Theoretical study of gallium nitride nanocage as a carrier for Cisplatin anticancer drug

In this paper, the possible interactions between cisplatin Cl2H6N2Pt as an anticancer drug and gallium nitride (Ga12N12) nanocage have been investigated using the DFT/b3lyp/lanl2dz(d,p) level of theory. Three different orientations were used to mimic the cisplatin adsorbed on Ga12N12. To investigate the interaction mechanism between the two components, the adsorption energies and thermodynamic parameters, the electronic properties such as the energies and orbitals distribution of the highest occupied molecular orbital (HOMO), the lowest unoccupied molecular orbital (LUMO), the HOMO-LUMO energy gaps (Eg), thermodynamic properties were also investigated. Additionally, some quantum molecular descriptors were calculated to understand molecular reactivity. The main results revealed that the adsorption process of the drug compound on the surface of the nanocage varies with the adsorption site. The process showed that different energies could be obtained, where the highest energy value was when the drug compound was adsorbed with the nanocage at the chlorine atom, with a value of (41.85) kcal/mol. On the other hand, the distance between the drug compound atoms was affected before and after adsorption, which proves the existence of an interaction between the drug compound and the nanocage and considers it as a drug delivery vehicle.

Exploring the Potential Mechanism of Chuanxiong Rhizoma Treatment for Migraine Based on Systems Pharmacology

Migraine is a disease whose aetiology and mechanism are not yet clear. Chuanxiong Rhizoma (CR) is employed in traditional Chinese medicine (TCM) to treat various disorders. CR is effective for migraine, but its active compounds, drug targets, and exact molecular mechanism remain unclear. In this study, we used the method of systems pharmacology to address the above issues. We first established the drug-compound-target-disease (D-C-T-D) network and protein-protein interaction (PPI) network related to the treatment of migraine with CR and then established gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. The results suggest that the treatment process may be related to the regulation of inflammation and neural activity. The docking results also revealed that PTGS2 and TRPV1 could directly bind to the active compounds that could regulate them. In addition, we found that CR affected 11 targets that were more highly expressed in the liver or heart but were the lowest in the whole brain. It also expounds the description of CR channel tropism in TCM theory from these angles. These findings not only indicate that CR can be developed as a potential effective drug for the treatment of migraine but also demonstrate the application of systems pharmacology in the discovery of herbal-based disease therapies.

A Deep-Learning Proteomic-Scale Approach for Drug Design

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multitarget therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach that computes interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning-based autoencoder to first reduce the dimensionality of CANDO-computed drug–proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this approach, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds were predicted to be significantly (p-value ≤ 0.05) more behaviorally similar relative to all corresponding controls, and 20/20 were predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design performed significantly better than those derived from natural sources (p-value ≤ 0.05), suggesting that the model learned an abstraction of rational drug design. We also show that the designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhanced thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. his study represents a significant step forward in automating holistic therapeutic design with machine learning, enabling the rapid generation of novel, effective, and safe drug leads for any indication.

Protein domain-based prediction of drug/compound–target interactions and experimental validation on LIM kinases

Predictive approaches such as virtual screening have been used in drug discovery with the objective of reducing developmental time and costs. Current machine learning and network-based approaches have issues related to generalization, usability, or model interpretability, especially due to the complexity of target proteins’ structure/function, and bias in system training datasets. Here, we propose a new method “DRUIDom” (DRUg Interacting Domain prediction) to identify bio-interactions between drug candidate compounds and targets by utilizing the domain modularity of proteins, to overcome problems associated with current approaches. DRUIDom is composed of two methodological steps. First, ligands/compounds are statistically mapped to structural domains of their target proteins, with the aim of identifying their interactions. As such, other proteins containing the same mapped domain or domain pair become new candidate targets for the corresponding compounds. Next, a million-scale dataset of small molecule compounds, including those mapped to domains in the previous step, are clustered based on their molecular similarities, and their domain associations are propagated to other compounds within the same clusters. Experimentally verified bioactivity data points, obtained from public databases, are meticulously filtered to construct datasets of active/interacting and inactive/non-interacting drug/compound–target pairs (~2.9M data points), and used as training data for calculating parameters of compound–domain mappings, which led to 27,032 high-confidence associations between 250 domains and 8,165 compounds, and a finalized output of ~5 million new compound–protein interactions. DRUIDom is experimentally validated by syntheses and bioactivity analyses of compounds predicted to target LIM-kinase proteins, which play critical roles in the regulation of cell motility, cell cycle progression, and differentiation through actin filament dynamics. We showed that LIMK-inhibitor-2 and its derivatives significantly block the cancer cell migration through inhibition of LIMK phosphorylation and the downstream protein cofilin. One of the derivative compounds (LIMKi-2d) was identified as a promising candidate due to its action on resistant Mahlavu liver cancer cells. The results demonstrated that DRUIDom can be exploited to identify drug candidate compounds for intended targets and to predict new target proteins based on the defined compound–domain relationships. Datasets, results, and the source code of DRUIDom are fully-available at: https://github.com/cansyl/DRUIDom.

