The role of activated androgen receptor in cofilin phospho-regulation depends on the molecular subtype of TNBC cell line and actin assembly dynamics.

Triple negative breast cancer (TNBC) is highly metastatic and of poor prognosis. Metastasis involves coordinated actin filament dynamics mediated by cofilin and associated proteins. Activated androgen receptor (AR) is believed to contribute to TNBC tumorigenesis. Our current work studied roles of activated AR and cofilin phospho-regulation during migration of three AR+ TNBC cell lines to determine if altered cofilin regulation can explain their migratory differences. Untreated or AR agonist-treated BT549, MDA-MB-453, and SUM159PT cells were compared to cells silenced for cofilin (KD) or AR expression/function (bicalutamide). Cofilin-1 was found to be the only ADF/cofilin isoform expressed in each TNBC line. Despite a significant increase in cofilin kinase caused by androgens, the ratio of cofilin:p-cofilin (1:1) did not change in SUM159PT cells. BT549 and MDA-MB-453 cells contain high p-cofilin levels which underwent androgen-induced dephosphorylation through increased cofilin phosphatase expression, but surprisingly maintain a leading-edge with high p-cofilin/total cofilin not found in SUM159PT cells. Androgens enhanced cell polarization in all lines, stimulated wound healing and transwell migration rates and increased N/E-cadherin mRNA ratios while reducing cell adhesion in BT549 and MDA-MB-453 cells. Cofilin KD negated androgen effects in MDA-MB-453 except for cell adhesion, while in BT549 cells it abrogated androgen-reduced cell adhesion. In SUM159PT cells, cofilin KD with and without androgens had similar effects in almost all processes studied. AR dependency of the processes were confirmed. In conclusion, cofilin regulation downstream of active AR is dependent on which actin-mediated process is being examined in addition to being cell line-specific. Although MDA-MB-453 cells demonstrated some control of cofilin through an AR-dependent mechanism, other AR-dependent pathways need to be further studied. Non-cofilin-dependent mechanisms that modulate migration of SUM159PT cells need to be investigated. Categorizing TNBC behavior as AR responsive and/or cofilin dependent can inform on decisions for therapeutic treatment.

Molecular Drivers of Crystallization Kinetics for Drugs in Supersaturated Aqueous Solutions.

In this study, we explore molecular properties of importance in solution-mediated crystallization occurring in supersaturated aqueous drug solutions. Furthermore, we contrast the identified molecular properties with those of importance for crystallization occurring in the solid state. A literature data set of 54 structurally diverse compounds, for which crystallization kinetics from supersaturated aqueous solutions and in melt-quenched solids were reported, was used to identify molecular drivers for crystallization kinetics observed in solution and contrast these to those observed for solids. The compounds were divided into fast, moderate, and slow crystallizers, and in silico classification was developed using a molecular K-nearest neighbor model. The topological equivalent of Grav3 (related to molecular size and shape) was identified as the most important molecular descriptor for solution crystallization kinetics; the larger this descriptor, the slower the crystallization. Two electrotopological descriptors (the atom-type E-state index for -Caa groups and the sum of absolute values of pi Fukui(+) indices on C) were found to separate the moderate and slow crystallizers in the solution. The larger these descriptors, the slower the crystallization. With these 3 descriptors, the computational model correctly sorted the crystallization tendencies from solutions with an overall classification accuracy of 77% (test set).

Physical stability of drugs after storage above and below the glass transition temperature: Relationship to glass-forming ability.

Amorphous materials are inherently unstable and tend to crystallize upon storage. In this study, we investigated the extent to which the physical stability and inherent crystallization tendency of drugs are related to their glass-forming ability (GFA), the glass transition temperature (Tg) and thermodynamic factors. Differential scanning calorimetry was used to produce the amorphous state of 52 drugs [18 compounds crystallized upon heating (Class II) and 34 remained in the amorphous state (Class III)] and to perform in situ storage for the amorphous material for 12h at temperatures 20°C above or below the Tg. A computational model based on the support vector machine (SVM) algorithm was developed to predict the structure-property relationships. All drugs maintained their Class when stored at 20°C below the Tg. Fourteen of the Class II compounds crystallized when stored above the Tg whereas all except one of the Class III compounds remained amorphous. These results were only related to the glass-forming ability and no relationship to e.g. thermodynamic factors was found. The experimental data were used for computational modeling and a classification model was developed that correctly predicted the physical stability above the Tg. The use of a large dataset revealed that molecular features related to aromaticity and π-π interactions reduce the inherent physical stability of amorphous drugs.

Experimental and computational prediction of glass transition temperature of drugs.

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.

Computational predictions of glass-forming ability and crystallization tendency of drug molecules.

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.

Data mining of solubility parameters for computational prediction of drug-excipient miscibility.

Abstract Computational data mining is of interest in the pharmaceutical arena for the analysis of massive amounts of data and to assist in the management and utilization of the data. In this study, a data mining approach was used to predict the miscibility of a drug and several excipients, using Hansen solubility parameters (HSPs) as the data set. The K-means clustering algorithm was applied to predict the miscibility of indomethacin with a set of more than 30 compounds based on their partial solubility parameters [dispersion forces (δd), polar forces (δp) and hydrogen bonding (δh)]. The miscibility of the compounds was determined experimentally, using differential scanning calorimetry (DSC), in a separate study. The results of the K-means algorithm and DSC were compared to evaluate the K-means clustering prediction performance using the HSPs three-dimensional parameters, the two-dimensional parameters such as volume-dependent solubility (δv) and hydrogen bonding (δh) and selected single (one-dimensional) parameters. Using HSPs, the prediction of miscibility by the K-means algorithm correlated well with the DSC results, with an overall accuracy of 94%. The prediction accuracy was the same (94%) when the two-dimensional parameters or the hydrogen-bonding (one-dimensional) parameter were used. The hydrogen-bonding parameter was thus a determining factor in predicting miscibility in such set of compounds, whereas the dispersive and polar parameters had only a weak correlation. The results show that data mining approach is a valuable tool for predicting drug-excipient miscibility because it is easy to use, is time and cost-effective, and is material sparing.