CPT: Pharmacometrics & Systems Pharmacology最新文献

{"title":"Current State-of-the-Art and Future Advancements of Model-Based Meta-Analysis","authors":"Phyllis Chan, Monica Simeoni, Matthew L. Zierhut, John Maringwa, Jonathan French","doi":"10.1002/psp4.70297","DOIUrl":"10.1002/psp4.70297","url":null,"abstract":"<p>Meta-analysis is a quantitative approach to combining data from multiple studies that has long been recognized as the highest level of evidence in evidence-based medicine—it is the lens through which stakeholders can appropriately view multiple sources of evidence [<span>1</span>]. In recent years, model-based meta-analysis (MBMA) has become an integral component of the model-informed drug development (MIDD) framework [<span>2</span>], due to its integrated nature, including the ability to incorporate longitudinal data and pharmacologic concepts like dose–response [<span>3, 4</span>].</p><p>As with traditional pairwise meta-analysis, the main purpose of MBMA in drug development involves the estimation of a treatment effect across multiple sources of evidence; however, MBMA also aims to create a predictive model that can be used to simulate unobserved scenarios. This predictive model includes parameters that help to explain between-study and within-study variability; thus, data from heterogeneous sources can be readily incorporated in the same analysis. As such, MBMA leverages publicly available summary data for indirect treatment comparisons and routinely incorporates covariate effects and random effects, leading to more accurate outcome predictions [<span>5, 6</span>]. Because of these advantages, the most common applications of MBMA have encompassed the quantification of the competitive landscape (via a comprehensive assessment of safety and/or efficacy targets necessary for differentiation, or a target product profile (TPP)) and the prediction of the probability of successful new drug development or differentiation from other available treatments. Additionally, resulting predictive models from MBMA can be utilized to guide a variety of critical decisions, such as informing trial design (e.g., dose or patient selection), trial operating characteristics optimization, and extending market access. Indeed, in survey results published in 2024 by the Oncology Dose Optimization Working Group of the International Consortium for Innovation and Quality in Pharmaceutical Development (IQ Consortium), 56% of the 18 member companies reported using MBMA routinely in their optimal dose/schedule selection process [<span>7</span>].</p><p>Despite MBMA providing indispensable value in the holistic understanding of disease areas and treatment risk–benefit profiles [<span>8</span>], publications of MBMA methodologies and use cases remain limited, potentially hindering the expansion of its utilization and ubiquitous adoption by pharmaceutical companies [<span>9</span>]. Therefore, the two main objectives of this themed issue were to (1) underscore the essential role of MBMA in MIDD and (2) highlight publications that incorporated MBMA approaches, including showcasing some novel applications.</p><p>Even though the precise definition of meta-analysis does not preclude the use of individual-level data, most of the MBMA literature refers to analyses of aggregate-level","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70297","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148429967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Clinical Study Designs for CAR-T Cell Therapy: Development of an Efficient Sampling Strategy Through Optimal Experimental Design","authors":"Fenja Klima,&nbsp;Anna M. Mc Laughlin,&nbsp;Robin Michelet,&nbsp;Wilhelm Huisinga,&nbsp;Katy Haussmann,&nbsp;Annette Künkele,&nbsp;Andrew C. Hooker,&nbsp;Charlotte Kloft","doi":"10.1002/psp4.70274","DOIUrl":"10.1002/psp4.70274","url":null,"abstract":"<p>CAR-T cell therapy is a cellular cancer immunotherapy that has impressively improved outcomes in hematological malignancies compared to conventional treatments. Yet, many patients do not respond permanently. Nonlinear mixed-effects modeling can support a better understanding of the unique and not fully understood dose-exposure and exposure-response relationships for CAR-T cell therapy by integrating available knowledge into a quantitative, physiology-motivated framework. However, to unfold its full potential, it requires informative and efficient study designs for clinical data collection. We aimed to develop an optimal experimental design framework informing a robust and feasible clinical CAR-T cell study design based on a published mechanistic model of CAR-T cell