Prediction Of Oral Bioavailability Research Articles

Research on prediction of human oral bioavailability of drugs based on improved deep forest

Human oral bioavailability is a crucial factor in drug discovery. In recent years, researchers have constructed a variety of different prediction models. However, given the limited size of human oral bioavailability data sets, the challenge of making accurate predictions with small sample sizes has become a critical issue in the field. The deep forest model, with its adaptively determinable number of cascade levels, can perform exceptionally well even on small-scale data. However, the original deep forest suffers unbalanced multi-grained scanning process and premature stopping of cascade forest training. In this paper, we propose a human oral bioavailability predict method based on an improved deep forest, called balanced multi-grained scanning mapping cascade forest (bgmc-forest). Firstly, the mordred descriptor method is selected to feature extraction, then enhanced features are obtained by the improved balanced multi-grained scanning, which solves the problem of missing features at both ends. And finally, the prediction results are obtained by feature mapping cascaded forests, which is based on principal component analysis and cascade forests, ensures the effectiveness of the cascade forest. The superiority of the model constructed in this paper is demonstrated through comparative experiments, while the effectiveness of the improved module is verified through ablation experiments. Finally the decision-making process of the model is explained by the shapley additive explanations interpretation algorithm.

Construction of An Oral Bioavailability Prediction Model Based on Machine Learning for Evaluating Molecular Modifications

ObjectiveThis study aims to explore the impact of ADME on the Oral Bioavailability (OB) of drugs and to construct a machine learning model for OB prediction. The model is then applied to predict the OB of modified berberine and atenolol molecules to obtain structures with higher OB. MethodsInitially, a drug OB database was established, and corresponding ADME characteristics were obtained. The relationship between ADME and OB was analyzed using machine learning, with Morgan fingerprints serving as molecular descriptors. Compounds from the database were input into Random Forest, XGBoost, CatBoost, and LightGBM machine learning models to train the OB 7prediction model and evaluate its performance. Subsequently, berberine and atenolol were modified using Chemdraw software with ten different substituents for mono-substitution, and chlorine atoms for a full range of double substitutions. The modified molecular structures were converted into the same format as the training set for OB prediction. The predicted OB values of the modified structures of berberine and atenolol were compared. ResultsAn OB database of 386 drugs was obtained. It was found that smaller molecular weight and a higher number of rotatable bonds (ten or less) could potentially lead to higher OB. The four machine learning models were evaluated using MSE, R2 score, MAE, and MFE as metrics, with Random Forest performing the best. The models' predictions for the test set were particularly accurate when OB ranged from 30% to 90%. After mono-substitution and double substitution of berberine and atenolol, the OB of both drugs was significantly improved. ConclusionsThis study found that some ADME properties of molecules do not have an absolute impact on OB. The database played a decisive role in the process of the machine learning OB prediction model, and the performance of the model was evaluated based on predictions within a range of strong generalization ability. In most cases, mono-substitution and double substitution were beneficial for enhancing the OB of berberine and atenolol. In summary, this study successfully constructed a machine learning regression prediction model that can accurately predict drug OB, which can guide drug design to achieve higher OB to some extent.

Improving on in-silico prediction of oral drug bioavailability

ABSTRACT Introduction Although significant development has been made in high-throughput screening of oral drug absorption and oral bioavailability, in silico prediction continues to play an important role in prediction of oral bioavailability and assisting in the proper selection of potential drug candidates. Areas covered This review describes the improvements and latest modeling methods and algorithms available for the prediction of this important parameter. We performed a PubMed database search with a focus on the literature published in the last 15 years. Expert opinion A tremendous efforts have been done in the past several years to develop reliable prediction tools that can provide accurate prediction for oral bioavailability. Several studies demonstrated new methodologies and techniques to develop either web-based in silico predictive tools or integrated PBPK models to predict oral bioavailability for new molecules. Improvements in the databases and the computational power will enhance the in silico prediction accuracy and reliability. Finally, introducing artificial intelligence to the drug development process will help improve the prediction tools.

Evaluating the Use of Graph Neural Networks and Transfer Learning for Oral Bioavailability Prediction.

