Predicting the correct dose in children: Role of computational Pediatric Physiological-based pharmacokinetics modeling tools

Optimizing vancomycin dosing in pediatrics: a machine learning approach to predict trough concentrations in children under four years of age

BACKGROUND

Vancomycin trough concentration is closely associated with clinical efficacy and toxicity. Predicting vancomycin trough concentrations in pediatric patients is challenging due to significant inter-individual variability and rapid physiological changes during maturation.

AIM

This study aimed to develop a machine learning model to predict vancomycin trough concentrations and determine optimal dosing regimens for pediatric patients < 4 years of age using ML algorithms.

METHOD

A single-center retrospective observational study was conducted from January 2017 to March 2020. Pediatric patients who received intravenous vancomycin and underwent therapeutic drug monitoring were enrolled. Seven ML models [linear regression, gradient boosted decision trees, support vector machine, decision tree, random forest, Bagging, and extreme gradient boosting (XGBoost)] were developed using 31 variables. Performance metrics including R-squared (R2), mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) were compared, and important features were ranked.

RESULTS

The study included 120 eligible trough concentration measurements from 112 patients. Of these, 84 measurements were used for training and 36 for testing. Among the seven algorithms tested, XGBoost showed the best performance, with a low prediction error and high goodness of fit (MAE = 2.55, RMSE = 4.13, MSE = 17.12, and R2 = 0.59). Blood urea nitrogen, serum creatinine, and creatinine clearance rate were identified as the most important predictors of vancomycin trough concentration.

CONCLUSION

An XGBoost ML model was developed to predict vancomycin trough concentrations and aid in drug treatment predictions as a decision-support technology.

Progress report on new medications for seizures and epilepsy: A summary of the 17th Eilat Conference on New Antiepileptic Drugs and Devices (EILAT XVII). II. Drugs in more advanced clinical development

The 17th Eilat Conference on New Antiepileptic Drugs and Devices took place in Madrid, Spain on May 5-8, 2024. As usual, the core part of the conference consisted of presentations on investigational drugs at various stages of development for epilepsy-related indications. Summaries of information on compounds in preclinical or early clinical development are included in an accompanying publication (Part I). In this article, we provide summaries for five compounds in more advanced clinical development, i.e. compounds for which some information on antiseizure activity in individuals with epilepsy is available. These investigational treatments include azetukalner (XEN1101), a potent, KV7.2/7.3-specific potassium channel opener in development for the treatment of focal seizures, generalized tonic-clonic seizures, and major depressive disorder; bexicaserin (LP352), a selective 5-HT2C receptor superagonist in development for the treatment of seizures associated with developmental and epileptic encephalopathies; radiprodil, a selective negative allosteric modulator of NR2B subunit-containing N-methyl-D-aspartate glutamate receptors, in development for the treatment of seizures and behavior manifestations associated with disorders caused by gain-of-function mutations in the GRIN1, -2A, -2B, or -2D genes; soticlestat (TAK-935), a selective inhibitor of cholesterol 24-hydroxylase in development for the treatment of seizures associated with Dravet syndrome and Lennox-Gastaut syndrome; and STK-001, an antisense oligonucleotide designed to upregulate Nav1.1 protein expression and improve outcomes in individuals with Dravet syndrome. The diversity in mechanisms of action of these agents illustrates different approaches being pursued in the discovery of novel treatments for seizures and epilepsy. For two of the compounds discussed in this report (azetukalner and soticlestat), clinical evidence of efficacy has already been obtained in a randomized placebo-controlled adjunctive-therapy trial. For the other compounds, adequately powered placebo-controlled efficacy trials have not been completed to date.

Application of physiologically based pharmacokinetic modeling of novel drugs approved by the U.S. food and drug administration

Physiologically based pharmacokinetic (PBPK) models which can leverage preclinical data to predict the pharmacokinetic properties of drugs rapidly became an essential tool to improve the efficiency and quality of novel drug development. In this review, by searching the Application Review Files in Drugs@FDA, we analyzed the current application of PBPK models in novel drugs approved by the U.S. Food and Drug Administration (FDA) in the past five years. According to the results, 243 novel drugs were approved by the FDA from 2019 to 2023. During this period, 74 Application Review Files of novel drugs approved by the FDA that used PBPK models. PBPK models were used in various areas, including drug-drug interactions (DDI), organ impairment (OI) patients, pediatrics, drug-gene interaction (DGI), disease impact, and food effects. DDI was the most widely used area of PBPK models for novel drugs, accounting for 74.2 % of the total. Software platforms with graphical user interfaces (GUI) have reduced the difficulty of PBPK modeling, and Simcyp was the most popular software platform among applicants, with a usage rate of 80.5 %. Despite its challenges, PBPK has demonstrated its potential in novel drug development, and a growing number of successful cases provide experience learned for researchers in the industry.

