1
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Yang YX, Lin ZY, Chen YC, Yao SJ, Lin DQ. Modeling multi-component separation in hydrophobic interaction chromatography with improved parameter-by-parameter estimation method. J Chromatogr A 2024; 1730:465121. [PMID: 38959659 DOI: 10.1016/j.chroma.2024.465121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 06/10/2024] [Accepted: 06/24/2024] [Indexed: 07/05/2024]
Abstract
Mechanistic models are powerful tools for chromatographic process development and optimization. However, hydrophobic interaction chromatography (HIC) mechanistic models lack an effective and logical parameter estimation method, especially for multi-component system. In this study, a parameter-by-parameter method for multi-component system (called as mPbP-HIC) was derived based on the retention mechanism to estimate the six parameters of the Mollerup isotherm for HIC. The linear parameters (ks,i and keq,i) and nonlinear parameters (ni and qmax,i) of the isotherm can be estimated by the linear regression (LR) and the linear approximation (LA) steps, respectively. The remaining two parameters (kp,i and kkin,i) are obtained by the inverse method (IM). The proposed method was verified with a two-component model system. The results showed that the model could accurately predict the protein elution at a loading of 10 g/L. However, the elution curve fitting was unsatisfactory for high loadings (12 g/L and 14 g/L), which is mainly attributed to the demanding experimental conditions of the LA step and the potential large estimation error of the parameter qmax. Therefore, the inverse method was introduced to further calibrate the parameter qmax, thereby reducing the estimation error and improving the curve fitting. Moreover, the simplified linear approximation (SLA) was proposed by reasonable assumption, which provides the initial guess of qmax without solving any complex matrix and avoids the problem of matrix unsolvable. In the improved mPbP-HIC method, qmax would be initialized by the SLA and finally determined by the inverse method, and this strategy was named as SLA+IM. The experimental validation showed that the improved mPbP-HIC method has a better curve fitting, and the use of SLA+IM reduces the error accumulation effect. In process optimization, the parameters estimated by the improved mPbP-HIC method provided the model with excellent predictive ability and reasonable extrapolation. In conclusion, the SLA+IM strategy makes the improved mPbP-HIC method more rational and can be easily applied to the practical separation of protein mixture, which would accelerate the process development for HIC in downstream of biopharmaceuticals.
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Affiliation(s)
- Yu-Xiang Yang
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Zhi-Yuan Lin
- Zhejiang University-University of Edinburgh Institute, Zhejiang University, Haining 314400, China
| | - Yu-Cheng Chen
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Shan-Jing Yao
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Dong-Qiang Lin
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.
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2
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Shi C, Chen XJ, Zhong XZ, Yang Y, Lin DQ, Chen R. Realization of digital twin for dynamic control toward sample variation of ion exchange chromatography in antibody separation. Biotechnol Bioeng 2024; 121:1702-1715. [PMID: 38230585 DOI: 10.1002/bit.28660] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Revised: 12/26/2023] [Accepted: 01/05/2024] [Indexed: 01/18/2024]
Abstract
Digital twin (DT) is a virtual and digital representation of physical objects or processes. In this paper, this concept is applied to dynamic control of the collection window in the ion exchange chromatography (IEC) toward sample variations. A possible structure of a feedforward model-based control DT system was proposed. Initially, a precise IEC mechanistic model was established through experiments, model fitting, and validation. The average root mean square error (RMSE) of fitting and validation was 8.1% and 7.4%, respectively. Then a model-based gradient optimization was performed, resulting in a 70.0% yield with a remarkable 11.2% increase. Subsequently, the DT was established by systematically integrating the model, chromatography system, online high-performance liquid chromatography, and a server computer. The DT was validated under varying load conditions. The results demonstrated that the DT could offer an accurate control with acidic variants proportion and yield difference of less than 2% compared to the offline analysis. The embedding mechanistic model also showed a positive predictive performance with an average RMSE of 11.7% during the DT test under >10% sample variation. Practical scenario tests indicated that tightening the control target could further enhance the DT robustness, achieving over 98% success rate with an average yield of 72.7%. The results demonstrated that the constructed DT could accurately mimic real-world situations and perform an automated and flexible pooling in IEC. Additionally, a detailed methodology for applying DT was summarized.
