An application of the Mantel-Haenszel statistic in process validation

Area of Science:

• Analytical Chemistry
• Pharmaceutical Sciences
• Statistical Analysis

Background:

• Bioequivalence studies are crucial for generic drug approval.
• Data discrepancies in a bioequivalence study raised concerns regarding analytical laboratory procedures.
• The analytical process in question was unvalidated, increasing the risk of procedural errors.

Purpose of the Study:

• To investigate the root causes of data discrepancies in a bioequivalence study.
• To explore the failure of an unvalidated analytical process using statistical and graphical methods.
• To provide a framework for analyzing process failures in similar contexts.

Main Methods:

• Application of statistical procedures to analyze study data.
• Utilization of graphical techniques for data visualization and pattern identification.
• Systematic exploration of the unvalidated analytical workflow to pinpoint procedural flaws.

Main Results:

• Identification of specific statistical outliers and graphical anomalies in the bioequivalence data.
• Pinpointing of procedural deviations within the unvalidated analytical laboratory process.
• Quantification of the impact of identified failures on study data integrity.

Conclusions:

• Statistical and graphical methods are effective tools for diagnosing process failures in analytical laboratories.
• Addressing unvalidated processes is critical for ensuring the reliability of bioequivalence study data.
• The findings highlight the importance of rigorous process validation and monitoring in pharmaceutical research.