Personalized weight change prediction in the first week of life

Applying Neural ODEs to Derive a Mechanism-Based Model for Characterizing Maturation-Related Serum Creatinine Dynamics in Preterm Newborns

Serum creatinine in neonates follows complex dynamics due to maturation processes, most pronounced in the first few weeks of life. The development of a mechanism-based model describing complex dynamics requires high expertise in pharmacometric (PMX) modeling and substantial model development time. A recently published machine learning (ML) approach of low-dimensional neural ordinary differential equations (NODEs) is capable of modeling such data from newborns automatically. However, this efficient data-driven approach in itself does not result in a clinically interpretable model. In this work, an approach to deriving an interpretable model with reasonable PMX-type functions is presented. This "translation" was applied to derive a PMX model for serum creatinine in neonates considering maturation processes and covariates. The developed model was compared to a previously published mechanism-based PMX model whereas both models had similar mechanistic structures. The developed model was then utilized to simulate serum creatinine concentrations in the first few weeks of life considering different covariate values for gestational age and birth weight. The reference serum creatinine values derived from these simulations are consistent with observed serum creatinine values and previously published reference values. Thus, the presented NODE-based ML approach to model complex serum creatinine dynamics in newborns and derive interpretable, mathematical-statistical components similar to those in a conventional PMX model demonstrates a novel, viable approach to facilitate the modeling of complex dynamics in clinical settings and pediatric drug development.

Changes in anthropometry in full-term breastfed newborns and associated factors for the first month

OBJECTIVE

We aim to assess the changes and associated factors in newborn anthropometry in the first month for full-term, healthy, and exclusive-breastfed infants.

METHODS

Neonatal anthropometric measurements were taken on day 5, day 15, and day 30 after delivery. Generalized estimating equations (GEE) analyzed the changes.

RESULTS

From 169 mother-newborn pairs, GEE showed that weight gain during the first month was influenced by maternal body mass index (BMI), delivery type, birth weight, and jaundice after adjusting confounding factors (p < .05). The neonatal length was affected by the smoking status of parents, gestational maternal health problems, maternal height, birth weight, and jaundice (p < .05). Neonatal head circumference was influenced by the smoking status of parents, gestational maternal health problems, maternal BMI, delivery type, maternal height, and birth weight.

CONCLUSION

Adverse perinatal factors including mother's smoke exposure, maternal obesity and diabetes, cesarean birth, and neonatal hyperbilirubinemia influence anthropometry in the first months of life.

Clinical nursing value of predictive nursing in reducing complications of pregnant women undergoing short-term massive blood transfusion during cesarean section

BACKGROUND

Cesarean hemorrhage is one of the serious complications, and short-term massive blood transfusion can easily cause postoperative infection and physical stress response. However, predictive nursing intervention has important clinical significance for it.

AIM

To explore the effect of predictive nursing intervention on the stress response and complications of women undergoing short-term mass blood transfusion during cesarean section (CS).

METHODS

A clinical medical record of 100 pregnant women undergoing rapid mass blood transfusion during sections from June 2019 to June 2021. According to the different nursing methods, patients divided into control group (n = 50) and observation group (n = 50). Among them, the control group implemented routine nursing, and the observation group implemented predictive nursing intervention based on the control group. Moreover, compared the differences in stress response, complications, and pain scores before and after the nursing of pregnant women undergoing rapid mass blood transfusion during CS.

RESULTS

The anxiety and depression scores of pregnant women in the two groups were significantly improved after nursing, and the psychological stress response of the observation group was significantly lower than that of the control group (P < 0.05). The heart rate and mean arterial pressure (MAP) of the observation group during delivery were lower than those of the control group, and the MAP at the end of delivery was lower than that of the control group (P < 0.05). Moreover, different pain scores improved significantly in both groups, with the observation group considerably less than the control group (P < 0.05). After nursing, complications such as skin rash, urinary retention, chills, diarrhea, and anaphylactic shock in the observation group were 18%, which significantly higher than in the control group (4%) (P < 0.05).

CONCLUSION

Predictive nursing intervention can effectively relieve the pain, reduce the incidence of complications, improve mood and stress response, and serve as a reference value for the nursing of women undergoing rapid mass transfusion during CS.

Challenges Related to Acquisition of Physiological Data for Physiologically Based Pharmacokinetic (PBPK) Models in Postpartum, Lactating Women and Breastfed Infants-A Contribution from the ConcePTION Project

Physiologically based pharmacokinetic (PBPK) modelling is a bottom-up approach to predict pharmacokinetics in specific populations based on population-specific and medicine-specific data. Using an illustrative approach, this review aims to highlight the challenges of incorporating physiological data to develop postpartum, lactating women and breastfed infant PBPK models. For instance, most women retain pregnancy weight during the postpartum period, especially after excessive gestational weight gain, while breastfeeding might be associated with lower postpartum weight retention and long-term weight control. Based on a structured search, an equation for human milk intake reported the maximum intake of 153 mL/kg/day in exclusively breastfed infants at 20 days, which correlates with a high risk for medicine reactions at 2-4 weeks in breastfed infants. Furthermore, the changing composition of human milk and its enzymatic activities could affect pharmacokinetics in breastfed infants. Growth in breastfed infants is slower and gastric emptying faster than in formula-fed infants, while a slower maturation of specific metabolizing enzymes in breastfed infants has been described. The currently available PBPK models for these populations lack structured systematic acquisition of population-specific data. Future directions include systematic searches to fully identify physiological data. Following data integration as mathematical equations, this holds the promise to improve postpartum, lactation and infant PBPK models.

