Model-based clinical dose optimization for phenobarbital in neonates: An illustration of the importance of data sharing and external validation

Prognostic models for predicting clinical disease progression, worsening and activity in people with multiple sclerosis

BACKGROUND

Multiple sclerosis (MS) is a chronic inflammatory disease of the central nervous system that affects millions of people worldwide. The disease course varies greatly across individuals and many disease-modifying treatments with different safety and efficacy profiles have been developed recently. Prognostic models evaluated and shown to be valid in different settings have the potential to support people with MS and their physicians during the decision-making process for treatment or disease/life management, allow stratified and more precise interpretation of interventional trials, and provide insights into disease mechanisms. Many researchers have turned to prognostic models to help predict clinical outcomes in people with MS; however, to our knowledge, no widely accepted prognostic model for MS is being used in clinical practice yet.

OBJECTIVES

To identify and summarise multivariable prognostic models, and their validation studies for quantifying the risk of clinical disease progression, worsening, and activity in adults with MS.

SEARCH METHODS

We searched MEDLINE, Embase, and the Cochrane Database of Systematic Reviews from January 1996 until July 2021. We also screened the reference lists of included studies and relevant reviews, and references citing the included studies.

SELECTION CRITERIA

We included all statistically developed multivariable prognostic models aiming to predict clinical disease progression, worsening, and activity, as measured by disability, relapse, conversion to definite MS, conversion to progressive MS, or a composite of these in adult individuals with MS. We also included any studies evaluating the performance of (i.e. validating) these models. There were no restrictions based on language, data source, timing of prognostication, or timing of outcome.

DATA COLLECTION AND ANALYSIS

Pairs of review authors independently screened titles/abstracts and full texts, extracted data using a piloted form based on the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies (CHARMS), assessed risk of bias using the Prediction Model Risk Of Bias Assessment Tool (PROBAST), and assessed reporting deficiencies based on the checklist items in Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD). The characteristics of the included models and their validations are described narratively. We planned to meta-analyse the discrimination and calibration of models with at least three external validations outside the model development study but no model met this criterion. We summarised between-study heterogeneity narratively but again could not perform the planned meta-regression.

MAIN RESULTS

We included 57 studies, from which we identified 75 model developments, 15 external validations corresponding to only 12 (16%) of the models, and six author-reported validations. Only two models were externally validated multiple times. None of the identified external validations were performed by researchers independent of those that developed the model. The outcome was related to disease progression in 39 (41%), relapses in 8 (8%), conversion to definite MS in 17 (18%), and conversion to progressive MS in 27 (28%) of the 96 models or validations. The disease and treatment-related characteristics of included participants, and definitions of considered predictors and outcome, were highly heterogeneous amongst the studies. Based on the publication year, we observed an increase in the percent of participants on treatment, diversification of the diagnostic criteria used, an increase in consideration of biomarkers or treatment as predictors, and increased use of machine learning methods over time. Usability and reproducibility All identified models contained at least one predictor requiring the skills of a medical specialist for measurement or assessment. Most of the models (44; 59%) contained predictors that require specialist equipment likely to be absent from primary care or standard hospital settings. Over half (52%) of the developed models were not accompanied by model coefficients, tools, or instructions, which hinders their application, independent validation or reproduction. The data used in model developments were made publicly available or reported to be available on request only in a few studies (two and six, respectively). Risk of bias We rated all but one of the model developments or validations as having high overall risk of bias. The main reason for this was the statistical methods used for the development or evaluation of prognostic models; we rated all but two of the included model developments or validations as having high risk of bias in the analysis domain. None of the model developments that were externally validated or these models' external validations had low risk of bias. There were concerns related to applicability of the models to our research question in over one-third (38%) of the models or their validations. Reporting deficiencies Reporting was poor overall and there was no observable increase in the quality of reporting over time. The items that were unclearly reported or not reported at all for most of the included models or validations were related to sample size justification, blinding of outcome assessors, details of the full model or how to obtain predictions from it, amount of missing data, and treatments received by the participants. Reporting of preferred model performance measures of discrimination and calibration was suboptimal.

AUTHORS' CONCLUSIONS

The current evidence is not sufficient for recommending the use of any of the published prognostic prediction models for people with MS in clinical routine today due to lack of independent external validations. The MS prognostic research community should adhere to the current reporting and methodological guidelines and conduct many more state-of-the-art external validation studies for the existing or newly developed models.

PICU Pharmacology

The care of the critically-ill child often includes medications used to optimize organ function, treat infections, and provide comfort. Pediatric pharmacology has some key differences that should be leveraged for safe pharmacologic management.

