Identification of Constituents and Exploring the Mechanism for Toutongning Capsule in the Treatment of Migraine

Toutongning capsule (TTNC) is an effective and safe traditional Chinese medicine used in the treatment of migraine. In this present study, a multiscale strategy was used to systematically investigate the mechanism of TTNC in treating migraine, which contained UPLC-UESI-Q Exactive Focus network pharmacology and experimental verification. First, 88 compounds were identified by the UPLC-UESI-Q Exactive Focus method for TTNC. Then, the target fishing for these compounds was performed by means of an efficient drug similarity search tool. Third, a series of network pharmacology experiments were performed to predict the key compounds, targets, and pathways. They were protein-protein interaction (PPI), KEGG pathway enrichment analysis, and herbs-compounds-targets-pathways (H-C-T-P) network construction. As a result, 18 potential key compounds, 20 potential key targets, and 6 potential signaling pathways were obtained for TTNC in treatment with migraine. Finally, molecular docking and experimental were carried out to verify the key targets. In short, the results showed that TTNC is able to treat migraine through multiple components, multiple targets, and multiple pathways. This work may provide a theoretical basis for further research on the molecular mechanism of TTNC in the treatment of migraine.

FDMine: a graph mining approach to predict and evaluate food-drug interactions

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. In this graph, all nodes representing drug, food and food composition are referenced as chemical structures. This homogenous representation enables us to take advantage of reported drug-drug interactions for accuracy evaluation, especially when accessible ground truth for FDIs is lacking. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions (DDIs) and 320 unique food items, composed of 563 unique compounds with 179 health effects. The potential number of interactions is 87,192 and 92,143 when two different versions of the graph referred to as disjoint and joint graphs are considered, respectively. We defined several similarity subnetworks comprising food-drug similarity (FDS), drug-drug similarity (DDS), and food-food similarity (FFS) networks, based on similarity profiles. A unique part of the graph is the encoding of the food composition as a set of nodes and calculating a content contribution score to re-weight the similarity links. To predict new FDI links, we applied the path category-based (path length 2 and 3) and neighborhood-based similarity-based link prediction algorithms. We calculated the precision@top (top 1%, 2%, and 5%) of the newly predicted links, the area under the receiver operating characteristic curve, and precision-recall curve. We have performed three types of evaluations to benchmark results using different types of interactions. The shortest path-based method has achieved a precision 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. We hypothesize that the proposed framework can be used to gain new insights on FDIs. FDMine is publicly available to support clinicians and researchers.

Prediction of the Drug–Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks

Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug–drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications. Recently, numerous text mining– and machine learning–based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug–drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types.

Drug Repurposing Using Modularity Clustering in Drug-Drug Similarity Networks Based on Drug–Gene Interactions

Drug repurposing is a valuable alternative to traditional drug design based on the assumption that medicines have multiple functions. Computer-based techniques use ever-growing drug databases to uncover new drug repurposing hints, which require further validation with in vitro and in vivo experiments. Indeed, such a scientific undertaking can be particularly effective in the case of rare diseases (resources for developing new drugs are scarce) and new diseases such as COVID-19 (designing new drugs require too much time). This paper introduces a new, completely automated computational drug repurposing pipeline based on drug–gene interaction data. We obtained drug–gene interaction data from an earlier version of DrugBank, built a drug–gene interaction network, and projected it as a drug–drug similarity network (DDSN). We then clustered DDSN by optimizing modularity resolution, used the ATC codes distribution within each cluster to identify potential drug repurposing candidates, and verified repurposing hints with the latest DrugBank ATC codes. Finally, using the best modularity resolution found with our method, we applied our pipeline to the latest DrugBank drug–gene interaction data to generate a comprehensive drug repurposing hint list.

Structures 4-n-propyl Piperazines as Non-Imidazole Histamine H3 Antagonists

Seven new low-temperature structures of 4-n-propylpiperazine derivatives, potential H3 receptor antagonists, have been determined by X-ray crystallography, with the following symmetry and unit cell parameters: 2-(4-propyl-piperazin-1-yl)oxazolo[4,5-c]pyridine (compound 1), P-1, 5.9496 Å, 12.4570 Å, 12.8656 Å, 112.445°, 95.687°, 103.040°; 2-(4-propyl-piperazin-1-yl)thia-zolo[4,5-c]pyridine (compound 2), I2/a, 22.2087 Å, 7.5519 Å, 19.9225 Å, β = 92.368°; 2-(4-propyl-piperazin-1-yl)oxazolo[5,4-c]pyridine (compound 3), C2/c, 51.1351 Å, 9.36026 Å, 7.19352 Å, β = 93.882°; 2-(4-propyl-piperazin-1-yl)thiazolo[5,4-c]pyridine (compound 4), Pbcn, 19.2189 Å, 20.6172 Å, 7.4439 Å; 2-(4-propylpiperazin-1-yl)[1,3]oxazolo[4,5-b]pyridine, hydrate (structure 5), Pbca, 7.4967 Å, 12.2531 Å, 36.9527 Å; 2-(4-propylpiperazin-1-yl)[1,3]oxazolo[4,5-b]pyridine, first polymorph (structure 6), P-1, 7.2634 Å, 11.1261 Å, 18.5460 Å, 80.561°, 80.848°, 76.840°; 2-(4-propylpiperazin-1-yl)[1,3]oxazolo[4,5-b]pyridine, second polymorph (structure 7), P21, 8.10852 Å, 7.06025 Å, 12.41650 Å, β = 92.2991°. All the compounds crystallized out as hydrobromides. Oxazole structures show a much greater tendency to form twin crystals than thiazole structures. All the investigated structures display N—H···Br hydrogen bonding. (ADME) analysis, including the assessment of absorption, distribution, metabolism, and excretion, determined the physicochemical properties, pharmacokinetics, drug similarity, and bioavailability radar, and confirmed the usefulness of the compounds in question for pharmaceutical utility. This work is a continuation of the research searching for a new lead of non-imidazole histamine H3 receptor antagonists.

