A meta-analysis of protein binding of flucloxacillin in healthy volunteers and hospitalized patients

Objectives: The aim of this study was to develop a mechanistic protein-binding model to predict the unbound flucloxacillin concentrations in different patient populations. Methods: A mechanistic protein-binding model was fitted to the data using non-linear mixed-effects modelling. Data were obtained from four datasets, containing 710 paired total and unbound flucloxacillin concentrations from healthy volunteers, non-critically ill and critically ill patients. A fifth dataset with data from hospitalized patients was used for evaluation of our model. The predictive performance of the mechanistic model was evaluated and compared with the calculation of the unbound concentration with a fixed unbound fraction of 5%. Finally, we performed a fit-for-use evaluation, verifying whether the model-predicted unbound flucloxacillin concentrations would lead to clinically incorrect dose adjustments. Results: The mechanistic protein-binding model predicted the unbound flucloxacillin concentrations more accurately than assuming an unbound fraction of 5%. The mean prediction error varied between -26.2% to 27.8% for the mechanistic model and between -30.8% to 83% for calculation with a fixed factor of 5%. The normalized root mean squared error varied between 36.8% and 69% respectively between 57.1% and 134%. Predicting the unbound concentration with the use of the mechanistic model resulted in 6.1% incorrect dose adjustments versus 19.4% if calculated with a fixed unbound fraction of 5%. Conclusions: Estimating the unbound concentration with a mechanistic protein-binding model outperforms the calculation with the use of a fixed protein binding factor of 5%, but neither demonstrates acceptable performance. When performing dose individualization of flucloxacillin, this should be done based on measured unbound concentrations rather than on estimated unbound concentrations from the measured total concentrations. In the absence of an assay for unbound concentrations, the mechanistic binding model should be preferred over assuming a fixed unbound fraction of 5%

Accelerated menopausal changes as human disease model 'FOCUM' for the development of osteoarthritis and other degenerative disorders:protocol for a prospective cohort study

INTRODUCTION: The incidence of degenerative disorders, including osteoarthritis (OA), increases rapidly in women after menopause. However, the influence of the menopause is still insufficiently investigated due to the slowness of menopausal transition. In this study, a novel human model is used in which it is expected that menopausal-related changes will occur faster. This is the Females discontinuing Oral Contraceptives Use at Menopausal age model. The ultimate aim is to link these changes to OA and other degenerative disorders, including cardiovascular diseases, diabetes, osteoporosis and tendinopathies. METHODS AND ANALYSIS: This is a pilot observational prospective cohort study with 2 years of follow-up. Fifty women aged 50–60 who use oral contraceptive (OC) and have the intention to stop are included. Measurements are performed once before stopping OC, and four times thereafter at 6 weeks, 6 months, 1 year and 2 years. At every time point, a questionnaire is filled in and a sample of blood is drawn. At the first and final time points, a physical examination, hand radiographs and a MRI scan of one knee are performed. The primary OA outcome is progression of the MRI Osteoarthritis Knee Score. Secondary OA outcomes are the development of clinical knee and hand OA, development of knee OA according to the MRI definition, and progression of radiographic features for hand OA. Principal component analysis will be used to assess which changes occur after stopping OC. Univariate and multivariate generalised estimating equation models will be used to test for associations between these components and OA. ETHICS AND DISSEMINATION: The study has been approved by the Medical Ethics Committee of the Erasmus MC University Medical Center Rotterdam (MEC-2019-0592). All participants must give informed consent before data collection. Results will be disseminated in national and international journals. TRIAL REGISTRATION NUMBER: NL70796.078.19

High unbound flucloxacillin fraction in critically ill patients

OBJECTIVES: To describe the unbound and total flucloxacillin pharmacokinetics in critically ill patients and to define optimal dosing strategies. PATIENTS AND METHODS: Observational multicentre study including a total of 33 adult ICU patients receiving flucloxacillin, given as intermittent or continuous infusion. Pharmacokinetic sampling was performed on two occasions on two different days. Total and unbound flucloxacillin concentrations were measured and analysed using non-linear mixed-effects modelling. Serum albumin was added as covariate on the maximum binding capacity and endogenous creatinine clearance (CLCR) as covariate for renal function. Monte Carlo simulations were performed to predict the unbound flucloxacillin concentrations for different dosing strategies and different categories of endogenous CLCR. RESULTS: The measured unbound concentrations ranged from 0.2 to 110 mg/L and the observed unbound fraction varied between 7.0% and 71.7%. An integral two-compartmental linear pharmacokinetic model based on total and unbound concentrations was developed. A dose of 12 g/24 h was sufficient for 99.9% of the population to achieve a concentration of >2.5 mg/L (100% fT>5×MIC, MIC = 0.5 mg/L). CONCLUSIONS: Critically ill patients show higher unbound flucloxacillin fractions and concentrations than previously thought. Consequently, the risk of subtherapeutic exposure is low

Long-term deficits in episodic memory after ischemic stroke: evaluation and prediction of verbal and visual memory performance based on lesion characteristics

We investigated the relationship between ischemic lesion characteristics (hemispheric side, cortical and subcortical level, volume) and memory performance, 1 year after stroke. Verbal and visual memory of 86 patients with stroke were assessed with Rey Auditory-Verbal Learning Test and the Doors Test, respectively. Lesion characteristics and presence of white matter lesions were assessed on magnetic resonance imaging early after stroke. Multiple regression analyses were used to investigate prediction of verbal and visual memory performance by lesion side (left v right hemisphere), lesion level (cortical v subcortical), and lesion volume. We controlled for the influence of demographic characteristics, language disability, and visuospatial difficulties on memory. The results demonstrated that poor verbal memory (immediate and delayed recall and recognition) could be predicted by lesion characteristics: patients with left hemispheric, subcortical, and large lesions showed poor memory performance. Poor visual recognition memory could not be predicted by lesion characteristics but only by low educational level. Our results suggest that lesion characteristics play an important role in episodic verbal memory poststroke if demographic and clinical characteristics are taken into accoun