Model-assisted process evaluation and optimization of continuous chromatography for antibody capture

doi:10.11949/0438-1157.20240583

Abstract

Abstract:

Continuous chromatography is a promising technology for antibody production, which has significant advantages of improving process productivity, saving operation cost and enhancing product quality. However, continuous chromatography modes are diverse and there are many influencing factors, making traditional experiment-based process development methods difficult. In this study, model-assisted process development and optimization method was proposed and applied to continuous capture of monoclonal antibody. Different continuous capture modes (twin columns, three columns and four columns) were compared systematically, and the best mode and operating conditions were determined and further verified by experiments. It was found that the model prediction was consistent with the experimental results. Compared with batch chromatography, process productivity of continuous capture was increased by 27.2%, and the resin capacity utilization was increased by 50.1%. Moreover, there were no significant changes in product quality. The results demonstrated that model-assisted process development is useful to determine the optimal operating mode and conditions for continuous capture, promote process optimization, and accelerate the industrial applications of continuous process.

Key words: continuous chromatography, antibody capture, model-assisted, protein A affinity chromatography, process development

摘要：

连续流层析具有提高过程产率和介质利用率、降低生产成本等显著优势，在抗体药物生产中具有良好的应用前景。但是，连续流层析模式多样，影响因素众多，传统的基于实验的过程开发方法存在困难。将模型辅助方法引入到连续流层析亲和捕获过程，建立了模型辅助过程优化方法，系统比较了两柱、三柱和四柱连续捕获模式，确定了最佳模式和操作条件，并经实验验证。结果表明：模型预测与实验结果基本一致；与批次层析相比，四柱连续捕获的过程产率提高了27.2%，介质利用率提高了50.1%，且产品质量稳定。由此说明，模型辅助方法有助于确定最佳连续捕获模式和操作条件，促进过程优化，加速抗体药物连续生产过程的工业实现。

关键词: 连续流层析, 抗体捕获, 模型辅助方法, 蛋白A亲和层析, 过程开发

CLC Number:

TQ 028.8

Yewei MA, Yanna SUN, Dong GAO, Haibin WANG, Shanjing YAO, Dongqiang LIN. Model-assisted process evaluation and optimization of continuous chromatography for antibody capture[J]. CIESC Journal, 2024, 75(11): 4274-4285.

马烨玮, 孙艳娜, 高栋, 王海彬, 姚善泾, 林东强. 模型辅助的单抗连续捕获工艺分析和过程优化[J]. 化工学报, 2024, 75(11): 4274-4285.

Figures/Tables 11

Fig.1 Operation mode of 4C-PCC continuous capture process

Table 1 Operation parameters of recovery and regeneration process units

步骤	溶液	保留时间/min	体积/CV	时间/min
冲洗	0.025 mol/L Tris + 0.025 mol/L NaCl缓冲液（pH 7.7）	6	4	24
淋洗	0.5 mol/L磷酸盐缓冲液（pH 6.0）	6	5	30
平衡	0.025 mol/L Tris + 0.025 mol/L NaCl缓冲液（pH 7.7）	6	3	18
洗脱	0.15 mol/L醋酸缓冲液（pH 2.8）	6	4	24
再生	0.1 mol/L NaOH溶液	6	5	30
再平衡	0.025 mol/L Tris + 0.025 mol/L NaCl缓冲液（pH 7.7）	6	3	18

Fig.2 Model calculated and experimental breakthrough curves

Table 2 Model parameters for protein A affinity chromatography

模型参数	数值
ε	0.38
ε_p	0.52
Q_max/(g/L)	130
K_d/(g/L)	0.08
D_ax/(10^-7 m²/s)	30
k_f/(10^-6 m/s)	20
D_s/(10^-14 m²/s)	0.5
D_p/(10^-12 m²/s)	4.9

Fig.3 Process performance of CaptureSMB continuous capture（Color contour maps show the changes of productivity; Black-line contour maps show the changes of capacity utilization; Red dash lines represent the upper flow rate of the resin; Star points are the optimal operation points）