kinetics and tumor dynamics. By considering variability between study populations and parameter uncertainty, we (1) identified the minimal population size required to inform model parameters, (2) assessed different sampling strategies, and (3) informed flexible sampling windows instead of fixed sampling timepoints. The optimized study design consisted of 60 patients, three fixed assessments of tumor burden (days 0, 30 and 90), and three feasible CAR-T cell sampling windows (days 2–4, 12–18 and 32–47 after infusion). In stochastic simulation and estimation, the optimized design with sampling windows showed better performance in informing model parameters, including those characterizing heterogeneous outcomes, than designs with fixed sampling timepoints, confirming its efficiency and robustness. This framework shall facilitate future feasible and resource-efficient CAR-T cell clinical data collection, thus showcasing the potential of optimal experimental design to advance the development and optimization of complex cancer immunotherapies in clinical trials and clinical practice.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70274","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148395681","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Best Practices in Physiologically Based Pharmacokinetic (PBPK) Modeling","authors":"Marylore Chenel,&nbsp;Andre Dallmann,&nbsp;Ibrahim Ince,&nbsp;Michaela Meyer,&nbsp;Jan-Frederik Schlender,&nbsp;Donato Teutonico,&nbsp;Erik Sjögren","doi":"10.1002/psp4.70295","DOIUrl":"https://doi.org/10.1002/psp4.70295","url":null,"abstract":"<p>Physiologically based pharmacokinetic (PBPK) modeling is a mechanistic cornerstone of model-informed drug development (MIDD), providing evidence for regulatory decisions. PBPK models integrate physiological and pharmacological data and have been applied across a plethora of contexts of use, including assessing drug–drug interactions (DDIs), optimizing dosing for special populations (e.g., pediatrics and those with organ impairment), and supporting clinical study waivers. However, despite the established history of PBPK modeling, a significant gap persists between the quality of submitted PBPK analyses and regulatory expectations. Recent reviews by the European Medicines Agency (EMA) and other regulatory bodies indicate that many PBPK models are not deemed adequately qualified for their intended application. This discrepancy highlights the need for consolidated guidance on model development, evaluation, and application, particularly to harmonize terminology and expectations with the ICH M15 guideline. To address this challenge, this paper provides comprehensive best-practice guidance for the development, verification, validation, and applicability assessment of PBPK models. We delineate strategies for defining explicit technical criteria to ensure models are appropriately qualified and demonstrably fit-for-purpose for a specific context of use. This guidance aims to enhance the quality, consistency, and regulatory acceptance of PBPK analyses, thereby strengthening the reliability of MIDD evidence supporting drug development and approval.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70295","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Use of a Population Pharmacokinetic Model for Characterizing the Pharmacokinetics of Vonoprazan in Pediatric Patients","authors":"Darcy J. Mulford,&nbsp;Axel Facius,&nbsp;Galen Witt,&nbsp;Colin W. Howden,&nbsp;Thomas Wagner,&nbsp;Eckhard Leifke,&nbsp;Carmelo Scarpignato","doi":"10.1002/psp4.70291","DOIUrl":"10.1002/psp4.70291","url":null,"abstract":"<p>Gastroesophageal reflux disease affects about 20% of children. While proton pump inhibitors are a standard treatment, vonoprazan offers more rapid, potent, and prolonged gastric acid suppression in adults. Despite its promising pharmacological properties, vonoprazan is not yet approved for pediatric use. We aimed to develop and iteratively refine a population pharmacokinetic model to guide pediatric dosing for vonoprazan, characterize its pharmacokinetics in children and adolescents, and evaluate drug exposure across age groups. We developed a two-compartment linear pharmacokinetic model using data from eight adult (18–54 years) studies, one adolescent (≥ 12 to 17 years) study, and one study in children (≥ 6 to &lt; 12 years). The model incorporated delayed absorption via three transit compartments parameterized by mean transit time, along with estimates for absorption rate constant and relative bioavailability. To capture variability, we quantified between-subject differences in clearance and central volume, accounting