AI in Pharmacovigilance: Predicting & Preventing Drug Risks

Healthcare is advancing rapidly, and artificial intelligence (AI) is now playing a vital role in improving drug safety. By analysing vast amounts of data, AI can monitor medicines, detect harmful interactions, and prevent side effects. This enables pharmaceutical companies to respond faster, ensuring patient safety and delivering effective treatment.¹

AI in drug safety is changing how pharmaceutical companies predict harmful interactions and prevent side effects, ensuring patient safety.

How AI Improves Drug Safety Monitoring

Traditional drug safety monitoring involved slow, manual processes that made it hard to handle large amounts of data. AI solves this problem by analysing diverse sources, including electronic health records, clinical trial results, and patient feedback. Using AI in drug safety, healthcare providers can analyse large datasets with greater accuracy, making faster decisions to protect patients. It uses trusted datasets like DrugBank and FDA reports to predict harmful drug interactions. Machine learning (ML) and natural language processing (NLP) help AI quickly identify patterns and risks, enabling faster action to prevent harm, improve patient care, and build trust in drug safety.⁵

Key Ways AI Enhances Drug Interaction Safety

Spotting Safety Issues Early

AI-powered tools quickly find patterns in safety data, going beyond what manual methods can do. They detect safety signals early, allowing faster responses to risks and improving patient safety.⁴ Pharmacovigilance innovations, driven by AI, enable early detection of adverse reactions and safer medication practices worldwide. For example, WHO’s VigiBase uses AI to analyse millions of reports from different countries to flag rare side effects or risky drug interactions. This real-time analysis helps healthcare providers and pharmaceutical companies act quickly, reducing risks and protecting patients.

Automating Reporting and Tracking

AI makes it easier to collect and analyse drug safety data from clinical trials, medical records, and patient feedback. Through pharmacovigilance innovations, healthcare providers can analyze data efficiently and make informed decisions to ensure patient safety. By formatting this data for regulators, AI reduces errors and saves time.

Interpreting Unstructured Data

Valuable safety information is often hidden in research papers or social media posts. AI uses natural language processing (NLP) to analyse this data and find unexpected side effects or risks. Combining these insights with traditional methods gives a clearer picture of drug safety.⁴

Post-market surveillance with AI

AI plays a critical role in ensuring drug safety after approval by analysing real-world patient experiences. Post-market surveillance examines patient registries to assess long-term outcomes and pharmacy records to identify patterns in prescriptions and side effects. Additionally, wearable devices, such as fitness trackers and smartwatches, provide continuous real-time data on metrics like heart rate, activity levels, and medication adherence.

AI analyses this data to detect early warning signs of adverse reactions. AI plays a vital role in preventing adverse drug reactions by identifying risks and enabling timely interventions. For instance, wearable devices can alert healthcare providers when a patient responds poorly to a medication, enabling timely interventions to prevent complications. This integration of wearable technology into pharmacovigilance ensures drugs remain safe and effective throughout their lifecycle while empowering patients to take an active role in their healthcare.

Transforming Drug Safety: AI in Action

• VigiBase by the World Health Organization: VigiBase, managed by the World Health Organization (WHO), serves as an international pharmacovigilance database, gathering safety information from more than 150 nations. AI tools analyse this extensive dataset to detect safety signals, such as rare adverse drug reactions or harmful interactions. This real-time analysis enables healthcare providers and pharmaceutical companies to respond proactively, ensuring global drug safety and patient protection.

• Pfizer and IBM Watson Collaboration: In 2016, Pfizer partnered with IBM Watson Health to leverage AI in immuno-oncology research. The collaboration aimed to utilise IBM Watson's machine learning and natural language processing capabilities to identify novel drug targets and optimise patient selection for clinical trials. While this initiative primarily focused on drug discovery, it indirectly contributes to drug safety by accelerating the development of safer treatments.

• Bayer and Tempus Partnership: Bayer has collaborated with Tempus, a company specialising in precision medicine and AI, to enhance patient access to genomic testing and improve treatment outcomes in oncology. This partnership utilises AI-driven insights from genomic and clinical data to better understand patient responses to specific treatments. While the focus is on personalised care, this approach indirectly enhances drug safety by minimising adverse reactions through tailored treatment strategies.