Fig.4 Process performance of 3C-PCC continuous capture（Color contour maps show the changes of productivity; Black-line contour maps show the changes of capacity utilization; Red dash line represents the boundary of phase Ⅰ‍-1 and phase Ⅰ‍-2; Blue dash line represents the boundary of phase Ⅱ and phase Ⅲ; Star points are the optimal operation points）

Fig.5 Process performance of 4C-PCC continuous capture（Color contour maps show the changes of productivity; Black-line contour maps show the changes of capacity utilization; Red dash line represents the boundary of phase Ⅰ-1 and phase Ⅰ-2; Blue dash line represents the boundary of phase Ⅱ and phase Ⅲ; Star points are the optimal operation points）

Fig.6 Capacity utilization and optimal productivities of batch, CaptureSMB, 3C-PCC and 4C-PCC at varying c0

Fig.7 UV curves of elution procedure of 4C-PCC continuous capture process

Table 3 Comparison of batch and continuous separation performance

项目	批次层析	连续捕获
纯度/%	97.3	99.3
收率/%	92.2	91.1
聚集体/%	2.2	0.7
HCP LRV	2.33	2.57
hcDNA LRV	4.04	2.36
过程产率/(g/(L·h))	11.17	14.21
介质利用率/%	59.9	89.9
缓冲液消耗/(L/g)	0.55	0.35