Study on Mechanism of Jiawei Chaiqin Wendan Decoction in Treatment of Vestibular Migraine Based on Network Pharmacology and Molecular Docking Technology

Objective. To predict the main active ingredients, potential targets, and key pathways of Jiawei Chaiqin Wendan decoction treatment in vestibular migraine and explore possible mechanisms by network pharmacology and molecular docking technology. Methods. The active ingredients and related targets of Jiawei Chaiqin Wendan decoction were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). The corresponding genes of the target were queried by UniProt database, and the “drug-compound-target-disease” network was constructed by Cytoscape 3.7.2 software. GO functional enrichment analysis and KEGG pathway enrichment analysis were carried out by R software and Bioconductor, and column chart and bubble chart were drawn by Prism software and OmicShare database for visualization. Finally, the mechanism and potential targets of Jiawei Chaiqin Wendan decoction in the treatment of vestibular migraine were predicted. Results. The “drug-compound-target-disease” network contains 154 active ingredients and 85 intersection targets. The key targets include AKT1, IL6, MAPK3, VEGFA, EGFR, CASP3, EGF, MAPK1, PTGS2, and ESR1. A total of 1939 items were obtained by GO functional enrichment analysis ( P < 0.05). KEGG pathway enrichment analysis screened 156 signal pathways ( P < 0.05), involving PI3K-Akt signal pathway, AGE-RAGE signal pathway in diabetes complications, MAPK signal pathway, HIF-1 signal pathway, IL-17 signal pathway, etc. Molecular docking results showed that quercetin, luteolin, kaempferol, tanshinone IIa, wogonin, naringenin, nobiletin, dihydrotanshinlactone, beta-sitosterol, and salviolone have good affinity with core target proteins IL6, PTGS2, MAPK1, MAPK3, and CGRP1. Conclusion. The active ingredients in Jiawei Chaiqin Wendan decoction may regulate the levels of inflammatory factors and neurotransmitters by acting on multiple targets such as IL6, MAPK3, MAPK1, and PTGS2, so as to play a therapeutic role in vestibular migraine.

Exploration of Ziziphi Spinosae Semen in Treating Insomnia Based on Network Pharmacology Strategy

Ziziphi Spinosae Semen (ZSS) is a common natural medicine used to treat insomnia, and to show clearly its method of action, we managed and did an in-depth discussion. Network pharmacology research is very suitable for the analysis of multiple components, multiple targets, and multiple pathways of Traditional Chinese Medicine (TCM). According to the relevant theory, we first carefully collected and screened the active ingredients in ZSS and received 11 active ingredients that may work. The targets going along with these active components were also strongly related to insomnia targets, 108 common genes were identified, and drug-compound-gene symbol-disease visualization network and protein-protein interaction network were constructed. Forty-eight core genes were identified by PPI analysis and subjected to GO functional analysis with KEGG pathway analysis. The results of GO analysis pointed that there were 998 gene ontology items for the treatment of insomnia, including terms of 892 biological processes, 47 cellular components, and 59 molecular functions. It mainly shows the coupling effect and transport mode of some proteins in the biological pathways of ZSS in the treatment of insomnia and explains the mechanism of action through the connection between the target and the cell biomembrane. KEGG enrichment analyzed 19 signaling pathways, which were collectively classified into seven categories. We have identified the potential pathways of ZSS against insomnia and obtained the regulatory relationship between core genes and pathways and know that the same target can be regulated by multiple components at the same time. The results of molecular docking also prove this conclusion. We sought to provide a new analytical approach to explore TCM treatments for diseases using network pharmacology analysis tools.

