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Demirden SF, Kimiz-Gebologlu I, Oncel SS. Animal Cell Lines as Expression Platforms in Viral Vaccine Production: A Post Covid-19 Perspective. ACS OMEGA 2024; 9:16904-16926. [PMID: 38645343 PMCID: PMC11025085 DOI: 10.1021/acsomega.3c10484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2023] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/23/2024]
Abstract
Vaccines are considered the most effective tools for preventing diseases. In this sense, with the Covid-19 pandemic, the effects of which continue all over the world, humanity has once again remembered the importance of the vaccine. Also, with the various epidemic outbreaks that occurred previously, the development processes of effective vaccines against these viral pathogens have accelerated. By these efforts, many different new vaccine platforms have been approved for commercial use and have been introduced to the commercial landscape. In addition, innovations have been made in the production processes carried out with conventionally produced vaccine types to create a rapid response to prevent potential epidemics or pandemics. In this situation, various cell lines are being positioned at the center of the production processes of these new generation viral vaccines as expression platforms. Therefore, since the main goal is to produce a fast, safe, and effective vaccine to prevent the disease, in addition to existing expression systems, different cell lines that have not been used in vaccine production until now have been included in commercial production for the first time. In this review, first current viral vaccine types in clinical use today are described. Then, the reason for using cell lines, which are the expression platforms used in the production of these viral vaccines, and the general production processes of cell culture-based viral vaccines are mentioned. Also, selection parameters for animal cell lines as expression platforms in vaccine production are explained by considering bioprocess efficiency and current regulations. Finally, all different cell lines used in cell culture-based viral vaccine production and their properties are summarized, with an emphasis on the current and future status of cell cultures in industrial viral vaccine production.
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Affiliation(s)
| | | | - Suphi S. Oncel
- Ege University, Bioengineering Department, Izmir, 35100, Turkiye
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Ao D, He X, Liu J, Xu L. Strategies for the development and approval of COVID-19 vaccines and therapeutics in the post-pandemic period. Signal Transduct Target Ther 2023; 8:466. [PMID: 38129394 PMCID: PMC10739883 DOI: 10.1038/s41392-023-01724-w] [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: 08/28/2023] [Revised: 11/24/2023] [Accepted: 12/06/2023] [Indexed: 12/23/2023] Open
Abstract
The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in significant casualties and put immense strain on public health systems worldwide, leading to economic recession and social unrest. In response, various prevention and control strategies have been implemented globally, including vaccine and drug development and the promotion of preventive measures. Implementing these strategies has effectively curbed the transmission of the virus, reduced infection rates, and gradually restored normal social and economic activities. However, the mutations of SARS-CoV-2 have led to inevitable infections and reinfections, and the number of deaths continues to rise. Therefore, there is still a need to improve existing prevention and control strategies, mainly focusing on developing novel vaccines and drugs, expediting medical authorization processes, and keeping epidemic surveillance. These measures are crucial to combat the Coronavirus disease (COVID-19) pandemic and achieve sustained, long-term prevention, management, and disease control. Here, we summarized the characteristics of existing COVID-19 vaccines and drugs and suggested potential future directions for their development. Furthermore, we discussed the COVID-19-related policies implemented over the past years and presented some strategies for the future.
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Affiliation(s)
- Danyi Ao
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 610041, Sichuan, People's Republic of China
| | - Xuemei He
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 610041, Sichuan, People's Republic of China
| | - Jian Liu
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 610041, Sichuan, People's Republic of China
| | - Li Xu
- State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, 610041, Sichuan, People's Republic of China.
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Fu Y, Zeng L, Huang P, Liao M, Li J, Zhang M, Shi Q, Xia Z, Ning X, Mo J, Zhou Z, Li Z, Yuan J, Wang L, He Q, Wu Q, Liu L, Liao Y, Qiao K. Severity-onset prediction of COVID-19 via artificial-intelligence analysis of multivariate factors. Heliyon 2023; 9:e18764. [PMID: 37576285 PMCID: PMC10415884 DOI: 10.1016/j.heliyon.2023.e18764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 07/13/2023] [Accepted: 07/26/2023] [Indexed: 08/15/2023] Open
Abstract
Progression to a severe condition remains a major risk factor for the COVID-19 mortality. Robust models that predict the onset of severe COVID-19 are urgently required to support sensitive decisions regarding patients and their treatments. In this study, we developed a multivariate survival model based on early-stage CT images and other physiological indicators and biomarkers using artificial-intelligence analysis to assess the risk of severe COVID-19 onset. We retrospectively enrolled 338 adult patients admitted to a hospital in China (severity rate, 31.9%; mortality rate, 0.9%). The physiological and pathological characteristics of the patients with severe and non-severe outcomes were compared. Age, body mass index, fever symptoms upon admission, coexisting hypertension, and diabetes were the risk factors for severe progression. Compared with the non-severe group, the severe group demonstrated abnormalities in biomarkers indicating organ function, inflammatory responses, blood oxygen, and coagulation function at an early stage. In addition, by integrating the intuitive CT images, the multivariable survival model showed significantly improved performance in predicting the onset of severe disease (mean time-dependent area under the curve = 0.880). Multivariate survival models based on early-stage CT images and other physiological indicators and biomarkers have shown high potential for predicting the onset of severe COVID-19.
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Affiliation(s)
- Yu Fu
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Lijiao Zeng
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Pilai Huang
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Mingfeng Liao
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Jialu Li
- Department of Biostatistics, HuaJia Biomedical Intelligence, Shenzhen, China
| | - Mingxia Zhang
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Qinlang Shi
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Zhaohua Xia
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Xinzhong Ning
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Jiu Mo
- Department of Biostatistics, HuaJia Biomedical Intelligence, Shenzhen, China
| | - Ziyuan Zhou
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Zigang Li
- Pingshan Translational Medicine Center, Shenzhen Bay Laboratory, and State Key Laboratory of Chemical Oncogenomics, School of Chemical Biology and Biotechnology, Peking University Shenzhen Graduate School, Shenzhen, China
| | - Jing Yuan
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Lifei Wang
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Qing He
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Qikang Wu
- Department of Clinical Laboratory, The First People's Hospital of Foshan, Foshan, China
| | - Lei Liu
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
| | - Yuhui Liao
- Molecular Diagnosis and Treatment Center for Infectious Diseases, Dermatology Hospital, Southern Medical University, Guangzhou, China
| | - Kun Qiao
- Department of Infectious Diseases, Department of Thoracic Surgery, Department of Radiology, National Clinical Research Center for Infectious Disease, The Second Affiliated Hospital of Southern University of Science and Technology, Shenzhen Third People's Hospital, Shenzhen, China
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