Micro-scale Solubility Assessments and Prediction Models for Active Pharmaceutical Ingredients in Polymeric Matrices

Esther S Bochmann, Dirk Neumann, Andreas Gryczke, Karl G Wagner

Eur J Pharm Biopharm. 2019 Aug;141:111-120.

PMID: 31100430

Abstract:

The number of models for assessing the solubility of active pharmaceutical ingredients (APIs) in polymeric matrices on the one hand and the extent of available associated data on the other hand has been rising steadily in the past few years. However, according to our knowledge an overview on the methods used for prediction and the respective experimental data is missing. Therefore, we compiled experimental data, the techniques used for their determination and the models used for estimating the solubility. Our focus was on polymers commonly used in spray drying and hot-melt extrusion to form amorphous solid dispersions (ASDs), namely polyvinylpyrrolidone grades (PVP), polyvinyl acetate (PVAc), vinylpyrrolidone-vinyl acetate copolymer (copovidone, COP), polyvinyl caprolactam-polyvinyl acetate-polyethylene glycol graft polymer (Soluplus®, SOL), different types of methacrylate copolymers (PMMA), polyethylene glycol grades (PEG) and hydroxypropyl-methylcellulose grades (HPMC). The literature data were further supplemented by our own results. The final data set included 37 APIs and two sugar derivatives. The majority of the prediction models was constituted by the melting point depression method, dissolution endpoint measurements, indirect solubility determination by Tg and the use of low molecular weight analogues. We observed that the API solubility depended more on the working group which conducted the experiments than on the measuring technique used. Furthermore, this compilation should assist researchers in choosing a prediction method suited for their investigations. Furthermore, a statistical assessment using recursive feature elimination was performed to identify descriptors of molecules, which are connected to the API solubility in polymeric matrices. It is capable of predicting the criterium 20% API soluble at 100 °C (Yes/No) for an unknown compound with a balanced accuracy of 71%. The identified 8 descriptors to be connected to API solubility in polymeric matrices were the number of hydrogen bonding donors, three descriptors related to the hydrophobicity of the molecule, glass transition temperature, fractional negative polar van der Waals surface area, out-of-plane potential energy and the fraction of rotatable bonds. Finally, in addition to our own model, the data set should help researchers in training their own solubility prediction models.

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