Technology transfer of a monitoring system to predict product concentration and purity of biopharmaceuticals in real‐time during chromatographic separation

Christler, Anna; Scharl, Theresa; Sauer, Dominik Georg; Köppl, Johannes; Tscheliessnig, Anne; Toy, Cabir; Melcher, Michael; Jungbauer, Alois; Dürauer, Astrid

doi:10.1002/bit.27870

Cited by 5 publications

(9 citation statements)

References 21 publications

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“…Routinely applied operations to counteract these effects are among others spectral smoothing, derivation, or corrections ( Anderson et al, 2020 ; Tulsyan et al, 2021 ). Spectral data may contain different levels of noise or baseline effects due to new data being recorded with a different spectrometer ( Mishra and Passos, 2021a ), biological variability ( Tulsyan et al, 2021 ) or as part of a technology transfer to another site ( Christler et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring

Schiemer,

Rüdt,

Hubbuch

2024

Front. Bioeng. Biotechnol.

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Chemometric modeling for spectral data is considered a key technology in biopharmaceutical processing to realize real-time process control and release testing. Machine learning (ML) models have been shown to increase the accuracy of various spectral regression and classification tasks, remove challenging preprocessing steps for spectral data, and promise to improve the transferability of models when compared to commonly applied, linear methods. The training and optimization of ML models require large data sets which are not available in the context of biopharmaceutical processing. Generative methods to extend data sets with realistic in silico samples, so-called data augmentation, may provide the means to alleviate this challenge. In this study, we develop and implement a novel data augmentation method for generating in silico spectral data based on local estimation of pure component profiles for training convolutional neural network (CNN) models using four data sets. We simultaneously tune hyperparameters associated with data augmentation and the neural network architecture using Bayesian optimization. Finally, we compare the optimized CNN models with partial least-squares regression models (PLS) in terms of accuracy, robustness, and interpretability. The proposed data augmentation method is shown to produce highly realistic spectral data by adapting the estimates of the pure component profiles to the sampled concentration regimes. Augmenting CNNs with the in silico spectral data is shown to improve the prediction accuracy for the quantification of monoclonal antibody (mAb) size variants by up to 50% in comparison to single-response PLS models. Bayesian structure optimization suggests that multiple convolutional blocks are beneficial for model accuracy and enable transfer across different data sets. Model-agnostic feature importance methods and synthetic noise perturbation are used to directly compare the optimized CNNs with PLS models. This enables the identification of wavelength regions critical for model performance and suggests increased robustness against Gaussian white noise and wavelength shifts of the CNNs compared to the PLS models.

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Section: Discussionmentioning

confidence: 99%

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring

Schiemer,

Rüdt,

Hubbuch

2024

Front. Bioeng. Biotechnol.

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“…Here we give an overview of the advantages and disadvantages of some selected machine learning methods (Table 1). When the number of predictors is large or even larger than the number of observations and the predictors are highly correlated, e.g., when using spectroscopic data, Partial Least Squares (PLS) regression (Wold et al, 2001) is frequently used (Brestich et al, 2018;Christler et al, 2021;Felfödi et al, 2020;Rüdt et al, 2017;Walch et al, 2019). PLS can easily be applied and has the big advantage that model training is very fast.…”

Section: Machine Leaning Methodsmentioning

confidence: 99%

“…A multiple sensor approach is only feasible if the chromatographic workstation is equipped with a central database (Oliveira, 2019;Steinwandter et al, 2019). For these multiple sensors the software solution XAMIris (Evon, Austria) was used for the recording of various signals, starting of the chromatographic runs, data export, time-alignment as well as the implementation of soft sensors for real-time monitoring of several CQAs (Christler et al, 2021;Sauer et al, 2019;Walch et al, 2019).…”

