HONG KONG.- Timely evaluation of COVID-19 vaccine effectiveness (VE) is desperately needed to inform and update vaccine design as novel genetic variants continually emerge. A research team led by Professor Maggie Haitian Wang and Professor Benny Chung Ying Zee, both from The Jockey Club School of Public Health and Primary Care at The Chinese University of Hong Kong’sFaculty of Medicine (CU Medicine), has developed a computational approach that can rapidly predict the protective effects of COVID-19 vaccines by analysing genetic distance (GD). The research findings have been published in the renowned journal Nature Medicine.

Genetic distance on the receptor-binding domain of spike protein is highly predictive of vaccine protection
Based on nearly two million SARS-CoV-2 sequences and 49 clinical trials and observational studies, researchers from The Jockey Club School of Public Health and Primary Care at CU Medicine have recently developed new algorithms that can be used to rapidly evaluate the VE of different types of vaccines against symptomatic COVID-19 infection. They found that the GD between the receptor-binding domain of the spike protein of the circulating viruses and the vaccine strain is highly predictive of vaccine protection. Their method demonstrated 95% VE prediction accuracy using genome analysis, validated on an independent dataset.

Traditionally, VE can only be achieved after people have been vaccinated and a portion of the population has been infected. After the emergence of new variants, scientists must redesign and repeat studies that observe vaccine performance. The innovative algorithms enable in silico real-time prediction of vaccine protection against novel variants through virus sequencing data. This approach can be applied to design vaccines with optimal estimated effectiveness, and improve vaccine clinical trial design and the evaluation of vaccines before they are deployed.

Technology facilitates selection of candidate vaccine antigen for optimal protection
Professor Benny Chung Ying Zee, Director of the Centre for Clinical Research and Biostatistics, from The Jockey Club School of Public Health and Primary Care at CU Medicine, remarked, “The development of in silico algorithms to rapidly evaluate VE is of huge significance to public health, as it can provide a snapshot of vaccine protection before mass vaccination and infection. Vaccine manufacturers can use this technology to select candidate vaccine antigens and inform clinical trial design. Healthcare workers and policymakers can estimate the scale of an upcoming epidemic caused by new variants with information about the predicted VE.”

Professor Maggie Haitian Wang, Associate Professor also from The Jockey Club School of Public Health and Primary Care at CU Medicine, added, “The algorithms presented in the paper can offer timely updates on the expected effectiveness of all types of COVID-19 vaccine. The VE-GD framework can be used to determine the optimal vaccine compositions that provide the maximum protection against circulating viruses. This can facilitate the design of high-efficacy vaccines against COVID-19, flu and other pathogens.”