Application of Gibbs Sampling in Modelling the Utilization Rate of Raw Materials for Drug Coating

Ge Song,Charlie Cheng-Jie Ji,Jiahui Yuan

doi:10.5539/ijsp.v10n2p28

Abstract

Drug coating is one of the most important processes in the modern pharmaceutical industry. Improving the utilization rate of raw materials (URRM) in the drug coating process is thus important for cost saving and efficiency enhancement. There is little existing research on this topic in the literature of applied statistics. In this paper, motivated by a real dataset collected from a pharmaceutical company in China, we propose to use a novel predictive model that integrates a Bayesian framework with the Gibbs sampling algorithm to characterize the pattern of URRM. Based on certain prior distributional assumptions, the Gibbs sampling algorithm is then applied to sample the posterior distribution of the parameters to obtain more accurate and robust estimation results. By using the proposed method, the drugs can be properly separated into several categories with different patterns of URRM, and the pattern of each category can be properly recognized with selected covariates, which achieves the goals of clustering, variable selection, and regression simultaneously, and provides valuable insights into the improvement of the URRM for drug coating. Numerical studies show that the proposed method works well in practice.

Highlights

With the rapid development of modern pharmaceutical production, a wide variety of drugs are devised, and their production processes are highly complicated
In this paper, motivated by a real dataset collected from a pharmaceutical company in China, we propose to use a novel predictive model that integrates a Bayesian framework with the Gibbs sampling algorithm to characterize the pattern of utilization rate of raw materials (URRM)
By using the proposed method, the drugs can be properly separated into several categories with different patterns of URRM, and the pattern of each category can be properly recognized with selected covariates, which achieves the goals of clustering, variable selection, and regression simultaneously, and provides valuable insights into the improvement of the URRM for drug coating

Summary

Introduction

With the rapid development of modern pharmaceutical production, a wide variety of drugs are devised, and their production processes are highly complicated. It is of great importance to model and to improve the utilization rate of raw materials in the drug coating process for cost saving and efficiency enhancement. We focus on the critical drug coating process, and apply modern computational statistical methods to assist in the improvement of the utilization rate of raw materials (URRM) used to coat tablets. The process of drug coating consists of several sequential stages, including drying of core tablets, feeding of raw materials, preheating, spraying, drying of the coated tablets, cooling, and discharging. After the aforementioned sequential steps with proper tuning parameter settings, tablets with enteric coating are produced, and the URRM of the tablets can be calculated and collected. The whole drug coating process involves many tuning parameters (e.g., roller rotation speed, air flow temperature) that need to be properly set up during the process. It is crucial to investigate statistically how the tuning parameters affect the final utilization rate, such that accurate and effective decisions and adjustments can be made in order to handle various scenarios

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Methods

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Conclusion