MM-Deacon: Multimodal molecular domain embedding analysis via contrastive learning

Molecular representation learning plays an essential role in cheminformatics. Recently, language model-based approaches have been popular as an alternative to traditional expert-designed features to encode molecules. However, these approaches only utilize a single modality for representing molecules. Driven by the fact that a given molecule can be described through different modalities such as Simplified Molecular Line Entry System (SMILES), The International Union of Pure and Applied Chemistry (IUPAC), and The IUPAC International Chemical Identifier (InChI), we propose a multimodal molecular embedding generation approach called MM-Deacon (multimodal molecular domain embedding analysis via contrastive learning). MM-Deacon is trained using SMILES and IUPAC molecule representations as two different modalities. First, SMILES and IUPAC strings are encoded by using two different transformer-based language models independently, then the contrastive loss is utilized to bring these encoded representations from different modalities closer to each other if they belong to the same molecule, and to push embeddings farther from each other if they belong to different molecules. We evaluate the robustness of our molecule embeddings on molecule clustering, cross-modal molecule search, drug similarity assessment and drug-drug interaction tasks.

Improving Drug Response Prediction Using Dual Similarity Regularization

Anti-cancer medicine for a particular patient has been a personal medical goal. Many computational models have been proposed by researchers to predict drug response. But predictive accuracy still remains a challenge. Base on this concept which “Similar cells have similar responses to drugs”, we developed the basic method of matrix factorization method by adding fines to similarity. So that the distance of latent factors to two cell lines or (drug) should be inversely related to similarity. This means that two similar drugs or similar cell lines should have a short distance, whereas two similar cell lines or non-similar drugs should have a large gap with their latent factors. We proposed a Dual similarity-regularized matrix factorization (DSRMF) model, then generated new data for drug similarity from the two-dimensional three-dimensional chemical structure, which were obtained from the CCLE and GDSC databases. In this research, by using the proposed model, and generating new drug similarity data we achieved the average Pearson correlation coefficient (PCC) about 0.96, and average mean square error (RMSE) Root about 0.30, between the observed value and the predicted value for the cell line response to the drug. Our analysis in this research showed, using heterogeneous data, has better results, and can be obtained with the proposed model, using other panels’ cancer cell lines, to calculate similarity between cells. Also, by imposing more restrictions on the similarity between cells, we were able to achieve more accurate prediction for the response of the cell line to the anticancer drug.

FDMine: a graph mining approach to predict and evaluate food-drug interactions

Food-drug interactions (FDIs) arise when nutritional dietary consumption regulates biochemical mechanisms involved in drug metabolism. These interactions can create unexpected adverse pharmacological effects. By contrast, particular foods can aid in the recovery process of a patient. Towards characterizing the nature of food’s influence on pharmacological treatment, it is essential to detect all possible FDIs. In this study, we propose FDMine, a novel systematic framework that models the FDI problem as a homogenous graph. In this graph, all nodes representing drug, food and food composition are referenced as chemical structures. This homogenous representation enables us to take advantage of reported drug-drug interactions for accuracy evaluation, especially when accessible ground truth for FDIs is lacking. Our dataset consists of 788 unique approved small molecule drugs with metabolism-related drug-drug interactions (DDIs) and 320 unique food items, composed of 563 unique compounds with 179 health effects. The potential number of interactions is 87,192 and 92,143 when two different versions of the graph referred to as disjoint and joint graphs are considered, respectively. We defined several similarity subnetworks comprising food-drug similarity (FDS), drug-drug similarity (DDS), and food-food similarity (FFS) networks, based on similarity profiles. A unique part of the graph is the encoding of the food composition as a set of nodes and calculating a content contribution score to re-weight the similarity links. To predict new FDI links, we applied the path category-based (path length 2 and 3) and neighborhood-based similarity-based link prediction algorithms. We calculated the precision@top (top 1%, 2%, and 5%) of the newly predicted links, the area under the receiver operating characteristic curve, and precision-recall curve. We have performed three types of evaluations to benchmark results using different types of interactions. The shortest path-based method has achieved a precision 84%, 60% and 40% for the top 1%, 2% and 5% of FDIs identified, respectively. We validated the top FDIs predicted using FDMine to demonstrate its applicability and we relate therapeutic anti-inflammatory effects of food items informed by FDIs. We hypothesize that the proposed framework can be used to gain new insights on FDIs. FDMine is publicly available to support clinicians and researchers.

Design and Antiproliferative and Antioxidant Activities of Furan-Based Thiosemicarbazides and 1,2,4-Triazoles: Their Structure-Activity Relationship and SwissADME Predictions

Due to the limited number of drugs in current clinical use, the diverse biological applications of furan have encouraged the preparation of a wide variety of thiosemicarbazide and triazole derivatives for the purpose of developing new drug agents. In this study aimed to investigate the antiproliferative and antioxidant activities of some thiosemicarbazides (1-12) and 1,2,4-triazoles (13-24). Compound 15 (IC50: 8.81±0.28 µM) showed the highest antiproliferative activity against the cervical (HeLa) cancer cell line among the compounds. Compounds 15, 20, 21, and 22 of the 1,2,4-triazole derivatives (13-24) exhibited excellent antioxidant activity. Moreover, the physicochemical properties, pharmacokinetic properties, drug similarity, and medicinal chemistry properties of all synthesized products were calculated using SwissADME. In addition, the effect of the structure–activity relationships of the 1,2,4-triazole derivatives (13-24) on the results of antiproliferative and antioxidant activity assays was evaluated.