AI in Pharma Safety: Enhancing Pharmacovigilance with Artificial Intelligence

In Pharma Now’s previous articles on artificial intelligence (AI) in the pharmaceutical industry, we’ve discussed AI's benefits, challenges, and use cases in various pharmaceutical industry sectors, including manufacturing, supply chain, regulatory affairs, clinical trials, and drug discovery. Continuing on this thread, this article discusses another crucial sector in the pharmaceutical industry: pharmacovigilance.

Pharmacovigilance is the process of monitoring the safety of drugs after their approval for use. From a pharmaceutical company's perspective, pharmacovigilance is a necessary, time-consuming, resource-consuming, and expensive process. It involves data gathering from patients and clinical trial volunteers, the analysis of gathered data, risk management, causality assessment, and many other activities. However, since pharmacovigilance studies are necessary to maintain regulatory compliance, pharma companies have no option but to complete them.

Consequently, many pharma companies are looking to simplify or automate their pharmacovigilance activities by implementing AI in pharmacovigilance. 

How can AI be Implemented in Pharmacovigilance?

Adverse event detection and reporting

AI algorithms can rapidly analyze large databases to identify potential adverse drug reactions. For example, AI systems can more efficiently identify anomalies in electronic records than people, making the identification of side effects easy.

AI systems can monitor multiple data sources simultaneously and flag concerns, which is challenging when done manually. This will allow the responsible teams to resolve the event swiftly.

Surveillance

AI systems have excellent potential in surveillance. They can be used to monitor social media for patient-reported side effects. They can also be used to process data from other sources like healthcare claims, insurance claims, or patient registries.

These detections can be used as early warnings of safety issues, allowing responsible teams to monitor these specific cases instead of the entire batch closely. To understand the full potential of AI across pharma workflows, including safety monitoring, explore how AI is transforming compliance in high-risk regulated environments.

Signal detection and analysis

Machine learning (ML) algorithms can identify patterns in safety data and flag potential risks earlier. As these models have higher accuracy than human detection, the number of false positives also decreases, reducing unnecessary alerts and allowing responsible teams to react swiftly to emergencies, improving resource allocation and usage.

Literature screening

Manually screening existing literature to find references to specific cases or side effects can take days or weeks. However, AI tools can automate this process.

They can scan existing literature to identify relevant information and even generate concise summaries of relevant articles, saving researchers the trouble of reading multiple articles.

Case processing

AI systems can also assist in case processing tasks. For example, repetitive tasks like filling out patient details can be automated to reduce manual work. Additionally, it can help create regulatory reports.

For example, AI systems can generate the relevant individual case safety report (ICSR) and periodic safety update report (PSUR) for regulatory submission.

Regulatory compliance

AI systems can also analyze and summarize existing regulatory guidelines for pharmacovigilance and identify potential non-compliance issues. AI can cross-check developed procedures with regulatory-approved processes or develop pharmacovigilance strategies for a particular drug according to regulatory requirements.

Advanced algorithms are also being deployed to strengthen regulatory compliance frameworks and reduce the risk of audit failures across global markets. Implementing these use cases of AI in pharmacovigilance will have several benefits.

Benefits of AI in Pharmacovigilance

Here are some benefits of AI in Pharmacovigilance:

• Speed: Implementing AI in pharmacovigilance can improve the speed of detection and response to safety issues. This will help prevent or resolve adverse events before they become severe.
• Accuracy: By reducing human involvement, AI can improve the accuracy of pharmacovigilance activities. This will primarily be achieved by reducing human errors in signal detection and documentation.
• Scalability: One of the most significant advantages of AI in pharmacovigilance is that the applications are scalable. The sample application can monitor multiple drugs and datasets with thousands—if not millions—of people.
• Cost reduction: By increasing the speed and accuracy of pharmacovigilance activities, the total cost involved can be reduced. Furthermore, automation can reduce manual workload and resource requirements, further reducing costs.

Several companies have already noted these advantages.

AI Technologies in Pharmacovigilance: Key AI-Powered Technologies Used in Pharmacovigilance

Natural language processing (NLP)

NLP can be used to extract medical information from unstructured data. For example, tools like Amazon Web Services Comprehend Medical use NLP to extract insights like symptoms and diagnoses from unstructured text. Other tools like Google Healthcare Natural Language API analyze unstructured data like medical records or insurance claims to generate structured insights.

