ABSTRACT
Introduction
A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.
Areas covered
This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.
Expert opinion
Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an ‘in silico era’ in which scientific partnerships, including a more and more increasing creation of an ‘ecosystem’ of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.
Article highlights
Vaccine development remains one of the most challenging part of drug development.
Current pipeline for major infectious diseases is a lengthy process and provides only partially effective vaccines.
The risk of failure is high: most of the vaccines fail to pass phase II/III.
The joint venture of artificial intelligence methods and systems biology approach has the potentiality to reduce failures while enhancing efficacy and speeding up the development process.
Regulatory authorities are currently being approached for qualification/approval of artificial intelligence in silico trials techniques fed by systems biology data in vaccine development pipeline.
This box summarizes the key points contained in the article.
Declaration of interest
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.
Reviewer disclosures
Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.
Correction Statement
This article has been republished with minor changes. These changes do not impact the academic content of the article.