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Affiliation(s)
- Ce Shi
- Process Development Downstream, Shanghai Engineering Research Center of Anti-tumor Biological Drugs, Shanghai Henlius Biotech, Inc., Shanghai, China
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China
| | - Xu-Jun Chen
- Process Development Downstream, Shanghai Engineering Research Center of Anti-tumor Biological Drugs, Shanghai Henlius Biotech, Inc., Shanghai, China
| | - Xue-Zhao Zhong
- Process Development Downstream, Shanghai Engineering Research Center of Anti-tumor Biological Drugs, Shanghai Henlius Biotech, Inc., Shanghai, China
| | - Yan Yang
- Process Development Downstream, Shanghai Engineering Research Center of Anti-tumor Biological Drugs, Shanghai Henlius Biotech, Inc., Shanghai, China
| | - Dong-Qiang Lin
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou, China
| | - Ran Chen
- Process Development Downstream, Shanghai Engineering Research Center of Anti-tumor Biological Drugs, Shanghai Henlius Biotech, Inc., Shanghai, China
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3
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Espinoza D, Tallvod S, Andersson N, Nilsson B. Automatic procedure for modelling, calibration, and optimization of a three-component chromatographic separation. J Chromatogr A 2024; 1720:464805. [PMID: 38471300 DOI: 10.1016/j.chroma.2024.464805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 02/08/2024] [Accepted: 03/06/2024] [Indexed: 03/14/2024]
Abstract
The current landscape of biopharmaceutical production necessitates an ever-growing set of tools to meet the demands for shorter development times and lower production costs. One path towards meeting these demands is the implementation of digital tools in the development stages. Mathematical modelling of process chromatography, one of the key unit operations in the biopharmaceutical downstream process, is one such tool. However, obtaining parameter values for such models is a time-consuming task that grows in complexity with the number of compounds in the mixture being purified. In this study, we tackle this issue by developing an automated model calibration procedure for purification of a multi-component mixture by linear gradient ion exchange chromatography. The procedure was implemented using the Orbit software (Lund University, Department of Chemical Engineering), which both generates a mathematical model structure and performs the experiments necessary to obtain data for model calibration. The procedure was extended to suggest operating points for the purification of one of the components in the mixture by means of multi-objective optimization using three different objectives. The procedure was tested on a three-component protein mixture and was able to generate a calibrated model capable of reproducing the experimental chromatograms to a satisfactory degree, using a total of six assays. An additional seventh experiment was performed to validate the model response under one of the suggested optimum conditions, respecting a 95 % purity requirement. All of the above was automated and set in motion by the push of a button. With these results, we have taken a step towards fully automating model calibration and thus accelerating digitalization in the development stages of new biopharmaceuticals.
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Affiliation(s)
- Daniel Espinoza
- Department of Chemical Engineering, Lund University, Lund, Sweden.
| | - Simon Tallvod
- Department of Chemical Engineering, Lund University, Lund, Sweden
| | - Niklas Andersson
- Department of Chemical Engineering, Lund University, Lund, Sweden
| | - Bernt Nilsson
- Department of Chemical Engineering, Lund University, Lund, Sweden
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4
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Eslami T, Jungbauer A. Control strategy for biopharmaceutical production by model predictive control. Biotechnol Prog 2024; 40:e3426. [PMID: 38199980 DOI: 10.1002/btpr.3426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 10/04/2023] [Accepted: 12/20/2023] [Indexed: 01/12/2024]
Abstract
The biopharmaceutical industry is rapidly advancing, driven by the need for cutting-edge technologies to meet the growing demand for life-saving treatments. In this context, Model Predictive Control (MPC) has emerged as a promising solution to address the complexity of modern biopharmaceutical production processes. Its ability to optimize operations and ensure consistent product yields has made it an attractive option for manufacturers in this sector. Furthermore, MPC's alignment with the Process Analytical Technology (PAT) initiative provides an additional layer of assurance, facilitating real-time monitoring and enabling swift adjustments to maintain process integrity. This comprehensive review delves into the various applications of MPC, ranging from robust control to stochastic model predictive control, thereby equipping biotechnologists and process engineers with a powerful toolset. By harnessing the capabilities of MPC, as elucidated in this review, manufacturers can confidently navigate the intricate bioprocessing landscape and unlock this approach's full potential in their production processes.