Oral bioavailability is a pharmacokinetic property that plays an important role in drug discovery. Recently developed computational models involve the use of molecular descriptors, fingerprints, and conventional machine-learning models. However, determining the type of molecular descriptors requires domain expert knowledge and time for feature selection. With the emergence of the graph neural network (GNN), models can be trained to automatically extract features that they deem important. In this article, we exploited the automatic feature selection of GNN to predict oral bioavailability. To enhance the prediction performance of GNN, we utilized transfer learning by pre-training a model to predict solubility and obtained a final average accuracy of 0.797, an F1 score of 0.840, and an AUC-ROC of 0.867, which outperformed previous studies on predicting oral bioavailability with the same test data set.

Synthesis by microwave irradiation of new pyrazole-imidazoline-pyrimidine analogs: Physicochemical and photophysical properties and their biological activity against Trypanosoma cruzi

Pyrazole derivatives are pharmacologically important agents that possess a wide spectrum of biological activities. Thus, the optimization of their synthetic approaches is of great interest for medicinal and pharmaceutical chemistry. In this work, eleven new 7-aryl-2,3-dihydro-7H-imidazo[1,2-c]pyrazolo[4,3-e]pyrimidines 1(a-k) were synthesized employing microwave irradiation conditions (150 W, 15–45 min). All compounds were completely characterized through Fourier Transform Infrared Spectroscopy (FT-IR), Nuclear Magnetic Resonance (NMR), and High-resolution Mass Spectrometry (HRMS) analyses. The in silico prediction of physicochemical properties showed that all analogs do not violate Lipinki's rule with prediction of good oral bioavailability. Analogs showed low activity against T. cruzi. Correlated experimental methods have been applied to investigate the photophysical properties of 1(a-k), describing their absorption properties in function of substituents. The molecular set exhibits a cyan emission, enabling it to investigate the basic fluorescence spectroscopy in solution.

Network Pharmacology Approach to Investigate the Mechanism of Danggui-Shaoyao-San against Diabetic Kidney Disease.

Danggui-Shaoyao-San (DSS) is a traditional Chinese medicine formula that has been widely used to treat a variety of disorders, including renal diseases. Despite being well-established in clinical practice, the mechanisms behind the therapeutic effects of DSS on diabetic nephropathy (DN) remain elusive. To explore the therapeutic mechanism, we explored the action mechanism of DSS on DN using network pharmacology strategies. All ingredients were selected from the relevant databases, and active ingredients were chosen on the basis of their oral bioavailability prediction and drug-likeness evaluation. The putative proteins of DSS were obtained from the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, whereas the potential genes of DN were obtained from the GeneCards and OMIM databases. Enrichment analysis using gene ontology (GO) and the Kyoto encyclopedia of genes and genomes (KEGG) was performed to discover possible hub targets and gene-related pathways. Afterwards, the underlying molecular mechanisms of DSS against DN were validated experimentally in vivo against db/db mice. We identified 91 phytochemicals using the comprehensive network pharmacology technique, 51 of which were chosen as bioactive components. There were 40 proteins and 20 pathways in the target-pathway network. The experimental validation results demonstrated that DSS may reduce the expression of TNF-α, IL-6, and ICAM-1, as well as extracellular matrix deposition, by blocking the JNK pathway activation, which protects against kidney injury. This study discovered the putative molecular mechanisms of action of DSS against diabetic kidney damage through a network pharmacology approach and experimental validation.

5-Nitroindazole-based compounds: further studies for activity optimization as anti-Trypanosoma cruzi agents

