUND: Linezolid in combination with rifampicin has been used in treatment of infective endocarditis especially for patients infected with staphylococci. OBJECTIVES: Because rifampicin has been reported to reduce the plasma concentration of linezolid, the present study aimed to characterize the population pharmacokinetics of linezolid for the purpose of quantifying an effect of rifampicin cotreatment. In addition, the possibility of compensation by dosage adjustments was evaluated. PATIENTS AND METHODS: Pharmacokinetic measurements were performed in 62 patients treated with linezolid for left-sided infective endocarditis in the Partial Oral Endocarditis Treatment (POET) trial. Fifteen patients were cotreated with rifampicin. A total of 437 linezolid plasma concentrations were obtained. The pharmacokinetic data were adequately described by a one-compartment model with first-order absorption and first-order elimination. RESULTS: We demonstrated a substantial increase of linezolid clearance by 150% (95% CI: 78%-251%), when combined with rifampicin. The final model was evaluated by goodness-of-fit plots showing an acceptable fit, and a visual predictive check validated the model. Model-based dosing simulations showed that rifampicin cotreatment decreased the PTA of linezolid from 94.3% to 34.9% and from 52.7% to 3.5% for MICs of 2 mg/L and 4 mg/L, respectively. CONCLUSIONS: A substantial interaction between linezolid and rifampicin was detected in patients with infective endocarditis, and the interaction was stronger than previously reported. Model-based simulations showed that increasing the linezolid dose might compensate without increasing the risk of adverse effects to the same degree.

Modelling inflammatory biomarker dynamics in a human lipopolysaccharide (LPS) challenge study using delay differential equations.

Clinical studies in healthy volunteers challenged with lipopolysaccharide (LPS), a constituent of the cell wall of Gram-negative bacteria, represent a key model to characterize the Toll-like receptor 4 (TLR4)-mediated inflammatory response. Here, we developed a mathematical modelling framework to quantitatively characterize the dynamics and inter-individual variability of multiple inflammatory biomarkers in healthy volunteer LPS challenge studies. Data from previously reported LPS challenge studies were used, which included individual-level time-course data for tumour necrosis factor α (TNF-α), interleukin 6 (IL-6), interleukin 8 (IL-8) and C-reactive protein (CRP). A one-compartment model with first-order elimination was used to capture the LPS kinetics. The relationships between LPS and inflammatory markers was characterized using indirect response (IDR) models. Delay differential equations were applied to quantify the delays in biomarker response profiles. For LPS kinetics, our estimates of clearance and volume of distribution were 35.7 L h-1 and 6.35 L, respectively. Our model adequately captured the dynamics of multiple inflammatory biomarkers. The time delay for the secretion of TNF-α, IL-6 and IL-8 were estimated to be 0.924, 1.46 and 1.48 h, respectively. A second IDR model was used to describe the induced changes of CRP in relation to IL-6, with a delayed time of 4.2 h. The quantitative models developed in this study can be used to inform design of clinical LPS challenge studies and may help to translate preclinical LPS challenge studies to humans.

A system pharmacology Boolean network model for the TLR4-mediated inflammatory response in early sepsis.

Sepsis is a life-threatening condition driven by the dysregulation of the host immune response to an infection. The complex and interacting mechanisms underlying sepsis remain not fully understood. By integrating prior knowledge from literature using mathematical modelling techniques, we aimed to obtain a deeper mechanistic insight into sepsis pathogenesis and to evaluate promising novel therapeutic targets, with a focus on Toll-like receptor 4 (TLR4)-mediated pathways. A Boolean network of regulatory relationships was developed for key immune components associated with sepsis pathogenesis after TLR4 activation. Perturbation analyses were conducted to identify therapeutic targets associated with organ dysfunction or antibacterial activity. The developed model consisted of 42 nodes and 183 interactions. Perturbation analyses suggest that over-expression of tumour necrosis factor alpha (TNF-α) or inhibition of soluble receptor sTNF-R, tissue factor, and inflammatory cytokines (IFN-γ, IL-12) may lead to a reduced activation of organ dysfunction related endpoints. Over-expression of complement factor C3b and C5b led to an increase in the bacterial clearance related endpoint. We identified that combinatory blockade of IFN-γ and IL-10 may reduce the risk of organ dysfunction. Finally, we found that combining antibiotic treatment with IL-1ß targeted therapy may have the potential to decrease thrombosis. In summary, we demonstrate how existing biological knowledge can be effectively integrated using Boolean network analysis for hypothesis generation of potential treatment strategies and characterization of biomarker responses associated with the early inflammatory response in sepsis.

