Vaccines are vital for human health, a fact that has been highlighted by the recent coronavirus disease 2019 (COVID-19) pandemic. They are regarded as the safest and most cost-effective approach against disease and have changed the way we live our lives.
Vaccine development. Image Credit: Treecha/Shutterstock.com
Numerous life-threatening diseases have been seemingly eradicated in some parts of the globe thanks to successful vaccination programs. For example, the UK had not seen a natural case of polio since 1984, and was declared free of the disease in 2003 (this was until 2022 when traces of the virus were detected in sewage water and a national incident was declared).
Traditional vaccine models have been used for many decades, however, they have numerous limitations and drawbacks. Now, modern models that rely on computational modeling are replacing traditional systems to more rapidly and safely establish novel vaccines, such as those that altered the course of the COVID-19 pandemic.
Traditional vs modern models of vaccine development
Designing novel vaccines is complicated. The traditional process is incredibly time-consuming, usually lasting 10 to 15 years. It is also expensive, costing an average of $200 and $500 million. Finally, as more and more antigenically diverse pathogens are uncovered, traditional vaccine design approaches are falling by the wayside due to their irrelevance in targeting these types of pathogens.
This is due to the diversity of the pathogens, the lack of information about the pathogen/host interaction, the absence of a permissive cell line, and a limited number of successful animal models to guide research. These drawbacks have challenged teams developing vaccines for pathogens that cause severe diseases, such as smallpox, HIV-AIDS, and tuberculosis.
The limitations of the traditional approach to vaccine development have encouraged a new era of vaccine development, one that leverages recombinant DNA technology, structural biology, rational vaccinology, conjugate vaccines, epitope-based vaccine design and next-generation technology. With this new technology, vaccine development is considered inexpensive compared with traditional development, while retaining its safety and efficacy levels.
In recent years, many in silico tools have been established for the development of immunotherapies as well as peptide-based drug discovery. The in silico approach in biology refers to the use of computational models and simulations applied to complex biological phenomena. In vaccine development, computational models are being used to improve and speed up the process, helping to establish safe and effective novel vaccines to prevent life-threatening diseases. Scientists believe that computational modeling in vaccine development will be fundamental to developing vaccines for diseases such as malaria, HIV-AIDS, and tuberculosis.
How computational modeling revolutionized the field of vaccine development during the COVID-19 pandemic
The COVID-19 pandemic illustrated the vital importance of establishing safe and effective vaccines against disease. Before the establishment of approved vaccines and national vaccine rollout programs, countries around the globe we affected by restrictions that changed the face of everyday life.
Entire industries were shut down, travel was limited, supply chains were disrupted, education was stalled, and people were prevented from seeing friends and family. Vaccine rollouts allowed these restrictions to be eased, as the threat of spreading the disease further reduced as more people were vaccinated. Computational modeling was instrumental in the unprecedented speed at which COVID-19 vaccines were developed.
While computational modeling systems have been used for many years by pharmaceutical companies to fine-tune drug dosing, it was the development of COVID-19 vaccines that really shone a light on their capabilities.
Computer modeling. Image Credit: Marcin Janiec/Shutterstock.com
Within six months of the first identification of the SARS-CoV-2 virus, scientists had already begun investigating vaccine candidates in clinical trials. Traditionally, this process had been incredibly time-consuming. The Ebola virus vaccine passed through clinical trials in a record-breaking five years, however, it took around ten years in pre-clinical development. The reason that COVID-19 researchers could identify vaccine targets within hours of the SARS-CoV-2 sequence being posted is that they were leveraging computational immunology.
The immune system is complicated, over millennia it has developed sophisticated mechanisms of defense to protect itself against invading pathogens. These systems are so complex that even today, scientists do not fully understand how it works. This is why many scientists have begun to turn to computational modeling to predict which parts of a particular pathogen may be recognized by the immune system’s B cells and T cells.
Such models allow scientists to rapidly identify vaccine targets from a genetic sequence, cutting out the need for years of preclinical research. Scientists believe that the mathematical models, statistical approaches, and machine learning techniques adopted by computer modeling methods are absolutely vital to understanding the complexities of the interactions between pathogens and the immune system.
In the future, it is likely that computational models will replace traditional vaccine development techniques. As machine learning and mathematical models become even more sophisticated, it is predicted that the field of immunology will greatly benefit from the development of next-generation tools that will allow for the rapid development of safe and effective vaccines.
References
- Arnold, C., 2020. How computational immunology changed the face of COVID-19 vaccine development. Nature Medicine. https://www.nature.com/articles/d41591-020-00027-9
- Dolgin, E., 2022. Could computer models be the key to better COVID vaccines?. Nature, 604(7904), pp.22-25. https://www.nature.com/articles/d41586-022-00924-8
- Guglielmi, G., 2022. What polio’s UK presence means for global health. Nature, 607(7918), pp.225-225. https://www.nature.com/articles/d41586-022-01802-z
- Sadria, M. and Layton, A., 2021. Modeling within-Host SARS-CoV-2 Infection Dynamics and Potential Treatments. Viruses, 13(6), p.1141. https://www.mdpi.com/1999-4915/13/6/1141
- Sunita, Sajid, A., Singh, Y. and Shukla, P., 2019. Computational tools for modern vaccine development. Human Vaccines & Immunotherapeutics, 16(3), pp.723-735. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7227725/
Further Reading
Public trust in COVID-19 vaccine science influences vaccine uptake in the US