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靶点介导的药物处置模型(Target-mediated drug disposition,TMDD)是研究单克隆抗体非线性PK的主要模型理论之一.然而全量TMDD模型参数较多,有限的临床数据并不能支持模型估算全部参数.因此,本研究旨在通过公式推导和模型仿真探讨TMDD模型的简化方法,提升TMDD模型在有限临床数据建模中的适用性.基于一项关于地诺单抗(denosumab)TMDD模型研究的结果及其模型参数,在群体水平上和个体水平上进行仿真分析.然后,利用准稳态近似模型,在模型拟合和参数估计的稳定性上,检验受体总浓度的两种假设的影响.结果表明,在治疗剂量下,总受体浓度对药物浓度变化的影响很小,而假设恒定总受体浓度的模型具有相同的预测能力.该简化方法可应用于单抗类药物研发中合理实验设计的制定和最优PK模型的选择.“,”Target-mediated drug disposition (TMDD) model is one of the main modeling theories for studying nonlinear pharmacokinetics (PK) of monoclonal antibodies.However,there are too many parameters in full TMDD model to be estimated based on limited clinical data,leading to instability of the final model.In the present study,we analyzed the predictive ability and applicability of a simplified quasi-steady state (QSS) model with the assumption that the total target concentration was a constant parameter during treatment with monoclonal antibody in clinical data modeling.Based on the parameters of a published TMDD model of denosumab,simulations were performed at population and individual levels.Then,a simplified TMDD model,QSS model,was used to examine the effects of hypotheses,in which the total receptor concentration was constant or variable on model fit and stability of parameter estimation.Both simulations at the population level and model fit results of simulated individual data showed that at the therapeutic doses,the total receptor concentration had little influence on changes in drug concentration,and the model with constant total receptor concentration had the same predictive power.The validated hypothesis could be applied to clinical trial design and selection of the optimal PK model in the development of monoclonal antibodies.