In this study, a new series of eleven 5-nitroindazole derivatives (10−20) and a related 6-nitroquinazoline (21) was synthesized and tested in vitro against different forms of the kinetoplastid parasite Trypanosoma cruzi, etiological agent of Chagas disease. Among these compounds, derivatives 11−14 and 17 showed trypanocidal profiles on epimastigotes (IC50 = 1.00−8.75 µM) considerably better than that of the reference drug benznidazole, BZ (IC50 = 25.22 µM). Furthermore, the lack of cytotoxicity observed for compounds 11, 12, 14, 17 and 18 over L929 fibroblasts, led to a notable selectivity (SI) on the extracellular replicative form of the parasite: SIEPI > 12.41 to > 256 µM. Since these five derivatives overpassed the cut-off value established by BZ (SIEPI ≥ 10), they were moved to a more specific assay against the intracellular and replicative form of T. cruzi, i.e, amastigotes. These molecules were not as active as BZ (IC50 = 0.57 µM) against this parasite form; however, all of them showed remarkable IC50 values lower than 7 µM. Special mention deserve compounds 12 and 17, whose SIAMA were > 246.15 and > 188.23, respectively. The results compiled in the present work, point to a positive impact over the trypanocidal activity of the electron withdrawing substituents introduced at position 2 of the N-2 benzyl moiety of these compounds, especially fluorine, i.e., derivatives 12 and 17. These outcomes, supported by the in silico prediction of good oral bioavailability and suitable risk profile, reinforce the 5-nitroindazole scaffold as an adequate template for preparing potential antichagasic agents.

Identification of molecular mechanisms underlying the therapeutic effects of Xintong granule in coronary artery disease by a network pharmacology and molecular docking approach.

Coronary artery disease (CAD) is a cardiovascular disease characterized by atherosclerosis, angiogenesis, thrombogenesis, inflammation, etc. Xintong granule (XTG) is considered a practical therapeutic strategy in China for CAD. Although its therapeutic role in CAD has been reported, the molecular mechanisms of XTG in CAD have not yet been explored.A network pharmacology approach including drug-likeness (DL) evaluation, oral bioavailability (OB) prediction, protein-protein interaction (PPI) network construction and analysis, and Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses was used to predict the active ingredients, potential targets, and molecular mechanisms of XTG associated with the treatment of CAD. Molecular docking analysis was performed to investigate the interactions between the active compounds and the underlying targets.Fifty-one active ingredients of XTG and 294 CAD-related targets were screened for analysis. Gene Ontology enrichment analysis showed that the therapeutic targets of XTG in CAD are mainly involved in blood circulation and vascular regulation. KEGG pathway analysis indicated that XTG intervenes in CAD mainly through the regulation of fluid shear stress and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, and the relaxin signaling pathway. Molecular docking analysis showed that each key active ingredient (quercetin, luteolin, kaempferol, stigmasterol, resveratrol, fisetin, gamma-sitosterol, and beta-sitosterol) of XTG can bind to the core targets of CAD (AKT1, JUN, RELA, MAPK8, NFKB1, EDN1, and NOS3).The present study revealed the CAD treatment-related active ingredients, underlying targets, and potential molecular mechanisms of XTG acting by regulating fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and relaxin signaling pathway.

Deep learning tools for advancing drug discovery and development.

A few decades ago, drug discovery and development were limited to a bunch of medicinal chemists working in a lab with enormous amount of testing, validations, and synthetic procedures, all contributing to considerable investments in time and wealth to get one drug out into the clinics. The advancements in computational techniques combined with a boom in multi-omics data led to the development of various bioinformatics/pharmacoinformatics/cheminformatics tools that have helped speed up the drug development process. But with the advent of artificial intelligence (AI), machine learning (ML) and deep learning (DL), the conventional drug discovery process has been further rationalized. Extensive biological data in the form of big data present in various databases across the globe acts as the raw materials for the ML/DL-based approaches and helps in accurate identifications of patterns and models which can be used to identify therapeutically active molecules with much fewer investments on time, workforce and wealth. In this review, we have begun by introducing the general concepts in the drug discovery pipeline, followed by an outline of the fields in the drug discovery process where ML/DL can be utilized. We have also introduced ML and DL along with their applications, various learning methods, and training models used to develop the ML/DL-based algorithms. Furthermore, we have summarized various DL-based tools existing in the public domain with their application in the drug discovery paradigm which includes DL tools for identification of drug targets and drug–target interaction such as DeepCPI, DeepDTA, WideDTA, PADME DeepAffinity, and DeepPocket. Additionally, we have discussed various DL-based models used in protein structure prediction, de novo design of new chemical scaffolds, virtual screening of chemical libraries for hit identification, absorption, distribution, metabolism, excretion, and toxicity (ADMET) prediction, metabolite prediction, clinical trial design, and oral bioavailability prediction. In the end, we have tried to shed light on some of the successful ML/DL-based models used in the drug discovery and development pipeline while also discussing the current challenges and prospects of the application of DL tools in drug discovery and development. We believe that this review will be useful for medicinal and computational chemists searching for DL tools for use in their drug discovery projects.

