The purpose of this study was to investigate if rapidly measured physical properties can predict glass-forming ability and glass stability of drug compounds. A series of 50 structurally diverse drug molecules were studied with respect to glass-forming ability and, for glass-formers (n=24), the physical stability upon 1 month of storage was determined. Spray-drying and melt-cooling were used to produce the amorphous material and the solid state was analysed by Differential Scanning Calorimetry (DSC) and Powder X-ray Diffraction. Thermal properties and molecular weight (Mw) were used to develop predictive models of (i) glass-forming ability and (ii) physical stability. In total, the glass-forming ability was correctly predicted for 90% of the drugs from their Mw alone. As a rule of thumb, drugs with Mw greater than 300 g/mole are expected to be transformed to its amorphous state by using standard process technology. Glass transition temperature and Mw predicted the physical stability upon storage correctly for 78% of the glass-forming compounds. A strong sigmoidal relationship (R(2) of 0.96) was identified between crystallization temperature and stability. These findings have the potential to rationalize decisions schemes for utilizing and developing amorphous formulations, through early predictions of glass-forming ability from Mw and physical stability from simple DSC characterization.

Amorphization is an attractive formulation technique for drugs suffering from poor aqueous solubility as a result of their high lattice energy. Computational models that can predict the material properties associated with amorphization, such as glass-forming ability (GFA) and crystallization behavior in the dry state, would be a time-saving, cost-effective, and material-sparing approach compared to traditional experimental procedures. This article presents predictive models of these properties developed using support vector machine (SVM) algorithm. The GFA and crystallization tendency were investigated by melt-quenching 131 drug molecules in situ using differential scanning calorimetry. The SVM algorithm was used to develop computational models based on calculated molecular descriptors. The analyses confirmed the previously suggested cutoff molecular weight (MW) of 300 for glass-formers, and also clarified the extent to which MW can be used to predict the GFA of compounds with MW < 300. The topological equivalent of Grav3_3D, which is related to molecular size and shape, was a better descriptor than MW for GFA; it was able to accurately predict 86% of the data set regardless of MW. The potential for crystallization was predicted using molecular descriptors reflecting Hückel pi atomic charges and the number of hydrogen bond acceptors. The models developed could be used in the early drug development stage to indicate whether amorphization would be a suitable formulation strategy for improving the dissolution and/or apparent solubility of poorly soluble compounds.

We present a novel computational tool which predicts the glass-forming ability of drug compounds solely from their molecular structure. Compounds which show solid-state limited aqueous solubility were selected, and their glass-forming ability was determined upon spray-drying, melt-quenching and mechanical activation. The solids produced were analyzed by differential scanning calorimetry (DSC) and powder X-ray diffraction. Compounds becoming at least partially amorphous on processing were classified as glass-formers, whereas those remaining crystalline regardless of the process method were classified as non-glass-forming compounds. A predictive model of the glass-forming ability, designed to separate between these two classes, was developed through the use of partial least-squares projection to latent structure discriminant analysis (PLS-DA) and calculated molecular descriptors. In total, ten of the 16 compounds were determined experimentally to be good glass-formers and the PLS-DA model correctly sorted 15 of the compounds using four molecular descriptors only. An external test set was predicted with an accuracy of 75%, and, hence, the PLS-DA model developed was shown to be applicable for the identification of compounds that have the potential to be designed as amorphous formulations. The model suggests that larger molecules with a low number of benzene rings, low level of molecular symmetry, branched carbon skeletons and electronegative atoms have the ability to form a glass. To conclude, we have developed a predictive, transparent and interpretable computational model for the identification of drug molecules capable of being glass-formers. The model allows an assessment of amorphization as a formulation strategy in the early drug development process, and can be applied before compound synthesis.

The alarming numbers of poorly soluble discovery compounds have centered the efforts towards finding strategies to improve the solubility. One of the attractive approaches to enhance solubility is via amorphization despite the stability issue associated with it. Although the number of amorphous-based research reports has increased tremendously after year 2000, little is known on the current research practice in designing amorphous formulation and how it has changed after the concept of solid dispersion was first introduced decades ago. In this review we try to answer the following questions: What model compounds and excipients have been used in amorphous-based research? How were these two components selected and prepared? What methods have been used to assess the performance of amorphous formulation? What methodology have evolved and/or been standardized since amorphous-based formulation was first introduced and to what extent have we embraced on new methods? Is the extent of research mirrored in the number of marketed amorphous drug products? We have summarized the history and evolution of amorphous formulation and discuss the current status of amorphous formulation-related research practice. We also explore the potential uses of old experimental methods and how they can be used in tandem with computational tools in designing amorphous formulation more efficiently than the traditional trial-and-error approach.

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We developed a step-by-step experimental protocol using differential scanning calorimetry (DSC), dynamic vapour sorption (DVS), polarized light microscopy (PLM) and a small-scale dissolution apparatus (μDISS Profiler) to investigate the mechanism (solid-to-solid or solution-mediated) by which crystallization of amorphous drugs occurs upon dissolution. This protocol then guided how to stabilize the amorphous formulation. Indapamide, metolazone, glibenclamide and glipizide were selected as model drugs and HPMC (Pharmacoat 606) and PVP (K30) as stabilizing polymers. Spray-dried amorphous indapamide, metolazone and glibenclamide crystallized via solution-mediated nucleation while glipizide suffered from solid-to-solid crystallization. The addition of 0.001%–0.01% (w/v) HPMC into the dissolution medium successfully prevented the crystallization of supersaturated solutions of indapamide and metolazone whereas it only reduced the crystallization rate for glibenclamide. Amorphous solid dispersion (ASD) formulation of glipizide and PVP K30, at a ratio of 50:50% (w/w) reduced but did not completely eliminate the solid-to-solid crystallization of glipizide even though the overall dissolution rate was enhanced both in the absence and presence of HPMC. Raman spectroscopy indicated the formation of a glipizide polymorph in the dissolution medium with higher solubility than the stable polymorph. As a complementary technique, molecular dynamics (MD) simulations of indapamide and glibenclamide with HPMC was performed. It was revealed that hydrogen bonding patterns of the two drugs with HPMC differed significantly, suggesting that hydrogen bonding may play a role in the greater stabilizing effect on supersaturation of indapamide, compared to glibenclamide.

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

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