Accelerated Stability Kinetic Modelling

Estimate the Šesták–Berggren kinetic model (degradation model) from experimental data. A closed-form (analytic) solution to the degradation model is implemented as a non-linear fit, allowing for the extrapolation of the degradation of a drug product - both in time and temperature. Parametric bootstrap, with kinetic parameters drawn from the multivariate t-distribution, and analytical formulae (the delta method) are available options to calculate the confidence and prediction intervals. The results (modelling, extrapolations and statistical intervals) can be visualised with multiple plots. The examples illustrate the accelerated stability modelling in drugs and vaccines development.

Accelerated Stability Kinetic Modelling

Overview

This package utilises the Šesták–Berggren equation alongside the Arrhenius equation to make a simple and consistent way for a user to carry out the calculations and predictions required by accelerated stability studies. Currently the package works with decreasing variables, you may choose to transform your increasing variable into a decreasing one but note that your choice of transformation can impact the outcome.

The available functions within the package are as follows:

• step1_down() Fit the one-step Šesták–Berggren kinetic model.
• step1_down_basic() Quickly fit the one-step Šesták–Berggren kinetic model.
• step1_down_rmse() Calculate Root Mean Square Error (RMSE) for the one-step Šesták–Berggren kinetic model.
• excursion() Predict a temperature excursion for a product.
• step1_plot_CI() Plot the stability data and visualise the predictions with confidence intervals.
• step1_plot_desc() Plot raw accelerated stability data.
• step1_plot_diagnostic() Generate residual diagnostic plots from a step1_down fit.
• step1_plot_PI() Plot the stability data and visualise the predictions with prediction intervals.
• step1_plot_pred() Plot the stability data and visualise the predictions.
• step1_plot_T() Plot the stability data and visualise the predictions with focus on one temperature.
• step1_sample_mvt() Take a selected number of samples from the multivariate t distribution (mvt).

Installation

Install AccelStab the following way -

install.packages("AccelStab")
library(AccelStab)

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