کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6490017 | 1416545 | 2018 | 6 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Optimization of a pH-shift control strategy for producing monoclonal antibodies in Chinese hamster ovary cell cultures using a pH-dependent dynamic model
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی شیمی
بیو مهندسی (مهندسی زیستی)
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Optimization of a pH-shift control strategy for producing monoclonal antibodies in Chinese hamster ovary cell cultures using a pH-dependent dynamic model Optimization of a pH-shift control strategy for producing monoclonal antibodies in Chinese hamster ovary cell cultures using a pH-dependent dynamic model](/preview/png/6490017.png)
چکیده انگلیسی
To optimize monoclonal antibody (mAb) production in Chinese hamster ovary cell cultures, culture pH should be temporally controlled with high resolution. In this study, we propose a new pH-dependent dynamic model represented by simultaneous differential equations including a minimum of six system component, depending on pH value. All kinetic parameters in the dynamic model were estimated using an evolutionary numerical optimization (real-coded genetic algorithm) method based on experimental time-course data obtained at different pH values ranging from 6.6 to 7.2. We determined an optimal pH-shift schedule theoretically. We validated this optimal pH-shift schedule experimentally and mAb production increased by approximately 40% with this schedule. Throughout this study, it was suggested that the culture pH-shift optimization strategy using a pH-dependent dynamic model is suitable to optimize any pH-shift schedule for CHO cell lines used in mAb production projects.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Bioscience and Bioengineering - Volume 125, Issue 2, February 2018, Pages 245-250
Journal: Journal of Bioscience and Bioengineering - Volume 125, Issue 2, February 2018, Pages 245-250
نویسندگان
Tomoharu Hogiri, Hiroshi Tamashima, Akitoshi Nishizawa, Masahiro Okamoto,