AI's Transformative Impact on Pharmaceutical Manufacturing
Artificial intelligence (AI) has significantly impacted the pharmaceutical business in several ways, from drug discovery and formulation to clinical trials and monitoring for adverse drug reactions.
But AI can also help improve pharma manufacturing, distribution, and supply chain management by improving efficiency, safety, quality, and other processes.
Here’s how AI can be used to improve the efficiency and effectiveness of pharmaceutical manufacturing.
How AI Is Used in Pharma Manufacturing
The pharmaceutical value chain is ripe for the efficiencies that AI provides: Over the past 40-odd years pharma research and development spending has increased by 51x, while clinical trial success rates have held steady at around 10 percent.
But this need for innovation goes beyond drug discovery, drug formulation, or R&D activities. It has implications across any activity that requires the collection, analysis, and utilization of large datasets and complex processes.
This includes the drug manufacturing process, where AI can be used to improve areas such as quality management, safety, and proper handling and transport to market.
The Benefits of AI and Pharma Manufacturing
The benefits of AI-driven pharmaceutical manufacturing include improved quality control, better resource allocation and risk management, and upgraded inventory and demand forecasting.
Here’s a closer look at some of the most significant benefits for pharmaceutical companies (and their customers):
Improved quality control and efficiency
One of the most important benefits of AI in pharma manufacturing are innovations that improve the precision of visual inspection processes.
Similar to how AI models trained on medical images can help detect anomalies in CT scans and MRIs, computer vision models can perform functions like object detection and image classification to identify anomalies and imperfections among large collections of manufactured pharmaceutical products.
AI can also be applied to software and internet-of-things (IoT)-enabled devices that monitor pharmaceutical dosage levels, temperature deviations, ingredient types, and other factors to improve quality management and product integrity. This helps pharma companies maintain constant compliance with industry standards across the entire lifecycle of a drug.
And quantum generative AI has been shown to improve micromanufacturing precision, which can lead to better therapeutic outcomes, and could one day even be used to spur “self-driving” manufacturing facilities that can be controlled remotely.
Better resource allocation and risk management
AI can scan massive amounts of data to give stakeholders actionable insights that help feed strategic plans and improve resource allocation. Predictive AI models, for example, can quickly spot potential risks from supply chain volatility, disruptions, or other issues before they become bigger problems.
And because pharmaceutical products (like any other products) are made from raw materials sourced from around the world, AI can help make these processes smoother and more efficient by identifying the best times to collect and ship products.
Upgraded inventory and demand forecasting
Similar to the above use case, predictive AI can monitor inventory and other signals to help forecast product demand, helping pharma companies plan future production levels. Models that monitor inventory and predict demand can even help companies anticipate changes in customer preferences or attitudes.
How AI Can Aid Good Manufacturing Practices (GxP)
AI maturity models can provide frameworks for implementing AI in compliance with Good Practice (GxP) regulations, which are quality control guidelines that ensure safety and efficacy in biopharmaceutical products.
Indeed, AI can help pharma companies automate and improve GxP practices to ensure quality and safety across all pharmaceutical manufacturing at a massive scale.
But while several pharma companies are currently running limited pilots around the implementation of AI around GxP, Erdmann et al. have introduced an AI maturity model that shows the stages of how companies can fully implement AI in these processes:
Maturity model: Control design stages
| Stage 1 | Stage 2 | Stage 3 | Stage 4 | Stage 5 |
|---|---|---|---|---|
| AI system used in parallel with GxP | System can execute automated GxP processes, but must be actively approved by a human operator | System can execute automated GxP processes, which can be revised by a human operator | The system runs automatically with no human intervention | The system runs automatically and can self-correct |
Maturity model: Autonomy stages
| Stage 0 | Stage 1 | Stage 2 | Stage 3 | Stage 4 | Stage 5 |
|---|---|---|---|---|---|
| Fixed algorithms with no machine learning | System is locked; updates are manually performed with new training data | Manual updates are performed after an alert by the system | Retraining is automated, but with a manual verification step | Fully automated system that learns independently, and with quantifiable optimization goals | Fully automated system that determines its own strategies and goals |
In the above examples, “control design” refers to the capability of a system to operate safety and quality controls independently; “autonomy” refers to the level of independence of the system.
Conclusion: AI in Pharma Manufacturing is Just Getting Started
AI is already well established across several links in the pharmaceutical value chain, from drug discovery to drug formulation. But it has an increasingly important role in improving the quality, safety, efficiency, and cost-effectiveness of pharmaceutical manufacturing and distribution.
AI can also help pharma companies implement more effective GxP in their manufacturing facilities.
But not every company has dedicated technical staff with the AI and machine learning expertise necessary to build and implement effective models to improve pharma manufacturing. That’s why many pharma and other medical and research companies work with CapeStart to develop and implement AI and machine learning models across their value chain.
Contact CapeStart today to learn more about how we can help scale your innovation—and your manufacturing—with the power of AI.