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To ensure the safe and effective dosing of gentamicin in children, therapeutic drug monitoring (TDM) is recommended. TDM utilizing Bayesian forecasting software is recommended but is unavailable, as no population model that describes the pharmacokinetics of gentamicin in pediatric oncology patients exists. This study aimed to develop and externally evaluate a population pharmacokinetic model of gentamicin to support personalized dosing in pediatric oncology patients. A nonlinear mixed-effect population pharmacokinetic model was developed from retrospective data. Data were collected from 423 patients for model building and a further 52 patients for external evaluation. A two-compartment model with first-order elimination best described the gentamicin disposition. The final model included renal function (described by fat-free mass and postmenstrual age) and the serum creatinine concentration as covariates influencing gentamicin clearance (CL). Final parameter estimates were as follow CL, 5.77 liters/h/70 kg; central volume of distribution, 21.6 liters/70 kg; peripheral volume of distribution, 13.8 liters/70 kg; and intercompartmental clearance, 0.62 liter/h/70 kg. External evaluation suggested that current models developed in other pediatric cohorts may not be suitable for use in pediatric oncology patients, as they showed a tendency to overpredict the observations in this population. The final model developed in this study displayed good predictive performance during external evaluation (root mean square error, 46.0%; mean relative prediction error, Ϫ3.40%) and may therefore be useful for the personalization of gentamicin dosing in this cohort. Further investigations should focus on evaluating the clinical application of this model. KEYWORDS pharmacokinetics, gentamicin, NONMEM, pediatrics, oncology, pharmacometrics, population pharmacokinetics F ebrile neutropenia induced by chemotherapy is a common complication in pediatric oncology patients. Neutropenic patients are more susceptible to the development of infections (1). Sepsis is the primary cause of mortality and morbidity in pediatric oncology patients with febrile neutropenia (2). The rate of mortality due to sepsis is 1.6-fold higher for oncology pediatric patients than it is for other pediatric patients (3). Aminoglycoside antibiotics, such as gentamicin, in combination with other broad-spectrum antibacterial agents play an important role in managing infectious complications in these individuals and are used as second-line therapies when treating Gram-negative bacterial infections and when resistance to first-line agents develops (4).Gentamicin has a narrow therapeutic window and displays large pharmacokinetic variability. High levels of and prolonged exposure to gentamicin has been associated with nephro-and ototoxicity (5, 6). Pediatric oncology patients often receive long

Background Common genetic variance in apolipoprotein E (APOE), β‐glucocerebrosidase (GBA), microtubule‐associated protein tau (MAPT), and α‐synuclein (SNCA) has been linked to cognitive decline in Parkinson's disease (PD), although studies have yielded mixed results. Objectives To evaluate the effect of genetic variants in APOE, GBA, MAPT, and SNCA on cognitive decline and risk of dementia in a pooled analysis of six longitudinal, non‐selective, population‐based cohorts of newly diagnosed PD patients. Methods 1002 PD patients, followed for up to 10 years (median 7.2 years), were genotyped for at least one of APOE‐ε4, GBA mutations, MAPT H1/H2, or SNCA rs356219. We evaluated the effect of genotype on the rate of cognitive decline (Mini‐Mental State Examanation, MMSE) using linear mixed models and the development of dementia (diagnosed using standardized criteria) using Cox regression; multiple comparisons were accounted for using Benjamini−Hochberg corrections. Results Carriers of APOE‐ε4 (n = 281, 29.7%) and GBA mutations (n = 100, 10.3%) had faster cognitive decline and were at higher risk of progression to dementia (APOE‐ε4, HR 3.57, P < 0.001; GBA mutations, HR 1.76, P = 0.001) than non‐carriers. The risk of cognitive decline and dementia (HR 5.19, P < 0.001) was further increased in carriers of both risk genotypes (n = 23). No significant effects were observed for MAPT or SNCA rs356219. Conclusions GBA and APOE genotyping could improve the prediction of cognitive decline in PD, which is important to inform the clinical trial selection and potentially to enable personalized treatment © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society

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Background Free-water imaging can predict and monitor dopamine system degeneration in people with Parkinson’s disease. It can also enhance the sensitivity of traditional diffusion tensor imaging (DTI) metrics for indexing neurodegeneration. However, these tools are yet to be applied to investigate cholinergic system degeneration in Parkinson’s (which involves both the pedunculopontine nucleus (PPN) and cholinergic basal forebrain (cBF)). Methods Free-water imaging, free-water-corrected DTI, and volumetry were used to extract structural metrics from the cBF and PPN in 99 people with Parkinson’s and 46 age-matched controls. Cognitive ability was tracked over 4.5-years. Results Pearson’s partial correlations revealed that free-water-corrected DTI metrics in the PPN were associated with performance on cognitive tasks that required participants to make rapid choices (behavioural flexibility). Volumetric, free-water content and DTI metrics in the cBF were elevated in a sub-group of people with Parkinson’s with evidence of cognitive impairment, and linear mixed modelling revealed that these metrics were differently associated with current and future changes to cognition. Conclusions Free water and free-water-corrected DTI can index cholinergic degeneration that could enable stratification of patients in clinical trials of cholinergic interventions for cognitive decline. In addition, degeneration of the PPN impairs behavioural flexibility in Parkinson’s, which may explain this region’s role in increased risk of falls.

A number of equations were identified that could be used to estimate renal function in paediatric oncology patients; however, none was found to be highly accurate. The Flanders metadata equation and univariate Schwartz performed the best in this study, and we would suggest that these two equations may be used cautiously in paediatric oncology patients for clinical decision making, understanding their limitations.

Based on a log-linear method of analysis, current dosing appears to be consistently producing gentamicin exposure below pre-defined pharmacokinetic targets, suggesting that an increase in the recommended starting dose of gentamicin may be required.

Dosing gentamicin in pediatric patients can be difficult due to its narrow therapeutic index. A significantly higher percentage of fat mass has been observed in children receiving oncology treatment than in those who are not. Differences in the pharmacokinetics of gentamicin between oncology and nononcology pediatric patients and individual dosage requirements were evaluated in this study, using normal fat mass (NFM) as a body size descriptor. Data from 423 oncology and 115 nononcology patients were analyzed. Differences in drug disposition were observed between the oncology and nononcology patients, with oncology patients having a 15% lower central volume of distribution and 32% lower intercompartmental clearance. Simulations based on the population pharmacokinetic model demonstrated low exposure target attainment in all individuals at the current clinical recommended starting dose of 7.5 mg/kg of body weight once daily, with 57.4% of oncology and 35.7% of nononcology subjects achieving a peak concentration (Cmax) of ≥25 mg/liter and 64.3% of oncology and 65.6% of nononcology subjects achieving an area under the concentration-time curve at 24 h postdose (AUC24) of ≥70 mg · h/liter after the first dose. Based on simulations, the extent of the impact of differences in drug disposition between the two cohorts appeared to be dependent on the exposure target under examination. Greater differences in achieving a Cmax target of >25 mg/liter than an AUC24 target of ≥70 mg · h/liter between the cohorts was observed. Further investigation into whether differences in the pharmacokinetics of gentamicin between oncology and nononcology patients are a consequence of changes in body composition is required.