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A Bayesian Approach to Kinetic Modeling of Accelerated Stability Studies and Shelf Life Determination

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Abstract

Kinetic modeling of accelerated stability data serves an important purpose in the development of pharmaceutical products, providing support for shelf life claims and expediting the path to clinical implementation. In this context, a Bayesian kinetic modeling framework is considered, accommodating different types of nonlinear kinetics with temperature and humidity dependent rates of degradation and accounting for the humidity conditions within the packaging to predict the shelf life. In comparison to kinetic modeling based on nonlinear least-squares regression, the Bayesian approach allows for interpretable posterior inference, flexible error modeling and the opportunity to include prior information based on historical data or expert knowledge. While both frameworks perform comparably for high-quality data from well-designed studies, the Bayesian approach provides additional robustness when the data are sparse or of limited quality. This is illustrated by modeling accelerated stability data from two solid dosage forms and is further examined by means of artificial data subsets and simulated data.

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Funding

This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors. All authors are employed by their affiliation as listed. The drug products used in the examples were provided by Janssen Pharmaceuticals.

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Authors and Affiliations

  1. Open Analytics, Antwerp, Belgium

    Joris Chau

  2. Statistics and Decision Sciences, Janssen Research, Raritan, New Jersey, USA

    Stan Altan

  3. Chemical and Pharmaceutical Development & Supply, Janssen Research, Beerse, Belgium

    Anneleen Burggraeve, Hana Prokopcova & Timothy Van Daele

  4. Statistics and Decision Sciences, Janssen Research, Beerse, Belgium

    Hans Coppenolle & Yimer Wasihun Kifle

  5. Chemical and Pharmaceutical Development & Supply, Janssen Research, Turnhoutseweg 30, 2340, Beerse, Belgium

    Hans Sterckx

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  1. Joris Chau
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Contributions

• Joris Chau: Inception of the work from a statistical perspective, contributions to conception of the work and drafting and reviewing the work. • Stan Altan: Inception of the work from a statistical perspective and reviewing the work. • Anneleen Burggraeve: Modeling of the first example and reviewing the work. • Hans Coppenolle: Inception of the work from a statistical perspective and reviewing of the work. • Yimer Wasihun Kifle: Contributions to drafting and reviewing of the work. • Hana Prokopcova: Inception of the work from a chemistry perspective and reviewing the work. • Timothy Van Daele: Modeling of the second example and reviewing the work. • Hans Sterckx: Inception of the work from a chemical perspective, contributions to conception of the work and drafting and reviewing the work.

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Correspondence to Hans Sterckx.

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Chau, J., Altan, S., Burggraeve, A. et al. A Bayesian Approach to Kinetic Modeling of Accelerated Stability Studies and Shelf Life Determination. AAPS PharmSciTech 24, 250 (2023). https://doi.org/10.1208/s12249-023-02695-5

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