Williams Jason H, Liao Kai H, Yin Donghua, Meng Xu
Pfizer Inc, San Diego, CA, USA.
Arcus Biosciences, Hayward, CA, USA.
AAPS J. 2024 Mar 7;26(2):31. doi: 10.1208/s12248-024-00901-1.
The interpretation of immunogenicity results for a mAb product and prediction of its clinical consequences remain difficult, despite enormous advances in methodologies and efforts toward the best practice for consistent data generation and reporting. To this end, the contribution from the clinical pharmacology discipline has been largely limited to comparing descriptively the pharmacokinetic (PK) profiles by antidrug antibodies (ADA) status or testing the significance of ADA as a covariate in a population PK setting, similar to the practice for small-molecule drugs in investigating the effect of an intrinsic/extrinsic factor on the drug disposition. There is a need for a mAb disposition framework that captures the dynamics of ADA formation and drug's interactions with the ADA and target as parts of the drug distribution and elimination. Here we describe such a framework and examine it against the PK, ADA, and clinical response data from a phase 3 trial in patients treated with adalimumab. The proposed framework offered a generalized understanding of how the dose, target affinity, and drug/ADA analyte forms affects the manifestation of ADA response with regard to its detections and alterations of drug disposition and effectiveness. Furthermore, as an example, its utility for dose considerations was demonstrated through predicting for late-stage trials of a PCSK9 inhibitor in terms of development in ADA incidence and titers, and consequences on the drug disposition, interaction with target, and downstream lowering effect on LDL-C.
尽管在方法学方面取得了巨大进展,并为生成和报告一致的数据付出了诸多努力以达到最佳实践,但对单克隆抗体(mAb)产品免疫原性结果的解读及其临床后果的预测仍然困难重重。为此,临床药理学学科的贡献在很大程度上局限于通过抗药物抗体(ADA)状态描述性地比较药代动力学(PK)概况,或者在群体PK环境中测试ADA作为协变量的意义,这与小分子药物在研究内在/外在因素对药物处置的影响时的做法类似。需要一个mAb处置框架,该框架能够捕捉ADA形成的动态过程以及药物与ADA和靶点的相互作用,将其作为药物分布和消除的一部分。在此,我们描述这样一个框架,并根据阿达木单抗治疗患者的3期试验中的PK、ADA和临床反应数据对其进行检验。所提出的框架提供了一种广义的理解,即剂量、靶点亲和力以及药物/ADA分析物形式如何影响ADA反应的表现,包括其对药物处置和有效性的检测及改变。此外,作为一个例子,通过预测PCSK9抑制剂后期试验中ADA发生率和滴度的变化,以及对药物处置、与靶点的相互作用和对低密度脂蛋白胆固醇(LDL-C)的下游降低作用的影响,展示了该框架在剂量考量方面的实用性。