Why we should sample sparsely and aim for a higher target: Lessons from model-based therapeutic drug monitoring of vancomycin in intensive care patients.

AIMS: To explore the optimal data sampling scheme and the pharmacokinetic (PK) target exposure on which dose computation is based in the model-based therapeutic drug monitoring (TDM) practice of vancomycin in intensive care (ICU) patients. METHODS: We simulated concentration data for 1 day following four sampling schemes, Cmin , Cmax + Cmin , Cmax + Cmid-interval + Cmin , and rich sampling where a sample was drawn every hour within a dose interval. The datasets were used for Bayesian estimation to obtain PK parameters, which were used to compute the doses for the next day based on five PK target exposures: AUC24 = 400, 500, and 600 mg·h/L and Cmin = 15 and 20 mg/L. We then simulated data for the next day, adopting the computed doses, and repeated the above procedure for 7 days. Thereafter, we calculated the percentage error and the normalized root mean square error (NRMSE) of estimated against "true" PK parameters, and the percentage of optimal treatment (POT), defined as the percentage of patients who met 400 ≤ AUC24 ≤ 600 mg·h/L and Cmin ≤ 20 mg/L. RESULTS: PK parameters were unbiasedly estimated in all investigated scenarios and the 6-day average NRMSE were 32.5%/38.5% (CL/V, where CL is clearance and V is volume of distribution) in the trough sampling scheme and 27.3%/26.5% (CL/V) in the rich sampling scheme. Regarding POT, the sampling scheme had marginal influence, while target exposure showed clear impacts that the maximum POT of 71.5% was reached when doses were computed based on AUC24 = 500 mg·h/L. CONCLUSIONS: For model-based TDM of vancomycin in ICU patients, sampling more frequently than taking only trough samples adds no value and dosing based on AUC24 = 500 mg·h/L lead to the best POT.

Optimizing Predictive Performance of Bayesian Forecasting for Vancomycin Concentration in Intensive Care Patients.

PURPOSE: Bayesian forecasting is crucial for model-based dose optimization based on therapeutic drug monitoring (TDM) data of vancomycin in intensive care (ICU) patients. We aimed to evaluate the performance of Bayesian forecasting using maximum a posteriori (MAP) estimation for model-based TDM. METHODS: We used a vancomycin TDM data set (n = 408 patients). We compared standard MAP-based Bayesian forecasting with two alternative approaches: (i) adaptive MAP which handles data over multiple iterations, and (ii) weighted MAP which weights the likelihood contribution of data. We evaluated the percentage error (PE) for seven scenarios including historical TDM data from the preceding day up to seven days. RESULTS: The mean of median PEs of all scenarios for the standard MAP, adaptive MAP and weighted MAP method were - 7.7%, -4.5% and - 6.7%. The adaptive MAP also showed the narrowest inter-quartile range of PE. In addition, regardless of MAP method, including historical TDM data further in the past will increase prediction errors. CONCLUSIONS: The proposed adaptive MAP method outperforms standard MAP in predictive performance and may be considered for improvement of model-based dose optimization. The inclusion of historical data beyond either one day (standard MAP and weighted MAP) or two days (adaptive MAP) reduces predictive performance.

Population pharmacokinetics of cefazolin before, during and after cardiopulmonary bypass to optimize dosing regimens for children undergoing cardiac surgery.

Objectives: The objective of this study was to characterize cefazolin serum pharmacokinetics in children before, during and after cardiopulmonary bypass (CPB), in order to derive an evidence-based dosing regimen. Patients and methods: This study included children who received cefazolin before surgical incision, before cessation of CPB and after surgery. Blood samples of total and unbound cefazolin concentrations were collected before, during and after CPB. The cefazolin concentration-time profiles were analysed using population pharmacokinetic modelling and predictors for interindividual variability in pharmacokinetic parameters were investigated. Subsequently, optimized dosing regimens were developed using stochastic simulations. Clinicaltrials.gov: NCT02749981. Results: A total of 494 total and unbound cefazolin concentrations obtained from 56 children (aged 6 days to 15 years) were included. A two-compartment model with first-order elimination plus an additional compartment for the effect of CPB best described the data. Clearance (1.56 L/h), central volume (1.93 L) and peripheral volume (2.39 L) were allometrically scaled by body weight. The estimated glomerular filtration rate (eGFR) was identified as a covariate on clearance and the serum albumin concentration was associated with maximum protein binding capacity. Our simulations showed that an additional bolus dose at the start of CPB improves the PTA in typical patients from 59% to >94%. Prolonged surgery and preserved renal function (i.e. drop in eGFR <25%) had a negative impact on PTA. Conclusions: We propose an optimized dosing regimen for cefazolin during cardiac surgery in paediatric patients to avoid treatment failure due to inadequate antibiotic prophylaxis.

