Methadone dosing strategies in preterm neonates can be simplified

Pharmacometric in silico studies used to facilitate a national dose standardisation process in neonatology - application to amikacin

BACKGROUND AND AIMS

Pharmacometric in silico approaches are frequently applied to guide decisions concerning dosage regimes during the development of new medicines. We aimed to demonstrate how such pharmacometric modelling and simulation can provide a scientific rationale for optimising drug doses in the context of the Swiss national dose standardisation project in paediatrics using amikacin as a case study.

METHODS

Amikacin neonatal dosage is stratified by post-menstrual age (PMA) and post-natal age (PNA) in Switzerland and many other countries. Clinical concerns have been raised for the subpopulation of neonates with a post-menstrual age of 30-35 weeks and a post-natal age of 0-14 days ("subpopulation of clinical concern"), as potentially oto-/nephrotoxic trough concentrations (Ctrough >5 mg/l) were observed with a once-daily dose of 15 mg/kg. We applied a two-compartmental population pharmacokinetic model (amikacin clearance depending on birth weight and post-natal age) to real-world demographic data from 1563 neonates receiving anti-infectives (median birth weight 2.3 kg, median post-natal age six days) and performed pharmacometric dose-exposure simulations to identify extended dosing intervals that would ensure non-toxic Ctrough (Ctrough <5 mg/l) dosages in most neonates.

RESULTS

In the subpopulation of clinical concern, Ctrough <5 mg/l was predicted in 59% versus 79-99% of cases in all other subpopulations following the current recommendations. Elevated Ctrough values were associated with a post-natal age of less than seven days. Simulations showed that extending the dosing interval to ≥36 h in the subpopulation of clinical concern increased the frequency of a desirable Ctrough below 5 mg/l to >80%.

CONCLUSION

Pharmacometric in silico studies using high-quality real-world demographic data can provide a scientific rationale for national paediatric dose optimisation. This may increase clinical acceptance of fine-tuned standardised dosing recommendations and support their implementation, including in vulnerable subpopulations.

Computing optimal drug dosing with OptiDose: implementation in NONMEM

Determining a drug dosing recommendation with a PKPD model can be a laborious and complex task. Recently, an optimal dosing algorithm (OptiDose) was developed to compute the optimal doses for any pharmacometrics/PKPD model for a given dosing scenario. In the present work, we reformulate the underlying optimal control problem and elaborate how to solve it with standard commands in the software NONMEM. To demonstrate the potential of the OptiDose implementation in NONMEM, four relevant but substantially different optimal dosing tasks are solved. In addition, the impact of different dosing scenarios as well as the choice of the therapeutic goal on the computed optimal doses are discussed.

Evaluation of machine learning methods for covariate data imputation in pharmacometrics

Missing data create challenges in clinical research because they lead to loss of statistical power and potentially to biased results. Missing covariate data must be handled with suitable approaches to prepare datasets for pharmacometric analyses, such as population pharmacokinetic and pharmacodynamic analyses. To this end, various statistical methods have been widely adopted. Here, we introduce two machine-learning (ML) methods capable of imputing missing covariate data in a pharmacometric setting. Based on a previously published pharmacometric analysis, we simulated multiple missing data scenarios. We compared the performance of four established statistical methods, listwise deletion, mean imputation, standard multiple imputation (hereafter "Norm"), and predictive mean matching (PMM) and two ML based methods, random forest (RF) and artificial neural networks (ANNs), to handle missing covariate data in a statistically plausible manner. The investigated ML-based methods can be used to impute missing covariate data in a pharmacometric setting. Both traditional imputation approaches and ML-based methods perform well in the scenarios studied, with some restrictions for individual methods. The three methods exhibiting the best performance in terms of least bias for the investigated scenarios are the statistical method PMM and the two ML-based methods RF and ANN. ML-based approaches had comparable good results to the best performing established method PMM. Furthermore, ML methods provide added flexibility when encountering more complex nonlinear relationships, especially when associated parameters are suitably tuned to enhance predictive performance.

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.

A genetic-based population PK/PD modeling of methadone in Chinese opiate dependence patients

PURPOSE

The full potential of methadone maintenance treatment (MMT) is often limited by the large inter-individual variability in both pharmacokinetics (PK) and pharmacodynamics (PD), and by the risk of torsade de pointes, a severe adverse effect caused by QTc prolongation. The current study aims to quantitate the contribution of genetic polymorphisms and other variables in PK/PD variability, and their contribution to the QTc interval prolongation in Chinese MMT patients.

METHODS

Population PK models were developed to fit (R)- and (S)-methadone PK data. Hierarchical models were tested to characterize the PK profile, the concentration-QTc relationship, and concentration-urinalysis illicit drug testing relationship, with demographics and genetic variants being included as covariates. Simulation based on the developed PK/PD models was performed to assess the effect of methadone dose and genetic variants on QTc interval prolongation.