HobPre: accurate prediction of human oral bioavailability for small molecules

Human oral bioavailability (HOB) is a key factor in determining the fate of new drugs in clinical trials. HOB is conventionally measured using expensive and time-consuming experimental tests. The use of computational models to evaluate HOB before the synthesis of new drugs will be beneficial to the drug development process. In this study, a total of 1588 drug molecules with HOB data were collected from the literature for the development of a classifying model that uses the consensus predictions of five random forest models. The consensus model shows excellent prediction accuracies on two independent test sets with two cutoffs of 20% and 50% for classification of molecules. The analysis of the importance of the input variables allowed the identification of the main molecular descriptors that affect the HOB class value. The model is available as a web server at www.icdrug.com/ICDrug/ADMET for quick assessment of oral bioavailability for small molecules. The results from this study provide an accurate and easy-to-use tool for screening of drug candidates based on HOB, which may be used to reduce the risk of failure in late stage of drug development.Graphical

In-Silico Prediction of Oral Drug Bioavailability: A multi-boluses approach

This work focuses on a new mathematical model able to describe in a simple manner the intestinal physiology, in order to better study drug absorption and bioavailability. The aim of our model is to overcome the limitations of physiological pharmacokinetics models of the literature, introducing a different modelling approach. The core of the new proposed model is a Discrete-Continuous Approach (DCA): a sequence of boluses travels in the investigated portion of the intestine, in counter-current with blood that flows in continuous mode. No empirical equations are implemented in this model. Simulation results show an excellent correlation between the predicted and experimental concentration profile used to validate our model. Our new approach provides a simple tool, with a good reliability, to analyze a very complex phenomenon, using only few parameters.

Structural Optimization and Biological Activity of Pyrazole Derivatives: Virtual Computational Analysis, Recovery Assay and 3D Culture Model as Potential Predictive Tools of Effectiveness against Trypanosoma cruzi.

Chagas disease, a chronic and silent disease caused by Trypanosoma cruzi, is currently a global public health problem. The treatment of this neglected disease relies on benznidazole and nifurtimox, two nitroheterocyclic drugs that show limited efficacy and severe side effects. The failure of potential drug candidates in Chagas disease clinical trials highlighted the urgent need to identify new effective chemical entities and more predictive tools to improve translational success in the drug development pipeline. In this study, we designed a small library of pyrazole derivatives (44 analogs) based on a hit compound, previously identified as a T. cruzi cysteine protease inhibitor. The in vitro phenotypic screening revealed compounds 3g, 3j, and 3m as promising candidates, with IC50 values of 6.09 ± 0.52, 2.75 ± 0.62, and 3.58 ± 0.25 µM, respectively, against intracellular amastigotes. All pyrazole derivatives have good oral bioavailability prediction. The structure–activity relationship (SAR) analysis revealed increased potency of 1-aryl-1H-pyrazole-imidazoline derivatives with the Br, Cl, and methyl substituents in the para-position. The 3m compound stands out for its trypanocidal efficacy in 3D microtissue, which mimics tissue microarchitecture and physiology, and abolishment of parasite recrudescence in vitro. Our findings encourage the progression of the promising candidate for preclinical in vivo studies.

Pharmacological Mechanisms Underlying the Anti-asthmatic Effects of Modified Guomin Decoction Determined by Network Pharmacology and Molecular Docking.

