Validations: Drift Monitoring & Re-Validation
Periodic Review of Model Health Periodic Review of Model Health in AI/ML Validation In the realm of pharmaceutical development and clinical operations, the emergence of artificial intelligence and machine learning (AI/ML) technologies is reshaping the landscape. However, with the implementation of these technologies comes the imperative need for rigorous validation processes to ensure compliance with Good Automated Manufacturing Practice (GxP)…
Outlier & Novelty Detection in Production: An In-Depth Guide for Pharmaceutical Validation In the rapidly evolving landscape of pharmaceutical development, the integration of artificial intelligence (AI) and machine learning (ML) into laboratory processes has revolutionized data analytics, particularly in Good Practice (GxP) environments. The validation of these models plays a critical role in ensuring compliance with regulatory standards mandated by…
Retraining Pipelines: Governance and Evidence Retraining Pipelines: Governance and Evidence Introduction to AI/ML Model Validation in GxP Analytics The rapid advancement of artificial intelligence (AI) and machine learning (ML) applications in the pharmaceutical and biopharmaceutical sectors has raised unprecedented opportunities for efficiencies and innovations in laboratory practices. However, along with these opportunities come significant regulatory responsibilities. This article provides a…
Human Override and Feedback Loops Human Override and Feedback Loops in AI/ML Model Validation Introduction to AI/ML in GxP Analytics In recent years, artificial intelligence (AI) and machine learning (ML) have gained significant traction in Good Practice (GxP) regulated environments such as laboratories (labs) in the pharmaceutical and biotechnology sectors. The utilization of AI/ML for data analysis and decision-making processes…
Small n/Imbalanced Data: Robust Monitoring The integration of artificial intelligence (AI) and machine learning (ML) in Good Practice (GxP) environments has garnered considerable attention, particularly regarding the validation of models utilizing small n/imbalanced data. Robust monitoring of these models is crucial for ensuring compliance with regulatory standards such as FDA, EMA, and MHRA. This guide details a step-by-step approach to…
Data Drift from New Markets/Sites Data Drift from New Markets/Sites In the rapidly advancing field of pharmaceuticals, the application of AI and machine learning (ML) models has become increasingly prevalent. However, navigating the complexities associated with data drift when using these models is essential to maintain compliance with Good Automated Manufacturing Practice (GxP) regulations. This step-by-step tutorial focuses on critical…
Shadow Mode/Champion–Challenger: Safe Rollouts Shadow Mode/Champion–Challenger: Safe Rollouts in AI/ML Model Validation Understanding the Importance of AI/ML Model Validation in Pharmaceutical Labs The integration of artificial intelligence (AI) and machine learning (ML) models in pharmaceutical labs is rapidly evolving, facilitating enhancements in drug development, clinical trials, and overall laboratory analytics. However, ensuring the validity and compliance of these AI/ML models…
Change Control for Models: Verification vs Re-Validation The integration of Artificial Intelligence (AI) and Machine Learning (ML) models in Good Practice (GxP) regulated environments has introduced complexities in pharmacovigilance, clinical operations, and regulatory compliance. As pharma professionals navigate these complexities, it’s essential to establish a robust framework for model validation, focusing on the distinction between verification and re-validation, particularly in…
Triggers & Escalations: From Alerts to Action Triggers & Escalations: From Alerts to Action The rapid integration of artificial intelligence (AI) and machine learning (ML) in Good Manufacturing Practice (GxP) analytics has initiated a need for rigorous validation processes. This tutorial outlines the comprehensive steps necessary for effective AI/ML model validation, focusing on the identification of triggers and escaling procedures…
A/B Tests and Backtests in Regulated Analytics A/B Tests and Backtests in Regulated Analytics In the realm of AI and machine learning (ML) used in Good Automated Manufacturing Practice (GxP) analytics, A/B testing and backtesting represent crucial methodologies for validation and monitoring of models implemented in various laboratories. With the increasing incorporation of advanced technologies in regulated environments, understanding these…