Objectives: There is uncertainty about the optimal teicoplanin regimens for neonates. The study aim was to determine the population pharmacokinetics (PK) of teicoplanin in neonates, evaluate currently recommended regimens and explore the exposure-effect relationships.Methods: An open-label PK study was conducted. Neonates from 26 to 44 weeks post-menstrual age were recruited (n ¼ 18). The teicoplanin regimen was a 16 mg/kg loading dose, followed by 8 mg/kg once daily. Therapeutic drug monitoring and dose adjustment were not conducted. A standard two-compartment PK model was developed, followed by models that incorporated weight. A PK/pharmacodynamic (PD) model with C-reactive protein serial measurements as the PD input was fitted to the data. Monte Carlo simulations (n¼ 5000) were performed using Pmetrics. The AUCs at steady state and the proportion of patients achieving the recommended drug exposures (i.e. C min .15 mg/L) were determined. The study was registered in the European Clinical Trials Database Registry (EudraCT: 2012-005738-12).Results: The PK allometric model best accounted for the observed data. The PK parameters medians were:). The individual time-course of C-reactive protein was well described using the Bayesian posterior estimates for each patient. The simulated median AUC 96-120 was 302.3 mg . h/L and the median C min at 120 h was 12.9 mg/L; 38.8% of patients attained a C min .15 mg/L by 120 h.Conclusions: Teicoplanin population PK is highly variable in neonates, weight being the best descriptor of PK variability. A low percentage of neonates were able to achieve C min .15 mg/L. The routine use of therapeutic drug monitoring and improved knowledge on the PD of teicoplanin is required.

Teicoplanin is frequently administered to treat Gram-positive infections in pediatric patients. However, not enough is known about the pharmacokinetics (PK) of teicoplanin in children to justify the optimal dosing regimen. The aim of this study was to determine the population PK of teicoplanin in children and evaluate the current dosage regimens. A PK hospital-based study was conducted. Current dosage recommendations were used for children up to 16 years of age. Thirty-nine children were recruited. Serum samples were collected at the first dose interval (1, 3, 6, and 24 h) and at steady state. A standard 2-compartment PK model was developed, followed by structural models that incorporated weight. Weight was allowed to affect clearance (CL) using linear and allometric scaling terms. 2). A significant rise in infections caused by methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-resistant coagulasenegative staphylococci (CoNS) has led to increased use of glycopeptides in the last decade (3, 4). Both vancomycin and teicoplanin are used for treatment of invasive infections caused by Gram-positive organisms, especially those that are resistant to ␤-lactam antibiotics (5, 6). The currently recommended regimen for teicoplanin in adults is 3 loading doses of 400 mg every 12 hours followed by a maintenance dose of 400 mg/day. In contrast, children receive 3 loading doses of 10 mg/kg of body weight every 12 hours followed by a maintenance dose of 10 mg/kg daily (7). However, there is a relative paucity of information to justify these regimens in children and even less information to identify optimal dosing strategies.Regulatory authorities such as the European Medicines Agency (EMA) have developed strategies to facilitate the safe and effective use of medicines in neonates and children (8). EMA supports the extrapolation of information from adults to children provided there are adequate safety data in the latter and the pharmacodynamics can reasonably assumed to be the same in both populations. This approach requires the development of robust population pharmacokinetic (PK) models in both adults and children, which facilitates the design of regimens that enable drug exposures in both populations to be matched (8).Teicoplanin is largely used without routinely measuring concentrations in the majority of pediatric patients. In our pediatric hospital setting, we have observed anecdotal cases of clinical failures with teicoplanin therapy and have observed "MIC creep" for CoNS with MICs at the breakpoint (4 mg/ liter) (unpublished data). To further investigate the clinical pharmacology of teicoplanin and to provide an insight into effective regimens for children, we performed a population PK study. The specific objectives of this study were to (i) describe the population PK of teicoplanin in children in a hospital setting, (ii) explore the percentage of patients attaining a predose minimum concentration (C min ) of Ͼ10 mg/liter, (iii) define the area under the concentration-time curve (AUC) distributions follo...

