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Amorphous solids exhibit enhanced solubility and dissolution rates relative to their crystalline counterparts. However, attaining optimal bioavailability presents a challenge, primarily due to the need to maintain the physical stability of amorphous solids. Moreover, the precise manner in which precipitation parameters, including the feeding rate of the anti-solvent, agitation speed, and aging time, influence the physical stability of amorphous solids remains incompletely understood. Consequently, this study aimed to investigate these three parameters during the precipitation process of the anticancer drug, nilotinib free base. The physical stability of the resultant samples was evaluated by employing characterization techniques including powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), focused beam reflectance measurement (FBRM), and data analysis methods such as pair distribution function (PDF), reduced crystallization temperature (Rc), and principal component analysis (PCA). This study's findings indicated that amorphous solids exhibited the greatest physical stability under particular conditions, namely a feeding rate of 5 mL/min, an agitation speed of 500 rpm, and an aging time of 10 min. Furthermore, the physical stability of the amorphous solids was primarily influenced by particle size and distribution, molecular interactions, microstructure, surface area, and interfacial energy. Notably, the parameters involved in the anti-solvent precipitation process, including the feeding rate of the anti-solvent, agitation speed, and aging time, exerted a significant impact on these factors. Consequently, they directly affected the physical stability of amorphous solids. Hence, this study comprehensively elucidated the mechanistic influence of these operational parameters on the physical stability of amorphous solids during the anti-solvent precipitation process. [ABSTRACT FROM AUTHOR]

The development of non-enzymatic and highly active electrocatalysts for glucose oxidation with excellent durability for blood glucose sensors has aroused widespread concern. In this work, we report a fast, simple, and low-cost NaBH4 reduction method for preparing ultrafine ternary PtPdCu alloy nanowires (NWs) with a 3D network nanostructure. The PtPdCu NWs catalyst presents significant efficiency for glucose oxidation-reduction (GOR), reaching an oxidative peak-specific activity of 0.69 mA/cm2, 2.6 times that of the Pt/C catalyst (0.27 mA/cm2). Further reaction mechanism investigations show that the NWs have better conductivity and smaller electron transfer resistance. Density functional theory (DFT) calculations reveal that the alloying effect of PtPdCu could effectively enhance the adsorption energy of glucose and reduce the activation energy of GOR. The obtained NWs also show excellent stability over 3600 s through a chronoamperometry test. These self-supported ultrafine PtPdCu NWs with 3D networks provide a new functional material for building blood glucose sensors and direct glucose fuel cells. [ABSTRACT FROM AUTHOR]

The amorphous form of poorly soluble drugs is physically unstable and prone to crystallization, resulting in decreased solubility and bioavailability. However, the conventional accelerated stability test for amorphous drugs is time-consuming and inaccurate. Therefore, there is an urgent need to develop rapid and accurate stability assessment technology. This study used the antitumor drug nilotinib free base as a model drug. The degree of disorder and physical stability in the amorphous form was assessed by applying the pair distribution function (PDF) and principal component analysis (PCA) methods based on powder X-ray diffraction (PXRD) data. Specifically, the assessment conditions, such as the PDF interatomic distance range, PXRD detector type, and PXRD diffraction angle range were also optimized. The results showed that more reliable PCA data could be obtained when the PDF interatomic distance range was 0–15 Å. When the PXRD detector was a semiconductor-type detector, the PDF data obtained were more accurate than other detectors. When the PXRD diffraction angle range was 5–40°, the intermolecular arrangement of the amorphous drugs could be accurately predicted. Finally, the accelerated stability test also showed that under the above-optimized conditions, this method could accurately and rapidly assess the degree of disorder and physical stability in the amorphous form of drugs, which has obvious advantages compared with the accelerated stability test. [ABSTRACT FROM AUTHOR]

The purpose of this study was to resolve the issue of physical instability in amorphous solid drugs, which can result in unwanted crystallization, affecting solubility and dissolution rates. The focus was on precipitating physically stable amorphous forms of the nilotinib free base, an anticancer drug, by monitoring preparation conditions such as precipitation temperature and filter cake thickness. A comprehensive set of characterization techniques, including powder X-ray diffraction (PXRD), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and focused beam reflectance measurement (FBRM), were used. These were supplemented by advanced data analysis methods that incorporated pair distribution function (PDF), reduced crystallization temperature (Rc), and principal component analysis (PCA) to evaluate the physical stability of the amorphous samples. Results emphasized that optimal physical stability was achieved when amorphous solids were prepared at a precipitation temperature of 10 °C and a filter cake thickness of 4 cm. Moreover, the integration of PDF analysis with Rc values was confirmed as an innovative approach for assessing physical stability, thus offering enhanced efficiency and accuracy over conventional accelerated stability testing methods. [ABSTRACT FROM AUTHOR]

Ligand-based virtual screening (LBVS) is a promising approach for rapid and low-cost screening of potentially bioactive molecules in the early stage of drug discovery. Compared with traditional similarity-based machine learning methods, deep learning frameworks for LBVS can more effectively extract high-order molecule structure representations from molecular fingerprints or structures. However, the 3D conformation of a molecule largely influences its bioactivity and physical properties, and has rarely been considered in previous deep learning-based LBVS methods. Moreover, the relative bioactivity benchmark dataset is still lacking. To address these issues, we introduce a novel end-to-end deep learning architecture trained from molecular conformers for LBVS. We first extracted molecule conformers from multiple public molecular bioactivity data and consolidated them into a large-scale bioactivity benchmark dataset, which totally includes millions of endpoints and molecules corresponding to 954 targets. Then, we devised a deep learning-based LBVS called EquiVS to learn molecule representations from conformers for bioactivity prediction. Specifically, graph convolutional network (GCN) and equivariant graph neural network (EGNN) are sequentially stacked to learn high-order molecule-level and conformer-level representations, followed with attention-based deep multiple-instance learning (MIL) to aggregate these representations and then predict the potential bioactivity for the query molecule on a given target. We conducted various experiments to validate the data quality of our benchmark dataset, and confirmed EquiVS achieved better performance compared with 10 traditional machine learning or deep learning-based LBVS methods. Further ablation studies demonstrate the significant contribution of molecular conformation for bioactivity prediction, as well as the reasonability and non-redundancy of deep learning architecture in EquiVS. Finally, a model interpretation case study on CDK2 shows the potential of EquiVS in optimal conformer discovery. The overall study shows that our proposed benchmark dataset and EquiVS method have promising prospects in virtual screening applications. [ABSTRACT FROM AUTHOR]