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      Editorial: Model-informed drug development and precision dosing in clinical pharmacology practice

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          Abstract

          Model-informed drug development (MIDD) refers to the integration and quantitative study of physiological, pharmacological, and disease process information using modeling and simulation technology to guide drug development and decision-making, whose aim is to make drug development more efficient and reduce unnecessary patient exposure by integrating data from in vivo/in vitro studies to predict drug effects (Li et al., 2020). Model-informed precision dosing (MIPD) integrates information related to patients, drugs, and diseases through mathematical modeling and simulation technology to provide a basis for precision medicine for patients. Compared with empirical medication, MIPD is a new method to formulate drug administration schedules based on physiological, pathological, genetic, disease, and other characteristics of patients, which can improve the safety, effectiveness, economy, and adherence of pharmacotherapy (Jiao et al., 2021). Common models of MIDD and MIPD include but are not limited to the population pharmacokinetics model, pharmacokinetics/pharmacodynamics model, population pharmacokinetics/pharmacodynamics model, physiologically based pharmacokinetics model, quantitative systems pharmacology, model-based meta-analysis, virtual twin, pharmacoeconomic modeling, artificial intelligence, and machine learning. MIDD and MIPD are essentially the same, given that they solve problems mainly through modeling and simulation. Their differences mainly lie in their different application scenarios, where MIDD mainly refers to modeling and simulation for new drug research and development (Wang et al., 2021; Mitra and Wang, 2022; Chen et al., 2023), while MIPD mainly refers to modeling and simulation for clinical precise drug delivery (Liu et al., 2021; Yin et al., 2022; Li et al., 2023). Clinical pharmacology is a discipline that studies the law of interaction between drugs and the human body, which based on pharmacology and clinical medicine expounds pharmacokinetics, pharmacodynamics, the nature and mechanism of toxic and side reactions, and the law of drug interaction (Giacomini and Huang, 2022; van der Graaf, 2022; Yao et al., 2022). The main tasks of clinical pharmacology are the clinical research and evaluation of new drugs, reevaluation of market drugs, clinical pharmacokinetic research, adverse drug reaction monitoring, and drug interaction research. With the development of modeling and simulation technology, MIDD and MIPD play an increasingly important role in the practice of clinical pharmacology. Thus, this Research Topic introduced the clinical pharmacological practice of MIDD and MIPD. Liang et al. found that in critically ill patients, age and albumin level were potentially important factors for the pharmacokinetic parameters of polymyxin B, mainly because older critically ill patients more likely had lower albumin levels, meaning that higher polymyxin B dosage was necessary for efficacy. Cai et al. studied polymyxin B population pharmacokinetics in lung transplantation patients and optimized its administration dosage, finding that renal function had a significant effect on the polymyxin B’s clearance and an adjustment of dosage was needful in lung transplantation patients with renal impairments. Additionally, in the early stage of adult liver transplantation, Cai et al. found the non-linear Michaelis–Menten model could offer credible evidence for tacrolimus dosage optimization in adult liver transplantation patients. Wang et al. reported a joint population pharmacokinetic model of venlafaxine and O-desmethyl venlafaxine in healthy volunteers and patients to estimate the influence of morbidity and drug combination, which may be conducive to achieve precision dosage in clinical pharmacology practice. In Zhu et al.’s research, olanzapine was used as an example to emphasize the feasibility of the real-time estimation of drug concentrations with stacking-based machine learning strategies without losing interpretability, thus further promoting MIPD. Macente et al. determined the dosage regimen recommendation for treatment initiation with sildenafil, specifically in the congenital diaphragmatic hernia indication. Upon treatment initiation, maternal sildenafil dosage should be adjusted on account of therapeutic drug monitoring. Li et al. revealed that toripalimab exposure from a 240 mg Q3W administration dosage was comparable to a 3 mg/kg Q2W administration dosage. In the meantime, the safety and efficacy of 240 mg Q3W was flat, indicating the 240 mg Q3W administration dosage is a preferred therapy dosage for toripalimab based on the convenience of the flat dosage. In brief, this Research Topic analyzed MIDD and MIPD in clinical pharmacology practice, concentrating principally on modeling and simulation to accelerate drug development and precision dosing.

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          Population pharmacokinetics of the anti-PD-1 antibody camrelizumab in patients with multiple tumor types and model-informed dosing strategy.

