Title : Ensuring reproducibility, reliability, and ethical compliance in AI-driven pharmacometrics: Emerging regulatory guidelines and best practices
Abstract:
The integration of artificial intelligence (AI) within pharmacometrics presents significant advancements in model precision, predictive capability, and clinical decision support. Nonetheless, the widespread adoption of AI-driven pharmacometric models necessitates rigorous assurance of reproducibility, reliability, and ethical adherence, particularly in the context of evolving regulatory frameworks. This study provides a comprehensive examination of emerging guidelines and best practices designed to address these critical dimensions, supported by evidence-based analyses from recent AI pharmacometric implementations. Central themes include standardized validation methodologies, transparency and explainability protocols, robust data governance and privacy safeguards, as well as ethical imperatives related to bias mitigation and informed consent. Through detailed case studies exemplifying successful regulatory alignment and model verification, this work underscores the necessity of standardized workflows and interdisciplinary collaboration. The proposed framework aims to equip researchers, clinicians, and regulatory stakeholders with actionable strategies to deploy AI-enabled pharmacometric models that are scientifically robust, transparent, and ethically responsible.
Keywords: Artificial Intelligence, Pharmacometrics, Population Pharmacokinetics, Reproducibility, Reliability, Ethical Compliance, Regulatory Guidelines.
