Arrhenius time-scaled least squares: a simple, robust approach to accelerated stability data analysis for bioproducts

Area of Science:

• Biopharmaceutical Development
• Chemical Kinetics
• Analytical Chemistry

Background:

• Ensuring bioproduct stability throughout shelf-life is critical.
• Accelerated stability studies are essential for predicting long-term stability.
• Current kinetic models for degradation analysis have limitations in accuracy and precision.

Purpose of the Study:

• To develop an improved analytical method for biopharmaceutical stability studies.
• To enhance the accuracy and precision of predicting product degradation profiles.
• To introduce the Arrhenius time-scaled (ATS) least squares approach.

Main Methods:

• Proposed a novel least-squares estimation approach termed Arrhenius time-scaled (ATS) least squares.
• Utilized a single nonlinear covariate and a polynomial to model changes in product attributes over time.
• Conducted a Monte Carlo simulation to compare ATS least squares with existing methods (log-linear Arrhenius and first-order nonlinear estimation).

Main Results:

• The ATS least squares method demonstrated accurate and precise predictions for various bioproduct degradation profiles.
• The proposed method showed superior performance compared to traditional least-squares estimation on the logarithmic Arrhenius equation and first-order models.
• ATS least squares accommodates diverse degradation patterns effectively.

Conclusions:

• The Arrhenius time-scaled (ATS) least squares approach offers a robust and user-friendly tool for biopharmaceutical stability assessment.
• This method enhances predictive capabilities for product degradation, aiding in development and shelf-life determination.
• ATS least squares provides an intuitive data presentation and is easily implementable.