Population pharmacokinetic analyses of methotrexate in pediatric patients: a systematic review

BACKGROUND AND OBJECTIVES

Methotrexate is widely utilized in the chemotherapy of malignant tumors and autoimmune diseases in the pediatric population, but dosing can be challenging. Several population pharmacokinetic models were developed to characterize factors influencing variability and improve individualization of dosing regimens. However, significant covariates included varied across studies. The primary objective of this review was to summarize and discuss population pharmacokinetic models of methotrexate and covariates that influence pharmacokinetic variability in pediatric patients.

METHODS

Systematic searches were conducted in the PubMed and EMBASE databases from inception to 7 July 2023. Reporting Quality was evaluated based on a checklist with 31 items. The characteristics of studies and information for model construction and validation were extracted, summarized, and discussed.

RESULTS

Eighteen studies (four prospective studies and fourteen retrospective studies with sample sizes of 14 to 772 patients and 2.7 to 93.1 samples per patient) were included in this study. Two-compartment models were the commonly used structural models for methotrexate, and the clearance range of methotrexate ranged from 2.32 to 19.03 L/h (median: 6.86 L/h). Body size and renal function were found to significantly affect the clearance of methotrexate for pediatric patients. There were limited reports on the role of other covariates, such as gene polymorphisms and co-medications, in the pharmacokinetic parameters of methotrexate pediatric patients. Internal and external evaluations were used to assess the performance of the population pharmacokinetic models.

CONCLUSION

A more rigorous external evaluation needs to be performed before routine clinical use to select the appropriate PopPK model. Further research is necessary to incorporate larger cohorts or pool analyses in specific susceptible pediatric populations to improve the understanding of predicted exposure profiles and covariate identification.

The Impact of Paediatric Obesity on Drug Pharmacokinetics: A Virtual Clinical Trials Case Study with Amlodipine

The incidence of paediatric obesity continues to rise worldwide and contributes to a range of diseases including cardiovascular disease. Obesity in children has been shown to impact upon the plasma concentrations of various compounds, including amlodipine. Nonetheless, information on the influence of obesity on amlodipine pharmacokinetics and the need for dose adjustment has not been studied previously. This study applied the physiologically based pharmacokinetic modelling and established a paediatric obesity population to assess the impact of obesity on amlodipine pharmacokinetics in children and explore the possible dose adjustments required to reach the same plasma concentration as non-obese paediatrics. The difference in predicted maximum concentration (Cmax) and area under the curve (AUC) were significant between children with and without obesity across the age group 2 to 18 years old when a fixed-dose regimen was used. On the contrary, a weight-based dose regimen showed no difference in Cmax between obese and non-obese from 2 to 9 years old. Thus, when a fixed-dose regimen is to be administered, a 1.25- to 1.5-fold increase in dose is required in obese children to achieve the same Cmax concentration as non-obese children, specifically for children aged 5 years and above.

Physiologically Based Pharmacokinetic Modeling in Neonates: Current Status and Future Perspectives

Rational drug use in special populations is a clinical problem that doctors and pharma-cists must consider seriously. Neonates are the most physiologically immature and vulnerable to drug dosing. There is a pronounced difference in the anatomical and physiological profiles be-tween neonates and older people, affecting the absorption, distribution, metabolism, and excretion of drugs in vivo, ultimately leading to changes in drug concentration. Thus, dose adjustments in neonates are necessary to achieve adequate therapeutic concentrations and avoid drug toxicity. Over the past few decades, modeling and simulation techniques, especially physiologically based pharmacokinetic (PBPK) modeling, have been increasingly used in pediatric drug development and clinical therapy. This rigorously designed and verified model can effectively compensate for the deficiencies of clinical trials in neonates, provide a valuable reference for clinical research design, and even replace some clinical trials to predict drug plasma concentrations in newborns. This review introduces previous findings regarding age-dependent physiological changes and pathological factors affecting neonatal pharmacokinetics, along with their research means. The application of PBPK modeling in neonatal pharmacokinetic studies of various medications is also reviewed. Based on this, we propose future perspectives on neonatal PBPK modeling and hope for its broader application.