Kinetic Modeling of the Antibody Disulfide Bond Reduction Reaction With Integrated Prediction of the Drug Load Profile for Cysteine-Conjugated ADCs

IF 3.5 2区 生物学 Q2 BIOTECHNOLOGY & APPLIED MICROBIOLOGY
Jan Tobias Weggen, Pedro González, Kimberly Hui, Ryan Bean, Michaela Wendeler, Jürgen Hubbuch
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Abstract

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.

Abstract Image

Abstract Image

半胱氨酸偶联adc抗体二硫键还原反应动力学建模及药物负荷谱综合预测
抗体药物共轭物(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共轭中,细胞毒性有效载荷被连接到还原的链间二硫化物上,这涉及到原生单克隆抗体(mAb)的还原。由于这种反应会影响 ADC 最终产品的关键质量属性 (CQA),如药物抗体比 (DAR) 和药物载量分布 (DLD),因此需要彻底了解和控制这种反应。然而,现有方法缺乏对工艺参数与 CQAs 之间关系的机理描述。在这种情况下,动力学建模可提供全面的反应理解,促进基于模型的还原反应参数优化,并有可能减少开发稳健工艺所需的实验工作量。在本研究中,我们引入了一个综合建模框架,其中包括一个针对 mAb 还原反应过程中形成的物种的还原动力学模型,结合一个回归模型,通过 DAR 和 DLD 量化共轭药物的数量。还原过程中形成的物种将通过分析性毛细管凝胶电泳(CGE)进行测量,而 DAR 和 DLD 将通过反相色谱法(RP)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,建立了还原动力学模型,以描述还原剂过量和反应温度对动力学的影响。其次,我们介绍了一种基于多元线性回归(MLR)的交叉分析方法,将 CGE 数据转换为 RP 导出的 DAR/DLD。通过将其与新开发的还原动力学模型相结合,我们创建了一个包含还原和共轭两个连续反应步骤的综合模型,以预测初始还原反应条件下的最终 DAR/DLD。最后,利用该综合模型进行硅筛选,分析还原条件、TCEP 过量、温度和反应时间对 ADC 最终产物的直接影响。
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来源期刊
Biotechnology and Bioengineering
Biotechnology and Bioengineering 工程技术-生物工程与应用微生物
CiteScore
7.90
自引率
5.30%
发文量
280
审稿时长
2.1 months
期刊介绍: Biotechnology & Bioengineering publishes Perspectives, Articles, Reviews, Mini-Reviews, and Communications to the Editor that embrace all aspects of biotechnology. These include: -Enzyme systems and their applications, including enzyme reactors, purification, and applied aspects of protein engineering -Animal-cell biotechnology, including media development -Applied aspects of cellular physiology, metabolism, and energetics -Biocatalysis and applied enzymology, including enzyme reactors, protein engineering, and nanobiotechnology -Biothermodynamics -Biofuels, including biomass and renewable resource engineering -Biomaterials, including delivery systems and materials for tissue engineering -Bioprocess engineering, including kinetics and modeling of biological systems, transport phenomena in bioreactors, bioreactor design, monitoring, and control -Biosensors and instrumentation -Computational and systems biology, including bioinformatics and genomic/proteomic studies -Environmental biotechnology, including biofilms, algal systems, and bioremediation -Metabolic and cellular engineering -Plant-cell biotechnology -Spectroscopic and other analytical techniques for biotechnological applications -Synthetic biology -Tissue engineering, stem-cell bioengineering, regenerative medicine, gene therapy and delivery systems The editors will consider papers for publication based on novelty, their immediate or future impact on biotechnological processes, and their contribution to the advancement of biochemical engineering science. Submission of papers dealing with routine aspects of bioprocessing, description of established equipment, and routine applications of established methodologies (e.g., control strategies, modeling, experimental methods) is discouraged. Theoretical papers will be judged based on the novelty of the approach and their potential impact, or on their novel capability to predict and elucidate experimental observations.
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