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  3. From Extractables To Exposure Data: Sensitivity Analysis Of Extrapolation Algorithms With Focus On Usp 〈665〉.
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  3. From Extractables To Exposure Data: Sensitivity Analysis Of Extrapolation Algorithms With Focus On Usp 〈665〉.

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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.

European Journal of Pharmaceutical Sciences : Official Journal of the European Federation for Pharmaceutical Sciences
|January 26, 2025

View abstract on PubMed

Summary
This summary is machine-generated.

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.

Keywords:
Exposure calculationsExtractables and leachables assessmentSensitivity analysis of exposure calculationsSingle-Use Systems

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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.