Exploring pharmacological active ingredients of traditional Chinese medicine by pharmacotranscriptomic map in ITCM.

With the emergence of high-throughput technologies, computational screening based on gene expression profiles has become one of the most effective methods for drug discovery. More importantly, profile-based approaches remarkably enhance novel drug-disease pair discovery without relying on drug- or disease-specific prior knowledge, which has been widely used in modern medicine. However, profile-based systematic screening of active ingredients of traditional Chinese medicine (TCM) has been scarcely performed due to inadequate pharmacotranscriptomic data. Here, we develop the largest-to-date online TCM active ingredients-based pharmacotranscriptomic platform integrated traditional Chinese medicine (ITCM) for the effective screening of active ingredients. First, we performed unified high-throughput experiments and constructed the largest data repository of 496 representative active ingredients, which was five times larger than the previous one built by our team. The transcriptome-based multi-scale analysis was also performed to elucidate their mechanism. Then, we developed six state-of-art signature search methods to screen active ingredients and determine the optimal signature size for all methods. Moreover, we integrated them into a screening strategy, TCM-Query, to identify the potential active ingredients for the special disease. In addition, we also comprehensively collected the TCM-related resource by literature mining. Finally, we applied ITCM to an active ingredient bavachinin, and two diseases, including prostate cancer and COVID-19, to demonstrate the power of drug discovery. ITCM was aimed to comprehensively explore the active ingredients of TCM and boost studies of pharmacological action and drug discovery. ITCM is available at http://itcm.biotcm.net.

Associations of dichlorophenols, trichlorophenols, and ortho-phenylphenol with the risk and prognosis of diabetes and prediabetes: A population-based study.

2,4-dichlorophenol (2,4-DCP), 2,5-DCP, 2,4,5-trichlorophenol (2,4,5-TCP), 2,4,6-TCP, and ortho-phenylphenol (OPP) are widely present in the environment. However, their associations with risk and prognosis of diabetes and prediabetes remains unclear. We investigated the associations of these five phenols with the risk of diabetes and prediabetes, and with all-cause and cardiovascular disease (CVD) mortality, in adults with diabetes or prediabetes (n=6419). Information on diabetes and prediabetes indicators, and mortality data was collected from the National Health and Nutrition Examination Survey. Logistic and Cox regression models were used to explore the associations of the five phenols with risk and prognosis of diabetes and prediabetes. Participants in the highest urinary 2,4-DCP and 2,5-DCP tertiles had higher odds of diabetes [adjusted odds ratio (aOR), 1.34, 95 % confidence interval (CI): 1.10, 1.62; aOR, 1.29, 95 % CI: 1.07, 1.56, respectively] than those in the lowest tertiles. Participants with urinary OPP concentrations above the limit of detection (LOD), but below median had an aOR of 1.25 (95 % CI: 1.08, 1.46) for prediabetes compared to those with concentrations below the LOD. In adults with diabetes, the highest 2,4-DCP and 2,5-DCP tertiles were associated with all-cause mortality [adjusted hazard ratio (aHR), 1.49; 95 % CI: 1.08, 2.06; aHR, 1.49; 95 % CI: 1.08, 2.05, respectively] and CVD mortality (aHR, 2.58; 95 % CI: 1.33, 4.97; aHR, 1.96; 95 % CI: 1.06, 3.60, respectively) compared with the lowest tertiles. Compared with 2,4,5-TCP concentrations below the LOD, those above median were associated with all-cause mortality (aHR: 1.75; 95 % CI: 1.24, 2.48) and CVD mortality (aHR: 2.34; 95 % CI: 1.19, 4.63) in adults with prediabetes. Furthermore, the associations between these phenols and mortality were strengthened in some subgroups. Environmental exposure to 2,4-DCP, 2,5-DCP, 2,4,5-TCP, and OPP increases the risk or adverse prognosis of diabetes or prediabetes in adults in the US. Further studies are required to confirm these findings.

Identification of immune-associated signatures and potential therapeutic targets for pulmonary arterial hypertension.