Machine learning (ML)

ML can be used to identify patterns and trends in large datasets to identify early signs of safety issues. Tools like Oracle Empirica can be used to detect, analyze, and manage safety signals. Empirica, especially, is a leading tool used by many pharmaceutical companies.

Machine learning's application in safety monitoring complements its growing role in drug discovery and development, particularly in predicting compound behavior and clinical outcomes.

Automation and workflow optimization

Automation and workflow optimization can be used to automate repetitive pharmacovigilance tasks like case intake and data entry. Tools like UiPath can automate the movement of clinical data and accelerate the creation of standard operating procedures.

Other tools like Blue Prism have also been used. For example, Pfizer used Blue Prism’s scaled RPA to process large volumes of clinical trial data during COVID-19.

To explore how companies are deploying AI automation tools in PV operations, read our analysis on UiPath, Blue Prism, and other emerging platforms.

Adverse event case processing tools

AI-powered adverse event detection tools can automate cases' intake, classification, and reporting. Tools such as Veeva Vault Safety can be used to manage safety information. This tool has been used by many pharma companies, including Merck.

Regulatory reporting software

AI-powered regulatory reporting tools can be used to automate regulatory submissions and meet compliance requirements. Tools like Oracle Argus can automate case processing and reduce workload by up to 50%. Other tools like ArisGlobal’s LifeSphere have been implemented by many leading pharma companies, including Roche, Novartis, AstraZeneca, and Johnson and Johnson, to automate and streamline regulatory workflows.

These above-given examples are just the tip of the iceberg. AI ha excellent potential in pharmacovigilance. However, a majority of pharmaceutical companies have still not implemented it.

Challenges facing AI in pharmacovigilance

The implementation of AI-powered tools and technologies in pharmacovigilance is limited by several challenges that are yet to be addressed:

Data quality and quantity

The successful implementation of AI in any application depends on high-quality data. However, in pharmacovigilance, data comes from various sources (electronic monitoring devices, patients, and reports), and ensuring the completeness and accuracy of all this data is challenging. Furthermore, there may not always be sufficient correct data to build a model.

Bias and fairness issues

In pharmacovigilance, reporting bias can lead to poor prediction accuracy. AI models may inadvertently use the same biases as the industry. Therefore, AI models' fairness must be thoroughly addressed and ensured before their implementation to ensure there are no ethical concerns down the line.

Regulatory compliance

Regulatory guidelines do not encourage or discourage the use of AI because there are no clear regulatory guidelines on AI implementation. The EU AI Act is the first effort to create a somewhat clear framework for implementing AI. Currently, most companies refrain from attempting AI implementation simply because they do not wish to risk non-compliance. Hence, additional efforts must be made to develop a clear framework for implementing AI.

Resource constraints

Developing and implementing AI algorithms is a challenging task requiring experts. Finding these experts is not easy. Furthermore, even if the right experts are found, they will need sufficient data and time to develop the model. Also, if they are provided with these sources, they will require sufficient funding. These many “ifs” make AI implementation more complicated.

Conclusion

As many have fairly assumed, AI is here to stay. It has many use cases and has demonstrated exceptional advantages in each. However, in the pharmacovigilance space, the implementation of AI is still at a nascent stage. While many tools like Oracle Argus and UiPath have been developed, tested, and implemented by many pharma companies, their widespread application is still subject to many challenges. However, we remain optimistic that AI has a positive outlook in pharmacovigilance.

FAQs

1. Why is AI being implemented in so many pharmaceutical processes?

AI is being implemented in many pharmaceutical processes because it can reduce time, workload, and resource requirements of processes while improving accuracy, which will eventually result in lowered costs for the overall project.

2. Does Big Pharma believe in the implementation of AI?

Yes, many Big Pharma companies have already implemented AI in various sectors. However, on a broad scale, the implementation of AI is still at a nascent stage.

3. What’s stopping pharma companies from implementing AI in all processes?

The implementation of AI is costly and subject to regulatory compliance. Hence, the implementation is rather slow. However, as clearer guidelines are developed, we’re likely to see adoption by most companies.