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Real-Time Bioinformatics Prediction Platform from CUHK

Image Credits: CUHK, Press Release

The rapid evaluation of COVID-19 vaccine effectiveness (VE) is an urgent need in order to inform and update vaccine design as new genetic variants of the virus continue to 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’s (CUHK) Faculty 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 roughly 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 recently developed new algorithms that can be used to rapidly evaluate the VE of different types of vaccines against symptomatic COVID-19 infection.

They discovered that the genetic distance between the receptor-binding domain of the spike protein of the circulating viruses and the vaccine strain is highly predictive of vaccine protection. Their research 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 are required to redesign and repeat studies that observe vaccine performance.

The innovative algorithms allow for in silico real-time prediction of vaccine protection against novel variants through virus sequencing data. This approach applies to designing vaccines with optimal estimated effectiveness and improving vaccine clinical trial design and the evaluation of vaccines before they are deployed.

Technology facilitates the selection of candidate vaccine antigens 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 stated that 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.

He noted that vaccine manufacturers can use this technology to select candidate vaccine antigens and inform clinical trial design. Healthcare workers and policymakers will also be able to 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 noted that 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.

The global vaccines market is projected to reach US$67.2 billion by 2026, at a CAGR of 10.2%. The global vaccines market (including COVID-19 vaccines) is projected to reach US$149.2 billion by 2026 during the forecast period.

There is increasing government support for and focus on the development of vaccines and immunisation strategies, as well as the growing prevalence of infectious diseases. These factors drive the vaccine market. However, for the development of vaccines the low purchasing power and huge capital may restrain the market growth in developing countries.

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