ReviewDrug design, development and therapy2026
Dose Exploration Based on Quantitative Pharmacokinetic Analysis of Monoclonal Antibodies for Pediatric Immune-Mediated Diseases: Current Evidence and Future Directions.
Review in Drug design, development and therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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5 authors.
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Abstract
Monoclonal antibodies have greatly changed the treatment of pediatric immune-mediated diseases (PIMDs) and have become the main treatment method for patients who do not respond to conventional therapy. However, there are many challenges in developing pediatric dosing regimens. The pharmacokinetics of monoclonal antibodies differ between adults and children due to dynamic, age-related physiological changes that affect target-mediated clearance, FcRn-mediated recycling, and fluid volume distribution. Pediatric dosing regimens typically employ weight-based and weight-stratified dosing that are extrapolated based on adult data and often result in subsequent treatment failure. This review provides a comprehensive analysis of current research on the pharmacokinetics of pediatric monoclonal antibodies, discussing the application of population pharmacokinetics and physiologically based pharmacokinetic modeling. In addition, the exposure-response relationship and the role of therapeutic drug monitoring in dose optimization are summarized. Overall, the current understanding of pharmacokinetics and pharmacodynamic behaviors specific to the child population is not comprehensive, and clinical translation remains hindered by the diversity of reported endpoints and the lack of external validation for existing models. Therefore, the future of precision dosing lies in moving beyond simple exposure matching to pharmacodynamic (PD) endpoint-based treatment regimens. Finally, we propose a feasible pathway that integrates next-generation mechanistic models with artificial intelligence (AI) and clinical decision support systems (CDSS) to bridge the gap between computational evidence and clinical applications.
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