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A newfangled study using risk silhouette and uncertainty approximation for quantification of acyclovir in diverse
Karan Mittal1, Riddhish Patadia1, Chintan Vora1
1Pharmacy Department, Faculty of Technology and Engineering, The Maharaja Sayajirao University of Baroda, Kalabhavan, Vadodara 390002, Gujarat, India.
Journal of Pharmaceutical Analysis
|February 7, 2018
Summary
This study evaluates risk assessment and uncertainty approximation for pharmaceutical analysis, focusing on acyclovir. Key findings highlight sample concentration and mass as major uncertainty contributors, validating a method per ICH guidelines.
Area of Science:
- Pharmaceutical Analysis
- Analytical Chemistry
- Quality Control
Background:
- Risk assessment and uncertainty approximation are crucial for reliable pharmaceutical process development.
- Traditional method validation checklists require enhancement for robust quality attribute measurement.
- A shift towards comprehensive validation is needed to ensure drug product quality.
Purpose of the Study:
- To evaluate the risk profile, combined standard uncertainty, and expanded uncertainty in acyclovir analysis.
- To validate a pharmaceutical analysis method according to ICH guidelines.
- To demonstrate the application of theoretical concepts in routine pharmaceutical analysis.
Main Methods:
- Cause-effect approach for uncertainty calculation.
- Method validation following International Council for Harmonisation (ICH) guidelines.
- Assessment of calibration model using lack-of-fit and Levene's tests.
Main Results:
- The calibration model was justified by lack of fit and Levene's test.
- Sample concentration and mass were identified as the primary contributors to uncertainty.
- The risk profile indicated potential future applications for the validated method.
Conclusions:
- The study successfully applied theoretical concepts of risk assessment and uncertainty approximation to routine pharmaceutical analysis.
- Validation using ICH guidelines and statistical tests confirmed method reliability.
- Understanding uncertainty sources is vital for accurate pharmaceutical quality assessment.
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