Population Model of Serum Creatinine as Time-Dependent Covariate in Neonates

Drug related adverse event assessment in neonates in clinical trials and clinical care

INTRODUCTION

Assessment of drug-related adverse events is essential to fully understand the benefit-risk balance of any drug exposure, weighing efficacy versus safety. This is needed for both drug labeling and clinical decision making. Assessment is based on seriousness, severity and causality, be it more difficult to apply in neonates. Adverse event detection or prevention in the neonatal clinical setting is also more complicated because of polypharmacy, and off-label or unlicensed pharmacotherapy.

AREAS COVERED

Tools became available to assess severity and causality of adverse events in neonates recruited in clinical trials. The first version of the Neonatal Adverse Event severity score (NAESS) reduced the inter-observer variability. Causality tools like the Naranjo score were also tailored to neonates. These tools are also instrumental to support proactive pharmacovigilance in clinical care, while multidisciplinary care teams and computerized pharmacovigilance using advanced data analysis, like machine learning are emerging approaches to develop effective decision strategies.

EXPERT OPINION

All stakeholders involved in development of medicines or its clinical use should be aware of the limitations of the currently available assessment tools. Extension and optimization of these tools, advanced data analysis approaches, and capturing the variability in time-dependent physiology are warranted to improve pharmacovigilance in neonates.

When will the Glomerular Filtration Rate in Former Preterm Neonates Catch up with Their Term Peers?

AIMS

Whether and when glomerular filtration rate (GFR) in preterms catches up with term peers is unknown. This study aims to develop a GFR maturation model for (pre)term-born individuals from birth to 18 years of age. Secondarily, the function is applied to data of different renally excreted drugs.

METHODS

We combined published inulin clearance values and serum creatinine (Scr) concentrations in (pre)term born individuals throughout childhood. Inulin clearance was assumed to be equal to GFR, and Scr to reflect creatinine synthesis rate/GFR. We developed a GFR function consisting of GFRbirth (GFR at birth), and an Emax model dependent on PNA (with GFRmax, PNA50 (PNA at which half ofGFR max is reached) and Hill coefficient). The final GFR model was applied to predict gentamicin, tobramycin and vancomycin concentrations.

RESULT

In the GFR model, GFRbirth varied with birthweight linearly while in the PNA-based Emax equation, GA was the best covariate for PNA50, and current weight for GFRmax. The final model showed that for a child born at 26 weeks GA, absolute GFR is 18%, 63%, 80%, 92% and 96% of the GFR of a child born at 40 weeks GA at 1 month, 6 months, 1 year, 3 years and 12 years, respectively. PopPK models with the GFR maturation equations predicted concentrations of renally cleared antibiotics across (pre)term-born neonates until 18 years well.

CONCLUSIONS

GFR of preterm individuals catches up with term peers at around three years of age, implying reduced dosages of renally cleared drugs should be considered below this age.

A Population Model of Time-Dependent Changes in Serum Creatinine in (Near)term Neonates with Hypoxic-Ischemic Encephalopathy During and After Therapeutic Hypothermia

The objective was to apply a population model to describe the time course and variability of serum creatinine (sCr) in (near)term neonates with moderate to severe encephalopathy during and after therapeutic hypothermia (TH). The data consisted of sCr observations up to 10 days of postnatal age in neonates who underwent TH during the first 3 days after birth. Available covariates were birth weight (BWT), gestational age (GA), survival, and acute kidney injury (AKI). A previously published population model of sCr kinetics in neonates served as the base model. This model predicted not only sCr but also the glomerular filtration rate normalized by its value at birth (GFR/GFR0). The model was used to compare the TH neonates with a reference full term non-asphyxiated population of neonates. The estimates of the model parameters had good precision and showed high between subject variability. AKI influenced most of the estimated parameters denoting a strong impact on sCr kinetics and GFR. BWT and GA were not significant covariates. TH transiently increased [Formula: see text] in TH neonates over the first days compared to the reference group. Asphyxia impacted not only GFR, but also the [Formula: see text] synthesis rate. We also observed that AKI neonates exhibit a delayed onset of postnatal GFR increase and have a higher [Formula: see text] synthesis rate compared to no-AKI patients. Our findings show that the use of [Formula: see text] as marker of renal function in asphyxiated neonates treated with TH to guide dose selection for renally cleared drugs is challenging, while we captured the postnatal sCr patterns in this specific population.