for correlation, and applied a combined additive and proportional error model to residual variability. Investigating relevant covariate effects allowed us to refine the model further. The model structure remained unchanged as pediatric data were added. The most significant parameter shifts occurred in the age effect on absorption rate and the weight effect on central volume. Predictions aligned closely with observed vonoprazan concentrations across age groups. Model-based simulations demonstrated consistent steady-state exposure among adults, adolescents, and children, with simulated pediatric doses aligning with the reference adult dose. In conclusion, the final two-compartment model successfully predicted vonoprazan pharmacokinetics and confirmed dose selection in children and adolescents aged 6 to 17 years.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70291","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148351039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Population Pharmacokinetic Analysis of Intravitreal Aflibercept Drug Products","authors":"Sébastien Bihorel,&nbsp;Joannellyn Chiu,&nbsp;Jason Chittenden,&nbsp;Thomas Eissing,&nbsp;Kenneth C. Turner,&nbsp;Joachim Höchel,&nbsp;Torsten Zimmermann,&nbsp;Zhongqing Will He,&nbsp;John D. Davis,&nbsp;A. Thomas DiCioccio,&nbsp;Lutz O. Harnisch","doi":"10.1002/psp4.70287","DOIUrl":"10.1002/psp4.70287","url":null,"abstract":"<p>Neovascular age-related macular degeneration (nAMD) and diabetic macular edema (DME) can cause substantial vision loss. The 2-mg drug product of aflibercept (AFL-2)—an anti-vascular endothelial growth factor agent—has established efficacy and safety in both diseases. In pivotal trials, a high-dose drug product (AFL-HD) using a formulation distinct from AFL-2 and delivering 8-mg aflibercept every 12 or 16 weeks (following initial monthly dosing) provided visual gains non-inferior to AFL-2 administered every 8 weeks (following initial monthly dosing) and comparable safety with fewer injections over 96 weeks. Free and bound aflibercept concentrations in plasma from 2744 participants in 16 clinical trials where aflibercept was administered intravitreally, subcutaneously, or intravenously were analyzed by population pharmacokinetic modeling. A semi-mechanistic model described the release of free aflibercept from the eye and its target-mediated elimination from plasma. The evaluated covariates (weight, age, sex, albumin, disease, racial classification, Japanese origin, renal and hepatic functions) resulted in only small numerical differences in systemic exposure. Ocular clearance (<i>Q</i>_E; characterizing the bidirectional transfer of free aflibercept between the eye and central compartment) was comparable across both diseases after adjusting for an age-dependent decrease. The change in drug product, and not just the dose increase, was responsible for a 39.4% slower <i>Q</i>_E and extended exposure in the ocular space for AFL-HD versus AFL-2. The median ocular concentrations of free aflibercept predicted at the end of the 8-week dosing interval for AFL-2 were typically reached 14 weeks after AFL-HD injections, versus 9–9.5 weeks if <i>Q</i>_E was identical for both drug products.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70287","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148359900","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Narrative Review of Artificial Intelligence for Drug Repurposing: Lessons From COVID-19 and Oncology (2020–2025)","authors":"Alaba Bukola Ogungbite,&nbsp;Malusi Sibiya","doi":"10.1002/psp4.70272","DOIUrl":"10.1002/psp4.70272","url":null,"abstract":"<p>Drug repurposing presents a cost-effective and time-efficient strategy to identify new therapeutic applications for existing drugs. Recent advances in artificial intelligence, including machine learning, deep learning, knowledge graphs, and natural language processing, have revolutionized this field by enabling automated discovery of drug-disease associations. This review examines the role of artificial intelligence in drug repurposing, drawing insights from two critical case areas: Coronavirus disease 2019 and oncology. We explore current trends, methodological frameworks, and technological innovations in artificial intelligence-driven drug repurposing, as well as challenges and emerging future directions. The findings of this paper underscore the transformative potential of artificial intelligence in biomedical research and justify its continued integration in pharmaceutical pipelines.