AI in vaccine development and rollout

How the technology helps pharma companies speed up vaccine design and handle administration

Oct 4, 2022

This article describes use cases and tools that AI healthcare companies and research teams built to facilitate vaccine design, speed up trials, predict mutations, prioritize patients, and address vaccine hesitancy.

Developing a vaccine is an expensive endeavor. It can amount to $500 million [1] starting from the research phase to vaccine registration, and the failure rate is high. The situation is aggravated by the fact that viruses mutate, rendering vaccines less effective. And even when vaccines are already in production, it is still a challenge to manage administration and protect the most endangered population segments.

Fortunately, it’s possible to accelerate and improve the processes by involving AI in vaccine development and rollout.

This article describes use cases and tools that AI healthcare companies [2] and research teams built to facilitate vaccine design, speed up trials, predict mutations, prioritize patients, and address vaccine hesitancy.

How AI contributes to vaccine development

Artificial intelligence can analyze massive datasets representing virus structure to pinpoint viable vaccine targets, predict virus mutation, assist in clinical trials, and help researchers organize and access a large volume of scientific publications.

AI identifies vaccine targets

Vaccine development is a data-intensive process as one needs to understand the virus itself and how the immune system will react to it. Machine learning algorithms [3] can analyze large datasets to identify which targets (or epitopes) of a virus are most likely to provoke an immune response. After obtaining a list of targets, scientists design matching vaccines.

While determining vaccine targets, one needs to be very careful to not enlist any entities similar to the host proteins that inhibit human bodies to avoid cross-reactions and undesirable side effects.

Protein-based AI vaccines

Machine learning algorithms can identify antigens from protein sequences and determine the most viable vaccine target. There are several research initiatives [4] that use AI models to fight COVID-19. One method employs AI to develop a vaccine that would contain both T-cell and B-cell epitopes (the part of an antigen that the immune system can recognize). This study discovered 17 potential vaccine peptides working with both immune cells.

In another example, a team of researchers from Baylor College of Medicine and Amity University in India built an AI-driven platform that facilitates vaccine target discovery [5]. Researchers used this software to develop a vaccine against Chagas disease. They identified eight main target proteins and top epitopes for each target, producing a multi-epitope vaccine. Since the emergence of the Delta coronavirus variant, the scientists have been collaborating with several pharmaceutical companies to design a new vaccine.

DNA and RNA-based AI vaccines

Such vaccines are supposed to mimic a partial genetic sequence of a virus. They encapsulate a part of the virus’s genetic code representing the targeted epitope in the form of RNA or DNA. When this code enters a human cell, it produces the epitope in question triggering an immune response. Given the ability of viruses to mutate, the vaccine needs to be based on a relatively stable genetic component to have a long-lasting effect. That’s where artificial intelligence becomes useful. AI algorithms can analyze enormous datasets containing genetically sequenced viruses to identify the more stable parts.

AI facilitates preclinical testing and clinical trials

Preclinical testing

The goal of this phase is to evaluate a vaccine’s safety and efficacy prior to testing it on people in clinical trials. Preclinical testing is typically conducted on a suitable animal model, but since the past decade, regulatory agencies have been calling for the use of alternative methods when possible. AI can be one of these methods as ML algorithms can predict compound toxicity.

Even if AI can’t fully replace preclinical testing, the technology can facilitate it by helping to set the proper dosage, anticipating some immune responses, and even selecting the best-suited animal model.

Clinical trials

Again, AI can’t make clinical trials virtual, but it can largely facilitate them. First, artificial intelligence can analyze the data obtained from preclinical testing and anticipate human immunogenic reactions.

Second, AI algorithms can help researchers find the best location for clinical trials. For example, the MIT School of Engineering built an ML-powered COVID-19 epidemiological model [6] that generates real-time insights about the pandemic and captures people’s behavior and health status (exposed, recovered, etc.). It can also predict how different governments would react to the challenge and their policy choices. All of this enabled the model to predict when and where COVID will spike, pinpointing ideal locations for clinical trials.

This AI tool could make intelligent predictions on 120 countries in addition to all 50 US states.

Third, AI accelerates vaccine rollout as it helps select the right people for trials through electronic health records mining. Provided that 86% of clinical trials can’t recruit [7] candidates within the expected time frame, any help is welcome.

AI outsmarts virus mutation

There is a general concern about viruses being able to change and adapt to medication. In light of the pandemic, SARS-CoV-2 is mutating and people are afraid that the publicly available vaccines will not provide long-lasting protection.

The scientific community is trying to stay ahead as researchers at USC Viterbi experiment with AI to predict and counter mutations [8]. This team has already used an AI-powered tool to determine potential vaccine targets using one B-cell and one T-cell epitope. Employing a wider dataset for AI-driven vaccine design will enable them to fight mutations more effectively. Especially that scientists claim their model can make accurate predictions using a set of 700,000 proteins.