The Blind Spot of Pharmacology: A Scoping Review of Drug Metabolism in Prematurely Born Children

The limit for possible survival after extremely preterm birth has steadily improved and consequently, more premature neonates with increasingly lower gestational age at birth now require care. This specialized care often include intensive pharmacological treatment, yet there is currently insufficient knowledge of gestational age dependent differences in drug metabolism. This potentially puts the preterm neonates at risk of receiving sub-optimal drug doses with a subsequent increased risk of adverse or insufficient drug effects, and often pediatricians are forced to prescribe medication as off-label or even off-science. In this review, we present some of the particularities of drug disposition and metabolism in preterm neonates. We highlight the challenges in pharmacometrics studies on hepatic drug metabolism in preterm and particularly extremely (less than 28 weeks of gestation) preterm neonates by conducting a scoping review of published literature. We find that >40% of included studies failed to report a clear distinction between term and preterm children in the presentation of results making direct interpretation for preterm neonates difficult. We present summarized findings of pharmacokinetic studies done on the major CYP sub-systems, but formal meta analyses were not possible due the overall heterogeneous approaches to measuring the phase I and II pathways metabolism in preterm neonates, often with use of opportunistic sampling. We find this to be a testament to the practical and ethical challenges in measuring pharmacokinetic activity in preterm neonates. The future calls for optimized designs in pharmacometrics studies, including PK/PD modeling-methods and other sample reducing techniques. Future studies should also preferably be a collaboration between neonatologists and clinical pharmacologists.

Knowledge gaps in late-onset neonatal sepsis in preterm neonates: a roadmap for future research

Late-onset neonatal sepsis (LONS) remains an important threat to the health of preterm neonates in the neonatal intensive care unit. Strategies to optimize care for preterm neonates with LONS are likely to improve survival and long-term neurocognitive outcomes. However, many important questions on how to improve the prevention, early detection, and therapy for LONS in preterm neonates remain unanswered. This review identifies important knowledge gaps in the management of LONS and describe possible methods and technologies that can be used to resolve these knowledge gaps. The availability of computational medicine and hypothesis-free-omics approaches give way to building bedside feedback tools to guide clinicians in personalized management of LONS. Despite advances in technology, implementation in clinical practice is largely lacking although such tools would help clinicians to optimize many aspects of the management of LONS. We outline which steps are needed to get possible research findings implemented on the neonatal intensive care unit and provide a roadmap for future research initiatives. IMPACT: This review identifies knowledge gaps in prevention, early detection, antibiotic, and additional therapy of late-onset neonatal sepsis in preterm neonates and provides a roadmap for future research efforts. Research opportunities are addressed, which could provide the means to fill knowledge gaps and the steps that need to be made before possible clinical use. Methods to personalize medicine and technologies feasible for bedside clinical use are described.

The PHARMACOM-EPI Framework for Integrating Pharmacometric Modelling Into Pharmacoepidemiological Research Using Real-World Data: Application to Assess Death Associated With Valproate

In pharmacoepidemiology, it is usually expected that the observed association should be directly or indirectly related to the pharmacological effects of the drug/s under investigation. Pharmacological effects are, in turn, strongly connected to the pharmacokinetic and pharmacodynamic properties of a drug, which can be characterized and investigated using pharmacometric models. Recently, the use of pharmacometrics has been proposed to provide pharmacological substantiation of pharmacoepidemiological findings derived from real-world data. However, validated frameworks suggesting how to combine these two disciplines for the aforementioned purpose are missing. Therefore, we propose PHARMACOM-EPI, a framework that provides a structured approach on how to identify, characterize, and apply pharmacometric models with practical details on how to choose software, format dataset, handle missing covariates/dosing data, how to perform the external evaluation of pharmacometric models in real-world data, and how to provide pharmacological substantiation of pharmacoepidemiological findings. PHARMACOM-EPI was tested in a proof-of-concept study to pharmacologically substantiate death associated with valproate use in the Danish population aged ≥ 65 years. Pharmacological substantiation of death during a follow-up period of 1 year showed that in all individuals who died (n = 169) individual predictions were within the subtherapeutic range compared with 52.8% of those who did not die (n = 1,084). Of individuals who died, 66.3% (n = 112) had a cause of death possibly related to valproate and 33.7% (n = 57) with well-defined cause of death unlikely related to valproate. This proof-of-concept study showed that PHARMACOM-EPI was able to provide pharmacological substantiation for death associated with valproate use in the study population.

