基于模型的方法评估抗药物抗体对生物制剂药物暴露的影响:以CD3 t细胞双特异性西比沙他单抗为例。

IF 3.1 3区 医学 Q2 PHARMACOLOGY & PHARMACY
Javier Sanchez, Philippe B. Pierrillas, Nicolas Frey, Gregor P. Lotz, Siv Jönsson, Lena E. Friberg, Nicolas Frances
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引用次数: 0

摘要

生物制剂的施用可导致触发抗药物抗体(ADA)形成的免疫原性反应。ADAs可以减少药物暴露。建立了群体药代动力学(popPK)模型,以描述在cea定向t细胞双特异性抗体西比沙他单抗的情况下,有无ada驱动暴露损失的临床PK数据。在两项临床研究中对西比沙他单抗的PK进行了评估(单独使用和与检查点抑制剂atezolizumab联合使用)。popPK模型是利用Monolix中实现的随机近似-期望最大化(SAEM)算法,在西比沙他单抗临床PK数据上建立的。西比沙他单抗的PK遵循双室模型,清除率随时间和ada相关暴露损失线性下降。通过考虑ADA的形成、与西比他单抗的可逆结合以及游离ADA和ADA-西比他单抗复合物从中央室的消除,在模型中实现了ADA驱动的暴露损失。在模型中,ADAs对PK暴露的影响是时间依赖的,ADA的形成被描述为时间的函数(从零开始增加,达到其估计的最大值,在某些患者中可能下降到该最大值的94%)。最终模型包括一个混合成分,用于区分因ADA形成而有暴露损失和没有暴露损失的患者(分别为75%和25%的患者)。被调查的患者人口统计学、剂量或给药计划或atezolizumab联合给药未被确定为影响ADAs导致的暴露损失的因素。开发的模型可用于区分ADA驱动暴露损失的患者和非ADA驱动暴露损失的患者,以及即使ADA形成的患者也可用于精确的PK表征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Model-Based Approach to Evaluate Anti-Drug Antibody Impact on Drug Exposure With Biologics: A Case Example With the CD3 T-Cell Bispecific Cibisatamab

The administration of biologics can lead to immunogenic responses that trigger anti-drug antibody (ADA) formation. ADAs can decrease drug exposure. A population pharmacokinetic (popPK) model was developed to describe clinical PK data with and without ADA-driven exposure loss with CEA-directed T-cell bispecific antibody cibisatamab. The PK of cibisatamab was evaluated in two clinical studies (as a single agent and in combination with the checkpoint inhibitor atezolizumab) in patients. The popPK model was developed on cibisatamab clinical PK data using the Stochastic Approximation –Expectation Maximization (SAEM) algorithm implemented in Monolix. Cibisatamab's PK followed a two-compartment model with linear clearance decreasing over time and ADA-associated exposure loss. ADA-driven exposure loss was implemented in the model by accounting for ADA formation, reversible binding to cibisatamab, and elimination of both free ADA and the ADA-cibisatamab complex from the central compartment. The impact of ADAs on PK exposure was time-dependent in the model, with the ADA formation described as a function of time (increasing from zero, reaching its estimated maximum value, and possibly decreasing down to 94% of this maximum value in some patients). The final model included a mixture component differentiating patients with and without exposure loss due to ADA formation (75% and 25% of patients, respectively). The investigated patient demographics, dose or dosing schedule, or atezolizumab coadministration were not identified as factors influencing exposure loss due to ADAs. The developed model can be used to differentiate patients with and without ADA-driven exposure loss, as well as for a precise PK characterization in patients even with ADA formation.

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来源期刊
CiteScore
5.00
自引率
11.40%
发文量
146
审稿时长
8 weeks
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