Harnessing Big Data and Artificial Intelligence for Pharmacovigilance in Precision Medicine
- 1. Welcome Unlocking The Potential : Leveraging Big Data And Artificial Intelligence For Pharmacovigilance In Precision Medicine M . Rema Rachal Pharm . D CLS_0051012024 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 1
- 2. Index Introduction Pharmacovigilance Revolution AI’s Game changing role. Big data analytics revolution The growing challenge of pharmacovigilance Case studies in AI’s enhanced pharmacovigilance Conclusion References www.clinosol.com | follow us on social media @clinosolresearch 2
- 3. INTRODUCTION 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 3 In the era of precision medicine the integration of big data and artificial intelligence is revolutionizing pharmacovigilance. This presentation explores the potential of leveraging these technologies to enhance drug safety and patient care.
- 4. PHARMACOVIGILANCE REVOLUTION: Embrace the future of Pharmacovigilance with the integration of AI and Big Data analytics. These advanced technologies are driving a revolutionary shift in the identification and mitigation of drug- related risks. Explore how healthcare professionals and regulatory authorities are gaining unprecedented insights, enhancing patient safety, and optimizing overall efficiency in Pharmacovigilance processes. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 4 BIG DATA DATA STORAGE DATA ANALYSIS DATA VISULISATION
- 5. AI's Game-Changing Role: AI's prowess in processing vast data volumes and identifying patterns is transforming Pharmacovigilance. Machine learning algorithms enable the analysis of medical records, adverse event reports, scientific literature, and even social media data to pinpoint potential safety concerns and adverse drug reactions. Witness how AI facilitates early detection, ensuring timely intervention and effective mitigation strategies. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 5
- 6. BIG DATA ANALYTICS REVOLUTION: Complementing AI, Big Data analytics is reshaping the landscape of Pharmacovigilance. Explore how the vast and diverse healthcare data landscape holds immense potential for identifying crucial drug safety signals. Uncover the significance of leveraging Big Data analytics in extracting actionable insights, providing a comprehensive understanding of the ever-increasing volume and variety of healthcare data. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 6
- 7. THE GROWING CHALLENGE OF PHARMACOVIGILANCE Before delving into the role of Big Data and AI, it’s essential to understand the evolving challenges of pharmacovigilance. In the past, adverse drug reactions (ADRs) were primarily detected through spontaneous reporting systems, which relied on healthcare professionals and patients voluntarily reporting adverse events. This approach, while valuable, had significant limitations. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 7
- 8. LIMITATIONS. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 8 Under reporting: A vast majority of ADRs went unreported, leading to a skewed understanding of a drug’s safety profile. Delayed Detection: It could take years to identify emerging safety concerns, leading to prolonged exposure of patients to potentially harmful drugs. Data Overload: The sheer volume of data generated from various sources, such as electronic health records (EHRs), social media, and scientific literature, became overwhelming for manual review. Signal Detection: Traditional methods for identifying safety signals lacked efficiency and precision.
- 9. ENTER BIG DATA 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 9 • Big Data analytics can identify subtle safety signals that may have been missed with traditional methods. •Diverse data sources, including EHRs, claims data, and wearable devices, provide a comprehensive view of a drug’s safety profile. •Advanced analytics can predict potential safety issues before they become widespread. • Big Data allows for real-time monitoring of adverse events, enabling faster detection of safety signals. Real-time Surveillance Predict Analytics Improved Signal Detection Data Variety
- 10. BIG DATA ANALYTICS REVOLUTION: Complementing AI, Big Data analytics is reshaping the landscape of Pharmacovigilance. Explore how the vast and diverse healthcare data landscape holds immense potential for identifying crucial drug safety signals. Uncover the significance of leveraging Big Data analytics in extracting actionable insights, providing a comprehensive understanding of the ever- increasing volume and variety of healthcare data. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 10
- 11. HARNESSING AI FOR PHARMACOVIGILANCE While Big Data provides the raw material, AI acts as the processing engine that transforms this data into actionable insights. AI technologies, such as machine learning and natural language processing (NLP), have several applications in pharmacovigilance: 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 11 Natural Language Processing Case Triage Predictive Modeling Signal Detection Automated Data Extraction
- 12. • Automated Data Extraction: AI-powered algorithms can extract relevant information from unstructured sources like medical literature, ensuring that no critical data is overlooked. • Signal Detection: Machine learning models can identify emerging safety signals by analyzing patterns in adverse event reports, EHRs, and social media conversations. • Predictive Modeling: AI can predict which patients are at a higher risk of experiencing adverse events, allowing for targeted interventions. • Case Triage: AI-driven algorithms can prioritize adverse event reports based on severity, helping pharmacovigilance teams focus on the most critical cases. • Natural Language Processing: NLP enables the analysis of free-text adverse event descriptions, making it easier to understand the context and nuances of reported events. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 12
- 13. CASE STUDIES IN AI-ENHANCED PHARMACOVIGILANCE To illustrate the impact of AI in pharmacovigilance, let’s explore a few real-world examples: • FAERS and FDA’s Sentinel Initiative: The FDA’s Adverse Event Reporting System (FAERS) and the Sentinel Initiative have adopted AI and Big Data analytics to monitor drug safety. These systems continuously analyze vast amounts of healthcare data, allowing regulators to quickly identify and investigate safety concerns. • Social Media Listening: Pharmaceutical companies are increasingly using AI to monitor social media platforms for mentions of their drugs. This approach can help detect early signs of adverse events and assess patient sentiment. • Electronic Health Records: AI-driven algorithms can sift through EHRs to identify patterns of adverse events associated with specific drugs. This enables healthcare providers to make informed decisions about treatment options. 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 13
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- 15. 15 www.clinosol.com | follow us on social media @clinosolresearch The convergence of Big Data and AI is reshaping the field of pharmacovigilance. It offers the potential to enhance drug safety by enabling real-time monitoring, early signal detection, and predictive analytics. However, it also raises important questions about data privacy, bias, and regulatory oversight. To fully harness the benefits of AI and Big Data in pharmacovigilance, stakeholders must collaborate, address these challenges, and prioritize patient safety. In doing so, we can usher in a new era of safer and more effective medicines, ultimately benefiting patients around the world. CONCLUSION:
- 16. REFERENCES: 1. Hauben M, Hartford CG. Artificial intelligence in pharmacovigilance: scoping points to consider. Clinical therapeutics. 2021 Feb 1;43(2):372-9. 2. Murali K, Kaur S, Prakash A, Medhi B. Artificial intelligence in pharmacovigilance: Practical utility. Indian Journal of Pharmacology. 2019 Nov;51(6):373. 3. Roy P. Artificial-Intelligence based Machine-Learning in Pharmacovigilance. Journal of Pharmacovigilance & Drug Safety. 2023;20(2):6-9. 4. Qian T, Zhu S, Hoshida Y. Use of big data in drug development for precision medicine: an update. Expert review of precision medicine and drug development. 2019 May 4;4(3):189-200. 5. Yu KH, Hart SN, Goldfeder R, Zhang QC, Parker SC, Snyder M. Harnessing big data for precision medicine: infrastructures and applications. InPACIFIC SYMPOSIUM ON BIOCOMPUTING 2017 2017 (pp. 635-639). 16 www.clinosol.com | follow us on social media @clinosolresearch
- 17. Thank You! www.clinosol.com (India | Canada) 9121151622/623/624 [email protected] 10/18/2022 www.clinosol.com | follow us on social media @clinosolresearch 17