V2 ACHER: Visualization of complex trial data in pharmacometric analyses with covariates

Pharmacometric models can enhance clinical decision making, with covariates exposing potential contributions to variability of subpopulation characteristics, for example, demographics or disease status. Intuitive visualization of models with multiple covariates is needed because sparsity of data in visualizations trellised by covariate values can raise concerns about the credibility of the underlying model. V² ACHER, introduced here, is a stepwise transformation of data that can be applied to a variety of static (non-ordinary-differential-equation-based) pharmacometric analyses. This work uses four examples of increasing complexity to show how the transformation elucidates the relationship between observations and model results and how it can also be used in visual predictive checks to confirm the quality of a model. V² ACHER facilitates consistent, intuitive, single-plot visualization of a multicovariate model with a complex data set, thereby enabling easier model communication for modelers and for cross-functional development teams and facilitating confident use in support of decisions.

External evaluation of the predictive performance of seven population pharmacokinetic models for phenobarbital in neonates

AIM

Several studies have reported population pharmacokinetic models for phenobarbital (PB), but the predictive performance of these models has not been well documented. This study aims to do external evaluation of the predictive performance in published pharmacokinetic models.

METHODS

Therapeutic drug monitoring data collected in neonates and young infants treated with PB for seizure control was used for external evaluation. A literature review was conducted through PubMed to identify population pharmacokinetic models. Prediction- and simulation-based diagnostics, and Bayesian forecasting were performed for external evaluation. The incorporation of allometric scaling for body size and maturation factors into the published models was also tested for prediction improvement.

RESULTS

A total of 79 serum concentrations from 28 subjects were included in the external dataset. Seven population pharmacokinetic studies of PB were identified as relevant in the literature search and included for our evaluation. The model by Voller et al showed the best performance concerning prediction-based evaluation. In simulation-based analyses, the normalized prediction distribution error of two models (those of Shellhaas et al and Marsot et al) obeyed a normal distribution. Bayesian forecasting with more than one observation improved predictive capability. Incorporation of both allometric size scaling and maturation function generally enhanced the predictive performance, with improvement as observed in the model of Vucicevic et al. CONCLUSIONS: The predictive performance of published pharmacokinetic models of PB was diverse. Bayesian forecasting and incorporation of both size and maturation factors could improve the predictability of the models for neonates.

What is the Best Predictor of Phenobarbital Pharmacokinetics to Use for Initial Dosing in Neonates?

Phenobarbital is a first-line treatment of various seizure types in newborns. Dosage individualization maximizing the proportion of patients with drug levels in therapeutic range or sufficient treatment response is still challenging. The aim of this review was to summarize the available evidence on phenobarbital pharmacokinetics in neonates and to identify its possible covariates suitable for individualization of initial drug dosing. Several covariates have been considered: body weight and height, body surface area, gestational and postnatal age, laboratory parameters of renal and hepatic functions, asphyxia, therapeutic hypothermia, extracorporeal membrane oxygenation (ECMO), drug interactions, and genetic polymorphisms. The most frequently studied and well-founded covariate for the estimation of phenobarbital dosing is actual body weight. Loading dose of 15-20 mg/kg followed by a maintenance dose of 3-5 mg/kg/day seems to be accurate. However, the evidence for the other covariates with respect to dosing individualization is not sufficient. Doses at the lower limit of suggested range should be preferred in patients with severe asphyxia, while the upper limit of the range should be targeted in neonates receiving ECMO support.

Pharmacotherapy of neonatal opioid withdrawal syndrome: a review of pharmacokinetics and pharmacodynamics

INTRODUCTION

Neonatal opioid withdrawal syndrome (NOWS) often arises in infants born to mothers who used opioids during pregnancy. Morphine, methadone, and buprenorphine are the most common first-line treatments, whereas clonidine and phenobarbital are generally reserved for adjunctive therapy. These drugs exhibit substantial pharmacokinetic (PK) and pharmacodynamic (PD) variability. Current pharmacological treatments for NOWS are based on institutional protocols and largely rely on empirical treatment of patient symptoms.

AREAS COVERED

This article reviews the PK/PD of NOWS pharmacotherapies with a focus on the implication of physiological development and maturation. Body size-standardized clearance is consistently low in neonates, except for methadone. This can be ascribed to underdeveloped metabolic and elimination pathways. The effects of pharmacogenetics have been clarified especially for morphine. The PK/PD relationship of medications used in the treatment of NOWS is generally understudied.

EXPERT OPINION

Providing an appropriate opioid dose in neonates is challenging. Advancements in quantitative pharmacology and PK/PD modeling approaches facilitate identification of key factors driving PK/PD variability and characterization of exposure-response relationships. PK/PD model-informed simulations have been widely employed to define age-appropriate pediatric dosing regimens. The model-informed approach holds promise to aid more rational use of medications in the treatment of NOWS.

