Yousef Malaz, Brocks Dion R, Löbenberg Raimar, Davies Neal M
Faculty of Pharmacy and Pharmaceutical Sciences, Katz Centre for Pharmacy and Health Research, University of Alberta, Edmonton, AB, T6G 2E1, Canada.
Clin Pharmacokinet. 2025 Aug 13. doi: 10.1007/s40262-025-01559-4.
Multiple peaking in pharmacokinetics refers to the occurrence of two or more peaks of drug plasma concentrations following a single dose administration. It complicates interpretation of pharmacokinetics parameters and influences clinical decision-making regarding drug efficacy and bioequivalence. This review re-examines and extends an earlier seminal review on the physicochemical and formulation-related causes and physiological mechanisms of multiple peaking. In addition to the previously discussed mechanisms, factors such as lymphatic drug uptake, enterogastric recycling, hepatoenteric recycling, dual absorption pathways, overdose scenarios, and pharmacobezoar formation have also been identified as contributors to the multiple peaking phenomenon. Furthermore, the role of specialized formulations, particularly pulsatile drug delivery systems (PDDS), has been explored in relation to their impact on this complex pharmacokinetics behavior. Moreover, this review highlights advanced modeling tools, namely physiologically based pharmacokinetic modeling (PBPK), illustrating how they can be applied to decipher complex absorption profiles, and highlights bioequivalence considerations for products exhibiting multiple peaks, such as partial area under the curve (pAUC). Improved identification and modeling of this phenomenon is critical to optimizing drug development, therapeutic monitoring, precision dosing, and regulatory decision-making.
药代动力学中的多峰现象是指单次给药后药物血浆浓度出现两个或更多峰值。这使药代动力学参数的解释变得复杂，并影响有关药物疗效和生物等效性的临床决策。本综述重新审视并扩展了之前一篇关于多峰现象的物理化学和制剂相关原因及生理机制的开创性综述。除了之前讨论的机制外，淋巴系统药物摄取、肠胃循环、肝肠循环、双重吸收途径、过量用药情况和药物胃石形成等因素也被确定为多峰现象的促成因素。此外，还探讨了特殊制剂，特别是脉冲式药物递送系统（PDDS）对这种复杂药代动力学行为的影响。此外，本综述强调了先进的建模工具，即基于生理学的药代动力学建模（PBPK），说明了如何将其应用于解读复杂的吸收曲线，并强调了对呈现多峰的产品进行生物等效性考量，如曲线下部分面积（pAUC）。更好地识别和建模这一现象对于优化药物研发、治疗监测、精准给药和监管决策至关重要。
