Untargeted proteomics reveals upregulation of stress response pathways during CHO-based monoclonal antibody manufacturing process leading to disulfide bond reduction

Kim

Biotech & Bioengineering

et al. 2023

Self Cite

Recently, the advancement in process analytical technology and artificial intelligence (AI) has enabled the generation of enormous culture data sets from biomanufacturing processes that produce various recombinant therapeutic proteins (RTPs), such as monoclonal antibodies (mAbs). Thus, now it is very important to exploit them for the enhanced reliability, efficiency, and consistency of the RTP‐producing culture processes and for the reduced incipient or abrupt faults. It is achievable by AI‐based data‐driven models (DDMs), which allow us to correlate biological and process conditions and cell culture states. In this work, we provide practical guidelines for choosing the best combination of model elements to design and implement successful DDMs for given hypothetical in‐line data sets during mAb‐producing Chinese hamster ovary cell culture, as such enabling us to forecast dynamic behaviors of culture performance such as viable cell density, mAb titer as well as glucose, lactate and ammonia concentrations. To do so, we created DDMs that balance computational load with model accuracy and reliability by identifying the best combination of multistep ahead forecasting strategies, input features, and AI algorithms, which is potentially applicable to implementation of interactive DDM within bioprocess digital twins. We believe this systematic study can help bioprocess engineers start developing predictive DDMs with their own data sets and learn how their cell cultures behave in near future, thereby rendering proactive decision possible.

Section: Methodsmentioning

confidence: 99%

Data‐driven prediction models for forecasting multistep ahead profiles of mammalian cell culture toward bioprocess digital twins

Kim

Biotech & Bioengineering

et al. 2023

Self Cite

“…In the present study, similar trends in mAb reduction were observed at all culture scales when the culture medium and process conditions were the same for all of them. Even when the same cell line was used, however, mAb reduction varied among culture scales when their culture media and process parameters differed (Cura et al 2020;Handlogten et al 2020;Park et al 2021). Here, we observed that mAb reduction trends also varied with culture medium composition (data not shown).…”

Section: Factor Determining Antibody Reductionmentioning

confidence: 99%

New high-throughput screening method for Chinese hamster ovary cell lines expressing low reduced monoclonal antibody levels: application of a system controlling the gas phase over cell lysates in miniature bioreactors and facilitating multiple sample setup

et al. 2023

Interchain disul de bonds in monoclonal antibodies (mAbs) may be reduced during large-scale mAb production using Chinese hamster ovary (CHO) cells. This reaction lowers the mAb product yield and purity; however, it may be prevented by screening cell lines that are unsusceptible to reduction and using them in mAb production. Antibody reduction susceptibility may be cell line-dependent. To the best of our knowledge, however, an e cient method of screening reduction-unsusceptible CHO cell lines has not been previously reported. Here, we report a novel screening method that can simultaneously detect and identify mAb reduction susceptibility in lysates containing ≤ 48 CHO cell lines. This evaluation system was equally effective and generated similar results at all culture scales, including 250 mL, 3 L, and 1,000 L. Furthermore, we discovered that reduction-susceptible cell lines contained higher total intracellular nicotinamide adenine dinucleotide phosphate (NADPH) and NADP + concentrations than reduction-unsusceptible cell lines, regardless of whether they expressed immunoglobulin (Ig)G4 or IgG1. NADPH or NADP + supplementation in the lysate of reductionunsusceptible cells resulted in mAb reduction. Application of the innovative CHO cell line screening approach could mitigate or prevent reductions in large-scale mAb generation from CHO cells. HighlightsA novel anaerobic environmental screening method with a miniature bioreactor e ciently assesses the risks of monoclonal antibody reduction in multiple Chinese hamster ovary (CHO) cell lines.Intracellular NADPH and NADP + concentrations determine the antibody reduction susceptibility of CHO cells.

“…This poses challenges in product purification due to the potential risks related to immunogenicity and regulatory compliance 6–9 . Some residual HCPs, for example, reductase and lipase, may also deteriorate the antibody quality or degrade the formulation components 10–14 . Therefore, it is critical to terminate the cell culture before extensive cell lysis to ensure product quality.…”

Section: Introductionmentioning

confidence: 99%

Adaptive modeling optimized by the data fusion strategy: Real‐time dying cell percentage prediction using capacitance spectroscopy

Wu,

Ketcham,

Corredor

et al. 2024

Biotechnology Progress

The previous research showcased a partial least squares (PLS) regression model accurately predicting cell death percentages using in‐line capacitance spectra. The current study advances the model accuracy through adaptive modeling employing a data fusion approach. This strategy enhances prediction performance by incorporating variables from the Cole–Cole model, conductivity and its derivatives over time, and Mahalanobis distance into the predictor matrix (X‐matrix). Firstly, the Cole–Cole model, a mechanistic model with parameters linked to early cell death onset, was integrated to enhance prediction performance. Secondly, the inclusion of conductivity and its derivatives over time in the X‐matrix mitigated prediction fluctuations resulting from abrupt conductivity changes during process operations. Thirdly, Mahalanobis distance, depicting spectral changes relative to a reference spectrum from a previous time point, improved model adaptability to independent test sets, thereby enhancing performance. The final data fusion model substantially decreased root‐mean squared error of prediction (RMSEP) by around 50%, which is a significant boost in prediction accuracy compared to the prior PLS model. Robustness against reference spectrum selection was confirmed by consistent performance across various time points. In conclusion, this study illustrates that the data fusion strategy substantially enhances the model accuracy compared to the previous model relying solely on capacitance spectra.