Published on 02/12/2025

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 within the regulated environment is paramount. As pharmaceutical professionals adeptly know, regulatory bodies such as the US FDA, EMA, and MHRA emphasize the significance of robust validation processes, documenting intended use, risk management, and ongoing drift monitoring.

Model validation, often termed model verification and validation (V&V), ensures that the AI/ML systems are developed and function as intended within their specified risk parameters. This article outlines a structured, step-by-step guide to implementing champion-challenger and shadow mode rollouts for AI/ML models, incorporating crucial aspects like explainability (XAI), bias and fairness testing, and data readiness curation in compliance with regulatory frameworks such as 21 CFR Part 11 and GAMP 5.

Step 1: Define Intended Use and Risk Assessment

Before deploying any AI/ML model, it is essential to clearly define its intended use case and perform a comprehensive risk assessment. This process aids in identifying potential risks associated with implementation, ensuring that the model adheres to the regulations set forth by both national and international governing bodies.

• Intended Use Definition: Specify what the AI/ML model is designed to achieve, whether it’s improving diagnostic accuracy, streamlining drug development processes, or enhancing operational efficiencies in the lab. Be explicit about the population it will serve and the data inputs it will utilize.
• Risk Assessment: Conduct a formal risk assessment to evaluate the implications of false positives, false negatives, or unforeseen consequences that could arise from model deployment. Risk matrices can be beneficial here, correlating likelihood to impact and establishing controls to mitigate identified risks.

Documenting the intended use and risks is a regulatory expectation and serves as a foundational component of your V&V process.

Step 2: Data Readiness and Curation

Data adequacy is a crucial pillar in AI/ML model performance. Before any model training occurs, ensure that your datasets are prepared appropriately. This involves curating and cleansing the data while also ensuring representativeness regarding the intended use.

• Data Quality Verification: Ensure that the data used for training the models is of high integrity. This means checking for completeness, consistency, accuracy, and relevancy.
• Data Bias Managing: Assess the datasets for potential biases that might influence the model’s output unfairly. Implement bias and fairness testing to identify any discrepancies that need addressing.
• Documentation of Data Sources: All data collections must be documented to create a reliable audit trail, ensuring traceability back to the data sources in compliance with regulations like 21 CFR Part 11.

Proper data readiness sets the stage for effective model training and functionality, directly influencing outcomes of V&V processes.

Step 3: Model Development and Preliminary Validation

Once data is adequately curated, the next phase encompasses developing the AI/ML models. During this phase, model verification occurs through iterative testing. Utilizing champion-challenger models allows for effective comparison and validation.

• Champion-Challenger Approaches: The champion model represents the current standard or best practice method, while challenger models are newer or alternative approaches intended to improve upon the current methods. Rigorous comparisons between the two provide insights into performance enhancements.
• Preliminary Validation: Validate each model during and after development to ensure that it meets the predefined performance criteria established during the risk assessment phase.
• Explainability (XAI): Incorporate XAI methodologies to facilitate understanding of the model’s processes and decisions, which is crucial for compliance and stakeholder trust.

Managing preliminary validations ensures that models are not only functional but also compliant with industry regulations and standards. Continual documentation throughout this phase assures a clear audit trail necessary for regulatory inspection.

Step 4: Implementation of Shadow Mode

Shadow mode is a validation approach wherein the model operates in parallel with the existing processes without affecting them. This allows you to gather insights and assess performance while minimizing risks associated with outright deployment.

• Operating in the Background: In this phase, the new AI/ML model runs simultaneously with the existing system, capturing outputs and comparing them against actual outcomes. No modifications to existing processes should occur at this stage, ensuring that current operations remain unaffected.
• Performance Monitoring: Maintain vigilant observation of the shadow model’s performance metrics against predetermined benchmarks. Monitor for any deviations from expectations closely.
• Documentation of Findings: Record all observations and findings meticulously. If any biases or critical discrepancies arise, address them through retraining, data remediation, or a systematic review.

This step highlights the need for thoroughness and compliance when transitioning from validation to deployment. Regulating entities like the FDA and EMA may require assurance of robustness before formal rollout.

Step 5: Transition to Champion Deployment

Upon satisfactory performance in shadow mode, transitioning to the champion deployment stage is feasible. Here, the challengers take precedence as operational models while continuous monitoring ensures that the performances remain on par with expectations.

• Controlled Deployment: Implement the new AI/ML model in a controlled environment. Maintain a fallback mechanism to re-establish the prior model if any performance issues arise during this phase.
• Monitoring and Validation: Continue monitoring the model’s output with an emphasis on drift detection and validation to ensure the model’s performance remains in line with regulatory standards and intended use.
• Documentation and Audit Trail: Keep a comprehensive record of adaptions during this transition phase, including decisions made, performance metrics, and stakeholder feedback.

Documenting all changes supports the model’s integrity in the event of a regulatory audit, aligning with expectations outlined in guidance documents by institutions such as PIC/S and GAMP 5.

Step 6: Drift Monitoring and Re-Validation

The final stage demands rigorous drift monitoring—assessing the model’s predictive accuracy over time. Drift can occur due to variations in incoming data, changing user behavior, or evolving regulations.

• Establish Drift Criteria: Define parameters to detect drift early; this could involve statistical thresholds on performance metrics or regular review intervals.
• Automated Monitoring Systems: Use automated systems to track performance metrics consistently, signaling alert criteria to initiate further evaluations or model retraining when drift is detected.
• Scheduled Re-Validation: Conduct periodic re-validations to ensure that models continue meeting regulatory standards and performance outputs. As per regulations, a re-validation plan must be documented, detailing the scope, responsibilities, and timelines.

By adhering to a systematic drift monitoring strategy, organizations can uphold compliance and ensure that AI/ML systems continuously align with operational goals and regulatory demands.

Conclusion: Ensuring Compliance and Robustness in AI/ML Models

The integration of AI/ML technologies in pharmaceutical labs presents opportunities for innovation but comes with responsibilities regarding regulatory compliance and ethical practices. Employing robust model validation techniques like champion-challenger and implementing shadow mode strategies serve as efficacious methods to ensure that AI/ML models are both reliable and compliant.

As pharmaceutical professionals, understanding these concepts and integrating them into operational frameworks is not just a regulatory obligation but also a commitment to quality and integrity in the industry. Regulatory references from the FDA, EMA, and similar bodies provide the necessary guidance to navigate this complex but rewarding landscape.

By adhering to best practices, continuous monitoring, and thorough documentation, labs can foster trust in their AI/ML systems, thereby paving the way for future advancements while maintaining compliance with global regulatory expectations.