A hybrid mechanistic-empirical model for in silico mammalian cell bioprocess simulation

O'Brien, Conor M.; Zhang, Qi; Daoutidis, Pródromos; Hu, Wei Shou

doi:10.1016/j.ymben.2021.03.016

Cited by 37 publications

(14 citation statements)

References 37 publications

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“…At Site B, the pre‐determined daily bolus of 100 µl glucose was sufficient to maintain glucose levels between 2 and 6 g/L until Day 9 at Site B. Glucose was fully consumed in cultures at Site A, despite the lower observed VCD, likely attributable to less efficient glucose metabolism due to lower oxygen levels due the smaller throw diameter on the shaker (Klaubert et al, 2021). The lactate concentration profiles were nearly identical, indicating that lactate consumption was achieved, which is the desired outcome in industrial settings (Mulukutla et al, 2014, 2015; O'Brien et al, 2021).…”

Section: Resultsmentioning

confidence: 93%

Generation of reference cell lines, media, and a process platform for CHO cell biomanufacturing

Cordova

Dahodwala

Elliott

et al. 2022

Biotech & Bioengineering

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Due to the favorable attributes of Chinese hamster ovary (CHO) cells for therapeutic proteins and antibodies biomanufacturing, companies generate proprietary cells with desirable phenotypes. One key attribute is the ability to stably express multi‐gram per liter titers in chemically defined media. Cell, media, and feed diversity has limited community efforts to translate knowledge. Moreover, academic, and nonprofit researchers generally cannot study “industrially relevant” CHO cells due to limited public availability, and the time and knowledge required to generate such cells. To address these issues, a university‐industrial consortium (Advanced Mammalian Biomanufacturing Innovation Center, AMBIC) has acquired two CHO “reference cell lines” from different lineages that express monoclonal antibodies. These reference cell lines have relevant production titers, key performance outcomes confirmed by multiple laboratories, and a detailed technology transfer protocol. In commercial media, titers over 2 g/L are reached. Fed‐batch cultivation data from shake flask and scaled‐down bioreactors is presented. Using productivity as the primary attribute, two academic sites aligned with tight reproducibility at each site. Further, a chemically defined media formulation was developed and evaluated in parallel to the commercial media. The goal of this work is to provide a universal, industrially relevant CHO culture platform to accelerate biomanufacturing innovation.

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Section: Resultsmentioning

confidence: 93%

Generation of reference cell lines, media, and a process platform for CHO cell biomanufacturing

Cordova

Dahodwala

Elliott

et al. 2022

Biotech & Bioengineering

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“…Both pH and osmolarity variations change the saturation equilibrium for both CO 2 and oxygen [39,40]. Some researchers have attempted to describe these interaction effects, however, not intending to be used in process control algorithm [21,22,41,42]. There is currently no process model that accurately represents these multiple interaction effects for mammalian cell cultures that can be integrated into process control simulation [43], as are available for bacteria and yeast fermentations [44].…”

Section: Resultsmentioning

confidence: 99%

PID controls: the forgotten bioprocess parameters

et al. 2022

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The ambr250 high-throughput bioreactor platform was adopted to provide a highly-controlled environment for a project investigating genome instability in Chinese hamster ovary (CHO) cells, where genome instability leads to lower protein productivity. Development of the baseline (control) and stressed process conditions highlighted the need to control critical process parameters, including the proportional, integral, and derivative (PID) control loops. Process parameters that are often considered scale-independent, include dissolved oxygen (DO) and pH; however, these parameters were observed to be sensitive to PID settings. For many bioreactors, control loops are cascaded such that the manipulated variables are adjusted concurrently. Conversely, for the ambr250 bioreactor system, the control levels are segmented and implemented sequentially. Consequently, each control level must be tuned independently, as the PID settings are independent by control level. For the CHO cell studies, it was observed that initial PID settings did not resulted in a robust process, which was observed as elevated lactate levels; which was caused by the pH being above the setpoint most of the experiment. After several PID tuning iterations, new PID settings were found that could respond appropriately to routine feed and antifoam additions. Furthermore, these new PID settings resulted in more robust cell growth and increased protein productivity. This work highlights the need to describe PID gains and manipulated variable ranges, as profoundly different outcomes can result from the same feeding protocol. Additionally, improved process models are needed to allow process simulations and tuning. Thus, these tuning experiments support the idea that PID settings should be fully described in bioreactor publications to allow for better reproducibility of results.

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“…A survey of the literature has shown applications of hybrid modeling in bioprocesses, [21][22][23][24][25][26][27] chemical and oil and gas process industries, [28][29][30][31][32] and polymer processes 33,34 for more accurate and scientifically consistent predictions. This survey has also shown many topical focuses of applications in bioprocessing and chemical engineering, including process control, [35][36][37][38] design of experiments, 39,40 process development and scale-up, 41,42 process design, 43 and optimization.…”

Section: Applications Of Hybrid Sgml Approach In Bioprocessing and Ch...mentioning

confidence: 99%

A hybrid science‐guided machine learning approach for modeling chemical processes: A review

Sharma

Liu

2022

AIChE Journal

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This study presents a broad perspective of hybrid process modeling combining the scientific knowledge and data analytics in bioprocessing and chemical engineering with a science-guided machine learning (SGML) approach. We divide the approach into two major categories: ML complements science, and science complements ML. We review the literature relating to the hybrid SGML approach, and propose a systematic classification of hybrid SGML models. For applying ML to improve science-based models, we present expositions of direct serial and parallel hybrid modeling and their combinations, inverse modeling, reduced-order modeling, quantifying uncertainty in the process and even discovering governing equations of the process model. For applying scientific principles to improve ML models, we discuss the science-guided design, learning and refinement. For each subcategory, we identify its requirements, strengths, and limitations, together with their published and potential applications. We also present several examples to illustrate different hybrid SGML methodologies for modeling chemical processes.

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A hybrid mechanistic-empirical model for in silico mammalian cell bioprocess simulation

Cited by 37 publications

References 37 publications

Generation of reference cell lines, media, and a process platform for CHO cell biomanufacturing

Generation of reference cell lines, media, and a process platform for CHO cell biomanufacturing

PID controls: the forgotten bioprocess parameters

A hybrid science‐guided machine learning approach for modeling chemical processes: A review

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