AI in Regulatory Compliance and Human Error Reduction in GMP Manufacturing

AI can transform regulatory decision-making by enhancing efficiency, compliance, and accuracy in GMP-regulated industries, including pharmaceuticals, biotechnology, and medical devices. It addresses core challenges like regulatory submissions, risk assessments, audits, clinical trial optimization, and post-market surveillance while minimizing human error.

Learning Objectives:-

• Decode the psychology behind human error.
• Navigate regulatory requirements for error management.
• Apply Root Cause Analysis and the Root Cause Determination Tool effectively.
• Establish and track human error metrics.
• Monitor CAPA effectiveness with AI-enhanced KPIs.
• Integrate AI for predictive error prevention.

Key Areas of Application:-

• Streamlining Regulatory Submissions:
  • Automation: AI-driven NLP tools generate and review regulatory submissions (e.g., INDs, NDAs), ensuring compliance with FDA, EMA, and ICH guidelines.
  • Smart Checklists: Dynamic validation tools confirm adherence to regulations like FDA's 21 CFR Part 11.
• Enhancing Risk Assessments:
  • Predictive Analytics: Anticipates adverse events and risks in clinical trials and manufacturing.
  • Real-Time Monitoring: AI sensors flag production anomalies for proactive intervention.
• Human Error Reduction:
  • Root Cause Analysis (RCA) using AI to identify systemic causes of errors.
  • AI-driven metrics for human error prediction, categorization, and trend analysis.
  • Training and environment design tools for reducing performance errors.
• Compliance and Audits:
  • Automated audit trails ensuring data integrity.
  • AI virtual assistants providing real-time compliance guidance.
• Post-Market Surveillance:
  • AI detects adverse events from diverse data sources like the FDA’s FAERS database, social media, and medical literature.
• Challenges and Solutions
  • Algorithm Transparency: Explainable AI is essential for building trust with regulators.
  • Data Privacy: Compliance with GDPR, HIPAA, and other data protection laws.
  • Validation: Rigorous testing ensures AI reliability in regulated environments.

Who Should Attend?

• QA/QC Staff
• Training Managers
• Process Improvement Specialists
• Regulatory Officers
• Operations and Manufacturing Leads
• Industrial Engineers.