The current & future impact of AI on pharmaceutical R&D

The current & future impact of AI on pharmaceutical R&D

Insights from Norstella's 2024 AI survey

Artificial intelligence (AI) is revolutionizing pharmaceutical R&D, helping to accelerate drug development, improve operational efficiencies and diagnostics whilst reducing associated costs. 

Drug candidates can be identified through data-driven algorithms that analyze a host of genetic, chemical and biological data. In clinical trials, AI can enhance patient recruitment, stratification and monitoring. Predictive AI can help foresee molecular and biological interactions, while similar technology can help streamline manufacturing and supply chain processes.

Early adopters of such technology are experiencing both its benefits and challenges - and the realms and remit of an industry professional is changing with rapid effect. In fact, according to a recent survey from Norstella, 66% of biopharma organizations say the skills required for their employees have changed due to the introduction of AI.

The study surveyed 125 senior decision-makers in the life sciences industry on their perceptions and expectations of AI to understand current AI adoption trends in addition to the future impact of AI on pharmaceutical R&D. 

Despite overwhelming market positivity, 71% of industry leaders say the main reason behind its limited adoption is a lack of expertise, followed by a lack of knowledge or awareness of AI and what it can do to improve pharmaceutical R&D workflows (43%).

As AI is set to become a more integral component of the industry, does this knowledge gap raise concern in relation to its benefits?

The full survey unveiled:

• Just under four in five industry professionals (79%) feel AI partnerships are moderately to extremely valuable in relation to pharmaceutical R&D success
• 81% of decision-makers are currently using AI in at least one development program, and over a half or organizations surveyed (56%) use two AI partnerships to help accelerate drug development. Of those yet to adopt, over 50% have plans to implement AI in future R&D workflows.
• Looking ahead, 95% of life sciences professionals believe AI will be used in at least one of their development programs; helping the industry in new drug candidate identification and selection, clinical trail protocol design, safety assessments and more.
• But despite market positivity on the impact and importance of AI, 71% state a lack of AI expertise is holding back industry adoption, and 43% lack the awareness and knowledge of how to get started.

The following commentary provides a snapshot of the survey’s findings in relation to AI adoption, its integration into pharmaceutical R&D and its impact in the future.

What is the current state of AI adoption?

81% of the survey participants are using AI in at least one development program. Of those that are not currently using AI, over 50% are planning to do so in future development programs.

Organizations are using AI the most in the following pharmaceutical workflows:

• 30% predictive modeling
• 28% new drug candidate identification and selection
• 27% safety assessments
• 23% generating labs
• 23% patient selection and recruitment

The majority of organizations encourage the use of AI and have hired new employees or allocated new departments to focus on AI in drug development. However, lack of AI expertise is the main reason for limited adoption of AI into pharmaceutical workflows (71%), followed by lack of awareness/knowledge of AI (43%).

How has AI integration changed pharmaceutical R&D?

The skills required within 66% of organizations have changed due to the introduction of AI.

Sponsors are partnering with organizations that have AI capabilities to accelerate drug development. 79% of the industry feel that AI partnerships are moderately to extremely valuable in relation to overall pharmaceutical R&D success.

The main benefits of using AI in drug development include:

• Improving operational efficiency
• Reducing costs
• Improving diagnostics

The main challenges however include:

• Integrating data from multiple sources
• Encountering biased AI algorithms
• Performing complex data analysis

What impact will AI have on pharmaceutical R&D in the future?

Nearly all decision-makers believe that existing and future AI policies and regulations will have some level of impact on AI use in drug development. 

Looking ahead, 95% of life sciences professionals believe that AI will be used in at least one of their development programs. Organizations predict that AI will be used the most in the following pharmaceutical R&D workflows:

• 58% predictive modeling
• 36% new drug candidate identification and selection
• 35% clinical trial protocol design
• 35% safety assessment
• 30% efficacy assessment

The future is bright with regards to AI integration in the pharmaceutical R&D landscape, with 74% of the survey participants feeling optimistic. In the next five years, organizations expect AI use across all phases of drug development, but most implementation in the discovery/development phase.

For more information on how Norstella is integrating AI into its data and advisory services, read Norstella’s whitepaper: https://www.norstella.com/norstella-embedding-artificial-intelligence/?utm_campaign=2024%3A%20Norstella&utm_source=norstella-ai-survey-infographic&utm_content=embedding-ai-whitepaper-download