Robust and Scalable GMP Manufacturing of Chondrocyte Cell Therapy for Cartilage Regeneration
By Vinayak Virupaksh Kedage, Rajesh Pratap Singh, Parvathi Chandran, Vidya Jadhav, Sujata Shinde, and Satyen Sanghavi
Volume 20, Open Access (Nov 2021)
Cell therapy has emerged as a promising technology that involves implanting live cells to replace/repair and restore normal function of damaged tissue. Autologous chondrocyte implantation (ACI) has been proven effective for the regeneration of articular cartilage in defective cartilage tissue. The process starts with the collection of healthy tissue from an eligible patient, then isolation and expansion of desired cells in vitro under good manufacturing practice (GMP) conditions, qualification before release of the final cell product, and finally, implantation into the patient. The promise to deliver autologous cell therapies has its own challenges in robust and reproducible manufacturing. To commercialize a cell therapy, it is imperative that a robust and scalable manufacturing process is set up that is consistent, in terms of quality and quantity, in order to deliver the intended therapeutic effect.
We analysed the manufacturing parameters of over 100 cartilage samples that were used to deliver our proprietary, commercialized autologous cell therapy. The paper addresses the most cited challenges in the manufacturing of autologous cell therapies and describes a robust process of in vitro human chondrocyte cell culture. Also included are key factors in manufacturing for attaining a high-quantity and quality product for articular cartilage regeneration.
Citation:
Kedage VV, Singh RP, Chandran P, Jadhav V, Shinde S, Sanghavi S. Robust and scalable GMP manufacturing of chondrocyte cell therapy for cartilage regeneration. BioProcess J, 2021; 20.
https://doi.org/10.12665/J20OA.Kedage
Posted online November 1, 2021.
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Optimization of a Conventional Glycosylation Analytical Method Using Machine Learning and Experimental Design
By Eliza Yeung and Philip Ramsey
Volume 20, Open Access (Oct 2021)
Glycosylation is one of the most common post-translational modifications in mammalian-expressed biologics, and is considered to be a critical quality attribute of therapeutic glycoproteins. Due to its biological relevance, physiochemical assessment on the glycosylation profile is always important to the success of a drug development initiative. This article describes the combination of experimental design and machine learning techniques applied to characterize and optimize a conventional, non-derivatized glycoprofiling method on glycans derived from a human immunoglobulin using high-performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD). Two independent experimental designs, a 16-run definitive screening design (DSD) and a 28-run central composite design (CCD), were incorporated with a machine learning technique known as “self-validating ensemble modeling (SVEM)” and used to build predictive models for four chromatographic responses. We show that the predictive models created using SVEM on the DSD data reliably predicted the behavior of the chosen responses when applied to CCD validation data. This demonstrates that the DSD is an efficient alternative to the larger, traditional CCD in which the combination of experimental design and machine learning can effectively characterize and optimize analytical methods.
Citation:
Yeung E, Ramsey P. Optimization of a conventional glycosylation analytical method using machine learning and experimental design. BioProcess J, 2021; 20.
https://doi.org/10.12665/J20OA.Yeung
Posted online October 15, 2021.
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Separation of Empty and Full Adeno-Associated Virus Capsids from a Weak Anion Exchanger by Elution with an Ascending pH Gradient at Low Ionic Strength
By Pete Gagnon, Blaž Goričar, Sara Drmota Prebil, Hana Jug, Maja Leskovec, and Aleš Štrancar
Volume 20, Open Access (Oct 2021)
Separation of empty and full AAV8 capsids was achieved during their elution from a weak anion exchanger with an ascending pH gradient at low conductivity. Experimental data suggest elution was mediated by loss of positive charge from the exchanger. The method produced a full capsid peak with fewer empty capsids than elution of a strong anion exchanger with a salt gradient. Elution of the weak exchanger by sodium chloride gradients or by pH gradients in the presence of sodium chloride gave inferior separation performance. Pre-elution of empty capsids with a pH step allowed full capsids to be eluted by salt without compromising separation. Loading at intermediate pH prevented empty capsid binding and enabled step elution of full capsids in a physiological buffer environment.
Citation:
Gagnon P, Goričar B, Drmota Prebil S, Jug H, Leskovec M, Štrancar A. Separation of empty and full adeno-associated virus capsids from a weak anion exchanger by elution with an ascending pH gradient at low ionic strength. BioProcess J, 2021; 20.
https://doi.org/10.12665/J20OA.Gagnon
Posted online October 11, 2021.
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A Novel, Risk-based Approach for Predicting the Optimum Set of Process and Cell Culture Parameters for Scaling Upstream Bioprocessing
By Adrian Stacey, Jochen Scholz, and Sinyee Yau-Rose
Volume 20, Open Access (September 2021)
The ability to scale a cell culture effectively and efficiently, from lab to manufacturing, is critical to maximizing productivity whilst minimizing the risk of run failures and delays that can cost millions of dollars per month. The task of scaling well, however, is still considered to be a challenge by many upstream scientists, and this can be an exercise in trial and error. Traditionally, scaling has most often been performed using arithmetic in a spreadsheet and/or simple “back of an envelope” calculations. For some, it may even come in the form of support from a team of data scientists using advanced analytical software. This dependency on what some consider to be complex mathematics or statistics has resulted in the common consideration of using just one scaling parameter at a time, one scale at a time.
