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Early-onset sepsis (EOS) is one of the most significant causes of morbidity and mortality in neonates. Currently, amoxicillin is empirically used to treat neonates with EOS. However, data on its effectiveness in neonates with EOS are still limited. Therefore, we aimed to evaluate the pharmacodynamics (PD) target attainment and effectiveness of a model-based amoxicillin dosage regimen in these neonates. We used a previously developed model and collected additional clinical data from the EOS neonates who used the model-based dosage regimen (25 mg/kg every 12 h). The primary outcomes were PD target attainment (free drug concentration above minimum inhibitory concentration during 70% of the dosing interval) and treatment failure rate. The secondary endpoints were length of amoxicillin treatment, duration of hospitalization etc. Seventy-five neonates (postmenstrual age 28.4-41.6 wk) were enrolled. A total of 70 (93.3%) neonates reached their PD target using 1 mg/L as the minimum inhibitory concentration breakpoint. The treatment failure rate was 10.7%.

Vancomycin is frequently used to treat Gram-positive bacterial infections in neonates. However, there is still no consensus on the optimal initial dosing regimen. This study aimed to assess the performance of pharmacokinetic model-based virtual trials to predict the dose-exposure relationship of vancomycin in neonates.The PubMed database was searched for clinical trials of vancomycin in neonates that reported the percentage of target attainment. Monte Carlo simulations were performed using nonlinear mixed-effect modeling to predict the dose-exposure relationship, and the differences in outcomes between virtual trials and real-world data in clinical studies were calculated.A total of 11 studies with 14 dosing groups were identified from the literature to evaluate dose-exposure relationships. For the ten dosing groups where the surrogate marker for exposure was the trough concentration, the mean ± standard deviation (SD) for the target attainment between original studies and virtual trials was 3.0 ± 7.3%. Deviations between - 10 and 10% accounted for 80% of the included dosing groups. For the other four dosing groups where the surrogate marker for exposure was concentration during continuous infusion, all deviations were between - 10 and 10%, and the mean ± SD value was 2.9 ± 4.5%.The pharmacokinetic model-based virtual trials of vancomycin exhibited good predictive performance for dose-exposure relationships in neonates. These results might be used to assist the optimization of dosing regimens in neonatal practice, avoiding the need for trial and error.

In recent years, population pharmacokinetics (PK) has been widely used in neonatal pharmacology. However, the sample size selection for neonatal PK studies has been highly variable and without clear consensus, especially for drugs with large individual variability. Therefore, this study's objective was to investigate the optimal sample size for use in neonatal PK studies.A comprehensive and reliable population PK model (1631 neonates) of vancomycin was selected as a reference model. The original sparse PK dataset was divided into several sub-datasets according to different sample sizes. NONMEM was used for sub-datasets PK analysis. Statistical powers were calculated to evaluate different sample sizes ( 80% was expected).During population clearance estimations, the average power was 40%, 85%, 100%, and 100% for sample sizes of 10, 25, 50, and 100 neonates, respectively. And the frequency of model-estimated median clearance values within ± 10% (relative errors) of target value (0.057 L/h) were 75.0%, 68.8%, 57.8%, and 35.0%, respectively. Regarding age sub-groups (postmenstrual age (PMA) or ≥ 37 weeks) clearance estimation, a sample size of 50 was better to complete the assessment of the neonatal age sub-group even in some cases of unbalanced age distribution.A sample size of 25 neonates provided a consistent estimation of the overall population (PMA: 23.3-52.4 weeks) clearance for a drug with high individual variability using a sparse PK sampling design. A sample size of 50 was recommended to complete neonatal age sub-group assessments.

