@article{Ghosh_D-2021_161399,
title = {State-of-the-art preclinical evaluation of COVID-19 vaccine candidates},
author = {Ghosh, D. and Bai, B. and Ji, Q. and Palliyil, S. and Yang, G. and Kumar, A. and Saxena, A.},
month = {dec},
year = {2021},
abstract = {The coronavirus disease 2019 (COVID-19) results from the infection of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and primarily affects the respiratory tissue. Since first reported from Wuhan, China in December 2019, the virus has resulted in an unprecedented pandemic. Vaccination against SARS-CoV-2 can control the further spread of the ongoing pandemic by making people immunised to SARS-CoV-2. Several vaccines have been approved for use in clinics, a lot many are in different stages of development. Diligent interpretations from the preclinical evaluation are crucial to identify the most effective and safest vaccine candidates. Multiple vaccine candidates/variants have been tested in small animal models with relative ease and further in non-human primate models before being taken into clinical development. Here, we review the state-of-the-art strategies employed for a thorough preclinical evaluation of COVID-19 vaccine candidates. We summarise the methods in place to identify indicators which make the vaccine candidate effective in controlling SARS-CoV-2 infection and/or COVID-19 and are safe for administration as inferred by their (1) biophysical/functional attributes (antigen expression, organization, functionality, and stability); (2) immunogenicity in animal models and protective correlates [SARS-CoV-2 specific binding/neutralising immunoglobulin titer, B/T-cell profiling, balanced T-helper type-1 (Th1) or type-2 (Th2) response (Th1:Th2), and anamnestic response]; (3) protective correlates as interpreted by controlled pathology of the respiratory tissue (pulmonary clinical and immunopathology); and finally, (4) strategies to monitor adverse effects of the vaccine candidates.},
pages = {440-460},
volume = {1},
journal = {Exploration of Immunology},
publisher = {Open Exploration Publishing Inc},
url = {https://doi.org/10.37349/ei.2021.00030},
}
