Modeling of quantitative relationships between physicochemical properties of active pharmaceutical ingredients and tensile strength of tablets using a boosted tree. (3rd July 2018)

• Record Type: Journal Article
• Authors: Hayashi, Yoshihiro; Oishi, Takuya; Shirotori, Kaede; Marumo, Yuki; Kosugi, Atsushi; Kumada, Shungo; Hirai, Daijiro; Takayama, Kozo; Onuki, Yoshinori
• Is Part Of: Drug development and industrial pharmacy. Volume 44:Number 7(2018)
• Page Start: 1090
• Page End: 1098
• Publication Date: 2018-07-03
• Subjects: Tablet -- quality by design -- machine learning -- boosted tree -- tensile strength -- physicochemical property; Pharmaceutical chemistry -- Periodicals; Pharmaceutical industry -- Periodicals; Drug Industry -- Periodicals; Technology, Pharmaceutical -- Periodicals; 615.05
• DOI: 10.1080/03639045.2018.1434195
• Languages: English
• ISSNs: 0363-9045

Abstract

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.