Abstract:Renal elimination of PG in (pre)term neonates is low, particularly compared with the reported percentage of 45% in adults, but it may increase with time after the first dose of PG. To study whether this increase is caused by an autoinduced increase in the renal secretion or a reduction of tubular reabsorption of PG, further research is needed.
“…Many external factors have been proposed to contribute to malfunction of neonatal kidneys, including maternal diet, stress, hypertension, and drugs administered to the mother or to the newborn [46, 47]. Drug formulations, in particular, contain propylene glycol and poly-ethylene glycol, which are co-solvents and could be a factor in the kidney injury [48–50] or a possible environmental contaminant.…”
Introduction
AKI staging has been developed in the adult and pediatric populations, but these do not yet exist for the neonatal population. Metabolomics was utilized to uncover biomarkers of normal and AKI associated renal function in preterm infants. The study was comprised of 20 preterm infants with an AKI diagnosis who were matched by gestational age and gender to 20 infants without an AKI diagnosis.
Methods
Urine samples from pre-term newborn infants collected on day 2 of life were analyzed using broad spectrum NMR-metabolomics. Multivariate analysis methods were used to identify metabolite profiles that differentiated AKI and no AKI, and to identify a metabolomics profile correlating with gestational age in infants with and without AKI.
Results
There was a clear distinction between the AKI and no-AKI profiles. Two previously identified biomarkers of AKI, hippurate and homovanillate, differentiated AKI and no-AKI. Pathway analysis revealed similarities to cholingeric neurons, prenatal nicotine exposure on pancreatic β cells, and Amitraz-induced inhibition of insulin secretion. Additionally, a pH difference was noted. Both pH and the metabolites were found to be associated of AKI; however, only the metabotype was a significant predictor of AKI. Pathways for the no-AKI group that were uniquely correlated to gestational age included aminoacyl-t-RNA biosynthesis, whereas pathways in the AKI group yielded potential metabolite changes in pyruvate metabolism.
Conclusions
Metabolomics was able to differentiate the urinary profiles of neonates with and without an AKI diagnosis and metabolic developmental profiles were correlated with gestational age. Further studies in larger cohorts are needed to validate these results.
“…Many external factors have been proposed to contribute to malfunction of neonatal kidneys, including maternal diet, stress, hypertension, and drugs administered to the mother or to the newborn [46, 47]. Drug formulations, in particular, contain propylene glycol and poly-ethylene glycol, which are co-solvents and could be a factor in the kidney injury [48–50] or a possible environmental contaminant.…”
Introduction
AKI staging has been developed in the adult and pediatric populations, but these do not yet exist for the neonatal population. Metabolomics was utilized to uncover biomarkers of normal and AKI associated renal function in preterm infants. The study was comprised of 20 preterm infants with an AKI diagnosis who were matched by gestational age and gender to 20 infants without an AKI diagnosis.
Methods
Urine samples from pre-term newborn infants collected on day 2 of life were analyzed using broad spectrum NMR-metabolomics. Multivariate analysis methods were used to identify metabolite profiles that differentiated AKI and no AKI, and to identify a metabolomics profile correlating with gestational age in infants with and without AKI.
Results
There was a clear distinction between the AKI and no-AKI profiles. Two previously identified biomarkers of AKI, hippurate and homovanillate, differentiated AKI and no-AKI. Pathway analysis revealed similarities to cholingeric neurons, prenatal nicotine exposure on pancreatic β cells, and Amitraz-induced inhibition of insulin secretion. Additionally, a pH difference was noted. Both pH and the metabolites were found to be associated of AKI; however, only the metabotype was a significant predictor of AKI. Pathways for the no-AKI group that were uniquely correlated to gestational age included aminoacyl-t-RNA biosynthesis, whereas pathways in the AKI group yielded potential metabolite changes in pyruvate metabolism.
Conclusions
Metabolomics was able to differentiate the urinary profiles of neonates with and without an AKI diagnosis and metabolic developmental profiles were correlated with gestational age. Further studies in larger cohorts are needed to validate these results.
“…Moreover, and in contrast to the rather selective compounds used in the in vitro experiments, current used drugs commonly have different routes of elimination. To further illustrate this, the main route of elimination of caffeine is age dependent (CYP1A2, CYP3A), and the main route of elimination of dextromethorphan evolves over the first year of life from CYP2D6‐ to CYP3A‐mediated metabolism, while alcohol dehydrogenase is the main route of elimination of alcohols (ethanol, propylene glycol) with an age‐dependent progressive increase in renal elimination capacity …”
Section: In Vitro Data To Guide Pediatric Drug Development: First Besmentioning
Capturing ontogeny of enzymes involved in phase I metabolism is crucial to improve prediction of dose‐concentration and concentration‐effect relationships throughout infancy and childhood. Once captured, these patterns can be integrated in semiphysiologically or physiology‐based pharmacokinetic models to support predictions in specific pediatric settings or to support pediatric drug development. Although these translational efforts are crucial, isoenzyme‐specific ontogeny‐based models should also incorporate data on variability of maturational and nonmaturational covariates (eg, disease, treatment modalities, pharmacogenetics). Therefore, this review provides a summary of the ontogeny of phase I drug‐metabolizing enzymes, indicating current knowledge gaps and recent progresses. Furthermore, we tried to illustrate that straightforward translation of isoenzyme‐specific ontogeny to predictions does not allow full exploration of scenarios of potential variability related to maturational (non–age‐related variability, other isoenzymes or transporters) or nonmaturational (disease, pharmacogenetics) covariates, and necessitates integration in a “systems” concept.
“…Its IV formulation contains propylene glycol (PG), which at toxic amounts can lead to lactic acidosis . Note should be taken that the PG metabolism is immature in preterm and term neonates . It is recommended to carefully monitor the osmol gap .…”
The population in question is very heterogeneous and this overview can aid clinicians and researchers in moving from practice-based sedation management towards more evidence- or model-based practice. Still, paediatric sedation management can be improved in other ways than pharmacology only, so future research should aim on sedation assessment and implementation strategies of protocolized sedation as well.
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