In sterile drug manufacturing, quality is non-negotiable. Batch after batch and site after site, ensuring quality is a promise to patients that the therapies they rely on are safe and efficacious. There are quality control (QC) and quality assurance (QA) checks throughout the entire manufacturing process, but quality is perhaps most tangible in final container inspection, where every vial must be assessed for visible particulates and other defects before it can be released and delivered to the patient. While the industry has long understood that any visible particulates in injectable products can jeopardize patient safety, regulators have been clear too. According to guidelines set by the US Food and Drug Administration (FDA), manufacturers must follow a holistic, risk-based approach for visible particulate control.
At the same time, demand for complex biologics and peptide-based therapies, such as GLP-1 medications, continues to rise, forcing manufacturers to focus on speed and quality in tandem. These persistent realities are why artificial intelligence (AI)-driven vial inspection is moving from a promising concept to a practical tool for enhancing sterile drug quality. This technology enables better insight into what is happening on the line, which is crucial as sterile injectable pipelines grow, and manufacturers look to meet tighter regulatory expectations.
Overcoming the challenge of particulate inspection in high-volume environments
Particulate inspection has typically relied on human expertise. While effective, manual inspection is inherently high in volume, highly repetitive and time-consuming. For high volume products, batch sizes can exceed 500,000 vials or syringes per batch, making manual inspection impractical for the majority of these products.
The transition from manual to automated inspection created an opportunity for variability in how and when defects are detected and a tendency to reject vials in question out of an abundance of caution. For example, vial handling may create very small bubbles that look like defects to the camera systems used for automated inspection, while the human eye can identify bubbles easily.
The high throughput and efficiency of AI-driven inspection offers a different path. When implemented correctly, it strengthens QC by bringing greater consistency to defect identification. These tools are also able to generate more data, which can offer insights into the root cause of a defect. CDMOs are able to train these models using extensive libraries of known defects and benign anomalies. This enables inspection teams to reject any product that does not meet its standards for quality and keep moving those that pass inspection.
A real-world case study of AI’s value in Monza, Italy
Quality must be pervasive across the drug development journey and incorporating next-generation technologies, like AI-driven vial inspection, are proven to help. According to a recent study conducted at Thermo Fisher Scientific’s pharmaceutical manufacturing site in Monza, Italy, combining human expertise with an AI model for vial particle inspection reduced the false rejection rate by 84 percent. When integrated into large-scale commercial programs, it can also help boost efficiency. Thermo Fisher saw a time savings of approximately 60 hours of human labor per batch after they introduced the AI-driven inspection technology.
With AI, drug manufacturers can narrow the gray zone where subjective interpretation can lead to inconsistent outcomes. It also makes the inspection process faster and more consistent without compromising patient safety. For biopharmaceutical companies focused on high demand therapies, empowering human inspectors with this hybrid approach can help make a meaningful shift in throughput and yield, all while keeping patient safety front and center.
A model for the future: human-led, AI-driven
The drug manufacturing industry is being asked to deliver more than ever before, and many companies are vetting new technologies to help. When implemented thoughtfully, AI-driven technologies can strengthen quality, improve yield and keep the patient and the center of every decision. In fact, last year, the FDA’s division that assesses drug safety noted an increase in the number of new drugs that are being developed with AI tools in the mix.
The study in Monza highlights the value of human-led, AI-driven workflows. As the industry focuses on bringing cutting-edge therapies to market and making personalized medicine available at scale, it’s clear that quality strategies must be as innovative as the therapies themselves.
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