Implementation and interoperability are key to achieving the benefits of AI in pharma.
Artificial intelligence is showing great promise in streamlining the development of new pharmaceuticals. In fact, a recent LinkedIn poll revealed that AI and emerging tech was the leading opportunity area identified for pharma R&D. But it’s not a silver bullet—implementing AI technologies comes with a range of complexities, especially in aligning them with the existing challenges of drug development. For AI-aided drug discovery to work, pharma companies need the right solutions, support, and expertise to gain the most benefit.
The Promising Future of AI-Aided Drug Discovery
Drug discovery is an incredibly complex, laborious, costly, and lengthy process. Traditionally, it requires extensive manual testing and documentation. On average, a new treatment costs $985 million to develop, with a high trial failure rate being a leading cause of sky-high costs. In fact, only about one in eight treatments that enter the clinical trial phase make it to the market, while the remaining seven are never developed.
AI has the ability to analyze significant volumes of data, predict outcomes, and uncover data similarities to help drive down costs. Making connections between data points in real-time boosts efficiency and reduces the time to discovery—that is, when AI technologies are implemented properly.
AI Challenges That Impact Drug Discovery
The advancement of AI and machine learning is showing great potential in combating some, if not all, of the challenges of traditional drug discovery. But AI-aided drug discovery also invites new challenges of its own.
One such challenge is the potential lack of data to properly feed AI and machine learning technologies. Typically, AI relies on large datasets from which to “learn.” But with unique diseases and rare conditions, there simply isn’t a lot of data for these technologies to ingest. What’s more, these tools typically need years of historical data to identify trends and patterns. Given the frequency of mergers and acquisitions in pharma, original data sources may be unavailable and, therefore, unusable.
McKinsey notes that one of the greatest challenges lies in delivering value at scale. AI should be fully integrated into the company’s scientific processes to gain the full benefit of AI-driven insights. AI-enabled discovery approaches (including via partnerships) are often kept at arm’s length from internal day-to-day R&D. They proceed as an experiment and are not anchored in a biopharma companies’ scientific and operational processes to achieve impact at scale.
Additionally, achieving interoperability limits the effectiveness of AI in drug research. Investment in digitized drug discovery capabilities and data sets within internal R&D teams is minimal. Companies frequently leverage partner platforms and enrich their IP rather than build biopharma’s end-to-end tech stack and capabilities. However, data needs to break out of silos and communicate with each other to contextualize the outputs. This is easier said than done when data comes from multiple sources in different structures and varying levels of reliability.
A part of this bigger challenge is the lack of data standardization. Using AI in drug discovery is still very new. The industry as a whole has not defined what constitutes a good data set, nor is there an agreed-upon set of data points that should be included in R&D processes. This opens the door for data bias, especially as some groups of the population have historically been omitted from medical datasets, which could lead to misdiagnoses or unreliable outcomes.
A lack of standardization also invites the potential for regulatory hurdles. Without a standardized way to structure, capture, and store data, pharma companies could be at risk of privacy concerns or non-compliance. The pharma industry is heavily regulated and requires careful documentation and disclosures at every stage of drug development. Adding the AI element to the process will introduce new regulatory considerations to ensure safety, privacy, and thoroughness.
How to Gain Support for AI-Aided Drug Discovery
AI is the future of daily human living—from how we travel, to what we buy, to the pharmaceuticals we take to live a higher quality of life. But in Life Sciences, AI will not replace Research Scientists, but Research Scientists that use AI will replace those that don’t. And Biotechs and Pharma companies conducting drug discovery and development need an experienced partner that understands that, for the effective implementation of AI technologies that drive results.
If your organization is looking to incorporate AI to boost your drug discovery goals, a strategic partner will help you navigate and circumvent the unavoidable hurdles and pitfalls from inception. At RCH Solutions, our Bio-IT consultants in Life Sciences understand the intricacies of the pharma industry and how they relate to the use of new technology. Implement new solutions in an intentional manner and give them staying power to achieve the greatest possible outcomes.
Download the Emerging Technologies eBook to learn more about the future of AI-aided drug discovery, and get in touch with our team for a consultation.
Sources:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7054832/
https://roboticsbiz.com/ai-in-drug-discovery-benefits-drawback-and-challenges/
https://arxiv.org/abs/2212.08104
https://www.weforum.org/agenda/2022/10/open-source-data-science-bias-more-ethical-ai-technology/