Section: Example For Predictive Chemometrics Approachmentioning

confidence: 99%

“…In same multi sensor setup UV/Vis spectroscopy was used as it mainly measures the primary structure, such as the content of aromatic amino acids (UV280nm), polypeptide backbone (UV214nm) or DNA content (UV260nm) (Christler et al, 2021;Sauer et al, 2019;Walch et al, 2019). The refractive index (RI) was included as it was previously used to quantify protein (Zhao et al, 2011).…”

Section: Impact Of Different Sensorsmentioning

confidence: 99%

“…If one sensor fails, the real-time monitoring can easily be based on an alternative model where the specific sensor is not used. The technology transfer of the monitoring system with multiple sensor (Sauer et al, 2019;Walch et al, 2019) revealed that only a subset of possible prediction models could be used for real-time prediction at the different sites as the fluorescence device was not robust enough (Christler et al, 2021). Simpler models without fluorescence could still be used at the different sites.…”

Section: Impact Of Different Sensorsmentioning

confidence: 99%

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Monitoring Product Quantity, Purity and Potency of Biopharmaceuticals in Real-time by Predictive Chemometrics and Soft sensors

Dürauer¹,

Jungbauer²,

Scharl

2023

Preprint

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The biopharmaceutical industry is still running in batch mode, mostly because it is a highly regulated industry sector. In the past, sensors were not readily available and in-process control was mainly executed off-line. The most important product parameters are quantity, purity and potency besides adventitious agents and bioburden. There is increasing economic pressure on time-to-market and also on the environmental sustainability of biopharmaceutical manufacturing. New concepts for manufacturing using disposable single-use technologies and integrated bioprocessing will dominate the future of bioprocessing. In order to ensure the quality of pharmaceuticals initiatives such as Process Analytical Technologies, Quality by Design and Continuous Integrated Manufacturing have been established. The vision must be that these initiatives together with technology development pave the way for process automation and autonomous bioprocessing without any human intervention. Then a real-time release would be realized leading to a highly predictive and robust biomanufacturing system. The steps toward such automated and autonomous bioprocessing are reviewed in context of monitoring and control. Starting from statistical treatment of single and multiple sensors, establishing soft sensors with predictive chemometrics and hybrid models. A scenario is described how to integrate soft sensors and predictive chemometrics into modern process control. This will be exemplified by selective downstream processing steps such as chromatography and membrane filtration, the most common unit operations for separation of biopharmaceuticals.

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Technology transfer of a monitoring system to predict product concentration and purity of biopharmaceuticals in real‐time during chromatographic separation

Christler

Scharl

Sauer

et al. 2021

Biotech & Bioengineering

Self Cite

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Technological developments require the transfer to their location of application to make use of them. We describe the transfer of a real‐time monitoring system for lab‐scale preparative chromatography to two new sites where it will be used and developed further. Equivalent equipment was used. The capture of a biopharmaceutical model protein, human fibroblast growth factor 2 (FGF‐2) was used to evaluate the system transfer. Predictive models for five quality attributes based on partial least squares regression were transferred. Six out of seven online sensors (UV/VIS, pH, conductivity, IR, RI, and MALS) showed comparable signals between the sites while one sensor (fluorescence) showed different signal profiles. A direct transfer of the models for real‐time monitoring was not possible, mainly due to differences in sensor signals. Adaptation of the models was necessary. Then, among five prediction models, the prediction errors of the test run at the new sites were on average twice as high as at the training site (model‐wise 0.9–5.7 times). Additionally, new prediction models for different products were trained at each new site. These allowed monitoring the critical quality attributes of two new biopharmaceutical products during their purification processes with mean relative deviations between 1% and 33%.

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Technology transfer of a monitoring system to predict product concentration and purity of biopharmaceuticals in real‐time during chromatographic separation

Cited by 5 publications

References 21 publications

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring

Generative data augmentation and automated optimization of convolutional neural networks for process monitoring

Monitoring Product Quantity, Purity and Potency of Biopharmaceuticals in Real-time by Predictive Chemometrics and Soft sensors

Technology transfer of a monitoring system to predict product concentration and purity of biopharmaceuticals in real‐time during chromatographic separation

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