Drug-drug interactions of simnotrelvir/ritonavir: an open-label, fixed-sequence, two-period clinical trial.

Objectives: Simnotrelvir is a small-molecule highly specific 3C-like protease inhibitor for anti-SARS-CoV-2 and was approved as a combination drug with ritonavir (simnotrelvir/ritonavir) in China. Simnotrelvir is a substrate of cytochrome P450 3A (CYP3A) and P-glycoprotein (P-gp), and a weak inhibitor of CYP3A. Ritonavir is a substrate and inhibitor of CYP3A and an inhibitor of P-gp.

Machine Learning Approach in Dosage Individualization of Isoniazid for Tuberculosis.

Introduction: Isoniazid is a first-line antituberculosis agent with high variability, which would profit from individualized dosing. Concentrations of isoniazid at 2 h (C), as an indicator of safety and efficacy, are important for optimizing therapy.

Objective: The objective of this study was to establish machine learning (ML) models to predict the C, that can be used for establishing an individualized dosing regimen in clinical practice.

Model-informed drug development in pediatric, pregnancy and geriatric drug development: States of the art and future.

The challenges of drug development in pediatric, pregnant and geriatric populations are a worldwide concern shared by regulatory authorities, pharmaceutical companies, and healthcare professionals. Model-informed drug development (MIDD) can integrate and quantify real-world data of physiology, pharmacology, and disease processes by using modeling and simulation techniques to facilitate decision-making in drug development. In this article, we reviewed current MIDD policy updates, reflected on the integrity of physiological data used for MIDD and the effects of physiological changes on the drug PK, as well as summarized current MIDD strategies and applications, so as to present the state of the art of MIDD in pediatric, pregnant and geriatric populations.

Optimal use of β-lactams in neonates: machine learning-based clinical decision support system.

Background: Accurate prediction of the optimal dose for β-lactam antibiotics in neonatal sepsis is challenging. We aimed to evaluate whether a reliable clinical decision support system (CDSS) based on machine learning (ML) can assist clinicians in making optimal dose selections.

Methods: Five β-lactam antibiotics (amoxicillin, ceftazidime, cefotaxime, meropenem and latamoxef), commonly used to treat neonatal sepsis, were selected.

A first-in-human phase 1 study of simnotrelvir, a 3CL-like protease inhibitor for treatment of COVID-19, in healthy adult subjects.

Safe and efficacious antiviral therapeutics are in urgent need for the treatment of coronavirus disease 2019. Simnotrelvir is a selective 3C-like protease inhibitor that can effectively inhibit severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We evaluated the safety, tolerability, and pharmacokinetics of dose escalations of simnotrelvir alone or with ritonavir (simnotrelvir or simnotrelvir/ritonavir) in healthy subjects, as well as the food effect (ClinicalTrials.

Machine Learning: A New Approach for Dose Individualization.

The application of machine learning (ML) has shown promising results in precision medicine due to its exceptional performance in dealing with complex multidimensional data. However, using ML for individualized dosing of medicines is still in its early stage, meriting further exploration. A systematic review of study designs and modeling details of using ML for individualized dosing of different drugs was performed.

Use of Machine Learning for Dosage Individualization of Vancomycin in Neonates.

Background And Objective: High variability in vancomycin exposure in neonates requires advanced individualized dosing regimens. Achieving steady-state trough concentration (C) and steady-state area-under-curve (AUC) targets is important to optimize treatment. The objective was to evaluate whether machine learning (ML) can be used to predict these treatment targets to calculate optimal individual dosing regimens under intermittent administration conditions.

Developmental Population Pharmacokinetics-Pharmacodynamics of Meropenem in Chinese Neonates and Young Infants: Dosing Recommendations for Late-Onset Sepsis.

The pharmacokinetic (PK) studies of meropenem in Chinese newborns with late-onset sepsis (LOS) are still lacking. Causative pathogens of LOS and their susceptibility patterns in China differ from the data abroad. We, therefore, conducted a developmental population pharmacokinetic−pharmacodynamic analysis in Chinese newborns with the goal to optimize meropenem dosing regimens for LOS therapy.

Clinical utility of a model-based amoxicillin dosage regimen in neonates with early-onset sepsis.

Early-onset sepsis (EOS) is one of the most significant causes of morbidity and mortality in neonates. Currently, amoxicillin is empirically used to treat neonates with EOS. However, data on its effectiveness in neonates with EOS are still limited.

Predictive Performance of Pharmacokinetic Model-Based Virtual Trials of Vancomycin in Neonates: Mathematics Matches Clinical Observation.

Background And Objective: Vancomycin is frequently used to treat Gram-positive bacterial infections in neonates. However, there is still no consensus on the optimal initial dosing regimen. This study aimed to assess the performance of pharmacokinetic model-based virtual trials to predict the dose-exposure relationship of vancomycin in neonates.

Optimal dose of meropenem for the treatment of neonatal sepsis: Dosing guideline variations and clinical practice deviations.

Aims: Meropenem is increasingly used to treat neonatal sepsis. There are several guidelines recommending different dosing regimens of meropenem in neonates. Furthermore, deviations from these guidelines regularly occur in daily clinical practice.

Amphiphilic small molecular mates match hydrophobic drugs to form nanoassemblies based on drug-mate strategy.

Nanomedicine has made great progress in the targeted therapy of cancer. Here, we established a novel drug-mate strategy by studying the formulation of nanodrugs at the molecular level. In the drug-mate combination, the drug is a hydrophobic drug that is poorly soluble in water, and the mate is an amphiphilic small molecule (SMA) that has both hydrophilic and lipophilic properties.

Optimal Sample Size for Use in Neonatal Pharmacokinetic Studies.

Background: In recent years, population pharmacokinetics (PK) has been widely used in neonatal pharmacology. However, the sample size selection for neonatal PK studies has been highly variable and without clear consensus, especially for drugs with large individual variability. Therefore, this study's objective was to investigate the optimal sample size for use in neonatal PK studies.

Drug Clearance in Neonates: A Combination of Population Pharmacokinetic Modelling and Machine Learning Approaches to Improve Individual Prediction.

Background: Population pharmacokinetic evaluations have been widely used in neonatal pharmacokinetic studies, while machine learning has become a popular approach to solving complex problems in the current era of big data.

Objective: The aim of this proof-of-concept study was to evaluate whether combining population pharmacokinetic and machine learning approaches could provide a more accurate prediction of the clearance of renally eliminated drugs in individual neonates.

Methods: Six drugs that are primarily eliminated by the kidneys were selected (vancomycin, latamoxef, cefepime, azlocillin, ceftazidime, and amoxicillin) as 'proof of concept' compounds.

Pilot trial on the efficacy and safety of pantethine in children with pantothenate kinase-associated neurodegeneration: a single-arm, open-label study.

Objective: This study aimed to explore the efficacy and safety of pantethine in children with pantothenate kinase-associated neurodegeneration (PKAN).

Methods: A single-arm, open-label study was conducted. All subjects received pantethine during the 24-week period of treatment.

Developmental Population Pharmacokinetics and Dosing Optimization of Cefepime in Neonates and Young Infants.

Objective: Cefepime is used to treat severe infections in neonates. Pharmacokinetic data have only been evaluated among preterm neonates and population pharmacokinetic model lacked external validation. Hence, our aim is to obtain the population pharmacokinetic parameters of cefepime with large sampling and optimize the cefepime dosage regimen for neonatal infection based on developmental pharmacokinetics-pharmacodynamics.