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From extractables to exposure data: Sensitivity analysis of extrapolation algorithms with focus on USP 〈665〉
Armin Hauk1, Alexander Wildschütz2, Ina Pahl1
1Sartorius Stedim Biotech GmbH, August-Spindler-Straße 11, Göttingen 37079, Germany.
Summary
Extrapolation algorithms accurately predict process equipment-related leachables (PERLs) in single-use systems (SUSs). These methods reliably assess PERL exposure, ensuring safety even with varied input data.
Area of Science:
- Pharmaceutical Science
- Materials Science
Background:
- Single-use systems (SUSs) are increasingly used in biopharmaceutical manufacturing.
- Assessing potential leachables from process equipment is crucial for patient safety.
Purpose of the Study:
- To evaluate algorithms for predicting process equipment-related leachables (PERLs) from extractables data.
- To assess the suitability of these algorithms for determining PERL exposure in SUSs and assemblies.
Main Methods:
- Tested algorithm robustness and sensitivity against variations in extractables data.
- Utilized data from standardized extractables protocols (USP 〈665〉) for short and long contact times.
- Analyzed extrapolation algorithms for both short and long contact time extractables data.
Main Results:
- Extrapolated data from SUSs and assemblies are suitable for safety assessments.
- Algorithms are non-sensitive to input data deviations, which propagate decreasingly.
- Extrapolated data do not systematically underestimate potential PERL exposure under specific experimental conditions (e.g., higher surface area to volume ratio).
Conclusions:
- Extrapolation algorithms provide reliable predictions for PERLs and their exposure in SUSs.
- Incorporating extractables data from semipolar organic solutions (e.g., ethanol) can enhance PERL exposure calculations.
- The validated algorithms support robust safety assessments for pharmaceutical manufacturing processes using SUSs.
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