Yoshihiro Hayashi1, Takuya Oishi1, Kaede Shirotori1, Yuki Marumo1, Atsushi Kosugi2, Shungo Kumada2, Daijiro Hirai2, Kozo Takayama3, Yoshinori Onuki1. 1. a Department of Pharmaceutical Technology, Graduate School of Medicine and Pharmaceutical Science for Research , University of Toyama , Toyama-shi , Japan. 2. b Formulation Development Department, Development and Planning Division , Nichi-Iko Pharmaceutical Co., Ltd. , Namerikawa-shi , Japan. 3. c Faculty of Pharmacy and Pharmaceutical Sciences , Josai University , Sakado , Japan.
Abstract
OBJECTIVES: The aim of this study was to explore the potential of boosted tree (BT) to develop a correlation model between active pharmaceutical ingredient (API) characteristics and a tensile strength (TS) of tablets as critical quality attributes. METHODS: First, we evaluated 81 kinds of API characteristics, such as particle size distribution, bulk density, tapped density, Hausner ratio, moisture content, elastic recovery, molecular weight, and partition coefficient. Next, we prepared tablets containing 50% API, 49% microcrystalline cellulose, and 1% magnesium stearate using direct compression at 6, 8, and 10 kN, and measured TS. Then, we applied BT to our dataset to develop a correlation model. Finally, the constructed BT model was validated using k-fold cross-validation. RESULTS: Results showed that the BT model achieved high-performance statistics, whereas multiple regression analysis resulted in poor estimations. Sensitivity analysis of the BT model revealed that diameter of powder particles at the 10th percentile of the cumulative percentage size distribution was the most crucial factor for TS. In addition, the influences of moisture content, partition coefficients, and modal diameter were appreciably meaningful factors. CONCLUSIONS: This study demonstrates that BT model could provide comprehensive understanding of the latent structure underlying APIs and TS of tablets.
OBJECTIVES: The aim of this study was to explore the potential of boosted tree (BT) to develop a correlation model between active pharmaceutical ingredient (API) characteristics and a tensile strength (TS) of tablets as critical quality attributes. METHODS: First, we evaluated 81 kinds of API characteristics, such as particle size distribution, bulk density, tapped density, Hausner ratio, moisture content, elastic recovery, molecular weight, and partition coefficient. Next, we prepared tablets containing 50% API, 49% microcrystalline cellulose, and 1% magnesium stearate using direct compression at 6, 8, and 10 kN, and measured TS. Then, we applied BT to our dataset to develop a correlation model. Finally, the constructed BT model was validated using k-fold cross-validation. RESULTS: Results showed that the BT model achieved high-performance statistics, whereas multiple regression analysis resulted in poor estimations. Sensitivity analysis of the BT model revealed that diameter of powder particles at the 10th percentile of the cumulative percentage size distribution was the most crucial factor for TS. In addition, the influences of moisture content, partition coefficients, and modal diameter were appreciably meaningful factors. CONCLUSIONS: This study demonstrates that BT model could provide comprehensive understanding of the latent structure underlying APIs and TS of tablets.