Severity parameters for asphyxia or hypoxic-ischemic encephalopathy do not explain inter-individual variability in the pharmacokinetics of phenobarbital in newborns treated with therapeutic hypothermia

BACKGROUND

The current study uses a population modeling approach to evaluate and quantify the impact of severity of asphyxia and hypoxic-ischemic encephalopathy (HIE) on the pharmacokinetics of phenobarbital in asphyxiated newborns treated with therapeutic hypothermia.

METHODS

Included newborns received phenobarbital (the TOBY trial protocol). 120 plasma samples were available from 50 newborns, median (IQR) weight 3.3 (2.8-3.5) kg and gestational age 39 (39-40) weeks. NONMEM^® version 7.2 was used for the data analysis. Age, body weight, sex, concomitant medications, kidney and liver function markers, as well as severity parameters of asphyxia and HIE were tested as potential covariates of pharmacokinetics of phenobarbital. Severe asphyxia was defined as pH of arterial umbilical cord blood ≤7.1 and Apgar 5 ≤5, and severe HIE was defined as time to normalization of amplitude-integrated electroencephalography (aEEG) >24 h.

RESULTS

Weight was found to be the only statistically significant covariate for the volume of distribution. At weight of 1 kg volume of distribution was 0.91 L and for every additional kg it increased in 0.91 L. Clearance was 0.00563 L/h. No covariates were statistically significant for the clearance of phenobarbital.

CONCLUSIONS

Phenobarbital dose adjustments are not indicated in the studied population, irrespective of the severity of asphyxia or HIE.

Pharmacokinetic variability of phenobarbital: a systematic review of population pharmacokinetic analysis

AIMS AND BACKGROUND

Population pharmacokinetics with Bayesian forecasting provides for an effective approach when individualized drug dosing, while phenobarbital is a narrow therapeutic index drug that requires therapeutic drug monitoring. To date, several population pharmacokinetic models have been developed for phenobarbital, these showing a number of significant predictors of phenobarbital clearance and volume of distribution. We have therefore conducted a systematic review to summarize how these predictors affect phenobarbital pharmacokinetics as well as their relationships with pharmacokinetic parameters.

METHOD

A systematic search for studies of phenobarbital population pharmacokinetics that were carried out in humans and that employed a nonlinear mixed-effect approaches was made using the PubMed, Scopus, CINAHL Complete, and ScienceDirect databases. The search covered the period from these databases' inception to March 2020.

RESULTS

Eighteen studies were included in this review, all of which used a one-compartment structure. The estimated phenobarbital clearance and volume of distribution ranged from 0.0034 to 0.0104 L/h/kg and 0.37 to 1.21 L/kg, respectively, with body weight, age, and concomitant antiepileptic drugs being the three most frequently identified predictors of clearance. Most models were validated through the use of an advanced internal approach.

CONCLUSION

Phenobarbital clearance may be predicted from previously developed population pharmacokinetic models and their significant covariate-parameter relationships along with Bayesian forecasting. However, when applying these models in a target population, an external evaluation of these models using the target population is warranted, and it is recommended that future research be conducted to investigate the link between population pharmacokinetics and pharmacodynamics.

Population Pharmacokinetics of Phenobarbital in Neonates and Infants on Extracorporeal Membrane Oxygenation and the Influence of Concomitant Renal Replacement Therapy

The objective of this study was to describe the pharmacokinetics (PK) of intravenous phenobarbital in neonates and infants on extracorporeal membrane oxygenation (ECMO) and to provide dosing recommendations in this population. We performed a retrospective single-center PK study of phenobarbital in neonates and infants on ECMO between January 1, 2014, and December 31, 2018. We developed a population PK model using nonlinear mixed-effects modeling, performed simulations using the final PK parameters, and determined optimal dosing based on attainment of peak and trough concentrations between 20 and 40 mg/L. We included 35 subjects with a median (range) age and weight of 14 days (1-154 days) and 3.4 kg (1.6-8.1 kg), respectively. A total of 194 samples were included in the analysis. Five children (14%) contributing 30 samples (16%) were supported by continuous venovenous hemodiafiltration (CVVHDF). A 1-compartment model best described the data. Typical clearance and volume of distribution for a 3.4-kg infant were 0.038 L/h and 3.83 L, respectively. Clearance increased with age and CVVHDF. Although on ECMO, phenobarbital clearance in children on CVVHDF was 6-fold higher than clearance in children without CVVHDF. In typical subjects, a loading dose of 30 mg/kg/dose followed by maintenance doses of 6-7 mg/kg/day administered as divided doses every 12 hours reached goal concentrations. Age did not impact dosing recommendations. However, higher doses were needed in children on CVVHDF. We strongly recommend therapeutic drug monitoring in children on renal replacement therapy (excluding slow continuous ultrafiltration) while on ECMO.