Challenges in Using AI for Drug Safety

• AI systems work with sensitive health information, so strong privacy protections are essential. Laws like GDPR and HIPAA require encryption and limited access to keep patient data safe. However, AI decisions can sometimes be hard to understand, making it difficult for healthcare providers to fully trust them.

• Clear and transparent tools are needed to help explain AI’s decision-making processes and build confidence in its use. Explainable AI (XAI) enhances transparency by showing how AI models arrive at their predictions, promoting trust and safety in critical tasks such as predicting drug interactions.⁵

• The ability of machine learning to process vast biomedical literature and unstructured data, including electronic medical records, allows for the early prediction of drug interactions that may not be evident during clinical trials.³

• Additionally, AI must be unbiased to guarantee fair treatment for all patients, regardless of their background. Efforts are underway to create guidelines that ensure ethical and responsible AI applications in healthcare, promoting reliable systems that prioritise patient safety and equity.

Future Directions for AI in Pharmacovigilance

Personalised Safety Models

AI can analyse individual patient data, such as medical history and genetics, to predict how someone might respond to a drug. This approach aligns with precision medicine, enabling safer, tailored treatments and reducing the risk of side effects.

Integration with Emerging Technologies

AI combined with technologies like blockchain and connected devices is set to change drug safety monitoring.

• Blockchain: By providing a secure and transparent method for recording transactions, blockchain can enhance the integrity of pharmacovigilance data. This ensures that drug safety information is tamper-proof and trustworthy, facilitating better tracking of adverse events and regulatory compliance.²
  

• IoT: The deployment of IoT devices enables continuous monitoring of patient health metrics in real-time. When integrated with AI, these devices can detect early signs of adverse drug reactions, allowing for prompt interventions and personalised treatment adjustments.

Global Data Sharing Networks

Establishing international networks for sharing drug safety data can significantly enhance AI's effectiveness in pharmacovigilance. Access to diverse and extensive datasets enables AI systems to identify safety signals more rapidly and accurately, leading to quicker responses to emerging drug safety concerns. Collaborative data sharing also fosters a unified approach to monitoring and mitigating adverse drug reactions on a global scale. By advancing in these areas, AI can play an important role in transforming drug safety practices, ensuring more personalised, secure, and efficient pharmacovigilance systems worldwide.

Conclusion

AI is transforming pharmacovigilance by enhancing drug safety, detecting risks early, and enabling swift responses. Wearable devices and AI tools are helping healthcare providers succeed in preventing adverse drug reactions by continuously monitoring patient health Analysing data from patient records, clinical trials, and wearable devices, ensures the timely identification of harmful interactions. Real-world examples, such as those from Pfizer and Bayer, show its effectiveness in drug monitoring. Emerging technologies like blockchain and IoT further boost AI’s potential, enabling secure data sharing and personalised care. Addressing challenges like privacy, bias, and transparency will ensure AI delivers safer, more efficient pharmacovigilance and better global health outcomes.

References

1.Basile, Anna O., et al.Artificial Intelligence for Drug Toxicity and Safety. Trends in Pharmacological Sciences, vol. 40, no. 9, Sept. 2019, pp. 624–35. DOI.org (Crossref), https://doi.org/10.1016/j.tips.2019.07.005.

2.Bathula, Archana, et al.Blockchain, Artificial Intelligence, and Healthcare: The Tripod of Future—a Narrative Review. Artificial Intelligence Review, vol. 57, no. 9, Aug. 2024, p. 238. Springer Link, https://doi.org/10.1007/s10462-024-10873-5.

3.Han, Ke, et al. ‘A Review of Approaches for Predicting Drug–Drug Interactions Based on Machine Learning’. Frontiers in Pharmacology, vol. 12, Jan. 2022. Frontiers, https://doi.org/10.3389/fphar.2021.814858.

4.Praveen, John, et al. Transforming Pharmacovigilance Using Gen AI: Innovations in Aggregate Reporting, Signal Detection, and Safety Surveillance’. The Journal of Multidisciplinary Research, Oct. 2023, pp. 9–16. saapjournals.org,  https://doi.org/10.37022/tjmdr.v3i3.484.

5.Vo, Thanh      Hoa, et al. ‘On the Road to Explainable AI in Drug-Drug Interactions Prediction: A Systematic Review’. Computational and Structural Biotechnology Journal, vol. 20, 2022, pp. 2112–23. PubMed, https://doi.org/10.1016/j.csbj.2022.04.021.