AI for Pharma: First Antibody Drug Designed Using AI Is Writing A New Chapter

Posted by Candy Swift on February 7th, 2023

Artificial intelligence (AI) technology has beaten humans at games of chess and become famous globally. Now, AI and machine learning (ML) have revolutionized almost all types of industries. In a data-driven age where companies across all parallels of the industry are adopting big data and AI technologies, the pharmaceutical industry is no exception.

Bimekizumab is the first-in-class monoclonal antibody, which has been approved for the treatment of plaque psoriasis in Europe and is in clinical trials for other indications including psoriatic arthritis and ankylosing spondylitis. What makes Bimekizumab unique is that it is the computational design that generated the exact final drug candidate.

Unlike the other bispecific antibodies that aim at two targets specifically with two arms, Bimekizumab has two identical arms and each arm hits both targets, which are IL-17A and IL-17F targets. If one arm is swapped to hit another target, the antibody can potentially cover three targets, which may contribute to the decreased immunogenicity risk and manufacturing cost. In addition, it may also be further modified into an antibody-drug conjugate.

That’s to say the possibilities are vast as the drug can be used as a platform for further drug discovery and development.

This first AI-assisted drug is actually writing a new chapter for his whole industry. Areas impacted include improved decision-making, reduced manual groundwork, and the upgraded pharma and healthcare systems across many areas in the healthcare sector, covering: 

l R&D

Pharma companies around the world are leveraging advanced ML algorithms and AI-powered tools to streamline the drug discovery process. For example, Creative Biolabs, a biotech company, has launched the AI-based Antibody Discovery Platform combining AI, ML, and big data, which can generate 10 times more antibody sequence clusters than a laboratory-based approach alone.

l Disease Diagnosis

Machine Learning systems can be established to collect, process, and analyze vast volumes of patients’ healthcare data, and ML technologies can help quicken the diagnosis process, thereby helping save millions of lives.

l Epidemic Prediction

AI and ML are already used by many healthcare organizations to monitor and forecast epidemic outbreaks across the globe, which can disparate sources on the web, and study the connection of various geological, environmental, and biological factors on the health of the population of different geographical locations.

l Remote Monitoring

Remote monitoring is a breakthrough in the pharma and healthcare sectors. Many pharma companies have already developed wearable patient monitoring devices powered by AI algorithms that can remotely monitor patients suffering from life-threatening diseases.

l Manufacturing

AI in the manufacturing process can be outstanding by higher productivity with improved efficiency. AI can be used to manage and improve all aspects of the manufacturing process, including quality control, predictive maintenance, waste reduction, design optimization, and process automation.

Currently, AI has been a chic catchphrase in health care. Its application is starting to have a significant impact on automation technologies used across the pharma industry. It has the potential to transform drug discovery and does what humans do, but more efficiently, more quickly, and at a lower cost.