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Published on: April 23, 2019
Is HPLC assay for drug substance a useful quality control attribute?
Jeffrey D Hofer1, Bernard A Olsen, Eugene C Rickard
1Analytical Sciences Research & Development, Eli Lilly & Company, Lilly Corporate Center, Indianapolis, IN 46285, USA.
High-performance liquid chromatography (HPLC) assay variability challenges drug substance quality control. A mass balance approach offers superior precision for process monitoring and stability studies, reducing false out-of-specification results.
Area of Science:
- Pharmaceutical Analysis
- Analytical Chemistry
- Drug Quality Control
Background:
- High-performance liquid chromatography (HPLC) is a standard method for drug substance assay.
- Recent studies question the reliability of HPLC assay for quality characterization.
- Variability in HPLC assay can impact batch acceptance and process monitoring.
Purpose of the Study:
- To evaluate the utility of traditional drug substance HPLC assay as a quality control parameter.
- To compare HPLC assay data with results from a mass balance approach.
- To assess the impact of assay variability on quality control and stability studies.
Main Methods:
- Collected HPLC assay data from over 100 batches for eight drug substances.
- Compared HPLC assay results with a mass balance approach (100% - impurities%).
- Analyzed variability estimates from own data and literature (0.6-1.1% R.S.D. for HPLC).
Main Results:
- HPLC assay variability is a significant portion of typical acceptance ranges and often exceeds manufacturing process variability.
- High HPLC variability can lead to a substantial percentage of false out-of-specification (OOS) results.
- Low HPLC precision hinders detection of process mean changes and degradation during stability studies.
Conclusions:
- The traditional HPLC assay's utility for drug substance quality control is questionable due to high variability.
- A mass balance approach provides similar average results but with significantly lower standard deviations (up to 10x less).
- Mass balance assay enables more precise process and stability monitoring, facilitating faster identification of process changes.
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