A Generic Multi-Compartmental CNS Distribution Model Structure for 9 Drugs Allows Prediction of Human Brain Target Site Concentrations.

PURPOSE: Predicting 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. METHODS: A 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. RESULTS: A 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%). CONCLUSIONS: A 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.

Quantitative modeling of tumor dynamics and development of drug resistance in non-small cell lung cancer patients treated with erlotinib.

Insight into the development of treatment resistance can support the optimization of anticancer treatments. This study aims to characterize the tumor dynamics and development of drug resistance in patients with non-small cell lung cancer treated with erlotinib, and investigate the relationship between baseline circulating tumor DNA (ctDNA) data and tumor dynamics. Data obtained for the analysis included (1) intensively sampled erlotinib concentrations from 29 patients from two previous pharmacokinetic (PK) studies, and (2) tumor sizes, ctDNA measurements, and sparsely sampled erlotinib concentrations from 18 patients from the START-TKI study. A two-compartment population PK model was first developed which well-described the PK data. The PK model was subsequently applied to investigate the exposure-tumor dynamics relationship. To characterize the tumor dynamics, models accounting for intra-tumor heterogeneity and acquired resistance with or without primary resistance were investigated. Eventually, the model assumed acquired resistance only resulted in an adequate fit. Additionally, models with or without exposure-dependent treatment effect were explored, and no significant exposure-response relationship for erlotinib was identified within the observed exposure range. Subsequently, the correlation of baseline ctDNA data on EGFR and TP53 variants with tumor dynamics' parameters was explored. The analysis indicated that higher baseline plasma EGFR mutation levels correlated with increased tumor growth rates, and the inclusion of ctDNA measurements improved model fit. This result suggests that quantitative ctDNA measurements at baseline have the potential to be a predictor of anticancer treatment response. The developed model can potentially be applied to design optimal treatment regimens that better overcome resistance.

Severe skin toxicity in pediatric oncology patients treated with voriconazole and concomitant methotrexate.

We report the occurrence of skin toxicities in pediatric oncology patients on concomitant treatment with voriconazole and methotrexate (MTX). Of 23 patients who received this combination, 11 patients suffered from cheilitis and/or photosensitivity. In contrast, only in 1 of 9 patients who received voriconazole without MTX was photosensitivity observed. A mechanism of action was not able to be identified. We describe two cases with severe skin toxicities. Caution is warranted when using voriconazole and concomitant MTX.

Optimizing drug development of anti-cancer drugs in children using modelling and simulation.

Modelling and simulation (M&S)-based approaches have been proposed to support paediatric drug development in order to design and analyze clinical studies efficiently. Development of anti-cancer drugs in the paediatric population is particularly challenging due to ethical and practical constraints. We aimed to review the application of M&S in the development of anti-cancer drugs in the paediatric population, and to identify where M&S-based approaches could provide additional support in paediatric drug development of anti-cancer drugs. A structured literature search on PubMed was performed. The majority of identified M&S-based studies aimed to use population PK modelling approaches to identify determinants of inter-individual variability, in order to optimize dosing regimens and to develop therapeutic drug monitoring strategies. Prospective applications of M&S approaches for PK-bridging studies have scarcely been reported for paediatric oncology. Based on recent developments of M&S in drug development there are several opportunities where M&S could support more informative bridging between children and adults, and increase efficiency of the design and analysis of paediatric clinical trials, which should ultimately lead to further optimization of drug treatment strategies in this population.

Surinabant, a selective cannabinoid receptor type 1 antagonist, inhibits Δ9-tetrahydrocannabinol-induced central nervous system and heart rate effects in humans.

