Research Progress in Nanopharmaceuticals with Different Delivery Routes in the Antivirus Field.

Human health is significantly threatened by infectious diseases caused by viral infection. Over the years, there have been numerous virus epidemics worldwide, causing millions of deaths. Traditional antiviral medications have many problems, including poor solubility and antiviral resistance. Additionally, because different drug delivery methods have different biological barriers to overcome, the drug's bioavailability will be significantly affected. Therefore, it is essential that researchers create more effective antiviral drugs. To serve as a guide for the future development of nanosized antiviral drugs with stronger and more precise therapeutic effects, research has been performed on nanotechnology in the field of antiviral therapy. This review summarizes the recent developments in antiviral nanopharmaceuticals with different delivery routes. Research on 7 typical viruses, including COVID-19, has been included in this review. After being loaded into nanoparticles, antiviral drugs can be delivered through several drug modes of delivery, overcoming biological barriers. Moreover, some nanoparticles themselves have the ability to combat infections, so they can be used in conjunction with antiviral medication. The use of nanoparticle medications through various routes of administration can result in their unique benefits. They can be capable of overcoming its limitations as well as retaining the advantages of this method of delivery. This will motivate researchers to conducted a new investigation on nanoparticle medicines from the standpoint of the route of administration in order to increase the practicability of antiviral medications.

Preparation and cytotoxicity evaluation of folic acid-modified YF8-OA self-assembled lipid prodrug nanoparticles.

This study aimed to improve the use of YF8, a matrine derivative obtained through chemical transformation of matrine extracted from Sophora alopecuroides. YF8 has demonstrated improved cytotoxicity compared to matrine, but its hydrophobic nature hinders its application. To overcome this, the lipid prodrug YF8-OA was synthesized by linking oleic acid (OA) to YF8 through an ester bond. Although YF8-OA could self-assemble into unique nanostructures in water, it was not sufficiently stable. To enhance the stability of YF8-OA lipid prodrug nanoparticles (LPs), we employed the strategy of PEGylation using DSPE-mPEG2000 or DSPE-mPEG2000 conjugated with folic acid (FA). This resulted in the formation of uniform spherical nanoparticles with greatly improved stability and a maximum drug load capacity upto 58.63%. Cytotoxicity was evaluated in A549, HeLa, and HepG2 cell lines. The results showed that in HeLa cells, the IC50 value of YF8-OA/LPs with FA-modified PEGylation was significantly lower than that of YF8-OA/LPs modified by PEGylation alone. However, no significant enhancement was observed in A549 and HepG2 cells. In conclusion, the lipid prodrug YF8-OA can form nanoparticles in aqueous solution to address its poor water solubility. Modification with FA resulted in further enhanced cytotoxicity, providing a potential avenue for exerting the antitumor activity of matrine analogs.

Predicting Drug Synergy and Discovering New Drug Combinations Based on a Graph Autoencoder and Convolutional Neural Network.

Drug synergy is a crucial component in drug reuse since it solves the problem of sluggish drug development and the absence of corresponding drugs for several diseases. Predicting drug synergistic relationships can screen drug combinations in advance and reduce the waste of laboratory resources. In this research, we proposed a model that utilizes graph autoencoder and convolutional neural networks to predict drug synergy (GAECDS). Our methods include a graph convolutional neural network as an encoder to encode drug features and use a matrix factorization method as a decoder. Multilayer perceptron (MLP) was applied to process cell line features and combine them with drug features. Furthermore, the latent vectors generated during the encoding process are being used to predict drug synergistic scores using a convolutional neural network. By measuring prediction performance using AUC, AUPR, and F1 score, GAECDS superior to other state-of-the-art models. In addition, four pairs of the predicted top 10 drug combinations were found to work well enough for evaluation. The case study shows that the GAECDS approach is useful for identifying potential drug synergy.

A compact review of progress and prospects of deep learning in drug discovery.

BACKGROUND: Drug discovery processes, such as new drug development, drug synergy, and drug repurposing, consume significant yearly resources. Computer-aided drug discovery can effectively improve the efficiency of drug discovery. Traditional computer methods such as virtual screening and molecular docking have achieved many gratifying results in drug development. However, with the rapid growth of computer science, data structures have changed considerably; with more extensive and dimensional data and more significant amounts of data, traditional computer methods can no longer be applied well. Deep learning methods are based on deep neural network structures that can handle high-dimensional data very well, so they are used in current drug development. RESULTS: This review summarized the applications of deep learning methods in drug discovery, such as drug target discovery, drug de novo design, drug recommendation, drug synergy, and drug response prediction. While applying deep learning methods to drug discovery suffers from a lack of data, transfer learning is an excellent solution to this problem. Furthermore, deep learning methods can extract deeper features and have higher predictive power than other machine learning methods. Deep learning methods have great potential in drug discovery and are expected to facilitate drug discovery development.

A Review of The Application of Spectroscopy to Flavonoids from Medicine and Food Homology Materials.

Medicinal and food homology materials are a group of drugs in herbal medicine that have nutritional value and can be used as functional food, with great potential for development and application. Flavonoids are one of the major groups of components in pharmaceutical and food materials that have been found to possess a variety of biological activities and pharmacological effects. More and more analytical techniques are being used in the study of flavonoid components of medicinal and food homology materials. Compared to traditional analytical methods, spectroscopic analysis has the advantages of being rapid, economical and free of chemical waste. It is therefore widely used for the identification and analysis of herbal components. This paper reviews the application of spectroscopic techniques in the study of flavonoid components in medicinal and food homology materials, including structure determination, content determination, quality identification, interaction studies, and the corresponding chemometrics. This review may provide some reference and assistance for future studies on the flavonoid composition of other medicinal and food homology materials.