The biopharma industry is poised for a seismic shift driven by advances in artificial intelligence (AI) and related technologies. It is thought even modest improvements in early-stage drug development success rates enabled by AI and machine learning could lead to an additional 50 novel therapies over 10 years, a more than $50 billion opportunity.
Techbio companies are launching strategic development projects with pharma and biotech companies, top sequencing organizations, cancer centers, and instrument manufacturers to integrate AI into their operations and provide solutions to challenges in translating scientific discoveries to the clinic.
Currently, advanced AI tools can be applied to some of biology’s most complex systems, giving the life science industry unique insights into the molecular underpinnings of diseases, their clinical course, and how they can be most effectively treated with new or repurposed drugs. However, understanding where best to apply these AI tools requires collaboration between technical and biological expertise to tackle unmet clinical needs.
AI-based, novel insights come with additional benefits: More robust solutions at a lower cost. As the connection between the life sciences, AI, and data science grows deeper, the time between diagnosis and treatment may get shorter, driving faster yet reliable outcomes for patients in need.
AI as a complementary scientific tool to improve pharmaceutical product development
How can AI become a partner alongside data science and wet lab operations in the pharmaceutical industry?
We’ve seen examples of large language models that can provide general solutions across many different industry verticals. While impressive, in the life sciences, we need AI that can work hand-in-hand with researchers to solve discrete, specific problems. AI-based tools can aid scientists but they may not replace human judgement. AI augments research by improving efficiency, making it more accessible and adaptable, thus, driving advancements in precision medicine.
The largest integration of AI tools has happened in the small molecule drug development world, with computational approaches facilitating target identification, drug design, candidate optimization, and other subtasks along the drug discovery and development pipeline. With AI-designed drugs now entering clinical trials , it's only a matter of time until the FDA approves its first AI-designed drug.
Such successes provide a solid foundation for AI’s recent shift into more complex and newer drug development applications, such as monoclonal antibodies and cell and gene therapy. One of the major bottlenecks in developing complex therapeutics is minimizing downstream unsafe impurities during manufacturing. There’s a growing portfolio of AI solutions to address safety concerns, manage the huge amounts of data generated, and control in-process variability in the drug development pipeline.
Emerging trends and opportunities
In silico design and development—a new category in pharmaceutical research—has emerged to integrate the use of AI in scientific workflows. Unlike contract research organizations (CROs) that focus on early research and assay development, a model-centric contract computation organization (CCO) is dedicated to in silico modeling for therapeutic design and development, facilitating more efficient and robust drug development, manufacturability testing, and biomanufacturing.
In silico models are fueled by a diverse dataset, combining proprietary and public data for training and testing, followed by wet lab biological validation. The goal of in silico design and development is to integrate large-scale in silico modeling into the existing R&D process, aiding in the selection of the most promising drug candidate for clinical and commercial success.