AI-driven manufacturing: lessons from the life sciences industry

The use of artificial intelligence for batch monitoring and digital twin development is redefining process control — enabling real-time deviation detection, predictive adjustments, and simulation-based optimisation to safeguard quality and reduce production risk.

It’s no secret that cell and gene therapy (CGT) manufacturing is an expensive process — a single cell therapy batch can cost upwards of $500,000 to produce. When a batch fails due to minor process variations, both manufacturers and patients pay the price in dollars and treatment delays.

The cell and gene therapy market is experiencing tremendous growth. With more than 2000 ongoing clinical trials globally and dozens of therapies now moving from clinical to commercial scale, manufacturers are feeling the heat to build processes that can scale up while keeping quality high and costs in check. And with the FDA giving the green light to multiple cell and gene therapies, companies are scrambling to turn these scientific breakthroughs into realities.

That’s why the manufacturing processes behind CGT manufacturing demand innovative monitoring solutions. Even minor variations can have outsized consequences in CGT manufacturing. Unlike conventional drugs, these therapies use living cells that respond dramatically to subtle environmental shifts. Minor temperature fluctuations of just 1–2°C can trigger cellular stress responses. Slight changes in media composition can also affect growth rates. Inconsistent centrifugation between batches can impact cell viability. These deviations compound throughout the process, compromising therapy potency and safety.

Traditional quality control methods, such as testing after production, usually come too late, with the damage to the batch already done. However, AI and predictive analytics are reforming CGT manufacturing by catching problems before they ruin a batch.

Detecting deviations before they occur

AI-powered batch monitoring systems function as tireless quality inspectors, simultaneously analysing thousands of process parameters to catch subtle patterns that human operators could miss. These systems harness several AI technologies:

• Computer vision examines bioreactor imagery to assess cell characteristics.
• Machine learning processes sensor data tracking pH, oxygen, glucose and metabolites.
• Natural language processing scrutinises batch records to find connections between procedural variations and outcomes.
   

AI’s true power lies in detecting deviations before they affect product quality. For example, when monitoring cell cultures, AI can identify early metabolite concentration shifts that signal potential future problems, allowing operators to make real-time adjustments to temperature, pH and nutrients.

With the help of AI-powered batch monitoring systems, manufacturers get fewer batch failures, more consistent quality and less waste. These systems go beyond providing basic alarms; they can be trained to understand how various factors impact each, enabling them to distinguish between normal variations and real anomalies that need intervention.

A staged implementation approach

Successfully implementing AI and digital twins in CGT typically requires a staged approach, starting with high-ROI use cases. Everything begins with data quality — ensuring sensors and collection systems deliver accurate measurements. This often requires equipment upgrades and calibration protocols.

Cross-functional teams comprising engineers, quality specialists and data scientists are crucial for creating actionable insights. Shrewd organisations start with historical batch analysis before attempting real-time monitoring, building confidence as they go.

This approach helps teams get comfortable with the technology while delivering immediate value, setting the stage for more advanced applications.

From batch failures to patient breakthroughs

As cell and gene therapies break into mainstream medicine, making manufacturing more efficient becomes key to getting these treatments to more patients at prices they can afford. AI and predictive analytics align with the FDA’s Quality by Design approach, which involves building quality into the process from the outset instead of simply testing at the end. With AI providing unprecedented insight into manufacturing, companies can show regulators exactly how they maintain control.

As systems get smarter, we’re seeing manufacturing that learns and improves on its own. These intelligent systems don’t just maintain quality — they improve it, bringing science closer to advanced therapies that work dependably in both production and patients.

Top image credit: iStock.com/PeopleImages