Global Artificial Intelligence (AI) Trends in Pharmaceutical Manufacturing

Ai In Pharmaceutical Manufacturing Market Report Overview

In GlobalData’s survey “Digital Transformation and Emerging Technology in the Healthcare Industry – 2023,” AI, followed by big data, was viewed as the most disruptive technology over the next two years. Incorporation of AI technologies in drug manufacturing can improve process controls, recognize early warning signals, and thus optimize production. Furthermore, AI can improve site planning, streamline production, and alleviate staff shortages. Key opinion leaders (KOLs) anticipate that their companies will soon use AI in manufacturing to support and improve human decision-making as well.

Although AI has enormous potential to disrupt drug production, pharma companies and regulators are likely to face challenges as they seek to exploit the capabilities of this new tool while adhering to the pharma industry’s extremely strict regulatory and quality standards. Since AI is inevitable in the foreseeable future, regulators must be open to accepting AI-generated simulation data and not necessarily stick to lab-generated data.

Most Disruptive Technologies in the Upcoming Two Years

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The ‘Global Artificial Intelligence (AI) Trends in Pharmaceutical Manufacturing’ research report assesses the current and future use of AI in pharma manufacturing. The report explores the practical, regulatory, and ethical challenges of implementing AI in the pharma sector. The report also discusses the role of pharmaceutical regulators such as the FDA and EMA in ensuring ethical manufacturing processes. Furthermore, the report identifies innovations generative AI can bring to drug manufacturing and supply chain management.

AI in Pharmaceutical Manufacturing – Current Scenario

Pharmaceutical companies that develop/market their drugs and do not offer contract services to other companies were among the most advanced users of AI, with more than 43% of respondents confirming that they are either using AI in manufacturing or planning to implement the technology very soon. Globally, around one-third of companies do not have an immediate plan to implement AI but anticipate its future use. In addition, over 75% of surveyed respondents confirmed that their partnering decisions do not yet depend on their partners/customers using AI.

AI has many different fields of application within pharma companies. Most of the respondents approved adaptive AI algorithms over AI, which can directly alter manufacturing processes. For instance, within the quality management system (QMS) space, pharma manufacturers expect any use of AI to be validated and/or include the human touch, meaning it can augment decision-making but cannot replace it.

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AI in Pharmaceutical Manufacturing – Future Considerations

Concerning the AI implementation pathway, respondents were equally divided between using in-house expert teams and external consultants/vendors to implement AI. Geographically, around 58% of European pharma companies employ in-house AI experts.

Moreover, the creation of manufacturing optimization plans, and real-time process monitoring were chosen as the top areas of pharma manufacturing that AI could help with the most. Likewise, respondents from contract manufacturing organizations believed that AI is best suited to help with the creation of manufacturing optimization plans, real-time process monitoring, investigation of faults, and small-molecule drug manufacturing.

Generative AI in Pharmaceutical Manufacturing

In pharma manufacturing, generative AI could create optimization plans and predictive maintenance schedules, mitigate risks, and improve communication. This could reduce the time to market for novel drugs and materials. Pharma supply chains are complex and comprise various stakeholders, and therefore require careful coordination and adherence to regulatory guidelines at every stage to ensure patients receive safe and effective medications. The speed and efficiency of generative AI could make it possible to improve the efficiency of supply chains, reduce the need for human intervention, improve safety, and cut costs. However, most of the surveyed respondents were not aware of any parts of the manufacturing process where generative AI is or could be used in drug manufacturing. In addition, many respondents based in North America and Europe believed that generative AI was hyped.

To date, in the pharmaceutical sector, the most common use cases of generative AI are in drug discovery, particularly in the creation of new molecules. As experience and trust with the technology grow, further examples of pharma companies using it in other areas will emerge.

Generative AI Use in Pharmaceutical Manufacturing

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Reasons to Buy

This report provides:

• A detailed survey of pharmaceutical industry Key Opinion Leaders (KOLs)
• A clear explanation of artificial intelligence and related terminologies
• A detailed analysis of current AI adoption in the pharmaceutical manufacturing sector
• An assessment of experts’ attitudes to AI adoption in the pharmaceutical manufacturing sector
• A guide to practical, regulatory, and ethical challenges to increased AI use in pharmaceutical manufacturing
• A detailed breakdown of the US Food and Drug Administration’s stance on AI
• Broader context of AI use in non-manufacturing sectors of pharma