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Modeling of hydrophobic interaction chromatography for the separation of antibody-drug conjugates and its application towards quality by design.
Journal of Biotechnology ( IF 4.1 ) Pub Date : 2020-05-01 , DOI: 10.1016/j.jbiotec.2020.04.018
Sebastian Andris 1 , Jürgen Hubbuch 1
Affiliation  

Antibody-drug conjugates (ADCs) are hybrid molecules based on monoclonal antibodies (mAbs) with covalently attached cytotoxic small-molecule drugs. Due to their potential for targeted cancer therapy, they form part of the diversifying pipeline of various biopharmaceutical companies, in addition to currently seven commercial ADCs. With other new modalities, ADCs contribute to the increasing complexity of biopharmaceutical development in times of growing costs and competition. Another challenge is the implementation of quality by design (QbD), which receives a lot of attention. In order to answer these challenges, mechanistic models are gaining interest as tools for enhanced process understanding and efficient process development. The drug-to-antibody ratio (DAR) is a critical quality attribute (CQA) of ADCs. After the conjugation reaction, the DAR can still be adjusted by including a hydrophobic interaction chromatography (HIC) step. In this work, we developed a mechanistic model for the preparative separation of cysteine-engineered mAbs with different degrees of conjugation with a non-toxic surrogate drug. The model was successfully validated for varying load compositions with linear and optimized step gradient runs, applying conditions differing from the calibration runs. In two in silico studies, we then present scenarios for how the model can be applied profitably to ensure a more robust achievement of the target DAR and for the efficient characterization of the design space. For this, we also used the model in a linkage study with a kinetic reaction model developed by us previously. The combination of the two models effectively widens system boundaries over two adjacent process steps. We believe this work has great potential to help advance the incorporation of digital tools based on mechanistic models in ADC process development by illustrating their capabilities for efficient process development and increased robustness. Mechanistic models can support the implementation of QbD and eventually might be the basis for digital process twins able to represent multiple unit operations.

中文翻译:

用于分离抗体-药物偶联物的疏水相互作用色谱的建模及其通过设计对质量的应用。

抗体-药物偶联物(ADC)是基于单克隆抗体(mAb)与共价连接的细胞毒性小分子药物的杂合分子。由于它们具有靶向癌症治疗的潜力,除了目前有七个商业ADC之外,它们还构成了各种生物制药公司多元化管道的一部分。在其他新的方式下,ADC在成本增加和竞争加剧的情况下,也加剧了生物制药开发的复杂性。另一个挑战是通过设计实现质量(QbD),这引起了很多关注。为了应对这些挑战,机械模型作为增强过程理解和有效过程开发的工具越来越受到关注。药物与抗体的比率(DAR)是ADC的关键质量属性(CQA)。共轭反应后 仍然可以通过包括疏水相互作用色谱(HIC)步骤来调节DAR。在这项工作中,我们开发了一种机制模型,用于半胱氨酸改造的单克隆抗体与无毒替代药物不同结合程度的制备分离。该模型已成功验证了线性和优化阶跃梯度运行对各种载荷成分的适应性,并应用了不同于校准运行的条件。然后,在两项计算机模拟研究中,我们提出了一些场景,说明如何以有利的方式应用模型,以确保更可靠地实现目标DAR,并有效表征设计空间。为此,我们还将模型用于我们以前开发的动力学反应模型的连锁研究中。两种模型的结合有效地拓宽了两个相邻工艺步骤的系统边界。我们相信这项工作具有巨大的潜力,可以通过说明其有效过程开发和增强鲁棒性的能力来帮助推动基于机械模型的数字工具在ADC过程开发中的整合。机械模型可以支持QbD的实施,并且最终可能成为能够代表多个单元操作的数字过程孪生的基础。
更新日期:2020-05-01
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