Aims The aims of the study were to compare [14C]‐paracetamol ([14C]‐PARA) paediatric pharmacokinetics (PK) after administration mixed in a therapeutic dose or an isolated microdose and to develop further and validate accelerator mass spectrometry (AMS) bioanalysis in the 0–2 year old age group. Methods [14C]‐PARA concentrations in 10–15 µl plasma samples were measured after enteral or i.v. administration of a single [14C]‐PARA microdose or mixed in with therapeutic dose in infants receiving PARA as part of their therapeutic regimen. Results Thirty‐four infants were included in the PARA PK analysis for this study: oral microdose (n = 4), i.v. microdose (n = 6), oral therapeutic (n = 6) and i.v. therapeutic (n = 18). The respective mean clearance (CL) values (SDs in parentheses) for these dosed groups were 1.46 (1.00) l h–1, 1.76 (1.07) l h–1, 2.93 (2.08) l h–1 and 2.72 (3.10) l h–1, t1/2 values 2.65 h, 2.55 h, 8.36 h and 7.16 h and dose normalized AUC(0‐t) (mg l–1 h) values were 0.90 (0.43), 0.84 (0.57), 0.7 (0.79) and 0.54 (0.26). Conclusions All necessary ethical, scientific, clinical and regulatory procedures were put in place to conduct PK studies using enteral and systemic microdosing in two European centres. The pharmacokinetics of a therapeutic dose (mg kg–1) and a microdose (ng kg–1) in babies between 35 to 127 weeks post‐menstrual age. [14C]‐PARA pharmacokinetic parameters were within a two‐fold range after a therapeutic dose or a microdose. Exploratory studies using doses significantly less than therapeutic doses may offer ethical and safety advantages with increased bionalytical sensitivity in selected exploratory paediatric pharmacokinetic studies.

Objectives: Following surgery, it is difficult to distinguish a postoperative inflammatory reaction from infection. This study examined the predictive value of the biomarkers; procalcitonin, C-reactive protein, lactate, neutrophils, lymphocytes, platelets, and the biphasic activated partial thromboplastin time waveform in diagnosing bacterial infection following cardiac surgery. Design: Prospective, observational study. Setting: A regional, PICU in the United Kingdom. Patients: Three-hundred sixty-eight children under the age of 16 admitted to the PICU for elective cardiac surgery were enrolled in the study. Interventions: All biomarker measurements were determined daily until postoperative day 7. Children were assessed for postoperative infection until day 28 and divided into four groups: bacterial infection, culture-negative sepsis, viral infection, and no infection. We used the Kruskal-Wallis test, chi-square test, analysis of variance, and area under the curve in our analysis. Measurements and Main Results: In total, 71 of 368 children (19%) developed bacterial infection postoperatively, the majority being surgical site infections. In those with bacterial infection, procalcitonin was elevated on postoperative days 1–3 and the last measurement prior to event compared with those without bacterial infection. The most significant difference was the last measurement prior to event; 0.72 ng/mL in the bacterial infection group versus 0.13 ng/mL in the no infection group (for all groups; p < 0.001). Longitudinal profiles of all biomarkers were indistinct in the bacterial infection and nonbacterial infection groups except in those with culture-negative infections who had distinct procalcitonin kinetics on postoperative days 1–4. Children with culture-negative sepsis required longer ventilatory support and PICU stay and were more likely to develop complications than the other groups. Conclusions: None of the biomarkers studied within 3 days of infection distinguished between infection and postoperative inflammatory reaction. However, procalcitonin kinetics peaked on postoperative day 2 and fell more sharply than C-reactive protein kinetics, which peaked at postoperative day 3. The monitoring of procalcitonin kinetics following cardiac surgery may help guide rational antimicrobial use.

Sepsis, defined as life-threatening organ dysfunction caused by infection is difficult to distinguish clinically from infection or post-operative inflammation. We hypothesized that in a heterogeneous group of critically ill children, there would be different metabolic profiles between post-operative inflammation, bacterial and viral infection and infection with or without organ dysfunction. 1D 1H nuclear magnetic resonance spectra were acquired in plasma samples from critically ill children. We included children with bacterial (n = 25) and viral infection (n = 30) and controls (n = 58) (elective cardiac surgery without infection). Principal component analysis was used for data exploration and partial least squares discriminant analysis models for the differences between groups. Area under receiver operating characteristic curve (AUC) values were used to evaluate the models. Univariate analysis demonstrated differences between controls and bacterial and viral infection. There was excellent discrimination between bacterial and control (AUC = 0.94), and viral and control (AUC = 0.83), with slightly more modest discrimination between bacterial and viral (AUC = 0.78). There was modest discrimination (AUC = 0.73) between sepsis with organ dysfunction and infection with no organ dysfunction. In critically ill children, NMR metabolomics differentiates well between those with a post-operative inflammation but no infection, and those with infection (bacterial and viral), and between sepsis and infection.