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Affiliation(s)
- Touraj Eslami
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
- Evon GmbH, St. Ruprecht an der Raab, Austria
| | - Alois Jungbauer
- Department of Biotechnology, University of Natural Resources and Life Sciences, Vienna, Austria
- Austrian Centre of Industrial Biotechnology, Vienna, Austria
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5
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Fan Y, Sun YN, Qiao LZ, Mao RQ, Tang SY, Shi C, Yao SJ, Lin DQ. Evaluation of dynamic control of continuous capture with periodic counter-current chromatography under feedstock variations. J Chromatogr A 2024; 1713:464528. [PMID: 38029658 DOI: 10.1016/j.chroma.2023.464528] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 11/19/2023] [Accepted: 11/22/2023] [Indexed: 12/01/2023]
Abstract
Multi-column periodic counter-current chromatography is a promising technology for continuous antibody capture. However, dynamic changes due to disturbances and drifts pose some potential risks for continuous processes during long-term operation. In this study, a model-based approach was used to describe the changes in breakthrough curves with feedstock variations in target proteins and impurities. The performances of continuous capture of three-column periodic counter-current chromatography under ΔUV dynamic control were systematically evaluated with modeling to assess the risks under different feedstock variations. As the concentration of target protein decreased rapidly, the protein might not breakthrough from the first column, resulting in the failure of ΔUV control. Small reductions in the concentrations of target proteins or impurities would cause protein losses, which could be predicted by the modeling. The combination of target protein and impurity variations showed complicated effects on the process performance of continuous capture. A contour map was proposed to describe the comprehensive impacts under different situations, and nonoperation areas could be identified due to control failure or protein loss. With the model-based approach, after the model parameters are estimated from the breakthrough curves, it can rapidly predict the process stability under dynamic control and assess the risks under feedstock variations or UV signal drifts. In conclusion, the model-based approach is a powerful tool for continuous process evaluation under dynamic changes and would be useful for establishing a new real-time dynamic control strategy.
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Affiliation(s)
- Yu Fan
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Yan-Na Sun
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Liang-Zhi Qiao
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Ruo-Que Mao
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Si-Yuan Tang
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Ce Shi
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Shan-Jing Yao
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China
| | - Dong-Qiang Lin
- Key Laboratory of Biomass Chemical Engineering of Ministry of Education, Zhejiang Key Laboratory of Smart Biomaterials, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310058, China.
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6
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Medeiros Garcia Alcântara J, Iannacci F, Morbidelli M, Sponchioni M. Soft sensor based on Raman spectroscopy for the in-line monitoring of metabolites and polymer quality in the biomanufacturing of polyhydroxyalkanoates. J Biotechnol 2023; 377:23-33. [PMID: 37879569 DOI: 10.1016/j.jbiotec.2023.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 10/04/2023] [Accepted: 10/16/2023] [Indexed: 10/27/2023]
Abstract
Polyhydroxyalkanoates (PHA) are among the most promising bio-based alternatives to conventional petroleum-based plastics. These biodegradable polyesters can in fact be produced by fermentation from bacteria like Cupriavidus necator, thus reducing the environmental footprint of the manufacturing process. However, ensuring consistent product quality attributes is a major challenge of biomanufacturing. To address this issue, the implementation of real-time monitoring tools is essential to increase process understanding, enable a prompt response to possible process deviations and realize on-line process optimization. In this work, a soft sensor based on in situ Raman spectroscopy was developed and applied to the in-line monitoring of PHA biomanufacturing. This strategy allows the collection of quantitative information directly from the culture broth, without the need for sampling, and at high frequency. In fact, through an optimized multivariate data analysis pipeline, this soft sensor allows monitoring cell dry weight, as well as carbon and nitrogen source concentrations with root mean squared errors (RMSE) equal to 3.71, 7 and 0.03 g/L, respectively. In addition, this tool allows the in-line monitoring of intracellular PHA accumulation, with an RMSE of 14 gPHA/gCells. For the first time, also the number and weight average molecular weights of the polymer produced could be monitored, with RMSE of 8.7E4 and 11.6E4 g/mol, respectively. Overall, this work demonstrates the potential of Raman spectroscopy in the in-line monitoring of biotechnology processes, leading to the simultaneous measurement of several process variables in real time without the need of sampling and labor-intensive sample preparations.
