Multi-wavelength UV-based PAT tool for measuring protein concentration

Ramakrishna, Anjali; Prathap, Vinay; Maranholkar, Vijay; Rathore, Anurag S.

doi:10.1016/j.jpba.2021.114394

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…Using the same methods as the first combined UV‐Vis and Raman calibration, we created new stock solutions containing 12.01 g/L total protein and 0.3% aggregation in the monomer‐enriched sample and 12.04 g/L total protein and 6.3% aggregation in the aggregates‐enriched sample. We were confident from our previous results (Figure 3) that our system was capable of producing stable mixtures for calibrating both UV‐Vis scans and Raman spectra, thus we updated our analysis of the Raman spectra to include methods that have been recently successful for calibration model building, including partial‐least squares (PLS) regression modeling (Brestrich et al, 2018; Wei et al, 2021) and PCR (McAvan et al, 2020; Ramakrishna et al, 2022; Silva et al, 2020), as well as nonlinear models such as Gaussian processes (GPs) (Tulsyan et al, 2020). Model hyperparameter selection was made by subjective judgment from published values and included: 10 for the number of PLS and PCA latent variables, and 60% as k , for the KNN model that we used instead of a GP model to reduce risk of overfitting.…”

Section: Resultsmentioning

confidence: 60%

“…Raman spectra, thus we updated our analysis of the Raman spectra to include methods that have been recently successful for calibration model building, including partial-least squares (PLS) regression modeling (Brestrich et al, 2018;Wei et al, 2021) and PCR (McAvan et al, 2020;Ramakrishna et al, 2022;Silva et al, 2020), as well as nonlinear models such as Gaussian processes (GPs) (Tulsyan et al, 2020) decreasing performance with increasing k (Figure 4b). We chose the value of 60 for k due to its centrality in a relatively stable region of the hyperparameter space with a lower impact on performance.…”

Section: Resultsmentioning

confidence: 99%

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Automated calibration and in‐line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Wang

Chen

Studts

et al. 2023

Biotech & Bioengineering

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Current manufacturing and development processes for therapeutic monoclonal antibodies demand increasing volumes of analytical testing for both real‐time process controls and high‐throughput process development. The feasibility of using Raman spectroscopy as an in‐line product quality measuring tool has been recently demonstrated and promises to relieve this analytical bottleneck. Here, we resolve time‐consuming calibration process that requires fractionation and preparative experiments covering variations of product quality attributes (PQAs) by engineering an automation system capable of collecting Raman spectra on the order of hundreds of calibration points from two to three stock seed solutions differing in protein concentration and aggregate level using controlled mixing. We used this automated system to calibrate multi‐PQA models that accurately measured product concentration and aggregation every 9.3 s using an in‐line flow‐cell. We demonstrate the application of a nonlinear calibration model for monitoring product quality in real‐time during a biopharmaceutical purification process intended for clinical and commercial manufacturing. These results demonstrate potential feasibility to implement quality monitoring during GGMP manufacturing as well as to increase chemistry, manufacturing, and controls understanding during process development, ultimately leading to more robust and controlled manufacturing processes.

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Section: Resultsmentioning

confidence: 60%

Section: Resultsmentioning

confidence: 99%

Automated calibration and in‐line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Wang

Chen

Studts

et al. 2023

Biotech & Bioengineering

View full text Add to dashboard Cite

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“…UV absorbance is a critical part of bioprocessing despite its limited dynamic range at 280 nm (Ramakrishna et al, 2022) and sensitivity to interfering nonprotein chromophores, requiring process limitations to avoid false positive detection of concentration. Raman spectroscopy operates on an equivalent mechanism as UV absorbance in that it is deployed in-line and relies on a light source to noninvasively monitor product and impurities dynamically under flow.…”

Section: The Reason For This Concentration Limitation Lies In the Sat...mentioning

confidence: 99%

Simultaneous prediction of 16 quality attributes during protein A chromatography using machine learning based Raman spectroscopy models

Wang,

Chen,

Studts

et al. 2024

Biotech & Bioengineering

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Several key technologies for advancing biopharmaceutical manufacturing depend on the successful implementation of process analytical technologies that can monitor multiple product quality attributes in a continuous in‐line setting. Raman spectroscopy is an emerging technology in the biopharma industry that promises to fit this strategic need, yet its application is not widespread due to limited success for predicting a meaningful number of quality attributes. In this study, we addressed this very problem by demonstrating new capabilities for preprocessing Raman spectra using a series of Butterworth filters. The resulting increase in the number of spectral features is paired with a machine learning algorithm and laboratory automation hardware to drive the automated collection and training of a calibration model that allows for the prediction of 16 different product quality attributes in an in‐line mode. The demonstrated ability to generate these Raman‐based models for in‐process product quality monitoring is the breakthrough to increase process understanding by delivering product quality data in a continuous manner. The implementation of this multiattribute in‐line technology will create new workflows within process development, characterization, validation, and control.

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“…The use of flow VPE to monitor protein concentration during a lab‐scale UFDF experiment is detailed in a study by Rolinger et al, [ 6 ] and its use in a small scale SPTFF experiment is detailed in a study by Arumkumar et al [ 7 ] Ramakrishna et al also demonstrated the use of variable wavelength UV‐based approach for dilution free on‐line concentration estimation in the range of 0.8–100 g/l. [ 8 ] The fact that the Flow VPE and Flow VPX spectrometers utilize the same technology used by the SoloVPE, makes it an appealing candidate for integration into a manufacturing TFF process. However, at the time this study was performed, this technology was not available in a format that could be integrated into a commercial scale biopharmaceutical manufacturing facility.…”

Section: Introductionmentioning

confidence: 99%

Refractive index as a process analytical technology to measure real time protein concentration for monitoring and control of commercial scale tangential flow filtration operations

Lam

McCann

Loman

et al. 2023

J Adv Manuf & Process

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Biological therapeutics are increasingly being formulated to high protein concentrations to decrease drug substance storage space and increase the flexibility of administration to patients. With the higher protein concentration targets comes added challenges to the downstream purification manufacturing process. Tangential flow filtration (TFF) operations are typically performed to reach target protein concentration. TFF operations for a given drug substance may include an Ultrafiltration and Diafiltration (UFDF) step, or a UFDF step followed by a single pass tangential flow filtration (SPTFF) step. Whether a TFF step achieves a target protein concentration is determined by at‐line protein concentration measurements performed at the completion of a process step. If the measured protein concentration is outside the specified range, the unit operation may need to be restarted, reprocessing may need to be performed, or a batch may need to be terminated. Out of specification protein concentration measurements may be a result of the TFF operation or sample measurement. Increased viscosities associated with high concentration TFF operations pose added challenges to the TFF process and sample measurement. Implementation of an inline process analytical technology (PAT) to monitor real‐time protein concentration during TFF operations has the potential to improve the accuracy of the operations in achieving target protein concentrations. This will result in improved process consistency and efficiency, increased operator confidence and decreased likelihood of batch failures. This paper studies the performance of a K‐Patents PR‐23 refractometer (Vaisala) as a PAT to monitor and control the UFDF and SPTFF unit operations of a commercial scale monoclonal antibody purification process.

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Multi-wavelength UV-based PAT tool for measuring protein concentration

Cited by 11 publications

References 21 publications

Automated calibration and in‐line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Automated calibration and in‐line measurement of product quality during therapeutic monoclonal antibody purification using Raman spectroscopy

Simultaneous prediction of 16 quality attributes during protein A chromatography using machine learning based Raman spectroscopy models

Refractive index as a process analytical technology to measure real time protein concentration for monitoring and control of commercial scale tangential flow filtration operations

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