Rapid Increase in Clearance of Phenobarbital in Neonates on Extracorporeal Membrane Oxygenation: A Pilot Retrospective Population Pharmacokinetic Analysis

OBJECTIVES

This study characterizes the changes in the pharmacokinetics of phenobarbital associated with extracorporeal membrane oxygenation treatment in neonates, to illustrate our findings and provide guidance on dosing.

DESIGN

Retrospective pilot population pharmacokinetic analysis.

SETTING

Neonatal ICU.

PATIENTS

Thirteen critically ill neonates (birth body weight, 3.21 kg [2.65-3.72 kg]; postnatal age at start of treatment: 2 d [0-7 d]; gestational age: 38 wk [38-41 wk]) receiving venovenous or venoarterial extracorporeal membrane oxygenation.

INTERVENTIONS

Phenobarbital administered in a loading dose of 7.5 mg/kg (8.5-16 mg/kg) and maintenance dose of 6.9 mg/kg/d (4.5-8.5 mg/kg/d).

MEASUREMENTS AND MAIN RESULTS

Therapeutic drug monitoring data were available, yielding 5, 31, and 19 phenobarbital concentrations before, during, and after extracorporeal membrane oxygenation, respectively. Population pharmacokinetic analysis was performed using NONMEM 7.3.0 (ICON Development Solutions, Ellicott City, MD). Maturation functions for clearance and volume of distribution were obtained from literature. In a one-compartment model, clearance and volume of distribution for a typical neonate off extracorporeal membrane oxygenation and with a median birth body weight (3.21 kg) at median postnatal age (2 d) were 0.0096 L/hr (relative SE = 11%)) and 2.72 L (16%), respectively. During extracorporeal membrane oxygenation, clearance was found to linearly increase with time. Upon decannulation, phenobarbital clearance initially decreased and subsequently increased slowly driven by maturation. Extracorporeal membrane oxygenation-related changes in volume of distribution could not be identified, possibly due to sparse data collection shortly after extracorporeal membrane oxygenation start. According to the model, target attainment is achieved in the first 12 days of extracorporeal membrane oxygenation with a regimen of a loading dose of 20 mg/kg and a maintenance dose of 4 mg/kg/d divided in two doses with an increase of 0.25 mg/kg every 12 hours during extracorporeal membrane oxygenation treatment.

CONCLUSIONS

We found a time-dependent increase in phenobarbital clearance during the first 12 days of extracorporeal membrane oxygenation treatment in neonates, which results in continuously decreasing phenobarbital exposure and increases the risk of therapeutic failure over time. Due to high unexplained variability, frequent and repeated therapeutic drug monitoring should be considered even with the model-derived regimen.

Population Pharmacokinetics of IV Phenobarbital in Neonates After Congenital Heart Surgery

OBJECTIVES

To develop a population pharmacokinetic model for IV phenobarbital in neonates following cardiac surgery and perform simulations to identify optimal dosing regimens.

DESIGN

Retrospective single-center pharmacokinetic study.

SETTING

Cardiac ICU at Children's Hospital of Philadelphia.

PATIENTS

Consecutive neonates who received greater than or equal to one dose of IV phenobarbital and had greater than or equal to one phenobarbital concentration drawn per standard of care from June 15, 2012, to October 15, 2018.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

A population pharmacokinetic model was developed using nonlinear mixed-effects modeling. Simulations were performed using the final model variables. Optimal phenobarbital loading doses were determined based on attainment of peak and maintenance concentrations between 20 and 40 mg/L. A total of 37 neonates contributed 159 pharmacokinetic samples. The median (range) weight, postmenstrual age, and postnatal age were 3.2 kg (1.3-3.8), 39 2/7 weeks (28 2/7 to 42 6/7), and 5 days (0-26 d), respectively. Twelve patients (32%) were on extracorporeal membrane oxygenation. An one-compartment model best described the data. The final population pharmacokinetic model included (1) weight and postnatal age for clearance and (2) weight, extracorporeal membrane oxygenation, and albumin for volume of distribution. In neonates not on extracorporeal membrane oxygenation, loading doses of 30 and 20 mg/kg reached goal concentration with albumin values less than or equal to 3 and 3.5 mg/dL, respectively. Loading doses of 30 mg/kg reached goal concentration on extracorporeal membrane oxygenation regardless of albumin values. Maintenance doses of 4-5 mg/kg/d reached goal concentration in all neonates.