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Affiliation(s)
- João Medeiros Garcia Alcântara
- Dept. of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, via Mancinelli 7, Milano 20131, Italy
| | - Francesco Iannacci
- Dept. of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, via Mancinelli 7, Milano 20131, Italy
| | - Massimo Morbidelli
- Dept. of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, via Mancinelli 7, Milano 20131, Italy
| | - Mattia Sponchioni
- Dept. of Chemistry, Materials and Chemical Engineering "Giulio Natta", Politecnico di Milano, via Mancinelli 7, Milano 20131, Italy.
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7
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Schini A, De Canditiis B, Sanchez C, Pierrelee M, Voltz KE, Jourdainne L. Influence of cell specific parameters in a dielectric spectroscopy conversion model used to monitor viable cell density in bioreactors. Biotechnol J 2023; 18:e2300028. [PMID: 37318800 DOI: 10.1002/biot.202300028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Revised: 05/25/2023] [Accepted: 06/06/2023] [Indexed: 06/16/2023]
Abstract
In the biopharmaceutical industry, the use of mammalian cells to produce therapeutic proteins is becoming increasingly widespread. Monitoring of these cultures via different analysis techniques is essential to ensure a good quality product while respecting good manufacturing practice (GMP) regulations. Process Analytical Technologies (PAT) tools provide real-time measurements of the physiological state of the culture and enable process automation. Dielectric spectroscopy is a PAT that can be used to monitor the viable cell concentration (VCC) of living cells after processing raw permittivity data. Several modeling approaches exist and estimate biomass with different accuracy. The accuracy of the Cole-Cole and Maxwell Wagner's equations are studied here in the determination of the VCC and cell radius in Chinese hamster ovary (CHO) culture. A sensitivity analysis performed on the parameters entering the equations highlighted the importance of the cell specific parameters such as internal conductivity (σi ) and membrane capacitance (Cm ) in the accuracy of the estimation of VCC and cell radius. The most accurate optimization method found to improve the accuracy involves in-process adjustments of Cm and σi in the model equations with samplings from the bioreactor. This combination of offline and in situ data improved the estimation precision of the VCC by 69% compared to a purely mechanistic model without offline adjustments.
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Affiliation(s)
- Adèle Schini
- Millipore S.A.S. (an affiliate of Merck KGaA), Darmstadt, Germany
| | | | - Célia Sanchez
- Millipore S.A.S. (an affiliate of Merck KGaA), Darmstadt, Germany
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8
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Kaltbeitzel J, Wich PR. Protein-based Nanoparticles: From Drug Delivery to Imaging, Nanocatalysis and Protein Therapy. Angew Chem Int Ed Engl 2023; 62:e202216097. [PMID: 36917017 DOI: 10.1002/anie.202216097] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 03/12/2023] [Accepted: 03/13/2023] [Indexed: 03/16/2023]
Abstract
Proteins and enzymes are versatile biomaterials for a wide range of medical applications due to their high specificity for receptors and substrates, high degradability, low toxicity, and overall good biocompatibility. Protein nanoparticles are formed by the arrangement of several native or modified proteins into nanometer-sized assemblies. In this review, we will focus on artificial nanoparticle systems, where proteins are the main structural element and not just an encapsulated payload. While under natural conditions, only certain proteins form defined aggregates and nanoparticles, chemical modifications or a change in the physical environment can further extend the pool of available building blocks. This allows the assembly of many globular proteins and even enzymes. These advances in preparation methods led to the emergence of new generations of nanosystems that extend beyond transport vehicles to diverse applications, from multifunctional drug delivery to imaging, nanocatalysis and protein therapy.