Meropenem is increasingly used to treat neonatal sepsis. There are several guidelines recommending different dosing regimens of meropenem in neonates. Furthermore, deviations from these guidelines regularly occur in daily clinical practice. Therefore, the current study aimed to evaluate the variations of meropenem dosing guidelines and compare the difference between guideline and clinical practice in terms of the probability of target attainment.This study is based on a population pharmacokinetic model. After defining the predictive performance of the model, Monte Carlo simulations were used to calculate the probability of target attainment of the currently existing dosing guidelines of meropenem and their use in daily clinical practice.Two guidelines and two labels were included in the Monte Carlo simulations. For 70% fTThis model-based population pharmacokinetics simulation showed that improper guidelines and/or clinical practice deviations will result in low probability of target attainment for patients infected with resistant bacteria and critically ill patients. It is important to develop and adhere to evidence-based and clinically pragmatic guidelines.

Objectives Mezlocillin is used in the treatment of neonatal infectious diseases. However, due to the absence of population pharmacokinetic studies in neonates and young infants, dosing regimens differ considerably in clinical practice. Hence, this study aimed to describe the pharmacokinetic characteristics of mezlocillin in neonates and young infants, and propose the optimal dosing regimen based on the population pharmacokinetic model of mezlocillin. Methods A prospective, open-label pharmacokinetic study of mezlocillin was carried out in newborns. Blood samples were collected using an opportunistic sampling method. HPLC was used to measure the plasma drug concentrations. A population pharmacokinetic model was developed using NONMEM software. Results Ninety-five blood samples from 48 neonates and young infants were included. The ranges of postmenstrual age and birth weight were 29–40 weeks and 1200–4000 g, respectively, including term and preterm infants. A two-compartment model with first-order elimination was developed to describe the population pharmacokinetics of mezlocillin. Postmenstrual age, current weight and serum creatinine concentration were the most important covariates. Monte Carlo simulation results indicated that the current dose of 50 mg/kg q12h resulted in 89.2% of patients achieving the therapeutic target, when the MIC of 4 mg/L was used as the breakpoint. When increasing the dosing frequency to q8h, a dose of 20 mg/kg resulted in 74.3% of patients achieving the therapeutic target. Conclusions A population pharmacokinetic model of mezlocillin in neonates and young infants was established. Optimal dosing regimens based on this model were provided for use in neonatal infections.

AIMS Early-onset sepsis (EOS) is a common disease in neonates with a high morbidity and mortality rate. Piperacillin/tazobactam has been used extensively and empirically for EOS treatment without clinically validated dosing regimens, although the population pharmacokinetics (PPK) of piperacillin in neonates has been reported. Therefore, we wanted to study the effectiveness and tolerance of a PPK model-based dosing regimen of piperacillin/tazobactam in EOS patients. METHODS A prospective, single-centre, phase II clinical study of piperacillin/tazobactam in neonates with EOS was conducted. The dosing regimen (90 mg·kg-1 , q8h) was determined based on a previous piperacillin PPK model in young infants using NONMEM v7.4. The pharmacodynamics (PD) target (70%fT > MIC, free drug concentration above MIC during 70% of the dosing interval) attainment was calculated using NONMEM combined with an opportunistic sampling design. The clinical treatment data were collected. RESULTS A total of 52 neonates were screened and 49 neonates completed their piperacillin/tazobactam treatment course and were included in this analysis. The median (range) values of postmenstrual age were 33.57 (range 26.14-41.29) weeks. Forty-seven (96%) neonates reached their PD target. Eight (16%) neonates experienced treatment failure clinically. The mean (SD, range) duration of treatment and length of hospitalization were 100.1 (62.2, 36.2-305.8) hours and 31 (30, 5-123) days. There were no obvious adverse events and no infection-related deaths occurred in the first month of life. CONCLUSIONS A model-based dosing regimen of piperacillin/tazobactam was evaluated clinically, was tolerated well and was determined to be effective for EOS treatment.

