Co-amorphous formulation of dipyridamole with p-hydroxybenzoic acid: Underlying molecular mechanisms, physical stability, dissolution behavior and pharmacokinetic study.

Coamorphization has been proven to be an effective approach to improve bioavailability of poorly soluble active pharmaceutical ingredients (APIs) by virtue of solubilization, and also contributes to overcome limitation of physical stability associated with amorphous drug alone. In current work, a co-amorphous formulation of dipyridamole (DPM), a poor solubility drug, with p-hydroxybenzoic acid (HBA) was prepared and investigated. At a molar ratio of 1:2, DPM and HBA were melted result in the formation of a binary co-amorphous system. The DPM-HBA co-amorphous was structurally characterized by powder X-ray diffraction (PXRD), temperature modulated differential scanning calorimetry (mDSC), high performance liquid chromatography (HPLC) and solution state 1H nuclear magnetic resonance (1H NMR). The molecular mechanisms in the co-amorphous were further analysed via Fourier-transform infrared (FTIR) and Raman spectroscopies, as well as density functional theory (DFT) calculation. All the results consistently revealed the presence of hydrogen bonding interactions between -OH of DPM and -COOH on HBA. Accelerated test and glass transition kinetics showed excellent physical stability of DPM-HBA co-amorphous compared with amorphous DPM along with glass transition temperatures (Tg). The phase-solubility study indicated that complexation occurred between DPM and HBA in solution, which contributed to the solubility and dissolution enhancement of DPM in co-amorphous system. Pharmacokinetic study of co-amorphous DPM-HBA in mouse plasma revealed that the DPM exhibited 1.78-fold and 2.64-fold improvement in AUC0­∞ value compared with crystalline and amorphous DPM, respectively. This current study revealed coamorphization is an effective approach for DPM to improve the solubility and biopharmaceutical performance.

CovC-ReDRNet: A Deep Learning Model for COVID-19 Classification.

Since the COVID-19 pandemic outbreak, over 760 million confirmed cases and over 6.8 million deaths have been reported globally, according to the World Health Organization. While the SARS-CoV-2 virus carried by COVID-19 patients can be identified though the reverse transcription-polymerase chain reaction (RT-PCR) test with high accuracy, clinical misdiagnosis between COVID-19 and pneumonia patients remains a challenge. Therefore, we developed a novel CovC-ReDRNet model to distinguish COVID-19 patients from pneumonia patients as well as normal cases. ResNet-18 was introduced as the backbone model and tailored for the feature representation afterward. In our feature-based randomized neural network (RNN) framework, the feature representation automatically pairs with the deep random vector function link network (dRVFL) as the optimal classifier, producing a CovC-ReDRNet model for the classification task. Results based on five-fold cross-validation reveal that our method achieved 94.94%, 97.01%, 97.56%, 96.81%, and 95.84% MA sensitivity, MA specificity, MA accuracy, MA precision, and MA F1-score, respectively. Ablation studies evidence the superiority of ResNet-18 over different backbone networks, RNNs over traditional classifiers, and deep RNNs over shallow RNNs. Moreover, our proposed model achieved a better MA accuracy than the state-of-the-art (SOTA) methods, the highest score of which was 95.57%. To conclude, our CovC-ReDRNet model could be perceived as an advanced computer-aided diagnostic model with high speed and high accuracy for classifying and predicting COVID-19 diseases.