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  3. Long-term Stability Prediction For Developability Assessment Of Biopharmaceutics Using Advanced Kinetic Modeling.
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  3. Long-term Stability Prediction For Developability Assessment Of Biopharmaceutics Using Advanced Kinetic Modeling.

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Long-Term Stability Prediction for Developability Assessment of Biopharmaceutics Using Advanced Kinetic Modeling.

Andreas Evers1, Didier Clénet2, Stefania Pfeiffer-Marek3

  • 1Global Research & Development, Discovery Technologies, Merck Healthcare KGaA, 64293 Darmstadt, Germany.

Pharmaceutics
|February 26, 2022

View abstract on PubMed

Summary
This summary is machine-generated.

Predicting therapeutic peptide stability using advanced kinetic modeling significantly accelerates pharmaceutical development. This approach accurately forecasts long-term drug product shelf-life, enabling faster clinical trial entry.

Keywords:
advanced kinetic analysisbiologicschemical stabilitydevelopabilityformulationin silico modelingpeptidesphysico–chemical propertiesshelf life prediction

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Area of Science:

  • Pharmaceutical Science
  • Biopharmaceutical Development
  • Chemical Kinetics

Background:

  • Long-term stability is critical for biopharmaceutical drug products, especially peptides in liquid formulations.
  • Ensuring stability over shelf life (e.g., 2 years at 5°C and 28 days at 30°C) is essential for patient safety and therapeutic efficacy.
  • Traditional stability testing is time-consuming, delaying drug development timelines.

Purpose of the Study:

  • To present a case study on predicting the long-term stability of a therapeutic peptide (SAR441255).
  • To evaluate the use of accelerated chemical degradation data and advanced kinetic modeling for stability predictions.
  • To validate these in silico predictions against real-world, long-term stability data.

Main Methods:

  • Chemical degradation of SAR441255 in various formulations and packaging was analyzed under accelerated conditions.
  • Advanced kinetic modeling was employed to predict long-term stability under recommended storage conditions.
  • In silico predictions were compared with analytical data obtained under long-term storage conditions.

    • Main Results:

    • The study successfully predicted the long-term stability of the therapeutic peptide SAR441255.
    • Predictions derived from accelerated data showed high accuracy when compared to subsequent long-term stability measurements.
    • Stability insights were obtained within weeks, a significant acceleration compared to traditional methods.

    Conclusions:

    • Advanced kinetic modeling using accelerated degradation data provides accurate long-term stability predictions for therapeutic peptides.
    • This in silico approach drastically reduces the time required for stability assessment, facilitating earlier clinical development.
    • This methodology offers a novel and efficient strategy for biopharmaceutical stability evaluation.