Modeling the thermal stability of enzyme-based in vitro diagnostics biosensors

Abstract

Performance of in vitro diagnostics biosensors may change over lifetime, particularly if environmental storage conditions such as temperature are not controlled. Biosensors are composed of diverse multiple components such as salts, polymers and biological components which may be differentially impacted by chemical and physical transformations induced by changes in temperature and exposure to humidity, oxygen and light. Mathematical models for predicting the influence of temperature on biosensor performance over time typically assume the changes follow first-order dynamics, with the temperature dependence of the rate of change described by an Arrhenius kinetic expression. However, the compositional diversity found in many biosensors may cause the assumption of first-order dynamics for sensor stability to be invalid. In this paper, a second-order dynamic model is developed to predict the change in biosensor performance over time for a single-use biosensor used in a point-of-care diagnostics system. The model consists of a reversible reaction followed by an irreversible reaction, with rate coefficients having Arrhenius temperature dependencies. The second-order dynamic model provides improved predictions, based on a comparison for two experimental datasets used for estimation, and on a validation dataset. The resulting model has applications for shelf-life prediction, designing accelerated testing experiments, biosensor improvement and the development of biosensor storage guidelines. Finally, it is shown that the concept of “mean kinetic temperature”, used widely in the pharmaceutical industry and based on first-order dynamics, can be applied successfully to a biosensor system exhibiting higher-order dynamic behaviour using a second-order model. This suggests that mean kinetic temperature concepts may be extended to in vitro diagnostics sensor applications.