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70272","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148344091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Butorphanol Pharmacokinetics Across Species: Meta-Analysis Integrating Allometric Scaling and Minimal Physiologically Based Pharmacokinetic Modeling","authors":"Ahmed M. Saeed,&nbsp;William J. Jusko","doi":"10.1002/psp4.70293","DOIUrl":"https://doi.org/10.1002/psp4.70293","url":null,"abstract":"<p>Butorphanol, an opioid agonist–antagonist used in human and veterinary medicine, lacks systematic interspecies comparisons. Butorphanol pharmacokinetics (PK) was assessed across species using a minimal physiologically based PK (mPBPK) model and allometric scaling, termed mPBPK allometric meta-analysis (MAMA). PK data from 13 species following intravenous (IV) and extravascular (EV) (intramuscular [IM], intranasal [IN], subcutaneous [SC]) doses were digitized and analyzed via MAMA. Clearance (<i>CL</i>) and steady-state volume of distribution (<i>V</i>_SS) were assessed for allometric relationships with body weight (BW). An mPBPK model including blood, three lumped tissues, and extravascular depot compartments was developed using species-specific physiological parameters. Nonlinear mixed effects modeling (NONMEM) was used for joint fittings of IV and IV + EV data. PK parameters were generally comparable across species, except for the Asian elephant. Allometric scaling revealed that <i>CL</i> and <i>V</i>_SS correlated with BW (<i>CL</i>: <i>R</i>² = 0.911, <i>b</i> = 0.700; <i>V</i>_SS: <i>R</i>² = 0.891, <i>b</i> = 0.824). The allometrically scaled mPBPK model captured the PK profiles across all 13 species. Joint fitting yielded a generalized tissue-to-plasma partition coefficient (<i>K</i>_p) of 4.07 (CV% 4.7), except the horse required a specific <i>K</i>_p of 1.14 (14.3) and a distinct fraction of cardiac output. Butorphanol exhibits consistent disposition across most species with generally BW proportional <i>CL</i> values, conserved <i>K</i>_p values, rapid absorption rates, and moderate to high bioavailability across three EV routes. The application of mPBPK modeling with allometric scaling offers a robust framework for characterizing the interspecies PK of butorphanol to assess both conservation and differences in determinants of PK.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70293","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148324819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Accelerating Subcutaneous Drug Development: A Mechanistic Absorption Model for the Open Systems Pharmacology Framework","authors":"Moriah Pellowe,&nbsp;Ilse Dubbelboer,&nbsp;Erik Sjögren","doi":"10.1002/psp4.70292","DOIUrl":"10.1002/psp4.70292","url":null,"abstract":"<p>This study describes the implementation of a mechanistic subcutaneous (SC) injection model for the Open Systems Pharmacology platform. As the SC route of administration is gaining increased popularity, there is a growing need for tools to predict, analyze, and understand the SC absorption process and the mechanisms involved. The interplay between molecular, formulation, administration, and physiological properties influences both the rate and extent of drug appearance in circulation. The primary objective of this study was to provide a structural modeling basis for mechanistic simulations of drug absorption after SC administration, considering fundamental molecular properties and systemic disposition characteristics. A key aspect of the model design was the intention to support generalizability and translational application across drug characteristics and species, providing a consistent structure for both small molecules and biologics. The SC model was implemented leveraging the structure and parameterization of PK-Sim to allow unified integration to the whole-body physiologically based pharmacokinetic model. An input-response analysis and a set of case examples were conducted to visualize model responsiveness and illustrate potential application in drug development. The generic framework may also serve as the backbone for further implementations to describe complex injection and formulation dependencies. Collectively, this framework establishes a mechanistic foundation for the simulation of SC drug absorption of both small molecules and biologics, providing a basis for further development and informed evaluation across preclinical and clinical stages within the Open Systems Pharmacology platform.