Leveraging Predictive Pharmacometrics-Based Algorithms to Enhance Perinatal Care-Application to Neonatal Jaundice

The field of medicine is undergoing a fundamental change, transforming towards a modern data-driven patient-oriented approach. This paradigm shift also affects perinatal medicine as predictive algorithms and artificial intelligence are applied to enhance and individualize maternal, neonatal and perinatal care. Here, we introduce a pharmacometrics-based mathematical-statistical computer program (PMX-based algorithm) focusing on hyperbilirubinemia, a medical condition affecting half of all newborns. Independent datasets from two different centers consisting of total serum bilirubin measurements were utilized for model development (342 neonates, 1,478 bilirubin measurements) and validation (1,101 neonates, 3,081 bilirubin measurements), respectively. The mathematical-statistical structure of the PMX-based algorithm is a differential equation in the context of non-linear mixed effects modeling, together with Empirical Bayesian Estimation to predict bilirubin kinetics for a new patient. Several clinically relevant prediction scenarios were validated, i.e., prediction up to 24 h based on one bilirubin measurement, and prediction up to 48 h based on two bilirubin measurements. The PMX-based algorithm can be applied in two different clinical scenarios. First, bilirubin kinetics can be predicted up to 24 h based on one single bilirubin measurement with a median relative (absolute) prediction difference of 8.5% (median absolute prediction difference 17.4 μmol/l), and sensitivity and specificity of 95.7 and 96.3%, respectively. Second, bilirubin kinetics can be predicted up to 48 h based on two bilirubin measurements with a median relative (absolute) prediction difference of 9.2% (median absolute prediction difference 21.5 μmol/l), and sensitivity and specificity of 93.0 and 92.1%, respectively. In contrast to currently available nomogram-based static bilirubin stratification, the PMX-based algorithm presented here is a dynamic approach predicting individual bilirubin kinetics up to 48 h, an intelligent, predictive algorithm that can be incorporated in a clinical decision support tool. Such clinical decision support tools have the potential to benefit perinatal medicine facilitating personalized care of mothers and their born and unborn infants.

Current and future physiologically based pharmacokinetic (PBPK) modeling approaches to optimize pharmacotherapy in preterm neonates

INTRODUCTION

There is a need for structured approaches to inform on pharmacotherapy in preterm neonates. With their proven track record up to regulatory acceptance, physiologically based pharmacokinetic (PBPK) modeling and simulation provide such a structured approach, and hold the promise to support drug development in preterm neonates.

AREAS COVERED

Compared to the general and pediatric use of PBPK modeling, its use to inform pharmacotherapy in preterms is limited. Using a systematic search (PBPK + preterm), we retained 25 records (20 research papers, 2 letters, 3 abstracts). We subsequently collated the published information on PBPK software packages (PK-Sim®, Simcyp®), and their applications and optimization efforts in preterm neonates. It is encouraging that these applications cover a broad range of scenarios (pharmacokinetic-dynamic analyses, drug-drug interactions, developmental pharmacogenetics, lactation related exposure) and compounds (small molecules, proteins). Furthermore, specific compartments (cerebrospinal fluid, tissue) or (patho)physiologic processes (cardiac output, biliary excretion, first pass metabolism) are considered.

EXPERT OPINION

Knowledge gaps exist, giving rise to various levels of model uncertainty in PBPK applications in preterm neonates. To improve this setting, we need cross talk between clinicians and modelers to generate and integrate knowledge (PK datasets, system knowledge, maturational physiology and pathophysiology) to further refine PBPK models.

Academy of Breastfeeding Medicine Clinical Protocol #2: Guidelines for Birth Hospitalization Discharge of Breastfeeding Dyads, Revised 2022

A central goal of the Academy of Breastfeeding Medicine is the development of clinical protocols for managing common medical problems that may impact breastfeeding success. These protocols serve only as guidelines for the care of breastfeeding mothers and infants and do not delineate an exclusive course of treatment or serve as standards of medical care. Variations in treatment may be appropriate according to the needs of an individual patient. The Academy of Breastfeeding Medicine recognizes that not all lactating individuals identify as women. Using gender-inclusive language, however, is not possible in all languages and all countries and for all readers. The position of the Academy of Breastfeeding Medicine (https://doi.org/10.1089/bfm.2021.29188.abm) is to interpret clinical protocols within the framework of inclusivity of all breastfeeding, chestfeeding, and human milk-feeding individuals.

