Artificial Intelligence (AI) in Vaccine Development
Vaccines have been saving lives for hundreds of years. Smallpox was eradicated globally in 1980, and the measles vaccine helped save an estimated 23.2 million lives between 2000 and 2018. Today, the COVID-19 pandemic is front and center, and vaccines from Moderna, Pfizer-BioNTech, AstraZeneca, and Johnson & Johnson are being administered globally. Behind the scenes, there is a growing trend toward using AI in vaccine development. AI is helping researchers develop vaccines faster, more accurately, and with fewer resources than ever before. In this article, we will explore how AI is revolutionizing vaccine development, the benefits and challenges of using AI models, the tools and technologies involved, and best practices for managing AI models in vaccine development.
How AI in Vaccine Development?
The inspiration for modern vaccines comes from the concept of inoculation, the process of infecting a person with a weaker or dead version of a pathogen to prevent infection from the virulent form. Historically, vaccines were made by exposing a pathogen (e.g., virus or bacteria) to a weakened or inactivated germ, modifying its structure to weaken its pathogenicity or by growing it in a culture medium for months. Later, researchers found that messenger RNA (mRNA) technology, which instructs cells on how to make proteins, could be used to develop vaccines for diseases such as COVID-19.
While these methods are effective, they are slow, expensive, and often fail to produce the desired vaccine. Enter AI. AI algorithms help scientists make better sense of data by identifying patterns to help researchers develop more precise vaccines faster.
How to Succeed in AI in Vaccine Development
To succeed in AI in vaccine development, researchers need to have an enormous amount of expertise in biology. They need to have a deep understanding of the virus, how it interacts with human cells, and how it can be weakened or eliminated. AI models can help by sorting through vast amounts of data to find patterns that the human brain may not recognize, but it cannot replace human expertise. In most cases, researchers are still required to validate the computer’s work, and the interpreted results must all be subject to human acceptance, interpretation and subject to implied biases.
The Benefits of AI in Vaccine Development
There are several benefits to using AI in vaccine development. Perhaps the most significant advantage is speeding up the vaccine development process. Early on in the pandemic, researchers and computational biologists ran millions of simulations of the COVID-19 virus before developing a model of the spike proteins, the key protein structure that allows the virus to infect human cells. In this way, scientists viewed hundreds of potential vaccine candidates in a virtual setting before testing a few selected vaccine candidates in rigorously designed clinical trials.
Another advantage is the potential to manipulate the virus’s genetic code to enhance its efficacy or increase its potency. In one example, researchers used AI to develop a new vaccine candidate for the H1N1 “”swine flu”” virus, increasing vaccine efficacy by 25%.
Challenges of AI in Vaccine Development and How to Overcome Them
Despite the numerous benefits of using AI in vaccine development, several challenges exist. One challenge is the cost of developing the AI models. High-powered computing resources, such as supercomputers, cloud computing platforms, and specialized software, can be expensive or hard to access, particularly for smaller labs, which may lead to teams relying on outdated software or hardware. This limitation could push smaller labs out of the scientific community and slow down the pace of discovery.
Another obstacle is the need to integrate AI into the current vaccine development landscape. From collaboration with drug companies, universities, and the government to strict quality control measures, vaccine development requires significant resource sharing and regulation. As AI makes rapid gains in specificity, we will be faced with the challenge of creating the policy and regulation required to ensure that AI is transparent, governable, and trusted—failures in such a realm could lead to a loss of life and damage to public health.
Tools and Technologies for Effective AI in Vaccine Development
The main tools and technologies used in AI vaccine development are machine learning algorithms, statistical methods, database integration, and bioinformatics. Combined with deep learning technology, algorithms can identify patterns, highlight potential vaccine candidates, and better predict the effects of each candidate. Alongside machine learning algorithms, sophisticated analytical tools are needed to analyze genome sequences and compute the proteins it produces.
Best Practices for Managing AI in Vaccine Development
It is essential to prioritize the review of the algorithm’s work by human experts in a vaccine development pipeline. Developers should have a thorough understanding of the limitations of the AI model and any potential biases to avoid erroneous results. To ensure that AI algorithms have been properly guided, the FDA released a new AI/ML-based Software-as-a-Medical-Device (SaMD) regulatory framework. The framework provides guidance on how to develop and maintain AI/ML models non-stop throughout their life-cycle by updating models as new data and information are gathered or inputs used to develop the model change.
Conclusion
AI is transforming the vaccine development landscape, making the process more efficient, cost-effective, and precise. The benefits are obvious, but researchers must tread carefully and continue to integrate with the traditional approaches of vaccine production. The fact is that without the human domain expertise that has always been crucial in vaccine development, AI would have obstacles in developing vaccines that are both practical and effective, and this is not something we can afford. Let’s continue to use AI to further innovation and efficiency, but let’s also recognize and embrace its limitations.