Semisynthetic model calibration for monitoring glucose in mammalian cell culture with in situ near infrared spectroscopy

Milligan, Michael; Lewin‐Koh, Nicholas; Coleman, Daniel A.; Arroyo, A.; Saucedo, Victor M.

doi:10.1002/bit.25161

Cited by 12 publications

(11 citation statements)

References 23 publications

(32 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Life Sci. 2016, 16,[25][26][27][28][29][30][31][32][33][34][35] www.biotecvisions.com days 2 and 6, measured and predicted TCD values were similar. When cell density exceeded 20 × 10 6 cells mL −1 , the discrepancy between measured and predicted values increased exponentially.…”

Section: Online In Situ Monitoringmentioning

confidence: 99%

Process analytical technology tools for perfusion cell culture

Mercier

Rouel

Lebrun³

et al. 2015

Engineering in Life Sciences

View full text Add to dashboard Cite

During cell cultivation processes for the production of biopharmaceuticals, good process performance and good product quality can be ensured by online monitoring of critical process parameters (e.g. temperature, pH, or dissolved oxygen). These data can be used in real‐time for process control, as suggested by the process analytical technology (PAT) initiative. Today, solutions for real‐time monitoring of parameters such as concentrations of cells, main nutrients, and metabolism by‐products are developing, but applications of these more complex tools in industrial settings are still limited. Here, we evaluated the use of dielectric spectroscopy (DS) and near‐infrared spectroscopy (NIRS) as PAT tools for a perfusion PER.C6® cultivation process. We showed that DS enabled predictions of viable cell density from the cultivation vessel, with a root mean square error of prediction (RMSEP) of 4.4% of the calibration range. Additionally, predictions of glucose and lactate concentrations from the cultivation vessel (RMSEP of 10 and 14%, respectively) and from the perfusion stream (RMSEP of 12 and 10%, respectively) were achieved with NIRS. We also showed that the perfusion stream offers great opportunities for noninvasive, yet frequent process monitoring. Accurate online monitoring of critical process parameters with PAT tools is the essential first step toward increased control of process output.

show abstract

Section: Online In Situ Monitoringmentioning

confidence: 99%

Process analytical technology tools for perfusion cell culture

Mercier

Rouel

Lebrun³

et al. 2015

Engineering in Life Sciences

View full text Add to dashboard Cite

show abstract

“…To improve the calibration and extend the calibration model to wider range of process conditions, cell culture samples were used for reconstitution to different concentrations of parameters (cell, glucose, lactate etc.) to be measured using multiple cell lines .…”

Section: Sensors Commonly Used In Bioprocessmentioning

confidence: 99%

Advances in process monitoring tools for cell culture bioprocesses

Zhao

Zhou

et al. 2015

Engineering in Life Sciences

View full text Add to dashboard Cite

Practical applicationThis review summarizes recent progress and current status of bioprocess monitoring. There has been an increasing emphasis on the applications of process analytical tools in bioprocessing for biologics manufacturing. The integration of in-line, on-line and at-line sensors and the real-time characterization of the physiological state of cells will lead to robust processes and enhanced product quality. AbstractThe productivity of cell culture manufacturing for biologics has increased momentously in the past decades. Increasingly, the process research efforts are devoted into improving product quality and consistency. Consistent process performance and successful implementation of quality by design (QbD) practice requires well-utilized process analytical technology (PAT). This review summarizes recent progress and current status of bioprocess monitoring. Many sensors for bioprocess monitoring have been available for decades while new ones, especially spectrometric sensors, are making their way into cell culture bioprocesses. On-line sampling devices have grown mature in the past decade thus making many instruments traditionally used for off-line analysis available for at-line use. With a general trend of using better defined medium for cell cultivation and increasing emphasis of process analytical tools, the spectrometric methods are also making headway in cell culture process monitoring. The integration of those sensing technologies will be important to advance the real-time monitoring of the state of cellular physiology for the control for process consistency and product quality.

show abstract

“…Considering NIR spectroscopy, Clavaud et al () and Kozma et al () have developed regression models taking into account parameters affecting spectroscopic measurements and bioprocess variations in order to increase robustness and accuracy of predictive models. In pursuit of the same goal, Milligan et al () have proposed to merge on‐line and off‐line measurements in a unique calibration dataset in order to develop “semi‐synthetic” models. Beyond classical metabolites quantifications, recent advances in Raman spectroscopic monitoring of CHO cell cultures have led to propose predictive models of recombinant antibody concentration (André et al, ; Ashton et al, ).…”

Section: Introductionmentioning

confidence: 99%

Developing global regression models for metabolite concentration prediction regardless of cell line

André

Lagresle²,

Sliva

et al. 2017

Biotech & Bioengineering

View full text Add to dashboard Cite

Following the Process Analytical Technology (PAT) of the Food and Drug Administration (FDA), drug manufacturers are encouraged to develop innovative techniques in order to monitor and understand their processes in a better way. Within this framework, it has been demonstrated that Raman spectroscopy coupled with chemometric tools allow to predict critical parameters of mammalian cell cultures in-line and in real time. However, the development of robust and predictive regression models clearly requires many batches in order to take into account inter-batch variability and enhance models accuracy. Nevertheless, this heavy procedure has to be repeated for every new line of cell culture involving many resources. This is why we propose in this paper to develop global regression models taking into account different cell lines. Such models are finally transferred to any culture of the cells involved. This article first demonstrates the feasibility of developing regression models, not only for mammalian cell lines (CHO and HeLa cell cultures), but also for insect cell lines (Sf9 cell cultures). Then global regression models are generated, based on CHO cells, HeLa cells, and Sf9 cells. Finally, these models are evaluated considering a fourth cell line(HEK cells). In addition to suitable predictions of glucose and lactate concentration of HEK cell cultures, we expose that by adding a single HEK-cell culture to the calibration set, the predictive ability of the regression models are substantially increased. In this way, we demonstrate that using global models, it is not necessary to consider many cultures of a new cell line in order to obtain accurate models. Biotechnol. Bioeng. 2017;114: 2550-2559. © 2017 Wiley Periodicals, Inc.

show abstract

Semisynthetic model calibration for monitoring glucose in mammalian cell culture with in situ near infrared spectroscopy

Cited by 12 publications

References 23 publications

Process analytical technology tools for perfusion cell culture

Process analytical technology tools for perfusion cell culture

Advances in process monitoring tools for cell culture bioprocesses

Developing global regression models for metabolite concentration prediction regardless of cell line

Contact Info

Product

Resources

About