AIM: Cannabinoid receptor type 1 (CB1 ) antagonists have been developed for the treatment of obesity and associated risk factors. Surinabant is a high affinity CB1 antagonist in vitro. The aim of this study was to assess the magnitude of inhibition by surinabant of CNS effects and heart rate induced by Δ(9) -tetrahydrocannabinol (THC) in humans. METHODS: This was a double-blind, placebo-controlled, randomized, four period six sequence crossover study. Thirty healthy young male occasional cannabis users (<1 per week) were included. A single oral dose of surinabant (5, 20 or 60 mg) or placebo was administered followed 1.5 h later by four intrapulmonary THC doses (2, 4, 6 and 6 mg) or vehicle, administered at 1 h intervals. The wash-out period was 14-21 days. Subjective and objective pharmacodynamic (PD) measurements were performed. A population PK-PD model for THC and surinabant quantified PK and PD effects. RESULTS: Surinabant 20 and 60 mg inhibited all THC-induced PD effects in a similar range for both doses with inhibition ratios ranging from 68.3% (95% CI = 32.5, 104.2; heart rate) to 91.1% (95% CI = 30.3, 151.8; body sway). IC50 ranged from 22.0 ng ml(-1) [relative standard error (RSE) = 45.2%; body sway] to 58.8 ng ml(-1) (RSE = 44.2%; internal perception). Surinabant 5 mg demonstrated no significant effects. CONCLUSIONS: The dose-related inhibition by surinabant, without any effect of its own, suggests that this compound behaves as a CB1 receptor antagonist in humans at these concentrations. A single surinabant dose between 5 to 20 mg and above was able to antagonize THC-induced effects in humans.

Population pharmacokinetics of lamotrigine in Indian epileptic patients.

PURPOSE: The aim of this analysis was to describe the pharmacokinetics of oral lamotrigine (LTG) in Indian epileptic patients using a population pharmacokinetic (PPK) modeling approach to confirm that the PK is similar to that of the Caucasian population, and to evaluate and confirm the impact of covariates predictive of inter-individual variability using a simulation platform. METHODS: Blood samples were obtained from 95 patients, and LTG plasma concentrations were determined. Population PK modeling was performed using NONMEM. A one-compartment PK model with first-order absorption and elimination was used to describe the LTG PK. Log-likelihood profiling and normalized prediction distribution errors (NPDE) were used for model evaluation. A simulation study was performed to investigate dose regimens. RESULTS: Clearance (CL) was estimated to be 2.27 L/h with inter-individual variability (IIV) of 29 CV%. Volume of distribution (V) was estimated to be 53.6 L (31 CV% IIV). Body weight and concurrent use of carbamazepine and valproate were identified as significant covariates on clearance. Log-likelihood profiling indicated that parameters could be estimated with adequate precision, and NPDE indicated that the model adequately described the data observed. The simulation study illustrated the impact of carbamazepine and valproate on LTG PK, and negligible differences in PK between Indian and Caucasian patients. CONCLUSIONS: This is the first PK analysis of LTG in Indian patients. The population PK model developed adequately described the data observed. Comparison of identified PK parameters with previous PK analyses in Caucasian patients indicates that CL of LTG is similar, while V is somewhat lower compared with Caucasian patients, but this is not expected to lead to relevant differences in PK profiles during steady state.

Evaluation of a Cardiovascular Systems Model for Design and Analysis of Hemodynamic Safety Studies.

Early prediction, quantification and translation of cardiovascular hemodynamic drug effects is essential in pre-clinical drug development. In this study, a novel hemodynamic cardiovascular systems (CVS) model was developed to support these goals. The model consisted of distinct system- and drug-specific parameter, and uses data for heart rate (HR), cardiac output (CO), and mean atrial pressure (MAP) to infer drug mode-of-action (MoA). To support further application of this model in drug development, we conducted a systematic analysis of the estimation performance of the CVS model to infer drug- and system-specific parameters. Specifically, we focused on the impact on model estimation performance when considering differences in available readouts and the impact of study design choices. To this end, a practical identifiability analysis was performed, evaluating model estimation performance for different combinations of hemodynamic endpoints, drug effect sizes, and study design characteristics. The practical identifiability analysis showed that MoA of drug effect could be identified for different drug effect magnitudes and both system- and drug-specific parameters can be estimated precisely with minimal bias. Study designs which exclude measurement of CO or use a reduced measurement duration still allow the identification and quantification of MoA with acceptable performance. In conclusion, the CVS model can be used to support the design and inference of MoA in pre-clinical CVS experiments, with a future potential for applying the uniquely identifiable systems parameters to support inter-species scaling.

