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《工程(英文)》 >> 2019年 第5卷 第6期 doi: 10.1016/j.eng.2019.10.011

使用数据驱动模型优化抗体纯化策略

a School of Management, Harbin Institute of Technology, Harbin 150001, China
b School of Management, Swansea University, Swansea SA1 8EN, UK
c Centre for Process Systems Engineering, Department of Chemical Engineering, University College London, London WC1E 7JE, UK

收稿日期: 2018-10-31 修回日期: 2018-12-15 录用日期: 2018-12-21 发布日期: 2019-10-18

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摘要

本工作致力于抗体片段纯化过程的多尺度优化。优化了生产过程中的色谱决策,包括色谱柱的数量及其大小,每批的循环数以及操作流速。使用基于微型实验数据的制造规模模拟数据集,建立了以负载质量、流速和柱床高度为输入的色谱通量数据驱动模型。与其他方法相比,分段线性回归建模方法具有简单、预测精度高的优点。提出了两种混合整数非线性规划(MINLP)模型,结合数据驱动模型,以最小化每克抗体纯化过程的总成本。然后,使用线性化技术和多参数分解将这些MINLP模型重新构造为混合整数线性规划(MILP)模型。研究了两个具有不同色谱柱尺寸替代品的工业相关案例,以证明所提出模型的适用性。

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参考文献

[ 1 ] Davis J, Edgar T, Porter J, Bernaden J, Sarli M. Smart manufacturing, manufacturing intelligence and demand-dynamic performance. Comput Chem Eng 2012;47:145–56. 链接1

[ 2 ] Thoben KD, Wiesner S, Wuest T. ‘‘Industrie 4.0” and smart manufacturing—a review of research issues and application examples. Int J Automat Technol 2017;11(1):4–16. 链接1

[ 3 ] Kang HS, Lee JY, Choi SS, Kim H, Park JH, Son JY, et al. Smart manufacturing: past research, present findings, and future directions. Int J Pr Eng Man-GT 2016;3(1):111–28. 链接1

[ 4 ] Bogle IDL. A perspective on smart process manufacturing research challenges for process systems engineers. Engineering 2017;3(2):161–5. 链接1

[ 5 ] Simaria AS, Turner R, Farid SS. A multi-level meta-heuristic algorithm for the optimisation of antibody purification processes. Biochem Eng J 2012;69:144–54. 链接1

[ 6 ] Brunet R, Guillén-Gosálbez G, Pérez-Correa JR, Caballero JA, Jiménez L. Hybrid simulation-optimization based approach for the optimal design of singleproduct biotechnological processes. Comput Chem Eng 2012;37:125–35. 链接1

[ 7 ] Allmendinger R, Simaria AS, Farid SS. Efficient discovery of chromatography equipment sizing strategies for antibody purification processes using evolutionary computing. In: Proceedings of the 12th International Conference on Parallel Problem Solving from Nature-Volume Part II; 2012 Sep 1–5; Taormina, Italy. Berlin: Springer; 2012. p. 468–77. 链接1

[ 8 ] Allmendinger R, Simaria AS, Turner R, Farid SS. Closed-loop optimization of chromatography column sizing strategies in biopharmaceutical manufacture. J Chem Technol Biotechnol 2014;89(10):1481–90. 链接1

[ 9 ] Allmendinger R, Simaria AS, Farid SS. Multiobjective evolutionary optimization in antibody purification process design. Biochem Eng J 2014;91:250–64. 链接1

[10] [0] Martagan T, Krishnamurthy A, Leland PA, Maravelias CT. Performance guarantees and optimal purification decisions for engineered proteins. Oper Res 2018;66(1):18–41. 链接1

[11] Asenjo JA, Montagna JM, Vecchietti AR, Iribarren OA, Pinto JM. Strategies for the simultaneous optimization of the structure and the process variables of a protein production plant. Comput Chem Eng 2000;24(9–10):2277–90. 链接1

