Process validation for drugmakers involves gathering documented evidence that the steps required to manufacture a product consistently meet specifications and cause high-quality outcomes. Workers can achieve the goal by identifying and mitigating production risks, working according to applicable regulations and retaining records to maximize transparency. Artificial intelligence could also help, especially since some vendors have specific AI pharmaceutical validation offerings. What are some of the best ways to capitalize on these solutions?
Enabling Digitalization Across Product Life Cycles
Bringing pharmaceutical products to the market requires years of collaboration between external and internal stakeholders. Digital tools that allow people to store forms, test results and other important paperwork in the cloud can streamline the necessary processes.
A recently announced tool designed for the life sciences industry features AI-based components that assist users in developing and validating processes for entire product life cycles. Its AI-powered validation assistant accelerates document creation by as much as 80% and shortens review timelines from weeks to hours.
The product’s validation life cycle suite automates commissioning, qualification and validation with several capabilities. One supports equipment qualification and manufacturing system validation to boost efficiency and consistency. Another helps people build digitally driven cleaning procedures according to traceability and regulatory requirements. Because the product also captures real-time operational data, it increases production visibility.
Image-based verification and content-analysis features automatically flag exceptions or compliance issues according to standard operating procedures. Decision-makers can integrate this platform with existing solutions, such as laboratory information management and manufacturing execution systems to scale up their digital transformation efforts.
Supporting Validation for Improved Efficacy
Pharmaceutical companies use specialty equipment to develop products that work as expected across intended groups, affecting patient outcomes. Microfluidizers promote particle uniformity, keeping them the desired size for the best results. The equipment applies high-shear forces to vaccines, facilitating effectiveness and stability. Drugmakers also add adjuvants to incite stronger immune responses and potentially reduce the antigen amounts required during production.
Companies using microfluidizers or products to add adjuvants must regularly test and calibrate the equipment and document the associated findings. AI pharmaceutical validation technologies support those steps by capturing data or verifying employees’ manual inputs. Many businesses in tightly controlled industries use connected sensors with AI algorithms working in the background to process incoming production line information and flag possible abnormalities.
Those setups send real-time alerts about machine faults that could disrupt production. Some also recognize environmental issues that could trigger recalls if employees do not address them quickly enough. For example, ultraviolet light degrades some cancer drugs, necessitating carefully controlled storage. Excessive heat and moisture exposure have similar effects on other products. Process validation identifies issues that could make products dangerous or unsellable, helping executives control their bottom lines and ensure safety.
Improving Packaging-Related Processes
Validation processes for pharmaceutical containers encompass all steps to check that the materials and design of the primary, secondary and tertiary packages protect the contents and meet other identified requirements. The processes also assess potential risks, such as whether a particular defect results in a cosmetic flaw or presents safety risks for patients or others who may handle the products.
Package-related needs vary based on a product’s formula and whether it requires sterile filling processes. Besides spanning caps, vials and labels, process validation procedures must verify that products have the correct labels and instructions. Variations or errors within that content pose safety risks and confuse patients, especially for medications that become life-threatening when taken incorrectly.
Many consumables manufacturers and restaurant managers use AI to ensure labels and menus contain the correct allergen warnings. Pharmaceutical executives can take a similar approach with machine vision tools that quickly and accurately scan text to check that it matches packages.
Ongoing research in this area could open up more opportunities. In one example, a Thai pharmacy student participating in a hackathon received an award for her proposed AI-based system. It recognizes misshapen or defective pills in real time as they fall into trays and removes them from production areas. The student designed her project to help pharmacists who traditionally rely on paper documentation and other error-prone processes. She believes this innovation will improve the quality of medication distributed to patients.
Motivated executives could easily implement similar factory technologies to prioritize AI pharmaceutical validation. They can ensure accuracy, quality and compliance in operations.
Reducing Untrustworthy Findings
Even manufacturing changes that accelerate production can slow it if unintended consequences occur. Imagine noticing an automated quality control tool repeatedly showing high false-positive rates. That issue reduces many frequently cited automation advantages by requiring conscientious employees to check the validity of the technology's conclusions.
One manufacturer of smart quality control systems targets that problem with an AI-powered inspection platform that assesses several key performance indicators to enhance results. The system significantly reduces untrue indications, such as incorrectly flagged quality issues. Because this platform understands complex anomalies, it helps users find the root causes of foreign objects, impurities or cosmetic flaws.
The tool interprets real-time production data to raise efficiency and reliability. Despite technology’s imperfections, products like this show how it can reduce discrepancies once people understand how to use it and regularly check for expected performance.
Furthering AI Pharmaceutical Validation
These artificial intelligence applications promote better worker productivity, patient safety, production consistency and regulatory compliance. Before choosing and implementing any solution, decision-makers should learn how to use it and give employees ample time to build skills and ask questions when bringing it into their work.
