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Effect of a statistical outlier in potency bioassays
Perceval Sondag1,2, Lingmin Zeng3, Binbing Yu3
1Pharmalex, Statistical Services, Fairfax, Virginia, USA.
Pharmaceutical Statistics
|August 17, 2018
Summary
Screening bioassay data for outliers before relative potency (RP) analysis is recommended by USP<1032> guidelines. Ignoring this can lead to incorrect lot acceptance or rejection, impacting biotherapeutic and vaccine quality control.
Area of Science:
- Pharmaceutical Sciences
- Biotechnology
- Statistical Analysis
Background:
- Potency bioassays are critical for biotherapeutic and vaccine quality control.
- USP<1032> guidelines recommend outlier screening before relative potency (RP) analysis.
- Lack of specific guidance on outlier removal in USP<1032> necessitates further investigation.
Purpose of the Study:
- To investigate the impact of ignoring outlier screening in bioassay data on relative potency (RP) analysis.
- To evaluate the consequences of outlier removal (or lack thereof) on similarity testing and RP estimation for biotherapeutics and vaccines.
Main Methods:
- Computer simulations were used to generate concentration-response curves using four-parameter logistic models.
- Scenarios included single, multiple, and whole-curve outliers.
- Effects on similarity testing and RP estimation were analyzed.
Main Results:
- Outliers in potency data can lead to incorrect lot acceptance or rejection.
- Ignoring outliers may result in failure to declare similarity or biased RP estimates.
- While outlier removal is not always necessary, results generally support USP<1032> recommendations.
Conclusions:
- Adherence to USP<1032> recommendations for outlier screening in bioassay data is crucial for accurate relative potency (RP) analysis.
- Proper outlier handling ensures reliable quality assessment of biotherapeutics and vaccines.
- Simulation results underscore the importance of outlier assessment in preventing erroneous drug lot release.
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