Novel Δ(9) -tetrahydrocannabinol formulation Namisol® has beneficial pharmacokinetics and promising pharmacodynamic effects.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT: • Cannabis based medicines are registered as a treatment for various indications, such as pain and spasms in multiple sclerosis (MS) patients, and anorexia and nausea in patients with HIV or receiving cancer treatment. • the pharmacokinetics of the various administration routes of cannabis and cannabis based medicines are variable and dosing is hard to regulate. WHAT THIS STUDY ADDS: • Namisol is a new tablet containing pure THC (>98%) that has a beneficial pharmacokinetic profile after oral administration. • Namisol gives a quick onset of pharmacodynamic effects in healthy volunteers, which implies a rapid initiation of therapeutic effects in patients. AIMS: Among the main disadvantages of currently available Δ(9) -tetrahydrocannabinol (THC) formulations are dosing difficulties due to poor pharmacokinetic characteristics. Namisol® is a novel THC formulation, designed to improve THC absorption. The study objectives were to investigate the optimal administration route, pharmacokinetics (PK), pharmacodynamics (PD) and tolerability of Namisol®. METHODS: This first in human study consisted of two parts. Panel I included healthy males and females (n = 6/6) in a double-blind, double-dummy, randomized, crossover study with sublingual (crushed tablet) and oral administration of Namisol® (5 mg THC). Based on these results, male and female (n = 4/5) participants from panel I received oral THC 6.5 and 8.0 mg or matching placebo in a randomized, crossover, rising dose study during panel II. PD measurements were body sway; visual analogue scales (VAS) mood, psychedelic and heart rate. THC and 11-OH-THC population PK analysis was performed. RESULTS: Sublingual administration showed a flat concentration profile compared with oral administration. Oral THC apparent t(1/2) was 72-80 min, t(max) was 39-56 min and C(max) 2.92-4.69 ng ml(-1) . THC affected body sway (60.8%, 95% CI 29.5, 99.8), external perception (0.078 log mm, 95% CI 0.019, 0.137), alertness (-2.7 mm, 95% CI -4.5, -0.9) feeling high (0.256 log mm, 95% CI 0.093, 0.418) and heart rate (5.6 beats min(-1) , 95% CI 2.7, 6.5). Namisol® was well tolerated. CONCLUSIONS: Oral Namisol® showed promising PK and PD characteristics. Variability and t(max) of THC plasma concentrations were smaller for Namisol® than reported for studies using oral dronabinol and nabilone. This study was performed in a limited number of healthy volunteers. Therefore, future research on Namisol® should study clinical effects in patient populations.

Anti-cancer treatment schedule optimization based on tumor dynamics modelling incorporating evolving resistance.

Quantitative characterization of evolving tumor resistance under targeted treatment could help identify novel treatment schedules, which may improve the outcome of anti-cancer treatment. In this study, a mathematical model which considers various clonal populations and evolving treatment resistance was developed. With parameter values fitted to the data or informed by literature data, the model could capture previously reported tumor burden dynamics and mutant KRAS levels in circulating tumor DNA (ctDNA) of patients with metastatic colorectal cancer treated with panitumumab. Treatment schedules, including a continuous schedule, intermittent schedules incorporating treatment holidays, and adaptive schedules guided by ctDNA measurements were evaluated using simulations. Compared with the continuous regimen, the simulated intermittent regimen which consisted of 8-week treatment and 4-week suspension prolonged median progression-free survival (PFS) of the simulated population from 36 to 44 weeks. The median time period in which the tumor size stayed below the baseline level (TTS

Pooled Population Pharmacokinetic Analysis for Exploring Ciprofloxacin Pharmacokinetic Variability in Intensive Care Patients.