[12] Montagna JM, Vecchietti AR, Iribarren OA, Pinto JM, Asenjo JA. Optimal design of protein production plants with time and size factor process models. Biotechnol Prog 2000;16(2):228–37. 链接1

[13] Pinto JM, Montagna JM, Vecchietti AR, Iribarren OA, Asenjo JA. Process performance models in the optimization of multiproduct protein production plants. Biotechnol Bioeng 2001;74(6):451–65. 链接1

[14] Simeonidis E, Pinto JM, Lienqueo ME, Tsoka S, Papageorgiou LG. MINLP models for the synthesis of optimal peptide tags and downstream protein processing. Biotechnol Prog 2005;21(3):875–84. 链接1

[15] Liu S, Simaria AS, Farid SS, Papageorgiou LG. Mixed integer optimisation of antibody purification processes. In: Kraslawski A, Turunen I, editors. Computer aided chemical engineering. Proceedings of the 23rd European Symposium on Computer Aided Process Engineering; 2013 June 9–12; Lappeenranta, Finland. Amsterdam: Elsevier; 2013. p. 157–62.

[16] Liu S, Simaria AS, Farid SS, Papageorgiou LG. Designing cost-effective biopharmaceutical facilities using mixed-integer optimization. Biotechnol Prog 2013;29(6):1472–83. 链接1

[17] Liu S, Simaria AS, Farid SS, Papageorgiou LG. Optimising chromatography strategies of antibody purification processes by mixed integer fractional programming techniques. Comput Chem Eng 2014;68:151–64. 链接1

[18] Liu S, Simaria AS, Farid SS, Papageorgiou LG. Mathematical programming approaches for downstream processing optimisation of biopharmaceuticals. Chem Eng Res Des 2015;94:18–31. 链接1

[19] Liu S, Farid SS, Papageorgiou LG. Integrated optimization of upstream and downstream processing in biopharmaceutical manufacturing under uncertainty: a chance constrained programming approach. Ind Eng Chem Res 2016;55(16):4599–612. 链接1

[20] Liu S, Gerontas S, Gruber D, Turner R, Titchener-Hooker NJ, Papageorgiou LG. Optimization-based framework for resin selection strategies in biopharmaceutical purification process development. Biotechnol Prog 2017;33(4):1116–26. 链接1

[21] Liu S, Papageorgiou LG. Optimal production of biopharmaceutical manufacturing. In: Yuan Z, Singh R, editors. Process systems engineering for pharmaceutical manufacturing. Computer aided chemical engineering. Amsterdam: Elsevier; 2018. p. 569–95. 链接1

[22] Liu S, Papageorgiou LG. Multi-objective optimisation for biopharmaceutical manufacturing under uncertainty. Comput Chem Eng 2018;119:383–93. 链接1

[23] Bhosekar A, Ierapetritou M. Advances in surrogate based modeling, feasibility analysis, and optimization: a review. Comput Chem Eng 2018;108:250–67. 链接1

[24] Wang GG, Shan S. Review of metamodeling techniques in support of engineering design optimization. J Mech Des 2007;129(4):370–80. 链接1

[25] Forrester A, Sóbester A, Keane A. Engineering Design via Surrogate Modelling: A Practical Guide. Chichester: John Wiley & Sons; 2008. 链接1

[26] Song M, Breneman CM, Bi J, Sukumar N, Bennett KP, Cramer S, et al. Prediction of protein retention times in anion-exchange chromatography systems using support vector regression. J Chem Inf Comput Sci 2002;42 (6):1347–57. 链接1

[27] Mandenius CF, Brundin A. Bioprocess optimization using design-ofexperiments methodology. Biotechnol Prog 2008;24(6):1191–203. 链接1

[28] Ghose S, Zhang J, Conley L, Caple R, Williams KP, Cecchini D. Maximizing binding capacity for protein A chromatography. Biotechnol Prog 2014;30 (6):1335–40. 链接1