International audience; Background Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data. Objective The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates. Methods Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds. Individual estimates of clearance obtained from population pharmacokinetic models were used as reference clearances, and diverse machine learning methods and nested cross-validation were adopted and evaluated against these reference clearances. The predictive performance of these combined methods was compared with the performance of two other predictive methods: a covariate-based maturation model and a postmenstrual age and body weight scaling model. Relative error was used to evaluate the different methods. Results The extra tree regressor was selected as the best-fit machine learning method. Using the combined method, more than 95% of predictions for all six drugs had a relative error of < 50% and the mean relative error was reduced by an average of 44.3% and 71.3% compared with the other two predictive methods. Conclusion A combined population pharmacokinetic and machine learning approach provided improved predictions of individual clearances of renally cleared drugs in neonates. For a new patient treated in clinical practice, individual clearance can be predicted a priori using our model code combined with demographic data. Bo-Hao Tang and Zheng Guan contributed equally to this work.

Ceftazidime is a third-generation cephalosporin with high activity against many pathogens. But the ambiguity and diversity of the dosing regimens in neonates and young infants impair access to effective treatment. Thus, we conducted a population pharmacokinetic study of ceftazidime in this vulnerable population and recommended a model-based dosage regimen to optimize sepsis therapy. Totally 146 neonates and young infants (gestational age (GA): 36–43.4 weeks, postnatal age (PNA): 1–81 days, current weight (CW): 900–4500 g) were enrolled based on inclusion and exclusion criteria. Ceftazidime bloods samples (203) were obtained using the opportunistic sampling strategy and determined by the high-performance liquid chromatography. The population pharmacokinetic-pharmacodynamic analysis was conducted by nonlinear mixed effects model (NONMEM). A one-compartment model with first-order elimination best described the pharmacokinetic data. Covariate analysis showed the significance of GA, PNA, and CW on developmental pharmacokinetics. Monte Carlo simulation was performed based on above covariates and minimum inhibitory concentration (MIC). In the newborns with PNA ≤ 3 days (MIC=8 mg/L), the dose regimen was 25 mg/kg twice daily (BID). For the newborns with PNA > 3 days (MIC=16 mg/L), the optimal dose was 30 mg/kg three times daily (TID) for those with GA ≤ 37 weeks and 40 mg/kg TID for those with GA > 37 weeks. Overall, on the basis of the developmental population pharmacokinetic-pharmacodynamic analysis covering the whole range of neonates and young infants, the evidence-based ceftazidime dosage regimens were proposed to optimize neonatal early-onset and late-onset sepsis therapy.

Objectives Nowadays, real-world data can be used to improve currently available dosing guidelines and to support regulatory approval of drugs for use in neonates by overcoming practical and ethical hurdles. This proof-of-concept study aimed to assess the population pharmacokinetics of azlocillin in neonates using real-world data, to make subsequent dose recommendations and to test these in neonates with early-onset sepsis (EOS). Methods This prospective, open-label, investigator-initiated study of azlocillin in neonates with EOS was conducted using an adaptive two-step design. First, a maturational pharmacokinetic-pharmacodynamic model of azlocillin was developed, using an empirical dosing regimen combined with opportunistic samples resulting from waste material. Second, a Phase II clinical trial (ClinicalTrials.gov: NCT03932123) of this newly developed model-based dosing regimen of azlocillin was conducted to assure optimized target attainment [free drug concentration above MIC during 70% of the dosing interval (‘70% fT>MIC’)] and to investigate the tolerance and safety in neonates. Results A one-compartment model with first-order elimination, using 167 azlocillin concentrations from 95 neonates (31.7–41.6 weeks postmenstrual age), incorporating current weight and renal maturation, fitted the data best. For the second step, 45 neonates (30.3–41.3 weeks postmenstrual age) were subsequently included to investigate target attainment, tolerance and safety of the pharmacokinetic-pharmacodynamic model-based dose regimen (100 mg/kg q8h). Forty-three (95.6%) neonates reached their pharmacokinetic target and only two neonates experienced adverse events (feeding intolerance and abnormal liver function), possibly related to azlocillin. Conclusions Target attainment, tolerance and safety of azlocillin was shown in neonates with EOS using a pharmacokinetic-pharmacodynamic model developed with real-world data.