          Camrelizumab, a programmed cell death 1 (PD-1) inhibitor, has been approved for the treatment of patients with relapsed or refractory classical Hodgkin lymphoma, nasopharyngeal cancer and non-small cell lung cancer. The aim of this study was to perform a population pharmacokinetic (PK) analysis of camrelizumab to quantify the impact of patient characteristics and to investigate the appropriateness of a flat dose in the dosing regimen. A total of 3092 camrelizumab concentrations from 133 patients in four clinical trials with advanced melanoma, relapsed or refractory classical Hodgkin lymphoma and other solid tumor types were analyzed using nonlinear mixed effects modeling. The PKs of camrelizumab were properly described using a two-compartment model with parallel linear and nonlinear clearance. Then, covariate model building was conducted using stepwise forward addition and backward elimination. The results showed that baseline albumin had significant effects on linear clearance, while actual body weight affected intercompartmental clearance. However, their impacts were limited, and no dose adjustments were required. The final model was further evaluated by goodness-of-fit plots, bootstrap procedures, and visual predictive checks and showed satisfactory model performance. Moreover, dosing regimens of 200 mg every 2 weeks and 3 mg/kg every 2 weeks provided similar exposure distributions by model-based Monte Carlo simulation. The population analyses demonstrated that patient characteristics have no clinically meaningful impact on the PKs of camrelizumab and present evidence for no advantage of either the flat dose or weight-based dose regimen for most patients with advanced solid tumors.
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            How to handle the delayed or missed dose of rivaroxaban in patients with non-valvular atrial fibrillation: model-informed remedial dosing

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              Handling Delayed or Missed Dose of Antiseizure Medications : A Model-Informed Individual Remedial Dosing

              Background and Objectives Delayed or missed antiseizure medications (ASMs) doses are common during long-term or lifelong antiepilepsy treatment. This study aims to explore optimal individualized remedial dosing regimens for delayed or missed doses of 11 commonly used ASMs. Methods To explore remedial dosing regimens, Monte Carlo simulation was used based on previously identified and published population pharmacokinetic models. Six remedial strategies for delayed or missed doses were investigated. The deviation time outside the individual therapeutic range was used to evaluate each remedial regimen. The influences of patients' demographics, concomitant medication, and scheduled dosing intervals on remedial regimens were assessed. RxODE and Shiny in R were used to perform Monte Carlo simulation and recommend individual remedial regimens. Results The recommended remedial regimens were highly correlated with delayed time, scheduled dosing interval, and half-life of the ASM. Moreover, the optimal remedial regimens for pediatric and adult patients were different. The renal function, along with concomitant medication that affects the clearance of the ASM, may also influence the remedial regimens. A web-based dashboard was developed to provide individualized remedial regimens for the delayed or missed dose, and a user-defined module with all parameters that could be defined flexibly by the user was also built. Discussion Monte Carlo simulation based on population pharmacokinetic models may provide a rational approach to propose remedial regimens for delayed or missed doses of ASMs in pediatric and adult patients with epilepsy.
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                Author and article information

                Contributors
                Journal
                Front Pharmacol
                Front Pharmacol
                Front. Pharmacol.
                Frontiers in Pharmacology
                Frontiers Media S.A.
                1663-9812
                30 June 2023
                2023
                : 14
                : 1224980
                Affiliations
                [1] 1 Jiangsu Key Laboratory of New Drug Research and Clinical Pharmacy and School of Pharmacy , Xuzhou Medical University , Xuzhou, Jiangsu, China
                [2] 2 Department of Clinical Pharmacology , Jiangsu Hengrui Pharmaceuticals Co., Ltd. , Shanghai, China
                [3] 3 Department of Pharmacy , Suzhou Hospital , Affiliated Hospital of Medical School , Nanjing University , Suzhou, Jiangsu, China
                [4] 4 Department of Pharmacy , Shanghai Chest Hospital , Shanghai Jiao Tong University School of Medicine , Shanghai, China
                Author notes

                Edited and reviewed by: Filippo Drago, University of Catania, Italy

                *Correspondence: Sumei He, hehe8204@ 123456163.com ; Zheng Jiao, jiaozhen@ 123456online.sh.cn ; Dongdong Wang, 13852029591@ 123456163.com
                [ † ]

                These authors share first authorship

                Article
                1224980
                10.3389/fphar.2023.1224980
                10348903
                37456757
                4ffee87b-abbf-4be8-a7ab-0237394c5102
                Copyright © 2023 Hu, Fu, Huang, He, Jiao and Wang.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 18 May 2023
                : 23 June 2023
                Funding
                This work was supported by the National Natural Science Foundation of China (No. 82104296), Xuzhou Special Fund for Promoting Scientific and Technological Innovation (No. KC21257), Initializing Fund of Xuzhou Medical University (No. RC20552111), Fusion Innovation Project of Xuzhou Medical University (No. XYRHCX2021011), Jiangsu Province Education Science Planning Project (No. C/2022/01/36), and Xuzhou Medical University Labor Education Special Support Project (No. X1d202209).
                Categories
                Pharmacology
                Editorial
                Custom metadata
                Experimental Pharmacology and Drug Discovery

                Pharmacology & Pharmaceutical medicine
                model-informed drug development,model-informed precision dosing,clinical pharmacology,modeling and simulation,practice

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