CONCLUSIONS

In neonates following cardiac surgery, phenobarbital clearance increased with postnatal age. Volume of distribution increased with extracorporeal membrane oxygenation and lower albumin values. Loading doses of 30 mg/kg on extracorporeal membrane oxygenation and 20-30 mg/kg without extracorporeal membrane oxygenation were needed to reach goal concentration based on simulations.

Enantiomer specific pharmacokinetics of ibuprofen in preterm neonates with patent ductus arteriosus

Aims

Racemic ibuprofen is widely used for the treatment of preterm neonates with patent ductus arteriosus. Currently used bodyweight‐based dosing guidelines are based on total ibuprofen, while only the S‐enantiomer of ibuprofen is pharmacologically active. We aimed to optimize ibuprofen dosing for preterm neonates of different ages based on an enantiomer‐specific population pharmacokinetic model.

Methods

We prospectively collected 210 plasma samples of 67 preterm neonates treated with ibuprofen for patent ductus arteriosus (median gestational age [GA] 26 [range 24–30] weeks, median body weight 0.83 [0.45–1.59] kg, median postnatal age [PNA] 3 [1–12] days), and developed a population pharmacokinetic model for S‐ and R‐ibuprofen.

Results

We found that S‐ibuprofen clearance (CL_S, 3.98 mL/h [relative standard error {RSE} 8%]) increases with PNA and GA, with exponents of 2.25 (RSE 6%) and 5.81 (RSE 15%), respectively. Additionally, a 3.11‐fold higher CL_S was estimated for preterm neonates born small for GA (RSE 34%). Clearance of R‐ibuprofen was found to be high compared to CL_S (18 mL/h [RSE 24%]), resulting in a low contribution of R‐ibuprofen to total ibuprofen exposure. Current body weight was identified as covariate on both volume of distribution of S‐ibuprofen and R‐ibuprofen.

Conclusion

S‐ibuprofen clearance shows important maturation, especially with PNA, resulting in an up to 3‐fold increase in CL_S during a 3‐day treatment regimen. This rapid increase in clearance needs to be incorporated in dosing guidelines by adjusting the dose for every day after birth to achieve equal ibuprofen exposure.

The Influence of Normalization Weight in Population Pharmacokinetic Covariate Models

In covariate (sub)models of population pharmacokinetic models, most covariates are normalized to the median value; however, for body weight, normalization to 70 kg or 1 kg is often applied. In this article, we illustrate the impact of normalization weight on the precision of population clearance (CL_pop) parameter estimates. The influence of normalization weight (70, 1 kg or median weight) on the precision of the CL_pop estimate, expressed as relative standard error (RSE), was illustrated using data from a pharmacokinetic study in neonates with a median weight of 2.7 kg. In addition, a simulation study was performed to show the impact of normalization to 70 kg in pharmacokinetic studies with paediatric or obese patients. The RSE of the CL_pop parameter estimate in the neonatal dataset was lowest with normalization to median weight (8.1%), compared with normalization to 1 kg (10.5%) or 70 kg (48.8%). Typical clearance (CL) predictions were independent of the normalization weight used. Simulations showed that the increase in RSE of the CL_pop estimate with 70 kg normalization was highest in studies with a narrow weight range and a geometric mean weight away from 70 kg. When, instead of normalizing with median weight, a weight outside the observed range is used, the RSE of the CL_pop estimate will be inflated, and should therefore not be used for model selection. Instead, established mathematical principles can be used to calculate the RSE of the typical CL (CL_TV) at a relevant weight to evaluate the precision of CL predictions.

Barriers and Challenges in Performing Pharmacokinetic Studies to Inform Dosing in the Neonatal Population

A number of barriers and challenges must be overcome in order to conduct the pharmacokinetic studies that are urgently needed to inform the selection and dosing of medication in neonates. However, overcoming these barriers can be difficult. This review outlines the common barriers researchers are confronted with, including issues with ethics approval and consent, study design for pharmacokinetic studies and the ability to measure the drug concentrations in the blood samples obtained. Strategies to overcome these challenges are also proposed.