BACKGROUND AND OBJECTIVE: Previous pharmacokinetic (PK) studies of ciprofloxacin in intensive care (ICU) patients have shown large differences in estimated PK parameters, suggesting that further investigation is needed for this population. Hence, we performed a pooled population PK analysis of ciprofloxacin after intravenous administration using individual patient data from three studies. Additionally, we studied the PK differences between these studies through a post-hoc analysis. METHODS: Individual patient data from three studies (study 1, 2, and 3) were pooled. The pooled data set consisted of 1094 ciprofloxacin concentration-time data points from 140 ICU patients. Nonlinear mixed-effects modeling was used to develop a population PK model. Covariates were selected following a stepwise covariate modeling procedure. To analyze PK differences between the three original studies, random samples were drawn from the posterior distribution of individual PK parameters. These samples were used for a simulation study comparing PK exposure and the percentage of target attainment between patients of these studies. RESULTS: A two-compartment model with first-order elimination best described the data. Inter-individual variability was added to the clearance, central volume, and peripheral volume. Inter-occasion variability was added to clearance only. Body weight was added to all parameters allometrically. Estimated glomerular filtration rate on ciprofloxacin clearance was identified as the only covariate relationship resulting in a drop in inter-individual variability of clearance from 58.7 to 47.2%. In the post-hoc analysis, clearance showed the highest deviation between the three studies with a coefficient of variation of 14.3% for posterior mean and 24.1% for posterior inter-individual variability. The simulation study showed that following the same dose regimen of 400 mg three times daily, the area under the concentration-time curve of study 3 was the highest with a mean area under the concentration-time curve at 24 h of 58 mg·h/L compared with that of 47.7 mg·h/L for study 1 and 47.6 mg·h/L for study 2. Similar differences were also observed in the percentage of target attainment, defined as the ratio of area under the concentration-time curve at 24 h and the minimum inhibitory concentration. At the epidemiological cut-off minimum inhibitory concentration of Pseudomonas aeruginosa of 0.5 mg/L, percentage of target attainment was only 21%, 18%, and 38% for study 1, 2, and 3, respectively. CONCLUSIONS: We developed a population PK model of ciprofloxacin in ICU patients using pooled data of individual patients from three studies. A simple ciprofloxacin dose recommendation for the entire ICU population remains challenging owing to the PK differences within ICU patients, hence dose individualization may be needed for the optimization of ciprofloxacin treatment.

Systematic evaluation of the descriptive and predictive performance of paediatric morphine population models.

PURPOSE: A framework for the evaluation of paediatric population models is proposed and applied to two different paediatric population pharmacokinetic models for morphine. One covariate model was based on a systematic covariate analysis, the other on fixed allometric scaling principles. METHODS: The six evaluation criteria in the framework were 1) number of parameters and condition number, 2) numerical diagnostics, 3) prediction-based diagnostics, 4) η-shrinkage, 5) simulation-based diagnostics, 6) diagnostics of individual and population parameter estimates versus covariates, including measurements of bias and precision of the population values compared to the observed individual values. The framework entails both an internal and external model evaluation procedure. RESULTS: The application of the framework to the two models resulted in the detection of overparameterization and misleading diagnostics based on individual predictions caused by high shrinkage. The diagnostic of individual and population parameter estimates versus covariates proved to be highly informative in assessing obtained covariate relationships. Based on the framework, the systematic covariate model proved to be superior over the fixed allometric model in terms of predictive performance. CONCLUSIONS: The proposed framework is suitable for the evaluation of paediatric (covariate) models and should be applied to corroborate the descriptive and predictive properties of these models.

Zebrafish larvae as experimental model to expedite the search for new biomarkers and treatments for neonatal sepsis.

Neonatal sepsis is a major cause of death and disability in newborns. Commonly used biomarkers for diagnosis and evaluation of treatment response lack sufficient sensitivity or specificity. Additionally, new targets to treat the dysregulated immune response are needed, as are methods to effectively screen drugs for these targets. Available research methods have hitherto not yielded the breakthroughs required to significantly improve disease outcomes, we therefore describe the potential of zebrafish (Danio rerio) larvae as preclinical model for neonatal sepsis. In biomedical research, zebrafish larvae combine the complexity of a whole organism with the convenience and high-throughput potential of in vitro methods. This paper illustrates that zebrafish exhibit an immune system that is remarkably similar to humans, both in terms of types of immune cells and signaling pathways. Moreover, the developmental state of the larval immune system is highly similar to human neonates. We provide examples of zebrafish larvae being used to study infections with pathogens commonly causing neonatal sepsis and discuss known limitations. We believe this species could expedite research into immune regulation during neonatal sepsis and may hold keys for the discovery of new biomarkers and novel treatment targets as well as for screening of targeted drug therapies.