BackgroundAsthma is a chronic inflammatory disease characterized by Th2-predominant inflammation and airway remodeling. Modified Guo Min decoction (MGMD) has been an extensive practical strategy for allergic disorders in China. Although its potential anti-asthmatic activity has been reported, the exact mechanism of action of MGMD in asthma remains unexplored.MethodsNetwork pharmacology approach was employed to predict the active components, potential targets, and molecular mechanism of MGMD for asthma treatment, including drug-likeness evaluation, oral bioavailability prediction, protein–protein interaction (PPI) network construction and analysis, Gene Ontology (GO) terms, and Reactome pathway annotation. Molecular docking was carried out to investigate interactions between active compounds and potential targets.ResultsA total of 92 active compounds and 72 anti-asthma targets of MGMD were selected for analysis. The GO enrichment analysis results indicated that the anti-asthmatic targets of MGMD mainly participate in inflammatory and in airway remolding processes. The Reactome pathway analysis showed that MGMD prevents asthma mainly through regulation of the IL-4 and IL-13 signaling and the specialized pro-resolving mediators (SPMs) biosynthesis. Molecular docking results suggest that each bioactive compounds (quercetin, wogonin, luteolin, naringenin, and kaempferol) is capable to bind with STAT3, PTGS2, JUN, VEGFA, EGFR, and ALOX5.ConclusionThis study revealed the active ingredients and potential molecular mechanism by which MGMD treatment is effective against airway inflammation and remodeling in asthma through regulating IL-4 and IL-13 signaling and SPMs biosynthesis.

Rational design of flavonoid based potential inhibitors targeting SARS-CoV 3CL protease for the treatment of COVID-19

The current outbreak of Coronavirus Disease 2019 (COVID-19) pandemic has reported thousands of deaths worldwide due to the rapid transmission rate and the lack of antiviral drugs and vaccinations. There is an urgent need to develop potential antiviral drug candidates for the prevention of COVID-19 infection. In the present study, a series of potential inhibitors targeting SARS-CoV 3CL protease were rationally designed by incorporating gamma lactam ring, and various fluoro substituted heterocyclic ring systems to the flavonoid scaffold. The prediction of drug-likeness, oral bioavailability, toxicity, synthetic accessibility, and ADMET properties was made by computational means. Quercetin was used as standard. The binding affinity of the ligands towards the 3CL protease target was examined using docking simulations. The designed ligands possess favourable pharmacokinetic and pharmacodynamic properties. Ligand L4, L8, and L14 appeared to be the lead compounds in the series and can be considered for further in-vivo and in-vitro validation.

Virtual Screening of the Multi-pathway and Multi-gene Regulatory Molecular Mechanism of Dachengqi Decoction in the Treatment of Stroke Based on Network Pharmacology.

Stroke is ranked second among diseases that cause mortality worldwide. Owing to its complicated pathogenesis, no satisfactory treatment strategies for stroke are available. Dachengqi decoction (DCQD), a traditional Chinese herbal medicine, has been widely used in China for a long time, as it has a good effect on stroke. However, the molecular mechanism underlying this effect of DCQD is unclear. In the present study, we aimed to reveal and explore the multi-pathway and multi-gene regulatory molecular mechanism of Dachengqi decoction in the treatment of stroke. In this study, a network pharmacology method, in combination with oral bioavailability prediction and drug-likeness evaluation, was employed to predict the active ingredients of DCQD. The target genes of the active components and the traced pathways related to these target genes were predicted. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were performed using clusterProfiler software package on the R platform and ClueGo+CluePedia plug-ins. Finally, the key DCQD targets were verified using the Gene Expression Omnibus (GEO) dataset. According to the ADME model, 52 active components were screened from 296 active components of DCQD. After prediction and screening, 215 stroke-related targets were obtained and analyzed via GO and KEGG analyses. GO analysis showed that DCQD targets were mainly involved in the regulation of oxidative stress, lipid metabolism, inflammation, and other biological processes. KEGG pathway analysis further revealed pathways involved in stroke, such as arachidonic acid metabolic, HIF-1 signaling pathway, estrogen signaling pathway, MAPK signaling pathway, PI3K-Akt signaling pathway, platelet activation pathway, VEGF signaling pathway, and cAMP signaling pathway. Network analysis revealed that DCQD might be involved in the regulation of lipid metabolism, blood pressure, inflammation, angiogenesis, neuroprotection, platelet aggregation, apoptosis, and oxidation in stroke treatment. GEO dataset analysis showed that DCQD's therapeutic effects might be exerted via the bidirectional regulation principle. Based on the methods of network pharmacology and GEO analysis, it was found that, during stroke treatment, DCQD regulates and controls multiple genes and multiple pathways in a synergistic manner, providing a new strategy for stroke treatment.

