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One-hit models for virus inactivation studies.

M Kundi1

  • 1Institute of Environmental Hygiene, University of Vienna, Austria.

Antiviral Research
|May 13, 1999
PubMed
Summary
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Maximum likelihood estimation improves viral safety testing for biologicals by accurately estimating the infectious dose 50 (ID50), overcoming limitations of older methods and ensuring product safety.

Area of Science:

  • Biostatistics
  • Virology
  • Biopharmaceutical Manufacturing

Background:

  • Biological products derived from human or animal sources face risks of viral contamination.
  • Assessing viral removal/inactivation is critical for ensuring the safety of biologicals.
  • The one-hit model is commonly used for estimating infectious particles and ID50 (infectious dose 50%).

Purpose of the Study:

  • To present advanced statistical methods for estimating ID50 and reduction factors in viral safety testing.
  • To introduce maximum likelihood estimation (MLE) as a superior alternative to traditional methods like Spearman-Kärber.
  • To provide guidance on experimental design and incorporate kinetic models for enhanced accuracy.

Main Methods:

  • Utilized maximum likelihood estimation procedures for parameter estimation, ID50 calculation, and confidence limits.

Related Experiment Videos

  • Performed 36,000 simulated experiments to evaluate model robustness.
  • Discussed the incorporation of kinetics models and provided experimental design recommendations.

    • Main Results:

    • Maximum likelihood estimation offers accurate ID50 and reduction factor estimates, surpassing the 10-30% bias of Spearman-Kärber.
    • The one-hit model parameters demonstrate robustness against random dose variations.
    • Ten-fold dilution series designs are less suitable and more susceptible to dose variability.

    Conclusions:

    • MLE provides a statistically sound and accurate method for viral safety assessment in biological production.
    • The study validates the robustness of the one-hit model under dose variability, enhancing confidence in safety testing.
    • Optimized experimental designs are crucial for reliable viral clearance data in biopharmaceutical manufacturing.