AI-ML applications in bioprocessing: ML as an enabler of real time quality prediction in continuous manufacturing of mAbs

Nikita, Saxena; Thakur, Garima; Jesubalan, Naveen G.; Kulkarni, Amey; Yezhuvath, Vinesh B.; Rathore, Anurag S.

doi:10.1016/j.compchemeng.2022.107896

Cited by 27 publications

(22 citation statements)

References 43 publications

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“…Random Forests are ensemble learning methods where the predictions are obtained by averaging over hundreds or even thousands of trees built on bootstrap samples, i.e., samples taken from the training data with replacement. Recently it was shown that these methods perform very well in downstream processing (Nikita et al, 2022). Random Forests are very popular due to the build-in permutation-based variable importance measure.…”

Section: Tree-based Methodsmentioning

confidence: 99%

“…Defining the linear regression function in this feature space, nonlinear function regression in the original space becomes a linear function regression in the feature space. SVMs have been included in recent studies on continuous biomanufacturing (Nikita et al, 2022). The selection of an appropriate kernel function is data dependent and needs expert knowledge.…”

Section: Support Vector Machines Regression -Svmmentioning

confidence: 99%

“…Nowadays, modern neural networks use the ReLU activation function instead of the sigmoid function and consist of multiple hidden layers. Deep learning was recently used for real-time quality prediction and process control (Nikita et al, 2022). They applied deep neural networks and compared it to SVM, decision trees regression and random forests on time series data of UV, conductivity and pH probes of 84 batches.…”

Section: Artificial Neural Network -Annmentioning

confidence: 99%

“…If only a single quality attribute is monitored such soft sensors are often based on single sensors, e.g., the monitoring of the product quantity by UV spectroscopy (Rolinger et al, 2021) or attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy (Rudt et al, 2019). However, multiple sensors must be combined even if only the process signals are used that a chromatographic workstation is typically equipped with, e.g., UV, conductivity and pH probe (Nikita et al, 2022). If several CQAs need to be modelled in real-time, it is indispensable to use a multiple sensor approach in order to capture the different properties of these CQAs (Sauer et al, 2019).…”

Section: Multiple-sensor Predictive Chemometrics Approachmentioning

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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Section: Tree-based Methodsmentioning

confidence: 99%

Section: Support Vector Machines Regression -Svmmentioning

confidence: 99%

Section: Artificial Neural Network -Annmentioning

confidence: 99%

Section: Multiple-sensor Predictive Chemometrics Approachmentioning

confidence: 99%

See 2 more Smart Citations

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

Dürauer¹,

Jungbauer²,

Scharl

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Multiple types of data-driven models have previously been used in the field. The most popular ones include (i) random forest regression (RFR), [7,11] (ii) partial least squares regression (PLSR), [10,12] (iii) Gaussian process regression (GPR), [13,14] and (iv) artificial neural networks (ANNs). [7,15] A special subtype of ANNs are (deep) convolutional neural networks (CNNs).…”

Section: Introductionmentioning

confidence: 99%

Explainable deep learning enhances robust and reliable real‐time monitoring of a chromatographic protein A capture step

Medl,

Leisch,

Dürauer

et al. 2024

Biotechnology Journal

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The application of model‐based real‐time monitoring in biopharmaceutical production is a major step toward quality‐by‐design and the fundament for model predictive control. Data‐driven models have proven to be a viable option to model bioprocesses. In the high stakes setting of biopharmaceutical manufacturing it is essential to ensure high model accuracy, robustness, and reliability. That is only possible when (i) the data used for modeling is of high quality and sufficient size, (ii) state‐of‐the‐art modeling algorithms are employed, and (iii) the input‐output mapping of the model has been characterized. In this study, we evaluate the accuracy of multiple data‐driven models in predicting the monoclonal antibody (mAb) concentration, double stranded DNA concentration, host cell protein concentration, and high molecular weight impurity content during elution from a protein A chromatography capture step. The models achieved high‐quality predictions with a normalized root mean squared error of <4% for the mAb concentration and of ≈10% for the other process variables. Furthermore, we demonstrate how permutation/occlusion‐based methods can be used to gain an understanding of dependencies learned by one of the most complex data‐driven models, convolutional neural network ensembles. We observed that the models generally exhibited dependencies on correlations that agreed with first principles knowledge, thereby bolstering confidence in model reliability. Finally, we present a workflow to assess the model behavior in case of systematic measurement errors that may result from sensor fouling or failure. This study represents a major step toward improved viability of data‐driven models in biopharmaceutical manufacturing.

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Sensors and chemometrics in downstream processing

Dürauer

Jungbauer

Scharl

2023

Biotech & Bioengineering

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The biopharmaceutical industry is still running in batch mode, mostly because it is highly regulated. In the past, sensors were not readily available and in‐process control was mainly executed offline. The most important product parameters are quantity, purity, and potency, in addition to adventitious agents and bioburden. New concepts using disposable single‐use technologies and integrated bioprocessing for manufacturing will dominate the future of bioprocessing. To ensure the quality of pharmaceuticals, initiatives such as Process Analytical Technologies, Quality by Design, and Continuous Integrated Manufacturing have been established. The aim is that these initiatives, together with technology development, will pave the way for process automation and autonomous bioprocessing without any human intervention. Then, 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 the context of monitoring and control. It is possible to integrate real‐time monitoring gradually, and it should be considered from a soft sensor perspective. This concept has already been successfully implemented in other industries and requires relatively simple model training and the use of established statistical tools, such as multivariate statistics or neural networks. This review describes a scenario for integrating soft sensors and predictive chemometrics into modern process control. This is 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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AI-ML applications in bioprocessing: ML as an enabler of real time quality prediction in continuous manufacturing of mAbs

Cited by 27 publications

References 43 publications

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

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

Explainable deep learning enhances robust and reliable real‐time monitoring of a chromatographic protein A capture step

Sensors and chemometrics in downstream processing

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