A Deep Learning Proteomic Scale Approach for Drug Design

Computational approaches have accelerated novel therapeutic discovery in recent decades. The Computational Analysis of Novel Drug Opportunities (CANDO) platform for shotgun multi-target therapeutic discovery, repurposing, and design aims to improve their efficacy and safety by employing a holistic approach by computing interaction signatures between every drug/compound and a large library of non-redundant protein structures corresponding to the human proteome fold space. These signatures are compared and analyzed to determine if a given drug/compound is efficacious and safe for a given indication/disease. In this study, we used a deep learning based autoencoder to first reduce the dimensionality of CANDO computed drug-proteome interaction signatures. We then employed a reduced conditional variational autoencoder to generate novel drug-like compounds when given a target encoded “objective” signature. Using this model, we designed compounds to recreate the interaction signatures for twenty approved and experimental drugs and showed that 16/20 designed compounds are predicted to be significantly (p-value ≤ .05) more behaviorally similar relative to all corresponding controls, and 20/20 are predicted to be more behaviorally similar relative to a random control. We further observed that redesigns of objectives developed via rational drug design perform significantly better than those derived from natural sources (p-value ≤.05), suggesting that the model has learned an abstraction of rational drug design. We also show that designed compounds are structurally diverse and synthetically feasible when compared to their respective objective drugs despite consistently high predicted behavioral similarity. Finally, we generated new designs that enhance thirteen drugs/compounds associated with non-small cell lung cancer and anti-aging properties using their predicted proteomic interaction signatures. This work represents a significant step forward in automating holistic therapeutic design with machine learning, and subsequently offers a reduction in the time needed to generate novel, effective, and safe drug leads for any indication.

In Silico Study of Vacuolin-1 as an Inhibitor of HSP27 for Precancerous Treatment of Breast Cancer

Breast cancer has a great chance of being cured if it is diagnosed and treated properly in its early stage. The pre-cancer stage is an early stage of cancer development characterized by the overexpression of HSP27. Therefore, HSP27 can be a therapeutic target of cancer. This study aims to analyze whether vacuolin-1, a small drug compound known for its ability to inhibit metastasis, can inhibit HSP27 to prevent precancerous development in breast cancer, as well as its ADME and biosafety aspects. Protein & ligand structures were obtained from RCSB PDB and PubChem database. Preparation was performed with Discovery Studio and PyRx. Drug-likeness/ADME analysis was performed in Swiss-ADME web server. Biosafety analysis was performed in MetaTox web server. Molecular docking was performed using PyRx. The visualization of docking results was performed using Discovery Studio. The docking result between vacuolin-1 and HSP27 showed that vacuolin-1 can act as an HSP27 inhibitor by interacting with S78 residue of HSP27 and blocking its phosphorylation as well as depolymerization process. The drug-likeness characterization result of this compound showed that vacuolin-1 violates one of the four Lipinski's Rule of Five. Biosafety analysis showed that vacuolin-1 has a low toxicity level with an estimated LD50 around 13,016.65 mg/kg.

Molecular Imprinted of Nylon 6 for Selective Separation of Procaine by Solid-Phase Extraction

The study is based on the selective binding ability of the drug compound procaine (PRO) on a surface imprinted with nylon 6 (N6) polymer. Physical characterization of the polymer template was performed by X-ray diffraction and DSC thermal analysis. The imprinted polymer showed a high adsorption capacity to trap procaine (237 µg/g) and excellent recognition ability with an imprinted factor equal to 3.2. The method was applied to an extraction column simulating a solid-phase extraction to separate the drug compound in the presence of tinoxicam and nucleosimide separately and in a mixture of them with a recovery rate more than the presence of tinoxicam and nucleosimide separately and in a mixture of them with a recovery rate of more than 82%. Separation efficiency and excellent selectivity for procaine were ensured using a mixed solution injected into an HPLC technique consisting of a C18 column with a mobile phase mixture of water-acetonitrile (75:25) at pH 3.3. The study of drug control using an imprinted polymer with procaine compound showed that the complete drug release process is faster at pH1 in a maximum period of 80 min. The proposed method was successfully applied on some of the available pharmaceuticals, and it showed high selectivity for the separation of PRO, RE % was < 1.18, and RSD was less than 0.447.

Molecular Basis for the Interaction of Catalase with D-Penicillamine : Rationalization of some of its Deleterious Effects

D-penicillamine (D-Pen) is a sulfur compound used in the management of rheumatoid arthritis, Wilson's disease (WD), and alcohol dependence. Many side effects are associated with its use, particularly after long-term treatment. However, the molecular bases for such side effects are poorly understood. Based on the well-known oxidase activity of hemoproteins, and the participation of catalase in cellular H2O2 redox signaling, we posit that D-Pen could inactivate catalase, thus disturbing H2O2 levels. Herein, we report on the molecular bases that could partly explain the side effects associated with this drug compound, and we demonstrate that it induces the formation of compound II, a temporarily inactive state of the enzyme, through two distinct mechanisms. Initially, D-Pen reacts with native catalase and/or iron metal ions, used to mimic non heme iron overload observed in long-term treated WD patients, to generate thiyl radicals. These partake into a futile redox cycling, thus producing superoxide radical anions and hydrogen peroxide H2O2.Then, either H2O2 unexpectedly reacts with native CAT-Fe(II) to produce compound II, or both aforementioned reactive oxygen species intervene into compound II generation through compound I formation then reduction. These findings support evidence that D-Pen could perturb H2O2 redox homeostasis through transient but recurring catalase inactivation, which may in part rationalize some deleterious effects observed with this therapeutic agent, as discussed.