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Track 9: R&D Quality and Compliance

This track explores the evolving landscape of quality and compliance across research and development, emphasizing proactive, risk-based approaches to ensure trial integrity, participant protections, and regulatory compliance in clinical trials, including in agile, pragmatic, decentralized, and technology-enabled clinical trial settings. Sessions focus on practical applications of GCP, GVP, GLP, and ISO 14155 standards, fostering a culture of quality, strengthening data governance, and leveraging emerging technologies, including AI/ML, to enhance quality oversight across clinical trials and pharmacovigilance activities throughout the product lifecycle.

  • Quality by Design (QbD) and Risk-Based Quality Management (RBQM) in Practice
  • Building and Sustaining a Culture of Quality in R&D: Prioritizing an environment that promotes quality, data reliability, and safety, naturally leading to inspection readiness
  • Implementing ICH E6(R3) Annex 1: Practical Approaches, Early Experiences and Lessons Learned
  • GCP Compliance in Agile, Pragmatic, and Decentralized Trials
  • Artificial Intelligence and Machine Learning Integrated into Clinical Trial Processes for R&D Quality and Compliance
  • Risk-Based Data Governance Strategies
  • Pharmacovigilance (PV) Data Quality and Signal Management
  • GCP and ISO 14155 Compliance: Best practices for managing quality in medical device and combination product trials
  1. What lessons have been learned from recent implementations of QbD and RBQM that can inform future best practices and compliance with ICH E6R3?
  2. What strategies and cultural shifts are critical to fostering a proactive quality mindset, transparency, and accountability within R&D and quality assurance functions?
  3. How can organizations effectively implement evolving regulatory standards (such as ICH E6(R3) and ISO 14155) to enhance quality, compliance, and risk management across diverse clinical trial designs and medical device studies?
  4. Howcan RBQM, including risk-based approaches to data governance and trial oversight be optimized to ensure data integrity, reliability, and patient safety?
  5. In what ways can emerging technologies, including AI and machine learning, be integrated into clinical trial processes to support decision-making, quality assurance, and regulatory compliance?
  6. How can organizations maintain high-quality, reliable pharmacovigilance processes amid new technologies and products to support sound decisions and transparency?

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