How artificial intelligence can drive greater speed and accuracy in vaccine development

Despite the tremendous advances in medical science over recent decades, there is still so much about the human body we are yet to discover. The mission of the life sciences industry, therefore, is to continually probe at the next frontier in expanding our collective understanding. The response to infectious diseases is just one aspect of this, yet presents a hugely significant area in improving global health and life expectancy.

What typically separates acute diseases from chronic conditions is a sense of urgency. When a new strain of the disease is identified, its viral nature combined with the interconnectedness of modern societies can quickly lead to an exponential rise in cases requiring treatment. This places immense pressure on governments and healthcare infrastructure as action is taken to restrict the spread and apply treatment so that normal social and economic life can be resumed.

Life sciences organizations have an essential role to play in this regard and, through the use of artificial intelligence (AI) and other advanced analytic technologies, can help to rapidly accelerate the path towards the development and dissemination of such treatments.

What is involved in developing a vaccine?

The sense of urgency that comes with infectious diseases can itself fast-track progress towards arriving at a vaccine. The need for immediate action unites and galvanizes a range of bodies – from researchers and clinicians through to regulatory bodies and manufacturers – in the quest to get an effective treatment into the hands of healthcare professionals as quickly as possible.

Even with these groups working tirelessly and with a determined focus, the end-to-end process can still take years. Several stages come before a treatment is ready to be put into the market. These include:

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• Exploratory stage; working through thousands of potential compounds to shortlist vaccine candidates, and research into immune response
• Pre-clinical stage; lab analysis to identify relevant antigens to arrive at a vaccine concept and design
• Clinical development; trailing of vaccine on test groups of varying characteristics
• Regulatory review and approval; verification of vaccine safety and compliance with health regulation
• Manufacturing and quality control; development of medicines in preparation for mass distribution

Each of these steps are essential in ensuring that the vaccine produced is effective and safe, that any side-effects are properly understood and that it can be produced at scale consistently until the threat of illness has been sufficiently minimized.

Historically, the complexity, regulation and cost involved in each of these stages has slowed down the response to emerging health issues. Now, due to advances in AI-related technologies, we have an opportunity to rapidly accelerate the process through which we can deploy treatments into the field.

How can AI assist in this process?

While we can never expect overnight success when dealing with something as complex as vaccine development, we can act to remove some of the constraints and bottlenecks that may hamper progress. Advances in automating data analysis and improving visualization of what is happening at each step of the discovery stage can address some of these inefficiencies, helping to accelerate the process of vaccine development and streamline operations to upscale production.

Here are some of the roles AI can play at each stage:

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• Exploratory/pre-clinical

  The initial phases of drug discovery often involve a filtering process to narrow down vaccine candidates based on prior studies and treatments. Researchers can use AI to process vast digital libraries of data (such as analyzing the properties of thousands of pharmaceutical compounds) with significantly more accuracy than manual processing, to arrive at potential treatment candidates. AI can also be used in these stages for DNA sequencing based on complex human data, allowing clinicians to conduct tests in genetic matching and immunity response.

• Clinical development and trials

  Once suitable compounds have been identified, the process moves towards live testing. Different patients will react differently to treatments based on factors such as age and prior medical history. The tests, therefore, need to be comprehensive enough to cover marginal cases where a patient may react badly to treatment.

  By training deep learning algorithms, researchers can conduct these tests at a previously-unimaginable scale, even before physically administering the vaccine candidate to test patients. These algorithms can be used to identify and sample antibodies to fight infectious diseases with drastic improvements in speed and cost. Advanced analytics and data visualization of human response to the potential vaccines can then be used to assist with rapid testing, allowing for more intricate analysis and lower error rates.

• Manufacturing and QA

  Upon regulatory approval of vaccine products, the race is on to develop and distribute the medicine across a vast network of hospitals and clinics. This has significant operational implications for the manufacturers making the products, requiring rapid decision-making on factors such as their output capacity, the quality of the product and optimum packaging solutions.

  Combining AI and sensor-based technologies, manufacturers can harness granular data to  bring greater supply chain efficiencies. This helps to avoid demand-supply misalignments in their production processes and minimizes the risk of products being spoiled in distribution.

• Faster treatments in times of need

  A viral outbreak can bring unforeseen challenges for those involved in public health management, from policymakers and health authorities to clinicians and manufacturers. While the former can take prompt action to test for infection and put containment measures in place on identified clusters, the latter are often under renewed pressure to deliver treatments at speed. Being able to find new efficiencies in the development of vaccines can make a considerable difference in treating identified cases, relieving pressures on healthcare infrastructure and contributing to better recovery rates.

  AI capabilities allow those involved in the development side to act faster under pressure. Techniques such as deep learning and advanced data visualization allow researchers to lean on the body of existing research undertaken to tackle the complexities involved in discovering suitable treatments for novel viruses. The utility of AI extends through to production and distribution, where manufacturers play a powerful role in getting these medicines into the field at rapid speed and amid conditions of great uncertainty. 

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