Algorithm for the early prediction of drug stability using bayesian inference and multiple measurements: Application for predicting the stability of silodosin tablets

Abstract

The stability of active pharmaceutical ingredients (APIs) and formulations has become a major chemistry, manufacturing, and control (CMC) concern in the pharmaceutical industry because it can determine the feasibility of research and development, the development period, and the development costs of a certain formulation. To streamline the research and development of pharmaceutical products and create useful pharmaceutical products at an early stage, a technology that predicts the stability of formulations at an early stage and with a high degree of accuracy is needed. When predicting the stability of a substance, highly reliable data are required; however, the stability data are affected by analytical variations that depend on the experimenter, measurement device, and conditions used. Although these variations greatly affect the prediction accuracy, a stability prediction method that considers these variations has not yet been developed. Here, short-term stability data under accelerated conditions were obtained at three institutions using silodosin tablets as a model sample. By combining Bayesian inference with the temporal change in the amount of the main degradation products obtained and the conventional humidity-corrected Arrhenius equation, we developed a new algorithm that provides a narrow confidence interval, even when using data with variations. By using this algorithm and setting an appropriate number of conditions, we were able to obtain a valid confidence intervals in a short period of time. Here, by performing more measurements than those suggested by the minimum measurement frequency indicated in the guideline specified in the International Council for Harmonisation (ICH) of Technical Requirements for Pharmaceuticals for Human Use, we developed a method that can be used to reasonably predict the long-term stability of the drugs, even if the data measurement interval is short. Our results will help solve various problems in today's pharmaceutical product development scenario and contribute to worldwide health and welfare.