RESULTS

The PK data were best-fit by a one-compartment, first-order absorption model. Clearance of (R)- and (S)-methadone was both affected by the weighted activity score derived from genetic variants. A linear model was used to describe both the methadone concentration-urinalysis illicit drug testing relationship and the methadone concentration-QTc relationship. Concentration of (R)- and (S)-methadone exhibits a comparable effect on QTc prolongation. Simulation showed that the percentage of QTc higher than 450 ms was almost doubled in the lowest clearance group as compared the highest when methadone dose was greater than 120 mg.

CONCLUSIONS

The large variability in PK/PD profiles can be partially explained by the genetic variants in an extent different from other population, which confirmed the necessity to conduct such a study in the specific Chinese patients.

Clinical pharmacology and dosing regimen optimization of neonatal opioid withdrawal syndrome treatments

In this paper, we review the management of neonatal opioid withdrawal syndrome (NOWS) and clinical pharmacology of primary treatment agents in NOWS, including morphine, methadone, buprenorphine, clonidine, and phenobarbital. Pharmacologic treatment strategies in NOWS have been mostly empirical, and heterogeneity in dosing regimens adds to the difficulty of extrapolating study results to broader patient populations. As population pharmacokinetics (PKs) of pharmacologic agents in NOWS become more well‐defined and knowledge of patient‐specific factors affecting treatment outcomes continue to accumulate, PK/pharmacodynamic modeling and simulation will be powerful tools to aid the design of optimal dosing regimens at the patient level. Although there is an increasing number of clinical trials on the comparative efficacy of treatment agents in NOWS, here, we also draw attention to the importance of optimizing the dosing regimen, which can be arguably equally important at identifying the optimal treatment agent.

Pharmacometric Evaluation of Umbilical Cord Blood Concentration-Based Early Initiation of Treatment in Methadone-Exposed Preterm Neonates

In methadone-exposed preterm neonates, early identification of those at risk of severe neonatal abstinence syndrome (NAS) and use of a methadone dosing regimen that can provide effective and safe drug exposure are two important aspects of optimal care. To this end, we reviewed 17 methadone dosing recommendations in the international guidelines and literature and explored their variability in key dosing strategies. We selected three of the reviewed dosing regimens for their pharmacokinetics (PK) characteristics and their exposure-response relationship in three gestational age groups of preterm neonates (28, 32 and 36 gestational age weeks) at risk for development of severe NAS (defined as an umbilical cord methadone concentration of ≤60 ng/mL, following fetal exposure). We applied early (12 h after birth) vs. typical (36 h after birth) initiation of treatment. We observed that use of universally recommended dosing regimens in preterm neonates can result in under- or over-exposure. Use of a PK-guided dosing regimen resulted in effective target exposures within 24 h after birth with early initiation of treatment (12 h after birth). Future prospective studies should explore the incorporation of umbilical cord methadone concentrations for early identification of preterm neonates at risk of developing severe NAS and investigate the use of a PK-guided methadone dosing regimen, so that treatment failure, prolonged length of stay and opioid over-exposure can be avoided.

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.

Methadone dosing strategies in preterm neonates can be simplified

AIMS

A dramatic increase in newborn infants with neonatal abstinence syndrome has been observed and these neonates are frequently treated with complex methadone dosing schemes to control their withdrawal symptoms. Despite its abundant use, hardly any data on the pharmacokinetics (PK) of methadone is available in preterm neonates. Therefore we investigated developmental PK of methadone and evaluated current dosing strategies and possible simplification in this vulnerable population.

METHODS

A single-centre open-label prospective study was performed to collect PK data after a single oral dose of methadone in preterm neonates. A population PK model was built to characterize developmental PK of (R)- and (S)-methadone. Model-based simulations were performed to identify a simplified dosing strategy to reach and maintain target methadone exposure.

RESULTS

A total of 121 methadone concentrations were collected from 31 preterm neonates. A one-compartment model with first order absorption and elimination kinetics best described PK data for (R)- and (S)-methadone. Clearance increases with advancing gestational age and differs between R- and S-enantiomer, being slightly higher for the former (0.244 vs 0.167 L/h). Preterm neonates reached target exposure after 48 hours with currently used dosing schedules. Output from simulations revealed that target exposures can be achieved with a simplified dosing strategy during the first 4 days of treatment.

CONCLUSION

Methadone clearance in preterm neonates increases with advancing gestational age and its disposition is influenced by its chirality. Simulations that account for developmental PK changes indicate a shorter methadone dosing strategy can maintain target exposure to control withdrawal symptoms.