Pulmonary arterial hypertension (PAH) comprises a heterogeneous group of diseases with diverse aetiologies. It is characterized by increased pulmonary arterial pressure and right ventricular (RV) failure without specific drugs for treatment. Emerging evidence suggests that inflammation and autoimmune disorders are common features across all PAH phenotypes. This provides a novel idea to explore the characteristics of immunological disorders in PAH and identify immune-related genes or biomarkers for specific anti-remodelling regimens. In this study, we integrated three gene expression profiles and performed Gene Ontology (GO) and KEGG pathway analysis. CIBERSORT was utilized to estimate the abundance of tissue-infiltrating immune cells in PAH. The PPI network and machine learning were constructed to identify immune-related hub genes and then evaluate the relationship between hub genes and differential immune cells using ImmucellAI. Additionally, we implemented molecular docking to screen potential small-molecule compounds based on the obtained genes. Our findings demonstrated the density and distribution of infiltrating CD4 T cells in PAH and identified four immune-related genes (ROCK2, ATHL1, HSP90AA1 and ACTR2) as potential targets. We also listed 20 promising molecules, including TDI01953, pemetrexed acid and radotinib, for PAH treatment. These results provide a promising avenue for further research into immunological disorders in PAH and potential novel therapeutic targets.

Network Proximity Analysis Deciphers the Pharmacological Mechanism of Osthole against D-Galactose Induced Cognitive Disorder in Rats.

Osthole, a natural coumarin found in various medicinal plants, has been previously reported to have neuroprotective effects. However, the specific mechanism by which Osthole alleviates dysmnesia associated with Alzheimer's disease (AD) remains unclear. This study aimed to investigate the neuroprotective properties of Osthole against cognitive impairment in rats induced by D-galactose and elucidate its pharmacological mechanism. The rat model was established by subcutaneously injecting D-galactose at a dose of 150 mg/kg/day for 56 days. The effect of Osthole on cognitive impairment was evaluated by behavior and biochemical analysis. Subsequently, a combination of in silico prediction and experimental validation was performed to verify the network-based predictions, using western blot, Nissl staining, and immunofluorescence. The results demonstrate that Osthole could improve memory dysfunction induced by D-galactose in Sprague Dawley male rats. A network proximity-based approach and integrated pathways analysis highlight two key AD-related pathological processes that may be regulated by Osthole, including neuronal apoptosis, i.e., neuroinflammation. Among them, the pro-apoptotic markers (Bax), anti-apoptotic protein (Bcl-2), the microgliosis (Iba-1), Astro-cytosis (GFAP), and inflammatory cytokines (TNF-R1) were evaluated in both hippocampus and cortex. The results indicated that Osthole significantly ameliorated neuronal apoptosis and neuroinflammation in D-galactose-induced cognitive impairment rats. In conclusion, this study sheds light on the pharmacological mechanism of Osthole in mitigating D-galactose-induced memory impairment and identifies Osthole as a potential drug candidate for AD treatment, targeting multiple signaling pathways through network proximity and integrated pathways analysis.

[Research progress of Acanthopanax senticosus in prevention and treatment of neurodegenerative diseases].

Neurodegenerative diseases are global public health problems that seriously affect the quality of human life. The incidence of neurodegenerative diseases is increasing year by year and there has been no effective treatment. Acanthopanax senticosus is a Chinese medicine for tonifying kidney and has a long medicinal and edible history. It contains many active ingredients such as saponins, coumarins, flavonoids, organic acids and polysaccharides, with pharmacological effects of anti-oxidation, anti-age, anti-inflammation, anti-fatigue and immune regulation. Modern medical studies have found that A. senticosus can act on the central nervous system, and its extracts and active ingredients can improve learning and memory ability, playing vital roles of anti-oxidation, anti-inflammation, anti-apoptosis, antagonizing against amyloid ß protein(Aß) toxicity, modulating neurotransmitter release, signaling pathways and brain energy metabolism, maintaining the structure and function of mitochondria, and epigenetic regulation. It treats neurodegenerative diseases via multiple components, multiple targets, and multiple pathways, with the characteristics of low toxic side effects. This study reviewed the pharmacological reports of A. senticosus on neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease and ischemic stroke in China and abroad in recent ten years, and summarized the active ingredients and the mechanism underlying the neuroprotective effects of A. senticosus. Additionally, the significant advantages of A. senticosus in the treatment of neurodegenerative diseases and the limitations of the reports were discussed from the aspects of traditional Chinese medicine(TCM) theory and modern medical research. This study provided theoretical support for the drug development and clinical application of A. senticosus in treating neurodegenerative diseases and also facilitated the prevention and treatment of neurodegenerative diseases by kidney-tonifying method in TCM.

Scores based on neutrophil percentage and lactate dehydrogenase with or without oxygen saturation predict hospital mortality risk in severe COVID-19 patients.

