[Paper Review] Risk management for analytical methods: conciliating objectives of methods, validation phase and routine decision rules

Myriam Maumy‐Bertrand, Boulanger, B.+3 more|ArXiv.org|Dec 31, 2007

Biosimilars and Bioanalytical Methods22 references3 citations

TL;DR

This paper proposes a risk management framework that aligns the objectives of analytical methods, their validation phase, and routine decision rules in pharmaceutical, chemical, and food industries. By integrating regulatory standards (e.g., ICH, FDA) with statistical risk assessment, it ensures method reliability and decision integrity through structured evaluation of measurement uncertainty and decision thresholds.

ABSTRACT

In the industries that involved either chemistry or biology, such as pharmaceutical industries, chemical industries or food industry, the analytical methods are the necessary eyes and hear of all the material produced or used. If the quality of an analytical method is doubtful, then the whole set of decision that will be based on those measures is questionable. For those reasons, being able to assess the quality of an analytical method is far more than a statistical challenge; it's a matter of ethic and good business practices. Many regulatory documents have been releases, primarily ICH and FDA documents in the pharmaceutical industry (FDA, 1995, 1997, 2001) to address that issue.

Motivation & Objective

• To address the ethical and operational risks arising from unreliable analytical methods in regulated industries.
• To reconcile conflicting objectives between method development, validation, and routine decision-making.
• To provide a systematic approach that ensures analytical decisions are both scientifically sound and compliant with regulatory standards.
• To integrate statistical risk assessment with regulatory requirements (e.g., ICH, FDA) for improved method reliability.
• To establish a coherent framework that links method performance, validation criteria, and operational decision rules.

Proposed method

• Adopts a risk-based approach to analytical method development and validation, focusing on measurement uncertainty and decision thresholds.
• Integrates regulatory guidelines (ICH, FDA) with statistical theory to define acceptable performance levels.
• Uses statistical models to quantify risks associated with false compliance or non-compliance decisions.
• Defines decision rules based on acceptable error rates and measurement uncertainty, ensuring alignment with method objectives.
• Applies a structured framework to evaluate method performance across validation and routine use phases.
• Employs a lifecycle perspective, ensuring consistency from method design through routine operation.

Experimental results

Research questions

• RQ1 How can analytical method objectives be consistently aligned with validation and routine decision rules?
• RQ2 What statistical and regulatory framework can minimize risks in analytical decisions across the method lifecycle?
• RQ3 How can measurement uncertainty be quantitatively linked to decision outcomes in regulated industries?
• RQ4 In what way do current validation practices fail to support routine decision-making, and how can this be corrected?
• RQ5 What risk assessment model ensures both scientific rigor and regulatory compliance in analytical methods?

Key findings

• The proposed risk management framework successfully reconciles method objectives, validation criteria, and routine decision rules.
• Integration of ICH and FDA guidelines with statistical risk models enhances method reliability and decision accuracy.
• Quantitative risk assessment based on measurement uncertainty reduces the likelihood of erroneous compliance decisions.
• The framework ensures that validation outcomes are directly traceable to operational decision-making, minimizing disconnects.
• The approach supports ethical and business-essential decision-making by ensuring analytical data integrity.
• The method is applicable across pharmaceutical, chemical, and food industries, demonstrating broad regulatory and technical relevance.