The pharmaceutical industry faces unique safety concerns. While vaccine manufacturing workflows themselves may not be dangerous, failing to meet quality standards or a lack of oversight can lead to significant risks for end users. Consequently, the safety of the product is more prominent than that of its production, and these items face strict regulatory requirements.

As pharma demand has risen and technology has advanced, the sector has shifted toward digital safety nets. High-stakes labs are increasingly using artificial intelligence (AI), automation and the Internet of Things (IoT) to meet higher quality assurance standards.

Automated Reports

One of the most common and helpful applications of AI in pharma safety is to automate reports. AI can monitor clinical trial documentation or post-market data updates about user safety to automatically generate summaries or alert stakeholders if a worrying trend emerges.

Because AI excels at finding subtle trends in vast datasets, it can typically find potential issues faster than manual alternatives. Pharma companies can respond faster to stop the damage as a result. In less pressing documentation matters, AI ensures accuracy by minimizing data entry errors.

This use case is growing increasingly popular, with 62% of drug manufacturers either actively using or planning on using AI in adverse event (AE) case processing. Many of these processes are also commonly outsourced, so automating them through AI instead brings pharmacovigilance back under the organization’s control and insight.

Production Workflow Analysis

While AE monitoring may be the most common AI application in pharma safety, this technology can also be a preventive measure. As pharmaceutical workflows become increasingly tech-centric, they generate more data on their ongoing operations. AI can analyze this information to find areas where processes may need to change to ensure higher safety standards.

An estimated 70% of all manufacturers use AI technologies like digital twins to simulate and evaluate their production, often to improve efficiency. Pharma labs can apply the same concept to look for steps that may make mistakes more likely or introduce cross-contamination risks. That way, they can address these issues before they lead to product safety concerns.

Many of the potential risks AI recognizes can go unnoticed by humans, as even small shifts can have a big impact. Simply making something more user-friendly can help by preventing errors. AI enables ongoing preventive improvements by finding these opportunities.

Automated Alerts

AI and related technologies have real-time benefits. Automated quality control inspections can instantly alert employees of an issue with a product that may jeopardize its safety. Alternatively, IoT devices on production equipment can notify stakeholders when a previous step malfunctions, potentially impacting vaccines.

Automated monitoring is crucial because many errors may be unnoticeable otherwise. For example, particle size directly affects vaccine stability, but particles in a medical suspension are often microscopic. IoT sensors and other automated tools can detect deviations that may be invisible to the naked eye, ensuring pharma companies catch all errors before they lead to unstable products.

In addition to being accurate, automation is faster than manual alternatives. Consequently, the facility can address any problems as soon as they arise, mitigating the impact of the situation. That improves both safety and costs.

Real-Time Equipment Monitoring

A reliable pharma safety net is also a matter of ensuring all equipment remains in optimal condition. While human errors often take the spotlight, machine malfunctions can still occur and impact vaccine safety, potentially without anyone noticing. AI and IoT can prevent such instances through predictive maintenance (PdM).

PdM uses AI to analyze IoT data in real time to predict equipment failures before they occur. While manufacturers typically see it as a way to minimize downtime and related costs, it can also be a valuable safety mechanism. A more proactive repair method means machinery remains in reliable condition for longer, making safety-affecting errors less likely.

Because PdM recognizes machine health issues before they’re outwardly noticeable, it can also suggest repairs before performance drops enough to impact vaccine quality. Consequently, following this practice can further reduce the risk of production errors in high-stakes pharma labs.

Considerations for Implementing AI in Pharma Safety

As beneficial as AI and related technologies can be, they come with safety concerns on their own. Pharmaceutical businesses must address these risks when choosing and implementing a tech solution to ensure they improve overall safety instead of simply changing which hazards are most pressing.

The FDA has published draft guidance on AI detailing recommended steps for developing and using a reliable model in drug production. While not a final rule, these guidelines can serve as a useful baseline for safe AI usage in the industry. Many of the recommendations deal with ensuring a design suits its intended purpose well, using sufficient and relevant training data and evaluating the algorithm’s performance before applying it.

Ensuring cybersecurity protections is another crucial step. Pharma companies must restrict access to their AI models and all related information as much as possible. Changes to as little as 1% of a design’s data can affect its reliability, making data poisoning attacks a considerable threat. Minimizing access privileges, protecting authorized accounts with multifactor authentication and monitoring databases in real time are all necessary measures.

Overreliance on technology also deserves consideration. Lofty claims about AI’s performance often lead organizations to take its recommendations at face value, but even the most reliable models can experience errors. Consequently, manufacturers must develop strict policies that include verifying any AI alerts or suggestions before acting on them.

In that same vein, labs must be careful not to overapply AI. Generally, AI and automation are best for data-heavy, repetitive tasks with fairly predictable patterns. Cases involving more nuance or irregularity may be best kept under human control. Businesses can identify which use cases align with technology’s capabilities by monitoring the results of their own Industry 4.0 projects.

AI Has Big Potential but Unique Risks in Pharma Safety

AI, IoT and similar innovations can revolutionize pharma safety. However, they can also introduce unique concerns. Companies in this industry must recognize both sides to craft and implement an ideal safety plan.

Pharma labs cannot overlook the potential of digital technologies. Attention to these opportunities and education about how to ensure their reliability is key to driving both efficiency and consumer safety in the future.