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A Mechanistic, Multiscale Mathematical Model of Immunogenicity for Therapeutic Proteins: Part 2—Model Applications

机译:治疗性蛋白质免疫原性的机械,多尺度数学模型:第2部分-模型应用

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AbstractA mechanistic, multiscale mathematical model of immunogenicity for therapeutic proteins was built by recapitulating key underlying known biological processes for immunogenicity. The model is able to simulate immune responses based on protein-specific antigenic properties (e.g., number of T-epitopes and their major histocompatibility complex (MHC)-II binding affinities) and host-specific immunological/physiological characteristics (e.g., MHC-II allele genotype, drug clearance rate). Preliminary validation was performed using mouse studies with antigens such as ovalbumin (OVA) or OVA-derived peptide. Further, using adalimumab as an example therapeutic protein, the model is able to simulate immune responses against adalimumab in individual subjects and in a population, and also provides estimations of immunogenicity incidence and drug exposure reduction that can be validated experimentally. This is a first attempt at modeling immunogenicity of biologics, so the model simulations should be used to help understand the immunogenicity mechanisms and impacting factors, rather than making direct predictions. This prototype model needs to be subjected to extensive experimental validation and refinement before fulfilling its ultimate mission of predicting immunogenicity. Nevertheless, the current model could potentially set up the starting framework to integrate various in silico, in vitro, in vivo, and clinical immunogenicity assessment results to help meet the challenge of immunogenicity prediction.CPT Pharmacometrics Syst. Pharmacol. (2014) 3, e134; doi:10.1038/psp.2014.31; published online 03 September 2014
机译:摘要通过概括免疫原性的关键基础已知生物学过程,建立了用于治疗性蛋白质的免疫原性的机械,多尺度数学模型。该模型能够基于蛋白质特异性抗原特性(例如T表位的数量及其主要组织相容性复合物(MHC)-II结合亲和力)和宿主特异性免疫/生理特性(例如MHC-II)来模拟免疫反应等位基因型,药物清除率)。使用小鼠研究对抗原进行初步验证,例如卵清蛋白(OVA)或OVA衍生肽。此外,使用阿达木单抗作为示例性治疗蛋白,该模型能够模拟个体受试者和人群中针对阿达木单抗的免疫应答,并且还提供了免疫原性发生率和药物暴露减少的估计,可以通过实验进行验证。这是对生物制剂免疫原性进行建模的首次尝试,因此应使用模型仿真来帮助理解免疫原性机制和影响因素,而不是直接进行预测。在完成其预测免疫原性的最终任务之前,需要对该原型模型进行广泛的实验验证和完善。然而,当前模型可能会建立一个起始框架,以整合各种计算机模拟,体外,体内和临床免疫原性评估结果,以帮助应对免疫原性预测的挑战。CPTPharmacometrics Syst。 Pharmacol。 (2014)3,e134; doi:10.1038 / psp.2014.31;在线发布于2014年9月3日

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