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Affiliation(s)
- Jonas Kaltbeitzel
- School of Chemical Engineering, University of New South Wales, Sydney, NSW 2052, Australia
- Australian Centre for NanoMedicine, University of New South Wales, Sydney, NSW 2052, Australia
| | - Peter R Wich
- School of Chemical Engineering, University of New South Wales, Sydney, NSW 2052, Australia
- Australian Centre for NanoMedicine, University of New South Wales, Sydney, NSW 2052, Australia
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9
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Ding C, Gerberich C, Ierapetritou M. Hybrid model development for parameter estimation and process optimization of hydrophobic interaction chromatography. J Chromatogr A 2023; 1703:464113. [PMID: 37267655 DOI: 10.1016/j.chroma.2023.464113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Revised: 05/26/2023] [Accepted: 05/27/2023] [Indexed: 06/04/2023]
Abstract
Hydrophobic Interaction Chromatography (HIC) is often employed as a polishing step to remove aggregates for the purification of therapeutic proteins in the biopharmaceutical industry. To accelerate the process development and save the costs of performing time- and resource-intensive experiments, advanced model-based process design and optimization are necessary. Due to the unclear adsorption mechanism of the salt-dependent interaction between the protein and resin, the development of an accurate mechanistic model to describe the complex HIC behavior is challenging. In this work, an isotherm derived from Wang et al. is modified by adding three extra parameters together with an equilibrium dispersive model to represent the HIC process. To reduce the development effort of isotherm equations and extract missing information from the available data, a hybrid model is constructed by combining a simple and well-known multi-component Langmuir isotherm (MCL) with a neural network (NN). It is observed that the structure of the hybrid model is of critical importance to the accuracy of the developed model. During parameter estimation, a regularization strategy is incorporated to prevent overfitting. Furthermore, the impact of NN structures and regularization rates are comprehensively investigated. One of the interesting findings was that a simple NN with one hidden layer with two nodes and sigmoid as the activation function, significantly outperforms the mechanistic model, with a 62% improvement in accuracy in calibration and 31.4% in validation. To ensure the generalizability of the developed hybrid model, an in-silico dataset is generated using the mechanistic model to test the extrapolation capability of the hybrid model. Process optimization is also carried out to find the optimal operating conditions under product quality constraints using the developed hybrid model.
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Affiliation(s)
- Chaoying Ding
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA
| | - Christopher Gerberich
- Biopharm Drug Substance Process Development, GlaxoSmithKline, King of Prussia, PA 19406, USA
| | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, DE 19716, USA.
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10
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Doyle K, Tsopanoglou A, Fejér A, Glennon B, del Val IJ. Automated assembly of hybrid dynamic models for CHO cell culture processes. Biochem Eng J 2022. [DOI: 10.1016/j.bej.2022.108763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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11
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Kim TK, Sechi B, Romero Conde JJ, Angelo J, Xu X, Ghose S, Morbidelli M, Sponchioni M. Design and economic investigation of a Multicolumn Countercurrent Solvent Gradient Purification unit for the separation of an industrially relevant PEGylated protein. J Chromatogr A 2022; 1681:463487. [PMID: 36115185 DOI: 10.1016/j.chroma.2022.463487] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 08/31/2022] [Accepted: 09/05/2022] [Indexed: 10/14/2022]
Abstract
Conjugation of biopharmaceuticals to polyethylene glycol chains, known as PEGylation, is nowadays an efficient and widely exploited strategy to improve critical properties of the active molecule, including stability, biodistribution profile, and reduced clearance. A crucial step in the manufacturing of PEGylated drugs is the purification. The reference process in industrial settings is single-column chromatography, which can meet the stringent purity requisites only at the expenses of poor product recoveries. A valuable solution to this trade-off is the Multicolumn Countercurrent Solvent Gradient Purification (MCSGP), which allows the internal and automated recycling of product-containing side fractions that are typically discarded in the batch processes. In this study, an ad hoc design procedure was applied to the single-column batch purification of an industrially relevant PEGylated protein, with the aim of defining optimal collection window, elution duration and elution buffer ionic strength to be then transferred to the MCSGP. This significantly alleviates the design of the continuous operation, subjected to manifold process parameters. The MCSGP designed by directly transferring the optimal parameters allowed to improve the yield and productivity by 8.2% and 17.8%, respectively, when compared to the corresponding optimized batch process, ensuring a purity specification of 98.0%. Once the efficacy of MCSGP was demonstrated, a detailed analysis of its cost of goods was performed and compared to the case of single-column purification. To the best of our knowledge, this is the first example of a detailed economic investigation of the MCSGP across different manufacturing scenarios and process cadences of industrial relevance, which demonstrated not only the viability of this continuous technology but also its flexibility.