Fig.8 Model-assisted optimization roadmap

References 31

1	David L, Schwan P, Lobedann M, et al. Side-by-side comparability of batch and continuous downstream for the production of monoclonal antibodies[J]. Biotechnology and Bioengineering, 2020, 117(4): 1024-1036.
2	Mahal H, Branton H, Farid S S. End-to-end continuous bioprocessing: impact on facility design, cost of goods, and cost of development for monoclonal antibodies[J]. Biotechnology and Bioengineering, 2021, 118(9): 3468-3485.
3	Rathore A S, Thakur G, Kateja N. Continuous integrated manufacturing for biopharmaceuticals: a new paradigm or an empty promise?[J]. Biotechnology and Bioengineering, 2023, 120(2): 333-351.
4	鲁伟, 应国清, 杨晓明, 等. 单克隆抗体工业生产中蛋白A亲和层析步骤的成本分析[J]. 高校化学工程学报, 2023, 37(2): 276-284.
	Lu W, Ying G Q, Yang X M, et al. Cost analysis of protein A affinity chromatography in industrial production of monoclonal antibody[J]. Journal of Chemical Engineering of Chinese Universities, 2023, 37(2): 276-284.
5	FDA. Quality considerations for continuous manufacturing[EB/OL]. [2024-05-30]. .
6	ICH. Continuous manufacturing of drug substances and drug products Q13[EB/OL]. [2024-05-30]. .
7	史策, 虞骥, 高栋, 等. 单抗制备的过程模拟和经济性分析[J]. 化工学报, 2018, 69(7): 3198-3207.
	Shi C, Yu J, Gao D, et al. Process simulation and economic evaluation of monoclonal antibody production[J]. CIESC Journal, 2018, 69(7): 3198-3207.
8	Bielser J M, Wolf M, Souquet J, et al. Perfusion mammalian cell culture for recombinant protein manufacturing — a critical review[J]. Biotechnology Advances, 2018, 36(4): 1328-1340.
9	Matanguihan C, Wu P. Upstream continuous processing: recent advances in production of biopharmaceuticals and challenges in manufacturing[J]. Current Opinion in Biotechnology, 2022, 78: 102828.
10	高宗晔, 史策, 姚善泾, 等. 双柱连续流层析亲和分离抗体的过程设计与应用[J]. 高校化学工程学报, 2019, 33(1): 117-127.
	Gao Z Y, Shi C, Yao S J, et al. Process design and application of twin-column continuous chromatography for antibody affinity separation[J]. Journal of Chemical Engineering of Chinese Universities, 2019, 33(1): 117-127.
11	Nicoud R M. The amazing ability of continuous chromatography to adapt to a moving environment[J]. Industrial & Engineering Chemistry Research, 2014, 53(10): 3755-3765.
12	荆淑莹, 史策, 姚善泾, 等. 连续流层析及用于抗体分离的新进展[J]. 高校化学工程学报, 2021, 35(1): 1-12.
	Jing S Y, Shi C, Yao S J, et al. Progress on continuous chromatography and its application in antibody separation[J]. Journal of Chemical Engineering of Chinese Universities, 2021, 35(1): 1-12.
13	ChromaCon. Contichrom CUBE-versatile benchtop process development tool[EB/OL]. [2024-05-30]. .
14	Cytica. ÄKTA pcc and BioProcess pcc continuous chromatography systems[EB/OL]. [2024-05-30]. .
15	Sartorius. Continuous Chromatography-BIOSMB PD and BIOSMBProcess[EB/OL]. [2024-05-30]. .
16	Novasep. BioSC continuous chromatography for biologics[EB/OL]. [2024-05-30]. .
17	Semba. Octave 10 chromatography system[EB/OL]. [2024-05-30]. .
18	Mahajan E, George A, Wolk B. Improving affinity chromatography resin efficiency using semi-continuous chromatography[J]. Journal of Chromatography A, 2012, 1227: 154-162.
19	Angarita M, Müller-Späth T, Baur D, et al. Twin-column CaptureSMB: a novel cyclic process for protein A affinity chromatography[J]. Journal of Chromatography A, 2015, 1389: 85-95.
20	Gao Z Y, Zhang Q L, Shi C, et al. Antibody capture with twin-column continuous chromatography: effects of residence time, protein concentration and resin[J]. Separation and Purification Technology, 2020, 253: 117554.
21	Lin D Q, Zhang Q L, Yao S J. Model-assisted approaches for continuous chromatography: current situation and challenges[J]. Journal of Chromatography A, 2021, 1637: 461855.
22	Shi C, Chen X J, Jiao B, et al. Model-assisted process design for better evaluation and scaling up of continuous downstream bioprocessing[J]. Journal of Chromatography A, 2022, 1683: 463532.
23	Narayanan H, Luna M, Sokolov M, et al. Hybrid models based on machine learning and an increasing degree of process knowledge: application to capture chromatographic step[J]. Industrial & Engineering Chemistry Research, 2021, 60(29): 10466-10478.
24	Chopda V, Gyorgypal A, Yang O, et al. Recent advances in integrated process analytical techniques, modeling, and control strategies to enable continuous biomanufacturing of monoclonal antibodies[J]. Journal of Chemical Technology & Biotechnology, 2022, 97(9): 2317-2335.
25	Guo J, Jin M, Kanani D. Optimization of single-column batch and multicolumn continuous protein A chromatography and performance comparison based on mechanistic model[J]. Biotechnology Journal, 2020, 15(10): e2000192.
26	Shi C, Zhang Q L, Jiao B, et al. Process development and optimization of continuous capture with three-column periodic counter-current chromatography[J]. Biotechnology and Bioengineering, 2021, 118(9): 3313-3322.
27	Shi C, Gao Z Y, Zhang Q L, et al. Model-based process development of continuous chromatography for antibody capture: a case study with twin-column system[J]. Journal of Chromatography A, 2020, 1619: 460936.
28	Sun Y N, Shi C, Zhang Q L, et al. Model-based process development and evaluation of twin-column continuous capture processes with protein A affinity resin[J]. Journal of Chromatography A, 2020, 1625: 461300.
29	Sun Y N, Shi C, Zhang Q L, et al. Comparison of protein A affinity resins for twin-column continuous capture processes: process performance and resin characteristics[J]. Journal of Chromatography A, 2021, 1654: 462454.
30	Sun Y N, Shi C, Zhong X Z, et al. Model-based evaluation and model-free strategy for process development of three-column periodic counter-current chromatography[J]. Journal of Chromatography A, 2022, 1677: 463311.
31	史策. 模型辅助的连续流层析过程开发和抗体分离应用研究[D]. 杭州: 浙江大学, 2021.
	Shi C. Model-assisted process development of continuous chromatography and its applications for antibody separation[D]. Hangzhou: Zhejiang University, 2021.

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