Prediction models for vaccine immunogenicity are mathematical and computational tools used to forecast how well a vaccine will provoke an immune response. These models take into account various factors, such as the type of antigen, the delivery method, and the characteristics of the immune system, to predict the likelihood that a vaccine will generate a protective immune response. Such models are essential for optimizing vaccine development, as they can help identify promising candidates early in the process, reducing the time and cost of trials. By integrating data from clinical trials, immunological studies, and genomic research, prediction models allow researchers to fine-tune vaccine formulations for maximum efficacy. In addition to predicting immunogenicity, these models can also be used to assess potential side effects or adverse reactions, helping ensure the safety of vaccines before they are approved for widespread use. They represent a crucial step in accelerating vaccine development and improving vaccine outcomes.
Khursheed Anwer
IMUNON, United States
Ping Xie
Rutgers University, United States
Regina Au
BioMarketing Insight, United States
Madhu Khanna
University of Delhi, India
Lara Isis Teodoro
Mayo Clinic, United States
Ahmed Abdulazeez
BHRUT Trust, United Kingdom
Elena Chiappini
University of Florence, Italy
Ayesha Zahid
Griffith University, Australia
Livia Daflon Silva
Federal University of State of Rio de Janeiro, Brazil
Neha Bhandari
Chitkara University, IndiaSubmit your abstract Today
Title : The importance of post-marketing surveillance and real-world data: For a product to be successful
Regina Au, BioMarketing Insight, United States
Title : A promising novel approach to DNA vaccines
Khursheed Anwer, IMUNON, United States
Title : Prophylactic and molecular approaches for mitigating human influenza A viruses: i. Evaluating influenza vaccine effectiveness in the older population ii. Down-regulation of influenza virus genes with novel sirna-chimeric-ribozyme constructs
Madhu Khanna, University of Delhi, India
Title : Homology analysis of MPXV and VACV peptides underscores the need to consider both MPXV clades for vaccine development
Lara Isis Teodoro, Mayo Clinic, United States
Title : Development of a platform UPLC-CAD method for high-throughput lipid quantitation and characterization in novel mRNA LNPs
Janet Muzulu, Sanofi, United States
Title : Commensal bacteria drive B-cell lymphomagenesis in the setting of innate immunodeficiency
Ping Xie, Rutgers University, United States
Title : High seroprevalence of RSV antibodies in adults indicates potential undetected transmission and requires further public health assessment
Lara Isis Teodoro, Mayo Clinic, United States
Title : Establishing a platform method for physical appearance assessment of new parenteral pharmaceuticals
Ying Wan, Merck & Co., United States
Title : Post COVID-19 syndrome is associated with sex and severity of first COVID-19 episode in Honduras
Manuel Antonio Sierra Santos, Central American Technological University, Honduras
Title : Evaluating the immunogenic impact of process impurities in mRNA vaccine production: Establishing integrated control strategies and specifications
Jesse Kuiper, Merck Research Laboratories, United States