The Global Harmonization Framework (GHF): A Unified Regulatory Model for AI-Augmented Pharmacovigilance and Automated ICSR Follow-up
Faculty Mentor
Ahmed Abu Fayyad
Major/Area of Research
Drug Regulatory Affairs; Pharmaceutical Sciences, Pharmacy Administration, Pharmacovigilance, Drug Regulatory Affairs
Description
INTRODUCTION: The integrity of global drug safety data is currently undermined by manual, fragmented methods—such as email, phone, and fax—used for Adverse Event (AE) follow-up. While Individual Case Safety Reports (ICSRs) are increasing in volume, a significant percentage remain incomplete, delaying signal detection. Current AI adoption is hindered by "policy friction" between conflicting global standards, specifically the divergence between the FDA’s risk-based validation, the EMA’s structural transparency requirements, and GDPR data privacy mandates.
METHOD: This research introduces the Global Harmonization Framework (GHF), a novel policy model designed to unify these divergent standards. The study employed a structured Delphi-Lite Protocol and a "Divergence Matrix" to analyze 12 critical compliance points in a digital pharmacovigilance (PV) workflow. By comparing requirements from 21 CFR Part 11 (FDA) and GVP Module VI (EMA), this methodology identified high-risk friction areas, such as the conflict between GDPR’s "Right to Erasure" and GVP’s mandatory 10-year data retention.
RESULTS: The GHF resolves these conflicts by proposing Novel Harmonized Regulatory Requirements (HRRs), notably the "Auditable Explainability Log", which provides a legal blueprint for AI accountability. The framework is currently undergoing validation via a Delphi-Lite Protocol. Preliminary data indicates a high level of expert agreement (Current Mean: 4.4/5.0).
DISCUSSION/CONCLUSION: This research serves as a definitive roadmap for Marketing Authorization Holders (MAHs) to implement an autonomous, AI-driven agent capable of compliant, real-time follow-up. By adopting this "Super-Standard," the pharmaceutical industry can achieve global regulatory convergence, significantly reducing operational costs while enhancing the quality of patient safety data.
The Global Harmonization Framework (GHF): A Unified Regulatory Model for AI-Augmented Pharmacovigilance and Automated ICSR Follow-up
INTRODUCTION: The integrity of global drug safety data is currently undermined by manual, fragmented methods—such as email, phone, and fax—used for Adverse Event (AE) follow-up. While Individual Case Safety Reports (ICSRs) are increasing in volume, a significant percentage remain incomplete, delaying signal detection. Current AI adoption is hindered by "policy friction" between conflicting global standards, specifically the divergence between the FDA’s risk-based validation, the EMA’s structural transparency requirements, and GDPR data privacy mandates.
METHOD: This research introduces the Global Harmonization Framework (GHF), a novel policy model designed to unify these divergent standards. The study employed a structured Delphi-Lite Protocol and a "Divergence Matrix" to analyze 12 critical compliance points in a digital pharmacovigilance (PV) workflow. By comparing requirements from 21 CFR Part 11 (FDA) and GVP Module VI (EMA), this methodology identified high-risk friction areas, such as the conflict between GDPR’s "Right to Erasure" and GVP’s mandatory 10-year data retention.
RESULTS: The GHF resolves these conflicts by proposing Novel Harmonized Regulatory Requirements (HRRs), notably the "Auditable Explainability Log", which provides a legal blueprint for AI accountability. The framework is currently undergoing validation via a Delphi-Lite Protocol. Preliminary data indicates a high level of expert agreement (Current Mean: 4.4/5.0).
DISCUSSION/CONCLUSION: This research serves as a definitive roadmap for Marketing Authorization Holders (MAHs) to implement an autonomous, AI-driven agent capable of compliant, real-time follow-up. By adopting this "Super-Standard," the pharmaceutical industry can achieve global regulatory convergence, significantly reducing operational costs while enhancing the quality of patient safety data.