Beyond the Hype: How is AI impacting Quality Systems in Pharmaceutical Companies Today?

AI in Pharma

We all know and have heard a thousand times that Artificial Intelligence (AI) is a transformative technology with the potential to revolutionize “the way we do things” across industries and functions. In this post, my goal is to cut through the hype and dig deeper to understand the tangible, more immediate impacts of AI, specifically in regard to quality systems within pharmaceutical companies.

While long-term potential impacts in areas like drug discovery, medical chatbots, and personalized medicine often grab the headlines, much of that still lies in the future. It’s in quality management that we are already seeing AI making a significant difference in the pharmaceutical industry. Here are the specific functional areas where the impact is being felt the most.

Enhanced Data Integrity and Compliance

One of AI’s most immediate and undeniable impacts on the pharmaceutical industry is the enhancement of data integrity and compliance. AI tools are very good at identifying mistakes and outliers. Since regulatory bodies such as the FDA and EMA have stringent data accuracy and traceability requirements, that ability is already widely deployed.

AI-powered tools can continuously monitor data streams from various sources—be it manufacturing processes, clinical trials, or laboratory environments – and look for anomalies. They do this by using advanced algorithms to flag potential errors and ensure that data remains consistent and reliable.

For instance, AI can automate the review of batch records and laboratory notebooks, reducing the time and effort required for manual inspections. By employing natural language processing (NLP), AI systems can parse and verify the accuracy of textual data, identifying discrepancies that human reviewers might overlook. This not only speeds up the process but also enhances the reliability of the quality control measures in place.

Predictive Maintenance and Downtime Reduction

A second area where AI is already having an impact is the use of predictive analytics capabilities in equipment maintenance.

Quality in pharmaceutical manufacturing is heavily dependent on the consistent performance of machinery. Traditional maintenance schedules, often based on fixed intervals, can lead to either unnecessary downtime or unexpected equipment failures, both of which can compromise quality.

In contrast, by analyzing historical data and identifying patterns in the data captured from equipment, AI can predict when a piece of equipment is likely to fail or require maintenance. This predictive maintenance approach ensures that interventions are made just in time—before any failure occurs and without unnecessary downtime. As a result, pharmaceutical companies can maintain optimal equipment performance, ensuring consistent product quality and compliance with regulatory standards.

Real-Time Quality Monitoring

A related area where AI is making a significant impact is real-time quality monitoring. The pharmaceutical manufacturing process involves numerous critical control points where quality needs to be assured. Traditional methods often rely on periodic sampling and offline testing, leading to delays in identifying and addressing quality issues.

AI-driven systems, however, can leverage the ability to continuously monitor production processes in real-time. Using sensors and advanced analytics, these systems can instantaneously detect deviations from established quality parameters. This real-time monitoring allows for immediate corrective actions, interrupting production, minimizing the production of off-spec products, and reducing waste.

Streamlined Documentation and Regulatory Submissions

Since the pharmaceutical industry is notoriously documentation-heavy, with extensive records required for regulatory submissions and quality assurance, AI has a significant role to play in streamlining documentation processes.

One way it can do so is by automating generating and managing reports and summaries. AI can collate data from various sources using machine learning algorithms, organize it coherently, and generate comprehensive reports that meet regulatory requirements.

This automation reduces the time and effort involved in preparing documentation and minimizes the risk of human error. As a result, pharmaceutical companies are accelerating their regulatory submissions, bringing products to market more swiftly while ensuring compliance with all necessary regulations.

Improved Supplier Quality Management

AI also has an impact on managing the quality of raw materials and components sourced from suppliers. AI can enhance supplier quality management by analyzing historical performance data and monitoring ongoing supplier activities. By evaluating trends and identifying potential risks, AI systems can help pharmaceutical companies make informed decisions about their supply chains.

For example, if the system detects a pattern of issues related to a supplier – a pattern that a human may not discern – an AI-based function can flag this and suggest changes in process or even alternative suppliers with better performance records. Additionally, AI can monitor real-time data from suppliers to ensure that the materials received meet the required quality standards, thereby preventing quality issues before they impact the manufacturing process.

AI is Already Delivering Real Impact on Pharma Quality

While it can be hard to cut through the hype around AI’s potential in the pharmaceutical industry, its most immediate and undeniable impacts are already being felt in the realm of quality systems.

From enhancing data integrity and compliance to predictive maintenance, real-time monitoring, streamlined documentation, and improved supplier management, AI is already transforming how pharmaceutical companies ensure quality.

However, tapping these capabilities can be a challenge for smaller, growing pharma companies. That is why Scigeniq is integrating these capabilities into the products we provide to customers. As a supplier of software solutions to growing pharma companies in emerging markets, we look for ways to ensure that all the components of your quality, document management, and regulatory systems work together with the new generation of AI capabilities to deliver the maximum impact on your business.