Recent advances to accelerate purification process development: A review with a focus on vaccines

Keulen, Daphne; Geldhof, Geoffroy; Bussy, Olivier Le; Pabst, Martin; Ottens, Marcel

doi:10.1016/j.chroma.2022.463195

Cited by 15 publications

(9 citation statements)

References 112 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Currently, there are several commercially available chromatographic mechanistic models software, such as GoSilico (now part of Cytivia, formerly known as ChromX), Aspen Chromatography, DelftChrom, CADET, and ChromaTech. While equilibrium and binding capabilities of membrane chromatography are typically restricted, membrane chromatography surpasses conventional packed bed chromatography in terms of productivity and bed utilization at high flow rates and short residence times [32].…”

Section: Evolution Of Ai In Chromatographymentioning

confidence: 99%

Automation in Analytical Chemistry: The Role of Ai in Chromatography

KALPANA,

PRASAD

2024

Int J App Pharm

View full text Add to dashboard Cite

Artificial Intelligence (AI) has facilitated significant breakthroughs in drug discovery, the design of materials, and organic synthesis. The advancements in the latter group are especially remarkable due to the abilities of the latest computational methods (molecular design algorithms) that enable the exploration of extensive chemical spaces and enhance research in fields such as predicting molecule properties, designing molecules, retrosynthesis, predicting reaction conditions, and predicting reaction outcomes. A literary review was conducted following PRISMA guidelines. This study aimed to review existing data on the application of AI in separation chromatography. The evolution and utilization of AI in the pharmaceutical industry and its future aspects were articulated in this study. The utilization of AI can completely transform the field of chromatography analysis by facilitating expedited, more precise, and more effective data processing. By automating chromatography analysis, AI can enhance efficiency and minimize the potential for human mistakes. This advancement enables scientists to dedicate their efforts towards addressing intricate and demanding analytical issues. With the evolution of technology and the increasing adoption, we can anticipate more progress in chromatography analysis and analytical chemistry.

show abstract

Section: Evolution Of Ai In Chromatographymentioning

confidence: 99%

Automation in Analytical Chemistry: The Role of Ai in Chromatography

KALPANA,

PRASAD

2024

Int J App Pharm

View full text Add to dashboard Cite

show abstract

“…To attain sufficient purity, chromatography is a method of choice due to its specificity and versatility. [ 2–4 ] However, the vast variety of commercially available resin types (e.g., ion exchange (IEX) or hydrophobic interaction chromatography (HIC)) and experimental conditions (e.g., salt concentrations, buffers, and pH) results in extensive experimental screening to obtain optimal separation conditions, driving both cost and development time. In‐silico preselection of resins and conditions prior to experimentation would allow a decrease in costs and development time by narrowing the empirical screening space.…”

Section: Introductionmentioning

confidence: 99%

Predicting protein retention in ion‐exchange chromatography using an open source QSPR workflow

Neijenhuis,

Le Bussy,

Geldhof

et al. 2024

Biotechnology Journal

Self Cite

View full text Add to dashboard Cite

Protein‐based biopharmaceuticals require high purity before final formulation to ensure product safety, making process development time consuming. Implementation of computational approaches at the initial stages of process development offers a significant reduction in development efforts. By preselecting process conditions, experimental screening can be limited to only a subset. One such computational selection approach is the application of Quantitative Structure Property Relationship (QSPR) models that describe the properties exploited during purification. This work presents a novel open‐source Python tool capable of extracting a range of features from protein 3D models on a local computer allowing total transparency of the calculations. As open‐source tool, it also impacts initial investments in constructing a QSPR workflow for protein property prediction for third parties, making it widely applicable within the field of bioprocess development. The focus of current calculated molecular features is projection onto the protein surface by constructing surface grid representations. Linear regression models were trained with the calculated features to predict chromatographic retention times/volumes. Model validation shows a high accuracy for anion and cation exchange chromatography data (cross‐validated R2 of 0.87 and 0.95). Hence, these models demonstrate the potential of the use of QSPR to accelerate process design.

show abstract

“…In vaccine production, downstream purification processes are pivotal [ 9 ]. These processes involve a series of steps designed to separate and refine intermediate products generated in the earlier stages [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Robustness Verification of Downstream Production Process for Inactivated SARS-CoV-2 Vaccine and Different Chromatography Medium Purification Effects

Pang,

Guo,

Hao

et al. 2024

Vaccines

View full text Add to dashboard Cite

Background: Large-scale vaccine production requires downstream processing that focuses on robustness, efficiency, and cost-effectiveness. Methods: To assess the robustness of the current vaccine production process, three batches of COVID-19 Omicron BA.1 strain hydrolytic concentrated solutions were selected. Four gel filtration chromatography media (Chromstar 6FF, Singarose FF, Bestarose 6B, and Focurose 6FF) and four ion exchange chromatography media (Maxtar Q, Q Singarose, Diamond Q, and Q Focurose) were used to evaluate their impact on vaccine purification. The quality of the vaccine was assessed by analyzing total protein content, antigen content, residual Vero cell DNA, residual Vero cell protein, and residual bovine serum albumin (BSA). Antigen recovery rate and specific activity were also calculated. Statistical analysis was conducted to evaluate process robustness and the purification effects of the chromatography media. Results: The statistical analysis revealed no significant differences in antigen recovery (p = 0.10), Vero HCP residue (p = 0.59), Vero DNA residue (p = 0.28), and BSA residue (p = 0.97) among the three batches of hydrolytic concentrated solutions processed according to the current method. However, a significant difference (p < 0.001) was observed in antigen content. Conclusions: The study demonstrated the remarkable robustness of the current downstream process for producing WIBP-CorV vaccines. This process can adapt to different batches of hydrolytic concentrated solutions and various chromatography media. The research is crucial for the production of inactivated SARS-CoV-2 vaccines and provides a potential template for purifying other viruses.

show abstract

Recent advances to accelerate purification process development: A review with a focus on vaccines

Cited by 15 publications

References 112 publications

Automation in Analytical Chemistry: The Role of Ai in Chromatography

Automation in Analytical Chemistry: The Role of Ai in Chromatography

Predicting protein retention in ion‐exchange chromatography using an open source QSPR workflow

Evaluation of the Robustness Verification of Downstream Production Process for Inactivated SARS-CoV-2 Vaccine and Different Chromatography Medium Purification Effects

Contact Info

Product

Resources

About