Pharmacokinetics during therapeutic hypothermia in neonates: from pathophysiology to translational knowledge and physiologically-based pharmacokinetic (PBPK) modeling

INTRODUCTION

Perinatal asphyxia (PA) still causes significant morbidity and mortality. Therapeutic hypothermia (TH) is the only effective therapy for neonates with moderate to severe hypoxic-ischemic encephalopathy after PA. These neonates need additional pharmacotherapy, and both PA and TH may impact physiology and, consequently, pharmacokinetics (PK) and pharmacodynamics (PD).

AREAS COVERED

This review provides an overview of the available knowledge in PubMed (until November 2022) on the pathophysiology of neonates with PA/TH. In vivo pig models for this setting enable distinguishing the effect of PA versus TH on PK and translating this effect to human neonates. Available asphyxia pig models and methodological considerations are described. A summary of human neonatal PK of supportive pharmacotherapy to improve neurodevelopmental outcomes is provided.

EXPERT OPINION

To support drug development for this population, knowledge from clinical observations (PK data, real-world data on physiology), preclinical (in vitro and in vivo (minipig)) data, and molecular and cellular biology insights can be integrated into a predictive physiologically-based PK (PBPK) framework, as illustrated by the I-PREDICT project (Innovative physiology-based pharmacokinetic model to predict drug exposure in neonates undergoing cooling therapy). Current knowledge, challenges, and expert opinion on the future directions of this research topic are provided.

Predictive Performance of Population Pharmacokinetic Models for Amikacin in Term Neonates

BACKGROUND AND OBJECTIVE

Amikacin is preferred in treating Gram-negative infections in neonates and it has a narrow therapeutic window. The population pharmacokinetic modeling approach can aid in designing optimal dosage regimens for amikacin in neonates. In this study, we attempted to identify the suitable population pharmacokinetic model from the published reports for the study population from an Indian setting.

METHODS

Published population pharmacokinetic studies for amikacin in neonates were identified. Data on structural models and typical pharmacokinetic parameters were extracted from the studies. For the clinical study, neonates who met the inclusion criteria were enrolled in the study from the NICU, Kasturba Medical College, Manipal, during Jan 2020 to March 2022. Drug concentrations were estimated, and demographic and clinical data were collected. Identified population pharmacokinetic models were used to predict the amikacin concentrations in neonates. Predicted concentrations were compared against the observed concentrations. Differences between predicted and observed concentrations were quantified using statistical measures. The population pharmacokinetic model, which was able to predict the data well, is considered a suitable model for the study population. Dosing regimens were suggested for neonates using the pharmacometric simulation approach generated by the selected model.

RESULTS

A total of 43 plasma samples were collected from 31 neonates. Twelve population pharmacokinetic models were found for amikacin in neonates. The predictive performance of the 12 studies was performed using clinical data. A two-compartment model reported by Illamola et al. predicted the amikacin concentrations better than other models. Illamola et al. reported creatinine clearance and body weight as the significant covariates impacting the pharmacokinetic parameters of amikacin. This model was able to predict the clinical data with 29.97% and 0.686 of relative median absolute prediction error and relative root mean square error, respectively, which is the best among the published models. The Illamola et al. model was selected as the final model to perform pharmacometric simulations for the subjects with different combinations of creatinine clearance and body weight. Dosage regimens were designed to attain target therapeutic concentrations for the virtual subjects and a nomogram was developed.

CONCLUSIONS

The population pharmacokinetic model reported by the Illamola et al. model was selected as the final model to explain the clinical data with the lowest relative median absolute prediction error and relative root mean square error when compared with other models. An amikacin nomogram was developed for the neonates whose creatinine clearance and body weight ranged between 10 and 90 mL/min and between 2 and 4 kg, respectively. A developed nomogram can assist clinicians to design an optimal dosage regimen of amikacin for term neonates.