[29] Wang G, Briskot T, Hahn T, Baumann P, Hubbuch J. Root cause investigation of deviations in protein chromatography based on mechanistic models and artificial neural networks. J Chromatogr A 2017;1515:146–53. 链接1

[30] Nagrath D, Messac A, Bequette BW, Cramer SM. A hybrid model framework for the optimization of preparative chromatographic processes. Biotechnol Prog 2004;20(1):162–78. 链接1

[31] Pirrung SM, van der Wielen LAM, van Beckhoven RFWC, van de Sandt EJAX, Eppink MHM, Ottens M. Optimization of biopharmaceutical downstream processes supported by mechanistic models and artificial neural networks. Biotechnol Prog 2017;33(3):696–707. 链接1

[32] Nascimento A, Pinto IF, Chu V, Aires-Barros MR, Conde JP, Azevedo AM. Studies on the purification of antibody fragments. Sep Purif Technol 2018;195:388–97. 链接1

[33] Scanlan C, Shumway J, Castano J, Wagner M, Waghmare R. Challenges and strategies for the downstream purification and formulation of Fab antibody fragments. Biopharm Int 2014;27(1):42–4. 链接1

[34] Gerontas S, Asplund M, Hjorth R, Bracewell DG. Integration of scaledown experimentation and general rate modelling to predict manufacturing scale chromatographic separations. J Chromatogr A 2010;1217 (44):6917–26. 链接1

[35] Boushaba R, Baldascini H, Gerontas S, Titchener-Hooker NJ, Bracewell DG. Demonstration of the use of windows of operation to visualize the effects of fouling on the performance of a chromatographic step. Biotechnol Prog 2011;27(4):1009–17. 链接1

[36] Shevade SK, Keerthi SS, Bhattacharyya C, Murthy KK. Improvements to the SMO algorithm for SVM regression. IEEE Trans Neural Netw 2000;11 (5):1188–93. 链接1

[37] MacKay DJC. Introduction to Gaussian processes. In: Bishop CM, editor. Neutral networks and machine learning, NATO ASI series: Ser. F: computer and systems science. Berlin: Springer; 1998. p. 133–66. 链接1

[38] Wang Y, Witten IH. Modeling for optimal probability prediction. In: Proceedings of the Nineteenth International Conference on Machine Learning; 2002 July 8–12; Sydney, NSW, Australia; 2002. p. 650–7. 链接1

[39] Box GEP, Draper NR. Response surfaces, mixtures, and ridge analyses. 2nd ed. Hoboken: John Wiley & Sons; 2007.

[40] Yang L, Liu S, Tsoka S, Papageorgiou LG. Mathematical programming for piecewise linear regression analysis. Expert Syst Appl 2016;44:156–67. 链接1

[41] Frank E, Hall MA, Witten IH. Data mining: practical machine learning tools and techniques. 4th ed. San Francisco: Morgan Kaufmann; 2016.

[42] GAMS Development Corporation. GAMS: a user’s guide. Washington, DC: GAMS Development Corporation; 2016. 链接1

[43] Glover F. Improved linear integer programming formulations of nonlinear integer problems. Manage Sci 1975;22(4):455–60. 链接1

[44] Kolodziej S, Castro PM, Grossmann IE. Global optimization of bilinear programs with a multiparametric disaggregation technique. J Glob Optim 2013;57(4):1039–63. 链接1

[45] Castro PM. Normalized multiparametric disaggregation: an efficient relaxation for mixed-integer bilinear problems. J Glob Optim 2016;64(4):765–84. 链接1

[46] Koleva MN, Styan CA, Papageorgiou LG. Optimisation approaches for the synthesis of water treatment plants. Comput Chem Eng 2017;106:849–71. 链接1

[47] Lin MH, Carlsson JG, Ge D, Shi J, Tsai JF. A review of piecewise linearization methods. Math Probl Eng 2013;2013:101376. 链接1

[48] Langer ES, Rader RA. Single-use technologies in biopharmaceutical manufacturing: a 10-year review of trends and the future. Eng Life Sci 2014;14(3):238–43. 链接1

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