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Kavianpour A, Hosseini SN, Ashjari M, Khatami M, Hosseini T, Soleimani H. Highly efficient strategy of lipopolysaccharide (LPS) decontamination from rHBsAg: synergistic effect of enhanced magnetic nanoparticles (MNPs) as an LPS affinity adsorbent (LAA) and surfactant as a dissociation factor. Prep Biochem Biotechnol 2024:1-10. [PMID: 39002143 DOI: 10.1080/10826068.2024.2377326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/15/2024]
Abstract
The interaction of lipopolysaccharide with a recombinant protein is a serious bottleneck, particularly in the purification step of bioprocessing. Recombinant hepatitis B surface antigen (rHBsAg), the active ingredient of the hepatitis B vaccine, is probably contaminated by extrinsic LPS like other biopharmaceuticals. This research intends to eliminate LPS from its mixture with rHBsAg efficiently. Immobilized polymyxin B on magnetic nanoparticles (PMB-MNPs) was synthesized and implemented as an enhanced LPS affinity adsorbent (LAA). The 20-80 EU/dose binary samples with and without surfactant were applied to PMB-MNPs. Formerly, dynamic light scattering (DLS) and transmission electron microscopy (TEM) were examined on the samples to qualitatively show the dissociation effect of the surfactant. Considering the high potential interaction of LPS with HBsAg, the dissociation effects of 0.5 and 1.5% Tween 20 on the binary samples were assessed using immunoaffinity chromatography (IAC) as a quantification tool. The dissociation effect of Tween 20 substantially diminished the interaction, leading to a proportional increase of free LPS up to 66%. The synergetic effect of Tween 20 and privileged LAA was highly effective in eliminating more than 80% of LPS with a remarkable LPS clearance factor of 5.8 and a substantial protein recovery rate of 97%.
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Affiliation(s)
- Alireza Kavianpour
- Department of Chemical Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran
| | - Seyed Nezamedin Hosseini
- Department of Hepatitis B Vaccine Production, Production and Research Complex, Pasteur Institute of Iran, Tehran, Iran
| | - Mohsen Ashjari
- Nanostructures and Bioresearch Lab, Faculty of Engineering, Department of Chemical Engineering, University of Kashan, Kashan, Iran
| | - Maryam Khatami
- Department of Hepatitis B Vaccine Production, Production and Research Complex, Pasteur Institute of Iran, Tehran, Iran
| | - Taravatsadat Hosseini
- Department of Chemical Engineering, Amirkabir University of Technology, Tehran, Iran
| | - Hosnsa Soleimani
- Department of Hepatitis B Vaccine Production, Production and Research Complex, Pasteur Institute of Iran, Tehran, Iran
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Rathore AS, Nikita S, Thakur G, Mishra S. Artificial intelligence and machine learning applications in biopharmaceutical manufacturing. Trends Biotechnol 2023; 41:497-510. [PMID: 36117026 DOI: 10.1016/j.tibtech.2022.08.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 07/08/2022] [Accepted: 08/22/2022] [Indexed: 11/30/2022]
Abstract
Artificial intelligence and machine learning (AI-ML) offer vast potential in optimal design, monitoring, and control of biopharmaceutical manufacturing. The driving forces for adoption of AI-ML techniques include the growing global demand for biotherapeutics and the shift toward Industry 4.0, spurring the rise of integrated process platforms and continuous processes that require intelligent, automated supervision. This review summarizes AI-ML applications in biopharmaceutical manufacturing, with a focus on the most used AI-ML algorithms, including multivariate data analysis, artificial neural networks, and reinforcement learning. Perspectives on the future growth of AI-ML applications in the area and the challenges of implementing these techniques at manufacturing scale are also presented.
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Affiliation(s)
- Anurag S Rathore
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India.
| | - Saxena Nikita
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Garima Thakur
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
| | - Somesh Mishra
- Department of Chemical Engineering, Indian Institute of Technology Delhi, New Delhi, India
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Mondal PP, Galodha A, Verma VK, Singh V, Show PL, Awasthi MK, Lall B, Anees S, Pollmann K, Jain R. Review on machine learning-based bioprocess optimization, monitoring, and control systems. BIORESOURCE TECHNOLOGY 2023; 370:128523. [PMID: 36565820 DOI: 10.1016/j.biortech.2022.128523] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 12/17/2022] [Accepted: 12/20/2022] [Indexed: 06/17/2023]
Abstract
Machine Learning is quickly becoming an impending game changer for transforming big data thrust from the bioprocessing industry into actionable output. However, the complex data set from bioprocess, lagging cyber-integrated sensor system, and issues with storage scalability limit machine learning real-time application. Hence, it is imperative to know the state of technology to address prevailing issues. This review first gives an insight into the basic understanding of the machine learning domain and discusses its complexities for more comprehensive applications. Followed by an outline of how relevant machine learning models are for statistical and logical analysis of the enormous datasets generated to control bioprocess operations. Then this review critically discusses the current knowledge, its limitations, and future aspects in different subfields of the bioprocessing industry. Further, this review discusses the prospects of adopting a hybrid method to dovetail different modeling strategies, cyber-networking, and integrated sensors to develop new digital biotechnologies.
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Affiliation(s)
- Partha Pratim Mondal
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Abhinav Galodha
- School of Interdisciplinary Research, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Vishal Kumar Verma
- Department of Biochemical Engineering and Biotechnology, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Vijai Singh
- Department of Biosciences, School of Science, Indrashil University, Rajpur, Mehsana, 382715, Gujarat, India
| | - Pau Loke Show
- Zhejiang Provincial Key Laboratory for Subtropical Water Environment and Marine Biological Resources Protection, Wenzhou University, Wenzhou 325035, China; Department of Sustainable Engineering, Saveetha School of Engineering, SIMATS, Chennai 602105, India; Department of Chemical and Environmental Engineering, University of Nottingham, Malaysia, 43500 Semenyih, Selangor Darul Ehsan, Malaysia
| | - Mukesh Kumar Awasthi
- College of Natural Resources and Environment, Northwest A&F University, Yangling, Shaanxi Province 712100, China
| | - Brejesh Lall
- Electrical Engineering Department, Indian Institute of Technology Delhi, Hauz-Khas, New Delhi 110016, India
| | - Sanya Anees
- Department of Electronics and Communication Engineering, Indian Institute of Information Technology Guwahati, Bongora, Guwahati 781015, India
| | - Katrin Pollmann
- Helmholtz-Zentrum Dresden-Rossendorf, Helmhholtz Institute Freiberg for Resource Technology, Bautzner Landstrasse 400, 01328 Dresden, Germany
| | - Rohan Jain
- Helmholtz-Zentrum Dresden-Rossendorf, Helmhholtz Institute Freiberg for Resource Technology, Bautzner Landstrasse 400, 01328 Dresden, Germany.
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Continuous fermentation of recombinant Pichia pastoris Mut+ producing HBsAg: Optimizing dilution rate and determining strain-specific parameters. FOOD AND BIOPRODUCTS PROCESSING 2019. [DOI: 10.1016/j.fbp.2019.09.011] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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