The Effect of Artificial Intelligence on Biopharma
Explore the ways artificial intelligence and machine learning have altered the current state of the biopharma industry, and how they will continue to change processes in the future.
What is Artificial Intelligence & Machine Learning?
One of the major technologies introduced to the biotech industry in recent years has been artificial intelligence. Artificial intelligence, commonly referred to as AI, is technology that utilizes human patterns and behaviors to mimic their problem solving in order to perform tasks like answering questions or providing relevant information.
Alternatively, machine learning (ML), a subset of AI, rely on mathematics algorithms to perform tasks like processing large bodies of data in order to draw conclusions and learn from the results without additional external human input. Artificial intelligence and machine learning are often mentioned in connection to one another.
Implications of AI on the Biopharma Industry
Projected to pass $20.6 billion in 2023, the AI market within the healthcare sector has had, and will continue to have a major impact on research and development, drug discovery, data analysis, and much more. As of 2023, there were nearly 270 AI-driven companies in the drug discovery industry. Over half of those companies were located in the US, followed by major hubs in Western Europe, Southeast Asia, and the United Kingdom.
The possible gain for the operational profit of pharma companies across the board with the strategic implementation of AI is estimated to be an additional $245 billion by 2030 worldwide.
Additionally, many mergers, acquisitions, and partnerships have resulted from the rise of AI. Since 2013, there have been around 161 deals focused on enhancing R&D, as well as 95 deals for development and commercialization licensing. We will explore the specific organizations taking part in these deals later in the article.
Using AI as a Strategic Tool
There are a variety of ways that AI can be helpfully implemented throughout biopharma operations, and as it is a relatively new tool, we are learning new ways to utilize it every day. However, four main areas in which AI has been shown to increase efficiency and improve processes in biopharma that we will discuss in this article include research and development (R&D), drug discovery, development and manufacturing processes, and large-scale data analysis.
Research and Development
Due to the pattern recognition and predictive capabilities of AI that far surpass human ability, AI has become a crucial tool during R&D. The research aspect of R&D can often involve large bodies of data, which AI can quickly comb through to reach helpful conclusions, including determining viable drug candidates and forecasting their safety and effectiveness.
The utilization of AI during R&D has been found to be historically time and money saving. Not only can AI comb through large bodies of information quickly to find target candidates, but it is also able to evaluate more of the intricate relationships between potential drug products and the multifaceted nature of human biology.
An example of a company making waves in the research and development space with AI is Cradle. Cradle utilizes machine learning to help you design improved variants of your target protein sequence. There are a variety of features available from their software, including a predicted performance score for generated sequences, improvements in variants after each experimental round. Overall, their ML software can drastically reduce time-to-market by expediting research and development.
Drug Discovery
Similarly to research and development, AI and ML are able to expedite the drug discovery process by processing high volumes of data and information to identify new solutions to new and existing health conditions. It should be noted that in addition to AI, we currently have greater availability of relevant and helpful biomedical data. AI algorithms have been used in drug discovery to predict molecular behavior, delineate pathways associated with certain diseases, expedite compound selection, and increase the probability of clinical trial success.
With the world’s largest proprietary chemical and biological datasets, Recursion Pharmaceuticals is using their Operating System (OS) to make major leaps within the drug discovery space. Drug discovery is becoming increasingly more costly and time consuming, so Recursion has thus decided to leverage computing power and technology, which is becoming faster and less expensive, in order to industrialize drug discovery.
Development and Manufacturing Processes
During everyday operations, the implementation of AI and ML can help to automate tedious and routine processes, allotting more time to employees to work on other tasks, which can overall increase operational efficiency. Specific algorithms can also help with minimizing waste, compliance monitoring, and task management. One particularly helpful implementation of AI for manufacturing and development processes is AI’s ability to run small experiments or run data to find correlations that will allow scientists to set optimal parameters once production begins.
Major biopharma company Eli Lilly is quantifying the effectiveness of AI and ML by measuring and growing what they have coined their “digital worker-equivalent workforce”. For example, according to an interview with Business Insider, as of June 2023, Eli Lilly estimated that AI was responsible for around 1.4 million hours of human equivalent activity. Not only is Eli Lilly utilizing AI in manufacturing processes and other tasks, they are also using the tool for drug discovery and research.
Large-Scale Data Analysis
As mentioned in all three applications above, AI’s ability to process large-scale data is helpful and relevant in almost every step of the bioprocess. Data analysis can be a tedious and long process, and can result in costly human error. By using AI and ML to conduct your data analysis, there is a better chance for accuracy, and computations and conclusions will be completed at a much quicker rate. AI can also mine other resources for conflicting or agreeing data. (Mention Recursion Pharmaceuticals)
Used by many major biopharma companies, Tellius offers an AI software focused on offering insights into a wide range of factors, such as pricing, clinical efficacy, patient outcomes, and more by using large-scale data analysis. With access to internal and third party data, Tellius AI helps biopharma companies get rid of bottlenecks in the traditional workflow of analytics.
AI and ML in Action in the Industry
Industry-wide, many companies are finding ways to incorporate AI into their products, services, and processes. As of 2023, the three companies leading the way with acquisition, merger or partnership deals with an AI-focus were Pfizer, Roche, and AstraZeneca. These deals grew across biopharma by 24% from 2013 to 2022 when compounded annually. Below are some detailed examples of companies that are paving the way for AI and ML in bioprocessing.
Genentech
Genentech has been part of both an acquisition and a strategic partnership in recent years to increase their AI capabilities. In 2021, Genentech acquired Prescient Design for their ML model which would hopefully help them retrain and modify protein-protein interactions. By having Prescient Design incorporated into Genentech as its own department, they were able to maintain the autonomy to continue to develop machine learning platforms, while acting as ML consultants and experts on various Genentech projects when needed. They were initially brought on to support Genetech’s antibody discovery efforts.
Additionally, in December of 2021, Genentech announced they would be collaborating with Recursion Pharmaceuticals in order to utilize their AI-guided high-content screening platform. With this new technology, Genetech hoped to identify new novel targets, specifically for oncology and neuroscience.
Novartis
Years before the Genetech deals were made, Novartis entered a collaboration agreement with Microsoft in the hopes of leveraging AI technology to alter the way they approached drug discovery, and to hopefully develop new, effective medicines. Part of the deal detailed that Novartis would create an AI innovation lab, which would be used to encourage employees across the company to leverage AI to its full potential. The main focus areas of the deal, as announced in Microsoft’s press release, were AI empowerment and AI exploration.
Discover More About AI and ML
As mentioned throughout the article, AI is a constantly evolving tool, and we are learning more about its uses daily. To learn more about artificial intelligence, visit SAS Institute’s detailed guide on the history of AI, and its possible next steps.
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About HPNE
As the industry needs grow, High Purity New England, Inc. continues to supply the biopharmaceutical industry with a range of innovative products, from drug discovery and development to fill-finish, including their flagship product, custom single-use assemblies, as well as pumps, sensors, bioreactor systems, storage and handling solutions and other single-use solutions. Along with their own manufactured products for the global market, they are also a distributor for more than 18 brands in North America.