Drug Disposition and Pharmacotherapy in Neonatal ECMO: From Fragmented Data to Integrated Knowledge

Extracorporeal membrane oxygenation (ECMO) is a lifesaving support technology for potentially reversible neonatal cardiac and/or respiratory failure. As the survival and the overall outcome of patients rely on the treatment and reversal of the underlying disease, effective and preferentially evidence-based pharmacotherapy is crucial to target recovery. Currently limited data exist to support the clinicians in their every-day intensive care prescribing practice with the contemporary ECMO technology. Indeed, drug dosing to optimize pharmacotherapy during neonatal ECMO is a major challenge. The impact of the maturational changes of the organ function on both pharmacokinetics (PK) and pharmacodynamics (PD) has been widely established over the last decades. Next to the developmental pharmacology, additional non-maturational factors have been recognized as key-determinants of PK/PD variability. The dynamically changing state of critical illness during the ECMO course impairs the achievement of optimal drug exposure, as a result of single or multi-organ failure, capillary leak, altered protein binding, and sometimes a hyperdynamic state, with a variable effect on both the volume of distribution (Vd) and the clearance (Cl) of drugs. Extracorporeal membrane oxygenation introduces further PK/PD perturbation due to drug sequestration and hemodilution, thus increasing the Vd and clearance (sequestration). Drug disposition depends on the characteristics of the compounds (hydrophilic vs. lipophilic, protein binding), patients (age, comorbidities, surgery, co-medications, genetic variations), and circuits (roller vs. centrifugal-based systems; silicone vs. hollow-fiber oxygenators; renal replacement therapy). Based on the potential combination of the above-mentioned drug PK/PD determinants, an integrated approach in clinical drug prescription is pivotal to limit the risks of over- and under-dosing. The understanding of the dose-exposure-response relationship in critically-ill neonates on ECMO will enable the optimization of dosing strategies to ensure safety and efficacy for the individual patient. Next to in vitro and clinical PK data collection, physiologically-based pharmacokinetic modeling (PBPK) are emerging as alternative approaches to provide bedside dosing guidance. This article provides an overview of the available evidence in the field of neonatal pharmacology during ECMO. We will identify the main determinants of altered PK and PD, elaborate on evidence-based recommendations on pharmacotherapy and highlight areas for further research.

Reproducible pharmacokinetics

Reproducibility is a highly desired feature of scientific investigation in general, and it has special connotations for research in pharmacokinetics, a vibrant field with over 500,000 publications to-date. It is important to be able to differentiate between genuine heterogeneity in pharmacokinetic parameters from heterogeneity that is due to errors and biases. This overview discusses efforts and opportunities to diminish the latter type of undesirable heterogeneity. Several reporting and research guidance documents and standards have been proposed for pharmacokinetic studies, but their adoption is still rather limited. Quality problems in the methods used and model evaluations have been examined in some empirical studies of the literature. Standardization of statistical and laboratory tools and procedures can be improved in the field. Only a small fraction of pharmacokinetic studies become pre-registered and only 9995 such studies have been registered in ClinicalTrials.gov as of August 2018. It is likely that most pharmacokinetic studies remain unpublished. Publication bias affecting the results and inferences has been documented in case studies, but its exact extent is unknown for the field at-large. The use of meta-analyses in the field is still limited. Availability of raw data, detailed protocols, software and codes is hopefully improving with multiple ongoing initiatives. Several research practices can contribute to greater transparency and reproducibility for pharmacokinetic investigations.

Phenobarbital, Midazolam Pharmacokinetics, Effectiveness, and Drug-Drug Interaction in Asphyxiated Neonates Undergoing Therapeutic Hypothermia

BACKGROUND

Phenobarbital and midazolam are commonly used drugs in (near-)term neonates treated with therapeutic hypothermia for hypoxic-ischaemic encephalopathy, for sedation, and/or as anti-epileptic drug. Phenobarbital is an inducer of cytochrome P450 (CYP) 3A, while midazolam is a CYP3A substrate. Therefore, co-treatment with phenobarbital might impact midazolam clearance.

OBJECTIVES

To assess pharmacokinetics and clinical anti-epileptic effectiveness of phenobarbital and midazolam in asphyxiated neonates and to develop dosing guidelines.

METHODS

Data were collected in the prospective multicentre PharmaCool study. In the present study, neonates treated with therapeutic hypothermia and receiving midazolam and/or phenobarbital were included. Plasma concentrations of phenobarbital and midazolam including its metabolites were determined in blood samples drawn on days 2-5 after birth. Pharmacokinetic analyses were performed using non-linear mixed effects modelling; clinical effectiveness was defined as no use of additional anti-epileptic drugs.

RESULTS

Data were available from 113 (phenobarbital) and 118 (midazolam) neonates; 68 were treated with both medications. Only clearance of 1-hydroxy midazolam was influenced by hypothermia. Phenobarbital co-administration increased midazolam clearance by a factor 2.3 (95% CI 1.9-2.9, p < 0.05). Anticonvulsant effectiveness was 65.5% for phenobarbital and 37.1% for add-on midazolam.