Synthesis, Biological Evaluation and in Silico Studies of Several Substituted Benzene Sulfonamides as Potential Antibacterial Agents

Four substituted benzene sulfonamides were synthesized to obtain antibacterial agents. The antibacterial activities were evaluated by the cup plate method. Compound 2d showed the most obvious inhibitory effect on seven kinds of strains with MIC ranging from 16 to 64µg/mL. The inhibitory effect of compound 2d on A. niger, C. albicans, B. subtilis, and S. aureus was better than that of the control drug SAN. Moreover, the cytotoxicity was evaluated by MTT assay. Finally, the binding patterns of the compounds with dihydrofolate synthetase and the prediction of oral bioavailability were preliminarily studied by computer simulation. The molecular docking result revealed that compound 2d had a stronger interaction with dihydrofolate synthetase than SAN. The oral bioavailability prediction by Molinspiration online program proved that these compounds exhibited drug-like properties. In conclusion, compound 2d is promising as a new antibacterial agent.

Integrated Network Pharmacology Analysis and Pharmacological Evaluation to Explore the Active Components and Mechanism of Abelmoschus manihot (L.) Medik. on Renal Fibrosis.

BackgroundRenal fibrosis is a common pathological outcome of chronic kidney diseases (CKD) that is considered as a global public health issue with high morbidity and mortality. The dry corolla of Abelmoschus manihot (L.) Medik. (AMC) has been used for chronic nephritis in clinic and showed a superior effect in alleviating proteinuria in CKD patients to losartan. However, the effective components and underlying mechanism of AMC in the treatment of renal fibrosis have not been systematically clarified.MethodsBased on drug-likeness evaluation, oral bioavailability prediction and compound contents, a systematic network pharmacology analysis was conducted to predict the active ingredients. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway analysis and protein–protein interaction analysis were applied to predict the potential pathway and target of AMC against renal fibrosis. The formula of component contribution index (CI) based on the algorithm was used to screen the principal active compounds of AMC in the treatment of renal fibrosis. Finally, pharmacological evaluation was conducted to validate the protective effect and primary predicted mechanism of AMC in the treatment of renal fibrosis on a 5/6 nephrectomy mice model.ResultsFourteen potential active components of AMC possessing favorable pharmacokinetic profiles and biological activities were selected and hit by 17 targets closely related to renal fibrosis. Quercetin, caffeic acid, 9.12-octadecadienoic acid, and myricetin are recognized as the more highly predictive components as their cumulative contribution rate reached 85.86%. The AMC administration on 5/6 nephrectomy mice showed a protective effect on kidney function and renal fibrosis. The hub genes analysis revealed that AMC plays a major role in inhibiting epithelial-to-mesenchymal transition during renal fibrosis.ConclusionOur results predicted active components and potential targets of AMC for the application to renal fibrosis from a holistic perspective, as well as provided valuable direction for further research of AMC and improved comprehension of renal fibrosis pathogenesis.

Theoretical Study of the Molecular Mechanism of Maxingyigan Decoction Against COVID-19: Network Pharmacology-based Strategy.

Maxingyigan (MXYG) decoction is a traditional Chinese medicine (TCM) prescription. However, how MXYG acts against coronavirus disease 2019 (COVID-19) is not known. We investigated the active ingredients and the therapeutic targets of MXYG decoction against COVID-19. A network pharmacology strategy involving drug-likeness evaluation, prediction of oral bioavailability, network analyses, and virtual molecular docking was used to predict the mechanism of action of MXYG against COVID-19. Thirty-three core COVID-19-related targets were identified from 1023 gene targets through analyses of protein-protein interactions. Eighty-six active ingredients of MXYG decoction hit by 19 therapeutic targets were screened out by analyses of a compound-compound target network. Via network topology, three "hub" gene targets (interleukin (IL-6), caspase-3, IL-4) and three key components (quercetin, formononetin, luteolin) were recognized and verified by molecular docking. Compared with control compounds (ribavirin, arbidol), the docking score of quercetin to the IL-6 receptor was highest, with a score of 5. Furthermore, the scores of three key components to SARS-CoV-2 are large as 4, 5, and 5, respectively, which are even better than those of ribavirin at 3. Bioinformatics analyses revealed that MXYG could prevent and treat COVID-19 through anti-inflammatory and immunity-based actions involving activation of T cells, lymphocytes, and leukocytes, as well as cytokine-cytokine-receptor interaction, and chemokine signaling pathways. The hub genes of COVID-19 helped to reveal the underlying pathogenesis and therapeutic targets of COVID-19. This study represents the first report on the molecular mechanism of MXYG decoction against COVID-19.