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Affiliation(s)
- Tae Keun Kim
- Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Via Mancinelli 7, Milano 20131, Italy
| | - Benedetta Sechi
- Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Via Mancinelli 7, Milano 20131, Italy
| | - Juan Jose Romero Conde
- Biologics Process Development, Global Product Development and Supply, Bristol Myers Squibb Inc., Devens, MA 01434, USA
| | - James Angelo
- Biologics Process Development, Global Product Development and Supply, Bristol Myers Squibb Inc., Devens, MA 01434, USA
| | - Xuankuo Xu
- Biologics Process Development, Global Product Development and Supply, Bristol Myers Squibb Inc., Devens, MA 01434, USA
| | - Sanchayita Ghose
- Biologics Process Development, Global Product Development and Supply, Bristol Myers Squibb Inc., Devens, MA 01434, USA
| | - Massimo Morbidelli
- Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Via Mancinelli 7, Milano 20131, Italy
| | - Mattia Sponchioni
- Department of Chemistry, Materials and Chemical Engineering, Politecnico di Milano, Via Mancinelli 7, Milano 20131, Italy.
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12
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Ding C, Ardeshna H, Gillespie C, Ierapetritou M. Process Design of a Fully Integrated Continuous Biopharmaceutical Process using Economic and Ecological Impact Assessment. Biotechnol Bioeng 2022; 119:3567-3583. [DOI: 10.1002/bit.28234] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 08/31/2022] [Accepted: 09/11/2022] [Indexed: 11/06/2022]
Affiliation(s)
- Chaoying Ding
- Department of Chemical and Biomolecular EngineeringUniversity of DelawareNewarkDE19716US
| | - Hiren Ardeshna
- Manufacturing Science and Technology, Biopharm and Steriles, GlaxoSmithKlinePhiladelphiaPA19112US
| | | | - Marianthi Ierapetritou
- Department of Chemical and Biomolecular EngineeringUniversity of DelawareNewarkDE19716US
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13
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Richelle A, Corbett B, Agarwal P, Vernersson A, Trygg J, McCready C. Model-based intensification of CHO cell cultures: One-step strategy from fed-batch to perfusion. Front Bioeng Biotechnol 2022; 10:948905. [PMID: 36072286 PMCID: PMC9443430 DOI: 10.3389/fbioe.2022.948905] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 07/06/2022] [Indexed: 11/13/2022] Open
Abstract
There is a growing interest in continuous processing of the biopharmaceutical industry. However, the technology transfer from traditional batch-based processes is considered a challenge as protocol and tools still remain to be established for their usage at the manufacturing scale. Here, we present a model-based approach to design optimized perfusion cultures of Chinese Hamster Ovary cells using only the knowledge captured during small-scale fed-batch experiments. The novelty of the proposed model lies in the simplicity of its structure. Thanks to the introduction of a new catch-all variable representing a bulk of by-products secreted by the cells during their cultivation, the model was able to successfully predict cellular behavior under different operating modes without changes in its formalism. To our knowledge, this is the first experimentally validated model capable, with a single set of parameters, to capture culture dynamic under different operating modes and at different scales.
