Weggen Jan Tobias, González Pedro, Hui Kimberly, Bean Ryan, Wendeler Michaela, Hubbuch Jürgen
Institute of Process Engineering in Life Sciences, Section IV: Biomolecular Separation Engineering, Karlsruhe Institute of Technology (KIT), Karlsruhe, Baden-Württemberg, Germany.
Purification Process Sciences, Biopharmaceutical Development, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, Maryland, USA.
Biotechnol Bioeng. 2025 Mar;122(3):579-593. doi: 10.1002/bit.28899. Epub 2024 Dec 17.
Antibody-drug conjugates (ADC) constitute a groundbreaking advancement in the field of targeted therapy. In the widely utilized cysteine conjugation, the cytotoxic payload is attached to reduced interchain disulfides which involves a reduction of the native monoclonal antibody (mAb). This reaction needs to be thoroughly understood and controlled as it influences the critical quality attributes (CQAs) of the final ADC product, such as the drug-to-antibody ratio (DAR) and the drug load distribution (DLD). However, existing methodologies lack a mechanistic description of the relationship between process parameters and CQAs. In this context, kinetic modeling provides comprehensive reaction understanding, facilitating the model-based optimization of reduction reaction parameters and potentially reduces the experimental effort needed to develop a robust process. With this study, we introduce an integrated modeling framework consisting of a reduction kinetic model for the species formed during the mAb reduction reaction in combination with a regression model to quantify the number of conjugated drugs by DAR and DLD. The species formed during reduction will be measured by analytical capillary gel electrophoresis (CGE), and the DAR and DLD will be derived from reversed-phase (RP) chromatography. First, we present the development of a reduction kinetic model to describe the impact of reducing agent excess and reaction temperature on the kinetic, by careful investigation of different reaction networks and sets of kinetic rates. Second, we introduce a cross-analytical approach based on multiple linear regression (MLR), wherein CGE data is converted into the RP-derived DAR/DLD. By coupling this with the newly developed reduction kinetic model, an integrated model encompassing the two consecutive reaction steps, reduction and conjugation, is created to predict the final DAR/DLD from initial reduction reaction conditions. The integrated model is finally utilized for an in silico screening to analyze the effect of the reduction conditions, TCEP excess, temperature and reaction time, directly on the final ADC product.
抗体药物偶联物(ADC)是靶向治疗领域的一项突破性进展。在广泛使用的半胱氨酸偶联中,细胞毒性药物有效载荷连接到还原的链间二硫键上,这涉及到天然单克隆抗体(mAb)的还原。由于该反应会影响最终ADC产品的关键质量属性(CQA),如药物与抗体比率(DAR)和药物负载分布(DLD),因此需要对其进行深入理解和控制。然而,现有方法缺乏对工艺参数与CQA之间关系的机理描述。在此背景下,动力学建模提供了全面的反应理解,有助于基于模型优化还原反应参数,并有可能减少开发稳健工艺所需的实验工作量。通过本研究,我们引入了一个综合建模框架,该框架由mAb还原反应过程中形成的物种的还原动力学模型与回归模型组成,用于通过DAR和DLD量化偶联药物的数量。还原过程中形成的物种将通过分析型毛细管凝胶电泳(CGE)进行测量,DAR和DLD将从反相(RP)色谱中得出。首先,我们通过仔细研究不同的反应网络和动力学速率集,提出了一个还原动力学模型,以描述还原剂过量和反应温度对动力学的影响。其次,我们引入了一种基于多元线性回归(MLR)的交叉分析方法,其中CGE数据被转换为RP衍生的DAR/DLD。通过将其与新开发的还原动力学模型相结合,创建了一个包含还原和偶联这两个连续反应步骤 的综合模型,以根据初始还原反应条件预测最终的DAR/DLD。最后,该综合模型被用于计算机模拟筛选,以直接分析还原条件、三(2-羧乙基)膦(TCEP)过量、温度和反应时间对最终ADC产品的影响。