However, it is difficult to determine easily or optimally, from the start, whether a process successfully transfers across scales based on only one process parameter, at one scale. In this article, we describe the benefits of using a risk-based approach to scaling, and the development of a software scaling tool known as BioPAT® Process Insights for predictive scale conversion across different bioreactor scales. BioPAT Process Insights can be used to consider multiple parameters and across multiple scales simultaneously, from the start of a scaling workflow. We briefly describe how it was used in a proof-of-concept scale-up study to allow a faster, more cost-effective process transfer from 250 mL to 2000 L. In summary, using BioPAT Process Insights, in conjunction with a bioreactor range that has comparable geometry and physical similarities across scales, has the potential to help biopharma manufacturing facilities reach 2000 L production-scale volumes with fewer process transfer steps, saving both time and money during scale-up of biologics and vaccines.
Citation:
Stacey A, Scholz J, Yau-Rose S. A novel, risk-based approach for predicting the optimum set of process and cell culture parameters for scaling upstream bioprocessing. BioProcess J, 2021; 20.
https://doi.org/10.12665/J20OA.Stacey
Posted online September 28, 2021.
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Monoclonal and Polyclonal Antibodies as Biological Reagents for SARS-CoV-2 Diagnosis Through Nucleocapsid Protein Detection
by Daily Hernández, Cristina García, Marcos González, Hilda Garay, David Diago, Luis Guzmán, Williams Ferro, Mayté Quintana, Leonardo Gómez, Bárbara Chávez, Virginia Capó, Hasel Aragón, Amalia Hernández, Samy Puertas, Pedro Puente, Regla Somoza, Grechen Menéndez, Sigifredo Padilla, Israel Borrajero, and Rodolfo Valdés
Volume 20, Open Access (June 2021)
SARS-CoV-2 is an enveloped, positive-strand RNA virus that contains four structural proteins: spike, envelope, membrane, and nucleocapsid (N-protein). The N-protein participates in virus RNA packaging and particle release, is conserved within SARS-CoV-2 isolates, is highly immunogenic, and is abundantly expressed during SARS-CoV-2 infection. For these reasons, the N-protein could be used as a marker for detecting SARS-CoV-2 in early infection when antibodies against SARS-CoV-2 have not been produced yet. This paper describes the production and characterization of mouse monoclonal antibodies (mAb) and rabbit polyclonal antibodies (pAb) specific for the M20P19 peptide (N-protein linear epitope) for detection purposes. For this study, B-cell hybridomas were generated from mice independently immunized with two different M20P19 peptide-carrier protein conjugates: (1) meningococcal protein P64K; and (2) the keyhole limpet hemocyanin (KLH). Rabbits were also independently immunized with these two immunogens. Study results demonstrated that the M20P19 peptide was very immunogenic in mice and rabbits, and both mAb and pAb specifically recognized the non-conjugated M20P19 peptide, conjugated M20P19 peptide, and N-protein with high affinity and specificity, which could allow SARS-CoV-2 detection by different analytical techniques. This study corroborated that specific and high affinity constant mAb and pAb against the M20P19 peptide can be used as biological reagents for specific and rapid SARS-CoV-2 detection, mainly in tissue samples.
Citation:
Hernández D et al. Monoclonal and polyclonal antibodies as biological reagents for SARS-CoV-2 diagnosis through nucleocapsid protein detection. BioProcess J, 2021; 20.
https://doi.org/10.12665/J20OA.Hernandez
Posted online June 23, 2021.
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Demonstrating the Equivalence of Traditional Versus Automated Buffer Preparation Methods Using In-Line Conditioning Control Modes to Manage Incoming Stock Solution Variability
by Karolina Busson, Robbie Kamperveen, and Enrique Carredano
Volume 20, Open Access (Apr 2021)
In-line conditioning (IC) is a form of dilution where a process buffer is formulated in-line from concentrated stock solutions of acids, bases, and salts that are mixed with the correct amount of water-for injection (WFI). This new buffer preparation strategy must prove its equivalency to buffers made the traditional way (i.e., weighing salts, stirring in water, titrating with acid or base). In this paper, such a demonstration is presented using two control modes: (1) ratio control with flow feedback; and (2) pH/conductivity feedback. To obtain the necessary parameters for an error propagation analysis, a robustness study has been performed. Our analysis showed that with low incoming variability, or when the uncertainty of the stock solutions is below 2%, the two modes of control give comparable performance. When the uncertainty increases, so does the uncertainty of ratio control with flow feedback, more with respect to conductivity than pH, while the precision of pH/conductivity feedback remains at the same level. The choice of control should therefore take into consideration the critical process parameters, their tolerances, and the input variability in the stock solution concentration. In situations where there are higher variabilities in stock solution concentrations or process temperatures, this study suggests that pH/conductivity feedback might be a better option.
Citation:
Busson K, Kamperveen R, Carredano E. Demonstrating the equivalence of traditional versus automated buffer preparation methods using in-line conditioning control codes to manage incoming stock solution variability. BioProcess J, 2021; 20.
https://doi.org/10.12665/J20OA.Busson
Posted online April 27, 2021.
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