Cuminaldehyde potentiates the antimicrobial actions of ciprofloxacin against Staphylococcus aureus and Escherichia coli.

Escherichia coli and Staphylococcus aureus are important agents of urinary tract infections that can often evolve to severe infections. The rise of antibiotic-resistant strains has driven the search for novel therapies to replace the use or act as adjuvants of antibiotics. In this context, plant-derived compounds have been widely investigated. Cuminaldehyde is suggested as the major antimicrobial compound of the cumin seed essential oil. However, this effect is not fully understood. Herein, we investigated the in silico and in vitro activities of cuminaldehyde, as well as its ability to potentiate ciprofloxacin effects against S. aureus and E. coli. In silico analyses were performed by using different computational tools. The PASS online and SwissADME programmes were used for the prediction of biological activities and oral bioavailability of cuminaldehyde. For analysis of the possible toxic effects and the theoretical pharmacokinetic parameters of the compound, the Osiris, SwissADME and PROTOX programmes were used. Estimations of cuminaldehyde gastrointestinal absorption, blood brain barrier permeability and skin permeation by using SwissADME; and drug likeness and score by using Osiris, were also evaluated The in vitro antimicrobial effects of cuminaldehyde were determined by using microdilution, biofilm formation and time-kill assays. In silico analysis indicated that cuminaldehyde may act as an antimicrobial and as a membrane permeability enhancer. It was suggested to be highly absorbable by the gastrointestinal tract and likely to cross the blood brain barrier. Also, irritative and harmful effects were predicted for cuminaldehyde if swallowed at its LD50. Good oral bioavailability and drug score were also found for this compound. Cuminaldehyde presented antimicrobial and anti-biofilm effects against S. aureus and E. coli.. When co-incubated with ciprofloxacin, it enhanced the antibiotic antimicrobial and anti-biofilm actions. We suggest that cuminaldehyde may be useful as an adjuvant therapy to ciprofloxacin in S. aureus and E. coli-induced infections.

Mechanism of Shuang-Huang-Lian Oral Liquid for Treatment of Mycoplasmal Pneumonia in Children on Network Pharmacology.

Mycoplasmal pneumonia (MP) can lead to inflammation, multiple system immune damage, and mixed infection in children. The pathogenesis is still unclear. Shuang-Huang-Lian (SHL) oral liquid can treat acute upper respiratory tract infection, acute bronchitis and light pneumonia. However, our current understanding of the molecular mechanisms supporting its clinical application still lags behind due to the lack of researches. It is difficult to understand the overall sensitization mechanism of SHL oral liquid. The purpose is to explain the mechanism of action of drugs in this study, which is useful to ensure the safety of medication for children. The therapeutic mechanism of SHL oral liquid was investigated by a system pharmacology approach integrating drug-likeness evaluation, oral bioavailability prediction, ADMET, protein-protein interaction worknet, Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes database pathway performance, C-T-P network construction and molecular docking. A total of 18 active ingredients contained in SHL oral liquid and 53 major proteins were screened out as effective players in the treatment of M. pneumoniae disease through some related pathways and molecular docking. The majority of targets, hubs and pathways were highly related to anti-mycoplasma therapy, immunity and inflammation process. This study shows that the anti-bacterial effect of SHL oral liquid has multicomponent, multi-target and multi-pathway phenomena. The proposed approach may provide a feasible tool to clarify the mechanism of traditional Chinese medicines and further develop their therapeutic potentials.