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70292","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148315805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Pharmacometric Model Development by Leveraging Low-Dimensional Neural ODEs and LASSO Regression","authors":"Dominic, Stefan Bräm,&nbsp;Bernhard Steiert,&nbsp;Britta Steffens,&nbsp;Marc Pfister,&nbsp;Gilbert Koch","doi":"10.1002/psp4.70285","DOIUrl":"10.1002/psp4.70285","url":null,"abstract":"<p>Current pharmacometrics (PMX) model development is a manual process with iterative model building, fitting, and evaluation, which can be resource-intensive and time-consuming. Existing automated model development approaches utilize algorithms that still rely on iterative processes and perform model selection based on goodness-of-fit criteria. Recent advances in machine learning and artificial intelligence, particularly neural ordinary differential equations (NODEs), have demonstrated strong potential for characterizing complex pharmacokinetic (PK) and pharmacodynamic (PD) dynamics directly from data. However, NODEs are inherently black-box models, which limits their interpretability and their ability to provide mechanistic insights, both of which are essential in PMX. We recently presented a promising concept that proposes interpretable ODE-based structural models from NODEs but relied on manually identifying functional relationships, which can be challenging and prevents full automation. In this work, we present an automated model development approach that combines NODEs with least absolute shrinkage and selection operator (LASSO) regression to automatically propose structural models based on the dynamics learned by NODEs. The approach leverages LASSO's feature selection capability, thereby linking data-driven modeling with interpretable mechanistic structures. We demonstrate the applicability of this automated NODE-LASSO model development approach in three different scenarios: neonatal weight development, bi-exponential PK data, and warfarin PK/PD data. The results indicate that our automated NODE-LASSO model development approach can recover meaningful, mechanism-based structures while reducing the need for extensive iterative and manual model development. This highlights its potential as a resource-efficient and interpretable modeling strategy for PMX and its applications in model-informed drug development and clinical research.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70285","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148307417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Population Pharmacokinetics and Pharmacodynamics of Paracetamol in Malaysian Patients With Plasmodium knowlesi Malaria","authors":"Thanaporn Wattanakul,&nbsp;Daniel J. Cooper,&nbsp;Katherine Plewes,&nbsp;Matthew J. Grigg,&nbsp;Giri S. Rajahram,&nbsp;Timothy William,&nbsp;Arjen M. Dondorp,&nbsp;Michael D. Edstein,&nbsp;Geoffrey W. Birrell,&nbsp;Nicholas M. Anstey,&nbsp;Richard M. Hoglund,&nbsp;Bridget E. Barber,&nbsp;Joel Tarning","doi":"10.1002/psp4.70283","DOIUrl":"10.1002/psp4.70283","url":null,"abstract":"<p>Paracetamol may improve renal function in patients with severe <i>Plasmodium knowlesi</i> malaria, particularly in those with acute kidney injury and hemolysis, via inhibition of cell-free hemoglobin mediated oxidative kidney damage. We developed a population pharmacokinetic/pharmacodynamic (PK/PD) model to assess effects of paracetamol on creatinine, hepatotoxicity, fever clearance, and parasite clearance among Malaysian patients with predominantly non-severe knowlesi malaria using data from the PACKNOW trial (Clinical Trials Registration: NCT03056391). A total of 372 patients were included in the PK/PD analyses (paracetamol: <i>n</i> = 183, control: <i>n</i> = 189). Paracetamol PK was described using a prior PK model published in patients with falciparum malaria. The PK/PD demonstrated that higher paracetamol exposures were associated with a faster decline in both creatinine and fever clearance time, supporting its renoprotective and antipyretic effects. Increased paracetamol exposure was not associated with hepatotoxicity or serious adverse events, despite a weak positive association with liver transaminases over time. No significant relationship was observed between paracetamol exposure and parasite clearance. Overall, these findings highlight an exposure-response relationship for paracetamol and a decline in creatinine, supporting its use as a renoprotective drug in treating <i>Plasmodium knowlesi</i> malaria.</p>","PeriodicalId":10774,"journal":{"name":"CPT: Pharmacometrics & Systems Pharmacology","volume":"15 7","pages":""},"PeriodicalIF":2.8,"publicationDate":"2026-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/psp4.70283","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148293849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}