Safe and effective vaccines are a critical component to establishing a robust response to combat current, re-emerging, or emerging pathogens. However, vaccine design, testing, and manufacturing are complex and expensive activities with a high rate of failure. The COVID-19 pandemic showed both the broad range of strategies that created effective vaccines and the value of systematic investments prior to the occurrence of a pandemic. Artificial intelligence (AI) and computational biology techniques provide an opportunity to optimize and streamline this process, but require:
• extensive and relevant data sets
• inclusion of both positive and negative results
• standardization of experimental protocols
Advancements in both computational and experimental approaches in immunology and vaccinology are required to address these gaps.
This Research Topic aims to address the lack of data and standardization in the field of vaccine development that may leverage advancements in biotechnology for rapid assessment of vaccine production for pandemic preparedness. We aim to present a comprehensive collection of articles that fill gaps in our understanding of the successes and failures of various vaccine platforms to optimize and systematize vaccine development.
We invite authors to submit original research articles, systematic reviews, methods, reviews, mini reviews, policy & practice reviews, perspectives, protocols, clinical trials, conceptual analyses, data reports (including negative data), brief research reports (including negative data), and opinions related to the following themes:
• Computational or experimental methods for evaluating vaccine efficacy
• Elucidation of immunological correlates of protection
• Approaches for comparing and understanding immune responses
• Evaluations of vaccine platform technologies including (but not limited to) viral vector, nucleic acid, subunit, protein conjugate, and nanoparticle vaccines
• Lessons learned and negative data relating to vaccine platform technologies or vaccine development for a specific pathogen
• Strategies, best practices, and protocols for standardizing vaccine evaluations
• Development and application of in vitro assays, including microphysiological systems and organs on a chip, for vaccine screening
Manuscripts presenting negative data that describe lessons learned from failed vaccine development efforts at various stages (pre-clinical through clinical trials) are highly encouraged in this Research Topic.
Dr. Evan Skowronski is CSO of TMG Biosciences, which focuses on technology integration and management consulting for biotechnology and global disease surveillance and public health programs. The other Topic Editors declare no competing interests with relation to the topic theme.
Safe and effective vaccines are a critical component to establishing a robust response to combat current, re-emerging, or emerging pathogens. However, vaccine design, testing, and manufacturing are complex and expensive activities with a high rate of failure. The COVID-19 pandemic showed both the broad range of strategies that created effective vaccines and the value of systematic investments prior to the occurrence of a pandemic. Artificial intelligence (AI) and computational biology techniques provide an opportunity to optimize and streamline this process, but require:
• extensive and relevant data sets
• inclusion of both positive and negative results
• standardization of experimental protocols
Advancements in both computational and experimental approaches in immunology and vaccinology are required to address these gaps.
This Research Topic aims to address the lack of data and standardization in the field of vaccine development that may leverage advancements in biotechnology for rapid assessment of vaccine production for pandemic preparedness. We aim to present a comprehensive collection of articles that fill gaps in our understanding of the successes and failures of various vaccine platforms to optimize and systematize vaccine development.
We invite authors to submit original research articles, systematic reviews, methods, reviews, mini reviews, policy & practice reviews, perspectives, protocols, clinical trials, conceptual analyses, data reports (including negative data), brief research reports (including negative data), and opinions related to the following themes:
• Computational or experimental methods for evaluating vaccine efficacy
• Elucidation of immunological correlates of protection
• Approaches for comparing and understanding immune responses
• Evaluations of vaccine platform technologies including (but not limited to) viral vector, nucleic acid, subunit, protein conjugate, and nanoparticle vaccines
• Lessons learned and negative data relating to vaccine platform technologies or vaccine development for a specific pathogen
• Strategies, best practices, and protocols for standardizing vaccine evaluations
• Development and application of in vitro assays, including microphysiological systems and organs on a chip, for vaccine screening
Manuscripts presenting negative data that describe lessons learned from failed vaccine development efforts at various stages (pre-clinical through clinical trials) are highly encouraged in this Research Topic.
Dr. Evan Skowronski is CSO of TMG Biosciences, which focuses on technology integration and management consulting for biotechnology and global disease surveillance and public health programs. The other Topic Editors declare no competing interests with relation to the topic theme.