The purpose of this study was to explore the potential of a quantitative structure-property relationship (QSPR) model to predict tablet density. First, we calculated 3381 molecular descriptors for 81 active pharmaceutical ingredients (API). Second, we obtained data that were merged with a dataset including powder properties that we had constructed previously. Next, we prepared 81 types of tablet, each containing API, microcrystalline cellulose, and magnesium stearate using direct compression at 120, 160, and 200 MPa, and measured the tablet density. Finally, we applied the boosted-tree machine learning approach to construct a QSPR model and to identify crucial factors from the large complex dataset. The QSPR model achieved statistically good performance. A sensitivity analysis of the QSPR model revealed that molecular descriptors related to the average molecular weight and electronegativity of the API were crucial factors in tablet density, whereas the effects of powder properties were relatively insignificant. Moreover, we found that these descriptors had a positive linear relationship with tablet density. This study indicates that a QSPR approach is possibly useful for in silico prediction of tablet density for tablets prepared using more than a threshold compression pressure, and to allow a deeper understanding of tablet density.
Keywords: Boosted tree; Density; In silico prediction; QSPR; Tablet.
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