Comprehensive characterization of putative genetic influences on plasma metabolome in a pediatric cohort

BACKGROUND

The human exposome is composed of diverse metabolites and small chemical compounds originated from endogenous and exogenous sources, respectively. Genetic and environmental factors influence metabolite levels, while the extent of genetic contributions across metabolic pathways is not yet known. Untargeted profiling of human metabolome using high-resolution mass spectrometry (HRMS) combined with genome-wide genotyping allows comprehensive identification of genetically influenced metabolites. As such previous studies of adults discovered and replicated genotype-metabotype associations. However, these associations have not been characterized in children.

RESULTS

We conducted the largest genome by metabolome-wide association study to date of children (N = 441) using 619,688 common genetic variants and 14,342 features measured by HRMS. Narrow-sense heritability (h²) estimates of plasma metabolite concentrations using genomic relatedness matrix restricted maximum likelihood (GREML) method showed a bimodal distribution with high h² (> 0.8) for 15.9% of features and low h² (< 0.2) for most of features (62.0%). The features with high h² were enriched for amino acid and nucleic acid metabolism, while carbohydrate and lipid concentrations showed low h². For each feature, a metabolite quantitative trait loci (mQTL) analysis was performed to identify genetic variants that were potentially associated with plasma levels. Fifty-four associations among 29 features and 43 genetic variants were identified at a genome-wide significance threshold p < 3.5 × 10^-12 (= 5 × 10^-8/14,342 features). Previously reported associations such as UGT1A1 and bilirubin; PYROXD2 and methyl lysine; and ACADS and butyrylcarnitine were successfully replicated in our pediatric cohort. We found potential candidates for novel associations including CSMD1 and a monostearyl alcohol triglyceride (m/z 781.7483, retention time (RT) 89.3 s); CALN1 and Tridecanol (m/z 283.2741, RT 27.6). A gene-level enrichment analysis using MAGMA revealed highly interconnected modules for dADP biosynthesis, sterol synthesis, and long-chain fatty acid transport in the gene-feature network.

CONCLUSION

Comprehensive profiling of plasma metabolome across age groups combined with genome-wide genotyping revealed a wide range of genetic influence on diverse chemical species and metabolic pathways. The developmental trajectory of a biological system is shaped by gene-environment interaction especially in early life. Therefore, continuous efforts on generating metabolomics data in diverse human tissue types across age groups are required to understand gene-environment interaction toward healthy aging trajectories.

Serum Creatinine Patterns in Neonates Treated with Therapeutic Hypothermia for Neonatal Encephalopathy

INTRODUCTION

There is large variability in kidney function and injury in neonates with neonatal encephalopathy (NE) treated with therapeutic hypothermia (TH). Acute kidney injury (AKI) definitions that apply categorical approaches may lose valuable information about kidney function in individual patients. Centile serum creatinine (SCr) over postnatal age (PNA) may provide more valuable information in TH neonates.

METHODS

Data from seven TH neonates and one non-TH-treated, non-NE control cohorts were pooled in a retrospective study. SCr centiles over PNA, and AKI incidence (definition: SCr ↑≥0.3 mg/dL within 48 h, or ↑ ≥1.5 fold vs. the lowest prior SCr within 7 days) and mortality were calculated. Repeated measurement linear models were applied to SCr trends, modeling SCr on PNA, birth weight or gestational age (GA), using heterogeneous autoregressive residual covariance structure and maximum likelihood methods. Findings were compared to patterns in the control cohort.

RESULTS

Among 1,136 TH neonates, representing 4,724 SCr observations, SCr (10th-25th-50th-75th-90th-95th) PNA centiles (day 1-10) were generated. In TH neonates, the AKI incidence was 132/1,136 (11.6%), mortality 193/1,136 (17%). AKI neonates had a higher mortality (37.2-14.3%, p < 0.001). Median SCr patterns over PNA were significantly higher in nonsurvivors (p < 0.01) or AKI neonates (p < 0.001). In TH-treated neonates, PNA and GA or birth weight explained SCr variability. Patterns over PNA were significantly higher in TH neonates to controls (801 neonates, 2,779 SCr).

CONCLUSIONS

SCr patterns in TH-treated NE neonates are specific. Knowing PNA-related patterns enable clinicians to better assess kidney function and tailor pharmacotherapy, fluids, or kidney supportive therapies.