Optimal Antibody Puri cation Strategies Using Data-Driven Models

作     者：Songsong Liu Lazaros GPapageorgiou 

作者机构：School of ManagementHarbin Institute of TechnologyHarbin 150001China School of ManagementSwansea UniversitySwansea SA18ENUK Centre for Process Systems EngineeringDepartment of Chemical EngineeringUniversity College LondonLondon WC1E 7JEUK 

出 版 物：《Engineering》 (工程（英文）)

年 卷 期：2019年第5卷第6期

页      面：1077-1092页

核心收录：

学科分类：08[工学] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

基　　金：Engineering and Physical Sciences Research Council, EPSRC, (EP/I033270/1, EP/M027856/1) Engineering and Physical Sciences Research Council, EPSRC 

主　　题：Antibody purification Multiscale optimization Antigen-binding fragment Mixed-integer programming Data-driven model Piecewise linear regression 

摘      要：This work addresses the multiscale optimization of the puri cation processes of antibody fragments. Chromatography decisions in the manufacturing processes are optimized, including the number of chromatography columns and their sizes, the number of cycles per batch, and the operational ow velocities. Data-driven models of chromatography throughput are developed considering loaded mass, ow velocity, and column bed height as the inputs, using manufacturing-scale simulated datasets based on microscale experimental data. The piecewise linear regression modeling method is adapted due to its simplicity and better prediction accuracy in comparison with other methods. Two alternative mixed-integer nonlinear programming (MINLP) models are proposed to minimize the total cost of goods per gram of the antibody puri cation process, incorporating the data-driven models. These MINLP models are then reformulated as mixed-integer linear programming (MILP) models using linearization techniques and multiparametric disaggregation. Two industrially relevant cases with different chromatography column size alternatives are investigated to demonstrate the applicability of the proposed models.