BackgroundSepsis and bacterial meningitis are major causes of mortality and morbidity in neonates and infants. Meropenem, a broad-spectrum antibiotic, is not licensed for use in neonates and infants below 3 months of age and sufficient information on its plasma and CSF disposition and dosing in neonates and infants is lacking.ObjectivesTo determine plasma and CSF pharmacokinetics of meropenem in neonates and young infants and the link between pharmacokinetics and clinical outcomes in babies with late-onset sepsis (LOS).MethodsData were collected in two recently conducted studies, i.e. NeoMero-1 (neonatal LOS) and NeoMero-2 (neonatal meningitis). Optimally timed plasma samples (n = 401) from 167 patients and opportunistic CSF samples (n = 78) from 56 patients were analysed.ResultsA one-compartment model with allometric scaling and fixed maturation gave adequate fit to both plasma and CSF data; the CL and volume (standardized to 70 kg) were 16.7 (95% CI 14.7, 18.9) L/h and 38.6 (95% CI 34.9, 43.4) L, respectively. CSF penetration was low (8%), but rose with increasing CSF protein, with 40% penetration predicted at a protein concentration of 6 g/L. Increased infusion time improved plasma target attainment, but lowered CSF concentrations. For 24 patients with culture-proven Gram-negative LOS, pharmacodynamic target attainment was similar regardless of the test-of-cure visit outcome.ConclusionsSimulations showed that longer infusions increase plasma PTA but decrease CSF PTA. CSF penetration is worsened with long infusions so increasing dose frequency to achieve therapeutic targets should be considered.

Pharmacodynamic (PD) count data can exhibit bimodality and nonequidispersion complicating the inclusion of drug effect. The purpose of this study was to explore four different mixture distribution models for bimodal count data by including both drug effect and distribution truncation. An example dataset, which exhibited bimodal pattern, was from rodent brief‐access taste aversion (BATA) experiments to assess the bitterness of ascending concentrations of an aversive tasting drug. The two generalized Poisson mixture models performed the best and was flexible to explain both under and overdispersion. A sigmoid maximum effect (Emax) model with logistic transformation was introduced to link the drug effect to the data partition within each distribution. Predicted density‐histogram plot is suggested as a model evaluation tool due to its capability to directly compare the model predicted density with the histogram from raw data. The modeling approach presented here could form a useful strategy for modeling similar count data types.

BackgroundThe capacity to accurately measure and report antimicrobial consumption is an essential element of antimicrobial stewardship (AMS). Though days of therapy (DOT) and defined daily doses (DDD) are common metrics used to measure consumption, no established metric exists in children. Our aim was to model metrics of Meropenem consumption based on administration data in a tertiary children’s hospital, and to analyse trends in Meropenem consumption and resistance over time.MethodsUsing an established electronic prescribing system, we extracted data on all Meropenem administrations from 2010 to 2016. Meropenem susceptibility of all Enterobacteriaceae isolates was extracted over the same period. Consumption was expressed as DOT per 1000 patient-days (DOT/1000 PD), DDD per 1000 patient-days (DDD/1000 PD). Time series analysis was undertaken to explore trends in both consumption and non-susceptibility of Enterobacteriaceae to Meropenem.ResultsMeropenem consumption was highly seasonal and increased over time. Between 2010 and 2016 there was an increase of 17.2% (95% CI 13.3% to 20.1%) in DOT/1000 PD. DDD and DOT showed similar seasonal variation, and similar overall increases between 2010 and 2015 (Figure 1). Resistance in E. coli increased to Piptazobactam (from 12% to 32%, p<0.001), Ceftazidime (20% to 46%, p<0.001), and Meropenem (from 0.3% to 8%, p<0.001).DiscussionUsing electronic prescribing we were able to compare DDD and DOT as measures of antimicrobial consumption in children of all ages in a large tertiary children’s hospital. In this single centre setting, DDD and DOT exhibited similar trends and may be considered equivalent measures. Time series analysis allows robust inferences regarding trends in Meropenem consumption and demonstrates an increase over the last 5 years. Contemporaneously there has been an increase in E. coli resistance to Piptazobactam, Ceftazidime and of significant concern, Meropenem.