CONCLUSIONS

Therapeutic hypothermia does not influence clearance of phenobarbital or midazolam in (near-)term neonates with hypoxic-ischaemic encephalopathy. A phenobarbital dose of 30 mg/kg is advised to reach therapeutic concentrations. Phenobarbital co-administration significantly increased midazolam clearance. Should phenobarbital be substituted by non-CYP3A inducers as first-line anticonvulsant, a 50% lower midazolam maintenance dose might be appropriate to avoid excessive exposure during the first days after birth.

Phenobarbital pharmacokinetics in neonates and infants during extracorporeal membrane oxygenation

INTRODUCTION

The disposition of drugs is potentially changed due to extracorporeal membrane oxygenation (ECMO) in neonates and infants.

METHODS

The aim of the study was to evaluate the individual pharmacokinetics (PK) of phenobarbital and the effect of PK covariates in neonates and infants undergoing ECMO. Sixteen patients (7 neonates, 9 infants) treated with phenobarbital during ECMO (centrifugal-flow pump circuits) were enrolled in the PK study. Phenobarbital serum concentrations were measured using a fluorescence polarization immunoassay. Individual PK parameters - volume of distribution (Vd) and clearance (CL) were calculated in a one-compartmental pharmacokinetic model.

RESULTS

The mean (SD) Vd and CL values in neonates were 0.46 (0.24) L/kg and 8.0 (4.5) mL/h/kg, respectively. Respective values in infants were 0.56 (0.23) L/kg and 8.5 (3.1) mL/h/kg. PK parameters in neonates and infants were not significantly different. We observed high inter-individual variability in PK parameters (coefficients of variation [CV] were 52% and 53% for CL and Vd, respectively). Doses were adjusted based on therapeutic drug monitoring (TDM) in 87.5% patients. Only 50% of the first measured phenobarbital serum concentrations in each patient were within the therapeutic range of 10-40 mg/L, in comparison with 88.6% concentration measured after TDM implementation. Linear regression models showed that both Vd and CL are significantly related with body weight (BW) and length. Median optimal phenobarbital loading dose (LD) and maintenance dose (MD), calculated from pharmacokinetic data, were 15 mg/kg and 4 mg/kg/day, respectively.

CONCLUSIONS

Body weight was shown to be the main PK covariate of phenobarbital disposition. Subsequent dosing nomograms are provided for phenobarbital dosing during ECMO.

Phenobarbital population pharmacokinetics across the pediatric age spectrum

OBJECTIVE

Phenobarbital is frequently used in pediatric patients for treatment and prophylaxis of seizures. Pharmacokinetic data for this patient population is lacking and would assist in dosing decisions.

METHODS

A retrospective population pharmacokinetic analysis was designed for all pediatric patients <19 years of age initiated on phenobarbital at our institution from January 2011 to June 2017. Patients were included if they were initiated on intravenous or enteral phenobarbital for treatment or prophylaxis of seizures and had a serum phenobarbital concentration monitored while an inpatient. Data collection included the following: age, weight, height, gestational age, core body temperature, serum creatinine, blood urea nitrogen, aspartase aminotransferase, alanine aminotransferase, urine output over the prior 12 hours, phenobarbital doses and serum concentrations, and potential drug-drug interactions. Descriptive statistical methods were used to summarize the data. Pharmacokinetic analysis was performed with NONMEM and simulation was performed for doses of 10, 20, 30, and 40 mg kg^-1  dose^-1 , iv, followed by enteral doses of 3, 4, 5, and 6 mg kg^-1  d^-1 .

RESULTS

A total of 355 patients (50.3% male, median gestational age 39 weeks (interquartile range [IQR] 35, 40), median age 0.28 years (IQR 0.06, 0.82). Median phenobarbital dose was enteral = 2.6 (IQR 1.9, 3.9) mg kg^-1  dose^-1 ; intravenous = 2.6 (IQR 2.2, 4.9) mg kg^-1  dose^-1 ) and mean serum concentration was 41.1 ± 23.9 mg/L at median 6.5 (IQR 2.9, 11.1) hours after a dose. A one-compartment proportional error model best fit the data where clearance and volume of distribution were allometrically scaled using fat-free mass. Significant covariates included serum creatinine, postmenstrual age, and drug-drug interactions on clearance, and age in years on volume of distribution.

SIGNIFICANCE

Phenobarbital dosing of 30 mg kg^-1  dose^-1 ,iv, followed by 4 mg kg^-1  d^-1 had the highest probability of attaining a therapeutic concentration at 7 days. Postmenstrual age and drug-drug interactions should be incorporated into dosing decisions.