Objective Bacterial Infections remains a leading cause of death in the Paediatric Intensive Care Unit (PICU). In this era of rising antimicrobial resistance, new tools are needed to guide antimicrobial use. The aim of this study was to investigate the accuracy of procalcitonin (PCT), neutrophil gelatinase-associated lipocalin (NGAL), resistin, activated partial thromboplastin time (aPTT) waveform and C-reactive protein (CRP) for the diagnosis of serious bacterial infection (SBI) in children on admission to PICU and their use as prognostic indicators. Setting A regional PICU in the United Kingdom. Patients Consecutive PICU admissions between October 2010 and June 2012. Measurements Blood samples were collected daily for biomarker measurement. The primary outcome measure was performance of study biomarkers for diagnosis of SBI on admission to PICU based on clinical, radiological and microbiological criteria. Secondary outcomes included durations of PICU stay and invasive ventilation and 28-day mortality. Patients were followed up to day 28 post-admission. Main results A total of 657 patients were included in the study. 92 patients (14%) fulfilled criteria for SBI. 28-day mortality was 2.6% (17/657), but 8.7% (8/92) for patients with SBI. The combination of PCT, resistin, plasma NGAL and CRP resulted in the greatest net reclassification improvement compared to CRP alone (0.69, p<0.005) with 10.5% reduction in correct classification of patients with SBI (p 0.52) but a 78% improvement in correct classification of patients without events (p <0.005). A statistical model of prolonged duration of PICU stay found log-transformed maximum values of biomarkers performed better than first recorded biomarkers. The final model included maximum values of CRP, plasma NGAL, lymphocyte and platelet count (AUC 79%, 95% CI 73.7% to 84.2%). Longitudinal profiles of biomarkers showed PCT levels to decrease most rapidly following admission SBI. Conclusion Combinations of biomarkers, including PCT, may improve accurate and timely identification of SBI on admission to PICU.

Our results in a heterogeneous UK PICU population found the Braden Q score performed well in the specific population it was validated for (PICU children aged 3 weeks to 8 years without CHD), however, it performed moderately well in the more heterogonous PICU population of term to 14 years including children with CHD.

Acute kidney injury (AKI), a common complication in paediatric intensive care units (PICU), is associated with increased morbidity and mortality. In this single centre, prospective, observational cohort study, neutrophil gelatinase-associated lipocalin in urine (uNGAL) and plasma (pNGAL) and renal angina index (RAI), and combinations of these markers, were assessed for their ability to predict severe (stage 2 or 3) AKI in children and young people admitted to PICU. In PICU children and young people had initial and serial uNGAL and pNGAL measurements, RAI calculation on day 1, and collection of clinical data, including serum creatinine measurements. Primary outcomes were severe AKI and renal replacement therapy (RRT). Secondary outcomes were length of stay, hospital acquired infection and mortality. The area under the Receiver Operating Characteristic (ROC) curves and Youden index was used to determine biomarker performance and identify optimum cut-off values. Of 657 children recruited, 104 met criteria for severe AKI (15∙8%) and 47 (7∙2%) required RRT. Severe AKI was associated with increased length of stay, hospital acquired infection, and mortality. The area under the curve (AUC) for severe AKI prediction for Day 1 uNGAL, Day 1 pNGAL and RAI were 0.75 (95% Confidence Interval [CI] 0∙69, 0∙81), 0∙64 (95% CI 0∙56, 0∙72), and 0.73 (95% CI 0∙65, 0∙80) respectively. The optimal combination of measures was RAI and day 1 uNGAL, giving an AUC of 0∙80 for severe AKI prediction (95% CI 0∙71, 0∙88). In this heterogenous PICU cohort, urine or plasma NGAL in isolation had poorer prediction accuracy for severe AKI than in previously reported homogeneous populations. However, when combined together with RAI, they produced good prediction for severe AKI.