The present study determined the pharmacokinetic profile of vancomycin in premature Malaysian infants. A one-compartment infusion model with first-order elimination was fitted to serum vancomycin concentration data (n ‫؍‬ 835 points) obtained retrospectively from the drug monitoring records of 116 premature newborn infants. Vancomycin concentrations were estimated by a fluorescence polarization immunoassay. Population and individual estimates of clearance and distribution volume and the factors which affected the variability observed for the values of these parameters were obtained using a population pharmacokinetic modeling approach. The predictive performance of the population model was evaluated by visual inspections of diagnostic plots and nonparametric bootstrapping with replacement. Dosing guidelines targeting a value of >400 for the area under the concentration-time curve over 24 h in the steady state divided by the MIC (AUC 24 /MIC ratio) were explored using Monte Carlo simulation. Body size (weight), postmenstrual age, and smallfor-gestational-age status are important factors explaining the between-subject variability of vancomycin pharmacokinetic parameter values for premature neonates. The typical population parameter estimates of clearance and distribution volume for a 1-kg premature appropriate-for-gestational-age neonate with a postmenstrual age of 30 weeks were 0.0426 liters/h and 0.523 liters, respectively. There was a 20% reduction in clearance for small-for-gestational-age infants compared to the level for the appropriate-for-gestational-age control. Dosage regimens based on a priori target response values were formulated. In conclusion, the pharmacokinetic parameter values for vancomycin in premature Malaysian neonates were estimated. Improved dosage regimens based on a priori target response values were formulated by incorporating body size, postmenstrual age, and small-for-gestational-age status, using Monte Carlo simulations with the modelestimated pharmacokinetic parameter values.Bacterial sepsis is a major cause of neonatal complications, prolonged hospital stay, and death in premature newborns, especially those in developing countries. Sepsis-related mortality and morbidity rates were even higher in extremely preterm neonates and intrauterine-growth-restricted infants because of their innate immunological immaturity (36). Because coagulase-negative staphylococcus (CoNS) is a common pathogen for late-onset (72-h-postbirth) septicemia, and because methicillin-resistant Staphylococcus aureus (MRSA) is an important pathogen (10), vancomycin continues to be widely prescribed in neonatal intensive care units.Kidney and vestibular/cochlear damage is a concern associated with vancomycin use. Very recently, it was shown that vancomycin trough concentrations were correlated with nephrotoxicity in hospitalized adult patients (27). Nonetheless, vancomycin-associated ototoxicity and nephrotoxicity among neonates are thought to be less frequent than those in adults (11), although more evidence-based i...

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 Δ9‐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 t1/2 was 72–80 min, tmax was 39–56 min and Cmax 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 tmax 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.

Increasing knowledge of intertumor heterogeneity, intratumor heterogeneity, and cancer evolution has improved the understanding of anticancer treatment resistance. A better characterization of cancer evolution and subsequent use of this knowledge for personalized treatment would increase the chance to overcome cancer treatment resistance. Model‐based approaches may help achieve this goal. In this review, we comprehensively summarized mathematical models of tumor dynamics for solid tumors and of drug resistance evolution. Models displayed by ordinary differential equations, algebraic equations, and partial differential equations for characterizing tumor burden dynamics are introduced and discussed. As for tumor resistance evolution, stochastic and deterministic models are introduced and discussed. The results may facilitate a novel model‐based analysis on anticancer treatment response and the occurrence of resistance, which incorporates both tumor dynamics and resistance evolution. The opportunities of a model‐based approach as discussed in this review can be of great benefit for future optimizing and personalizing anticancer treatment.

PurposeA 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.MethodsThe 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.ResultsThe 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.ConclusionsThe 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.

PurposePredicting 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.MethodsA 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.ResultsA 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%).ConclusionsA 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.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-016-2065-3) contains supplementary material, which is available to authorized users.

Treatment failure of antibiotic therapy due to insufficient efficacy or occurrence of toxicity is a major clinical challenge, and is expected to become even more urgent with the global rise of antibiotic resistance. Strategies to optimize treatment in individual patients are therefore of crucial importance. Currently, therapeutic drug monitoring plays an important role in optimizing antibiotic exposure to reduce treatment failure and toxicity. Biomarker-based strategies may be a powerful tool to further quantify and monitor antibiotic treatment response, and reduce variation in treatment response between patients. Host response biomarkers, such as CRP, procalcitonin, IL-6, and presepsin, could potentially carry significant information to be utilized for treatment individualization. To achieve this, the complex interactions among immune system, pathogen, drug, and biomarker need to be better understood and characterized. The purpose of this tutorial is to discuss the use and evidence of currently available biomarker-based approaches to inform antibiotic treatment. To this end, we also included a discussion on how treatment response biomarker data from preclinical, healthy volunteer, and patient-based studies can be further characterized using pharmacometric and system pharmacology based modeling approaches. As an illustrative example of how such modeling strategies can be used, we describe a case study in which we quantitatively characterize procalcitonin dynamics in relation to antibiotic treatments in patients with sepsis.

A one-compartmental population pharmacokinetic/pharmacodynamic model was developed using a multi-level Markov transition model. No (clinically relevant) effect of moderate therapeutic hypothermia on phenobarbital pharmacokinetics could be identified. The observed responsiveness was 66%. While we still advise an initial loading dose of 20 mg/kg, clinicians should not be reluctant to administer an additional dose of 10-20 mg/kg. An additional dose should be given before switching to a second-line anticonvulsant drug. Based on our pharmacokinetic/pharmacodynamic model, administration of phenobarbital under hypothermia seems to reduce the transition rate from a continuous normal voltage (CNV) to discontinuous normal voltage aEEG background level in hypothermic asphyxiated newborns, which may be attributed to the additional neuroprotection of phenobarbital in infants with a CNV pattern.

Background Sepsis is a major contributor to neonatal mortality, particularly in low-income and middle-income countries (LMICs). WHO advocates ampicillin-gentamicin as first-line therapy for the management of neonatal sepsis. In the BARNARDS observational cohort study of neonatal sepsis and antimicrobial resistance in LMICs, common sepsis pathogens were characterised via whole genome sequencing (WGS) and antimicrobial resistance profiles. In this substudy of BARNARDS, we aimed to assess the use and efficacy of empirical antibiotic therapies commonly used in LMICs for neonatal sepsis.Methods In BARNARDS, consenting mother-neonates aged 0-60 days dyads were enrolled on delivery or neonatal presentation with suspected sepsis at 12 BARNARDS clinical sites in