title: Arrhenius time-scaled least squares: a simple, robust approach to accelerated stability data analysis for bioproducts.
abstract: Defining a suitable product presentation with an acceptable stability profile over its intended shelf-life is one of the principal challenges in bioproduct development. Accelerated stability studies are routinely used as a tool to better understand long-term stability. Data analysis often employs an overall mass action kinetics description for the degradation and the Arrhenius relationship to capture the temperature dependence of the observed rate constant. To improve predictive accuracy and precision, the current work proposes a least-squares estimation approach with a single nonlinear covariate and uses a polynomial to describe the change in a product attribute with respect to time. The approach, which will be referred to as Arrhenius time-scaled (ATS) least squares, enables accurate, precise predictions to be achieved for degradation profiles commonly encountered during bioproduct development. A Monte Carlo study is conducted to compare the proposed approach with the common method of least-squares estimation on the logarithmic form of the Arrhenius equation and nonlinear estimation of a first-order model. The ATS least squares method accommodates a range of degradation profiles, provides a simple and intuitive approach for data presentation, and can be implemented with ease.
authors: inVentiv Health Clinical, Princeton, NJ, 08540.
authors: Adam P
authors: AP
authors: Rauk
authors: Kevin
authors: K
authors: Guo
authors: Yanling
authors: Y
authors: Hu
authors: Suntara
authors: S
authors: Cahya
authors: William F
authors: WF
authors: Weiss
mesh_terms: D003198:Computer Simulation; D004355:Drug Stability; D004356:Drug Storage; D007700:Kinetics; D016018:Least-Squares Analysis; D008956:Models, Chemical; D009010:Monte Carlo Method; D066329:Protein Aggregates; D055550:Protein Stability; D013696:Temperature
chemical_list: D066329:Protein Aggregates
keywords: formulation; kinetics; monte carlo; nonlinear regression; protein aggregation; stability