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Affiliation(s)
- Anne Richelle
- Sartorius Corporate Research, Brussels, Belgium
- *Correspondence: Anne Richelle,
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14
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Parameter-by-parameter method for steric mass action model of ion exchange chromatography: Theoretical considerations and experimental verification. J Chromatogr A 2022; 1680:463418. [PMID: 36001908 DOI: 10.1016/j.chroma.2022.463418] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 08/08/2022] [Accepted: 08/10/2022] [Indexed: 12/30/2022]
Abstract
Ion exchange chromatography (IEC) is one of the most widely-used techniques for protein separation and has been characterized by mechanistic models. However, the time-consuming and cumbersome model calibration hinders the application of mechanistic models for process development. A new methodology called "parameter-by-parameter method (PbP)" was proposed with mechanistic derivations of the steric mass action (SMA) model of IEC. The protocol includes four steps: (1) first linear regression (LR1) for characteristic charge; (2) second linear regression (LR2) for equilibrium coefficient; (3) linear approximation (LA) for shielding factor; (4) inverse method (IM) for kinetic coefficient. Four SMA parameters could be one-by-one determined in sequence, reducing the number of unknown parameters per species from four to one, and predicting almost consistent retention. Numerical single-component experiments were investigated firstly, and the PbP method showed excellent agreement between experiments and simulations. The effects of loadings on the PbP and Yamamoto methods were compared. It was found that the PbP method had higher accuracy and robustness than the Yamamoto method. Moreover, a five-experiment strategy was suggested to implement the PbP method, which is straightforward to reduce the cost of calibration experiments. Finally, a real-world multi-component separation was challenged and further confirmed the feasibility of the PbP method. In general, the proposed method can not only reliably estimate the SMA parameters with comprehensive physical understanding but also accurately predict retention over a wide range of loading conditions.
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Schwaminger SP, Zimmermann I, Berensmeier S. Current research approaches in downstream processing of pharmaceutically relevant proteins. Curr Opin Biotechnol 2022; 77:102768. [PMID: 35930843 DOI: 10.1016/j.copbio.2022.102768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/04/2022] [Accepted: 07/12/2022] [Indexed: 11/03/2022]
Abstract
Biopharmaceuticals and their production are on the rise. They are needed to treat and to prevent multiple diseases. Therefore, an urgent need for process intensification in downstream processing (DSP) has been identified to produce biopharmaceuticals more efficiently. The DSP currently accounts for the majority of production costs of pharmaceutically relevant proteins. This short review gathers essential research over the past 3 years that addresses novel solutions to overcome this bottleneck. The overview includes promising studies in the fields of chromatography, aqueous two-phase systems, precipitation, crystallization, magnetic separation, and filtration for the purification of pharmaceutically relevant proteins.
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Affiliation(s)
- Sebastian P Schwaminger
- Division of Medicinal Chemistry, Otto Loewi Research Center, Medical University of Graz, Graz, Austria; Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, Garching, Germany.
| | - Ines Zimmermann
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, Garching, Germany
| | - Sonja Berensmeier
- Bioseparation Engineering Group, School of Engineering and Design, Technical University of Munich, Garching, Germany.
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Narayanan H, Luna M, Sokolov M, Butté A, Morbidelli M. Hybrid Models Based on Machine Learning and an Increasing Degree of Process Knowledge: Application to Cell Culture Processes. Ind Eng Chem Res 2022. [DOI: 10.1021/acs.iecr.1c04507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Harini Narayanan
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | - Martin Luna
- Institute of Chemical and Bioengineering, Department of Chemistry and Applied Biosciences, ETH Zurich, 8093 Zurich, Switzerland
| | | | | | - Massimo Morbidelli
- DataHow AG, Zürichstrasse 137, 8600 Dübendorf, Switzerland
- Dipartimento di Chimica, Materiali e Ingegneria Chimica, Giulio Natta, Politecnico di Milano, 20131 Milano, Italy
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Automation of Modeling and Calibration of Integrated Preparative Protein Chromatography Systems. Processes (Basel) 2022. [DOI: 10.3390/pr10050945] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
With the increasing global demand for precise and efficient pharmaceuticals and the biopharma industry moving towards Industry 4.0, the need for advanced process integration, automation, and modeling has increased as well. In this work, a method for automatic modeling and calibration of an integrated preparative chromatographic system for pharmaceutical development and production is presented. Based on a user-defined system description, a system model was automatically generated and then calibrated using a sequence of experiments. The system description and model was implemented in the Python-based preparative chromatography control software Orbit.
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