Math models used to predict most effective new RSV vaccine

In a first, scientists used computer simulations to identify the vaccines most likely to be effective against respiratory syncytial virus (RSV), the most common cause of infant severe pneumonia worldwide.

 

Although there is no vaccine yet available for RSV, a viral infection that annually kills up to 200,000 children under five globally, a study published today in Vaccine suggested that the most effective vaccine would be one that stops RSV from spreading in the general population rather than one that completely prevented disease in RSV-infected individuals.

 

“This approach radically alters the way we decide which promising vaccine to develop. Choosing which new vaccines to develop from many possible candidates is an expensive process. As using mathematical modelling helps do that more efficiently, we expect that the pharmaceutical industry will use this approach more and more in the future,” says study leader Prof. Lisa White, of the University of Oxford, and Head of Mathematical and Economic MODelling (MAEMOD) at the Mahidol Oxford Tropical Medicine Research Unit (MORU) in Bangkok, Thailand.

 

“When we used mathematical modelling simulations to find the best choices among candidate anti-RSV vaccines, we were surprised to find that the most effective vaccines would not provide solid immunity to reinfection but would reduce the infectiousness of infected individuals, thereby protecting the community at large by reducing the amount of virus in circulation,” explained study co-author Dr Wirichada Pan-Ngum, Deputy Head of Mathematical Modelling at MORU.

 

The study, a collaboration between researchers linked to the universities of Oxford, Warwick and Manchester in the UK, Kenya and Mahidol University, Thailand, in partnership with global vaccine developer GSK-Biologicals, Belgium, examined which properties RSV vaccines under development would need to have to be most effective in preventing RSV in young children.

 

The researchers were linked by a new network of mathematical modelers based in the Tropics (TDMODNET). The network is a highly innovative environment which nurtures talented mathematicians from Asia and Africa. “We have proven that true world-class innovation can come from a South-South collaboration of scientists,” said Dr Tim Kinyanjui, study co-author formerly in KEMRI and now in the School of Mathematics, University of Manchester, and Manchester M13 9PL, United Kingdom.

 

Unlike vaccines that currently control common childhood diseases, new vaccines must target diseases with complex and poorly understood immunity. These diseases nevertheless cause a huge amount of suffering and death.

 

“RSV is the most important cause of infant severe lower respiratory tract disease worldwide, estimated to be responsible for 3 million hospital admissions annually. Occurring in seasonal outbreaks, RSV causes an inflammatory immune response and that constricts airflow, with many children developing pneumonia or bronchiolitis,” says study co-author and major RSV researcher Prof. James Nokes, of the University of Warwick and Kenya Medical Research Institute (KEMRI)/Wellcome Trust in Kilifi, Kenya.

 

“New vaccines demand new development pathways and this research is the first to use computer simulation to support the process going a long way to demonstrate that our researchers are in the lead in thinking of innovative ways of improving the effectiveness of our vaccines,” Said Dr. Gerald Mkoji the Ag. Director of KEMRI

Please contact cmauncho@kemri-wellcome.org for more information.