Paul Bogdan, Associate Professor of Electrical and Computer Engineering at USC Viterbi, pointed out that their AI-based method could also vastly accelerate vaccine design, “This AI framework, applied to the specifics of this virus, can provide vaccine candidates within seconds and move them to clinical trials quickly to achieve preventive medical therapies without compromising safety.”

AI organizes data and makes it available for researchers

Researchers keep adding new reports to the already enormous stack of literature on the novel coronavirus and other viruses for that matter. It is becoming increasingly challenging to sift through all these publications. And again, scientists turn to AI to extract valuable insights from these papers.

For example, the Allen Institute built a resource called CORD-19, [9] which offers scientific articles on COVID-19 in a machine-readable format. Other researchers can develop AI algorithms to access this platform and answer queries.

How AI supports vaccine rollout

AI technology’s potential spans beyond vaccine development to its distribution, tracking, administration, and offering counseling. Artificial intelligence prioritizes people for vaccination

Many hospitals prioritize patients solely based on their age and rush to vaccinate everyone in the 65+ age category without further discrimination. For more awareness in vaccine distribution, AI-powered algorithms can help medical facilities identify the most fragile population segments.

Sanford Health, a Dakota-based healthcare organization, deployed AI to identify people at risk of having poor outcomes from COVID-19 [10]. They ran an algorithm on their patients of the age of 65 and older to produce a prioritized list based on various health-related factors, such as obesity, kidney disease, heart disease, and diabetes among others.

Artificial intelligence monitors vaccine distribution and tracking

Using artificial intelligence in vaccine distribution, handling, and storage can have many benefits. Cheryl Rodenfels, Healthcare Strategist at Nutanix, mentions some of them: [11] “Relying on the technology [AI] to manage distribution data eliminates human error and ensures that healthcare organizations are accurately tracking the vast amounts of data associated with the vaccine rollout.”

However, deploying AI at this level is difficult, as every manufacturer has its own procedures for vaccine storage and handling. There are no unified standards on, for example, how many vaccines a medical facility must store.

Eases vaccine hesitancy

The spread of misinformation and vaccine hesitancy presents another problem that AI can help address. AI-powered chatbots that combine knowledge of psychology, public health, and infectious diseases can offer counseling and answer some of the sensitive questions. A recent study conducted in France [12] shows that bots can make people feel more positive towards vaccines.

Johns Hopkins Bloomberg School of Public Health teamed with IBM to develop a chatbot named Vira (Vaccine Information Resource Assistant). [12] They trained the bot through conversations with healthcare workers. Now Vira is used by regular people, and it continues to improve and learn.

Obstacles on the way to AI deployment

No doubt that AI can analyze large volumes of data much faster than humans. According to Dr. Kamal Rawal, Associate Professor at Amity University, who participated in building an AI-driven platform [13] for vaccine development, “The key innovation is using artificial intelligence to combine several hundred parameters to mine several thousand proteins and genes to reach to the right targets and design vaccine using these proteins.”

One interesting characteristic of AI is that it doesn’t make assumptions about what is right and wrong, so it can test the options that researchers tend to discard based on biased beliefs. However, there are things to consider when deploying AI in vaccine development and administration:

  • Black-box models [14] are powerful, but their results can’t be justified, and bias can sneak in unnoticed. It is advisable to use explainable AI to understand how algorithms arrive at their conclusions. However, this will compromise their predictive power, so there is a tradeoff to make.
  • The performance of machine learning algorithms depends on the training dataset, and immunology models are being trained on significantly smaller datasets [15] than the ones available for other disciplines, such as voice recognition.
  • AI ethics is still a complex topic to approach. Using AI in vaccine development might grant it access to patient records, and the issue of privacy comes in. Another ethical concern arises when using AI in vaccine prioritization. Research shows [16] that race and ethnicity contribute to higher hospitalization risks in the case of COVID, but is it ethical to use such data?

Salesforce launched a Vaccine Cloud tool which is expected to help healthcare organizations manage vaccine administration. The company faced the same ethical concern. Here is what a Salesforce spokesperson told Healthcare IT News: “Our Principles for the Ethical Use of COVID-19 Vaccine Technology Solutions explicitly state that AI should not be used to predict personal characteristics or beliefs that would affect a person’s or group’s prioritization for access to vaccines, and we work closely with our partners and teams on this guidance.”

On a final note

With its analytical power, AI still can’t foresee everything. As Oren Etzioni, CEO at the Allen Institute for Artificial Intelligence, said, [17] “The human body is so complex that our models cannot necessarily predict with reliability what this molecule or this vaccine will do for the body.” So, using AI can’t replace clinical trials and can’t make vaccine development entirely virtual and fully automated.

Still, artificial intelligence can analyze large volumes of data and detect patterns that escape the human eye. With all the applications mentioned above, AI can vastly accelerate vaccine development and control their rollouts.

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