Population pharmacokinetics of propofol in neonates and infants: Gestational and postnatal age to determine clearance maturation

AIMS

Develop a population pharmacokinetic model describing propofol pharmacokinetics in (pre)term neonates and infants, that can be used for precision dosing (e.g. during target-controlled infusion) of propofol in this population.

METHODS

A nonlinear mixed effects pharmacokinetic analysis (Monolix 2018R2) was performed, based on a pooled study population in 107 (pre)term neonates and infants.

RESULTS

In total, 836 blood samples were collected from 66 (pre)term neonates and 41 infants originating from 3 studies. Body weight (BW) of the pooled study population was 3.050 (0.580-11.440) kg, postmenstrual age (PMA) was 36.56 (27.00-43.00) weeks and postnatal age (PNA) was 1.14 (0-104.00) weeks (median and min-max range). A 3-compartment structural model was identified and the effect of BW was modelled using fixed allometric exponents. Elimination clearance maturation was modelled accounting for the maturational effect on elimination clearance until birth (by gestational age [GA]) and postpartum (by PNA and GA). The extrapolated adult (70 kg) population propofol elimination clearance (1.64 L min^-1 , estimated relative standard error = 6.02%) is in line with estimates from previous population pharmacokinetic studies. Empirical scaling of BW on the central distribution volume in function of PNA improved the model fit.

CONCLUSIONS

It is recommended to describe elimination clearance maturation by GA and PNA instead of PMA on top of size effects when analyzing propofol pharmacokinetics in populations including preterm neonates. Changes in body composition in addition to weight changes or other physio-anatomical changes may explain the changes in central distribution volume. The developed model may serve as a prior for propofol dose finding and target-controlled infusion in (preterm) neonates.

Pharmacometrics and Machine Learning Partner to Advance Clinical Data Analysis

Clinical pharmacology is a multidisciplinary data sciences field that utilizes mathematical and statistical methods to generate maximal knowledge from data. Pharmacometrics (PMX) is a well-recognized tool to characterize disease progression, pharmacokinetics, and risk factors. Because the amount of data produced keeps growing with increasing pace, the computational effort necessary for PMX models is also increasing. Additionally, computationally efficient methods, such as machine learning (ML) are becoming increasingly important in medicine. However, ML is currently not an integrated part of PMX, for various reasons. The goals of this article are to (i) provide an introduction to ML classification methods, (ii) provide examples for a ML classification analysis to identify covariates based on specific research questions, (iii) examine a clinically relevant example to investigate possible relationships of ML and PMX, and (iv) present a summary of ML and PMX tasks to develop clinical decision support tools.

Population Pharmacokinetic Modeling in the Presence of Missing Time-Dependent Covariates: Impact of Body Weight on Pharmacokinetics of Paracetamol in Neonates

Body weight is the primary covariate in pharmacokinetics of many drugs and dramatically changes during the first weeks of life of neonates. The objective of this study is to determine if missing body weights in preterm and term neonates affect estimates of model parameters and which methods can be used to improve performance of a population pharmacokinetic model of paracetamol. Data for our analysis were obtained from previously published studies on the pharmacokinetics of intravenous paracetamol in neonates. We adopted a population model of body weight change in neonates to implement three previously introduced methods of handling missing covariates based on data imputation, likelihood function modification, and full random effects modeling. All models were implemented in NONMEM 7.4, and population parameters were estimated using the FOCE method. Our major finding was that missing body weights minimally affect population estimates of pharmacokinetic parameters but do affect the covariate relationship parameters, particularly the one describing dependence of clearance on body weight. None of the tested methods changed estimates of between-subject variability nor impacted the predictive performance of the model. Our analysis shows that a modeling approach towards handling missing covariates allows borrowing information gathered in various studies as long as they target the same population. This approach is particularly useful for handling time-dependent missing covariates.

Physiologically based pharmacokinetic (PBPK) modeling and simulation in neonatal drug development: how clinicians can contribute

Introduction: Legal initiatives to stimulate neonatal drug development should be accompanied by development of valid research tools. Physiologically based (PB)-pharmacokinetic (PK) modeling and simulation are established tools, accepted by regulatory authorities. Consequently, PBPK holds promise to be a strong research tool to support neonatal drug development. Area covered: The currently available PBPK models still have poor predictive performance in neonates. Using an illustrative approach on distinct PK processes of absorption, distribution, metabolism, excretion, and real-world data in neonates, we provide evidence on the need to further refine available PBPK system parameters through generation and integration of new knowledge. This necessitates cross talk between clinicians and modelers to integrate knowledge (PK datasets, system knowledge, maturational physiology) or test and refine PBPK models. Expert opinion: Besides refining these models for 'small molecules', PBPK model development should also be more widely applied for therapeutic proteins and to determine exposure through breastfeeding. Researchers should also be aware that PBPK modeling in combination with clinical observations can also be used to elucidate age-related changes that are almost impossible to study based on in vivo or in vitro data. This approach has been explored for hepatic biliary excretion, renal tubular activity, and central nervous system exposure.