BACKGROUND: Risk scores are needed to predict the risk of death in severe coronavirus disease 2019 (COVID-19) patients in the context of rapid disease progression. METHODS: Using data from China (training dataset, n = 96), prediction models were developed by logistic regression and then risk scores were established. Leave-one-out cross validation was used for internal validation and data from Iran (test dataset, n = 43) was used for external validation. RESULTS: A NSL model (area under the curve (AUC) 0.932) and a NL model (AUC 0.903) were developed based on neutrophil percentage and lactate dehydrogenase with and without oxygen saturation (SaO2) using the training dataset. AUCs of the NSL and NL models in the test dataset were 0.910 and 0.871, respectively. The risk scoring systems corresponding to these two models were established. The AUCs of the NSL and NL scores in the training dataset were 0.928 and 0.901, respectively. At the optimal cut-off value of NSL score, the sensitivity and specificity were 94% and 82%, respectively. The sensitivity and specificity of NL score were 94% and 75%, respectively. CONCLUSIONS: These scores may be used to predict the risk of death in severe COVID-19 patients and the NL score could be used in regions where patients' SaO2 cannot be tested.

Systems pharmacology approach uncovers Ligustilide attenuates experimental colitis in mice by inhibiting PPARγ-mediated inflammation pathways.

Inflammatory bowel disease (IBD) is a chronic idiopathic disorder causing inflammation in the gastro-intestinal tract, which is lack of effective drug targets and medications. To identify novel therapeutic agents against consistent targets, we exploited a systems pharmacology-driven framework that incorporates drug-target networks of natural product and IBD disease genes. Our in silico approach found that Ligustilide (LIG), one of the major active components of Angelica acutiloba and Cnidium Officinale, potently attenuated IBD. The following in vivo and in vitro results demonstrated that LIG prevented experimental mice colitis induced by dextran sulfate sodium (DSS) via suppressing inflammatory cell infiltration, the activity of MPO and iNOS, and the expression and production of IL-1ß, IL-6, and TNF-α. Subsequently, the network analysis helped to validate that LIG alleviated colitis by inhibiting NF-κB and MAPK/AP-1 pathway through activating PPARγ, which were further confirmed in RAW 264.7 cells and bone marrow-derived macrophages in vitro. In summary, this study reveals that LIG activated PPARγ to inhibit the activation of NF-κB and AP-1 signaling thus eventually alleviated DSS-induced colitis, which has promising activities and may serve as a candidate for the treatment of IBD.Graphical abstract This study suggested novel computational and experimental pharmacology approaches to identify potential IBD therapeutic agents by exploiting polypharmacology of natural products. We demonstrated that LIG could attenuate inflammation in IBD by inhibiting NF-κB and AP-1 pathways via PPARγ activation to reduce the expression of pro-inflammatory cytokines in macrophages. These findings offer comprehensive pre-clinical evidence that LIG may serve as a promising candidate for IBD therapy in the future. Graphical headlights: 1. Systems pharmacology uncovered Ligustilide attenuates experimental colitis in mice. 2. Network-based analysis predicted the mechanism of Ligustilide against IBD, which was validated by inhibiting PPARγ-mediated inflammation pathways. 3. Ligustilide activated PPARγ to inhibit NF-κB and AP-1 activation thus eventually alleviated DSS-induced colitis.4. Ligustilide has promising activities and may serve as a candidate for the treatment of IBD.

Potential targets for intervention against doxorubicin-induced cardiotoxicity based on genetic studies: a systematic review of the literature.

Cardiotoxicity is a well-known adverse effect of doxorubicin (Dox) administration, but the underlying molecular mechanism of this effect is not fully understood. Over the past two decades, considerable efforts have focused on the potential molecular targets of cardiotoxicity in the hope that novel targeted therapies will be generated to attenuate Dox-induced cardiotoxicity. Here, we provide a comprehensive overview of genetically modified animals that show enhanced or reduced susceptibility to the cardiotoxic effects of Dox. We focused on the process by which the molecules involved in DNA damage, oxidative stress, apoptosis, autophagy and necrosis are affected in the presence of Dox. We also present a protein-protein interaction network and explain the contribution of the components to the process of Dox-induced cardiotoxicity. More importantly, data from the literature have indicated that PI3Kγ and Rac1 are potential targets with therapeutic advantages in cancer therapy; molecules that target these proteins can simultaneously attenuate Dox-induced cardiotoxicity and enhance its anticancer activity. This review highlights the potential molecular targets that are critical regulators involved in Dox-mediated cardiotoxicity, thus providing further insight into the development of potential treatment strategies to prevent the cardiotoxic effects and enhance the anticancer efficiency of Dox in cancer patients.

Harnessing endophenotypes and network medicine for Alzheimer's drug repurposing.

Following two decades of more than 400 clinical trials centered on the "one drug, one target, one disease" paradigm, there is still no effective disease-modifying therapy for Alzheimer's disease (AD). The inherent complexity of AD may challenge this reductionist strategy. Recent observations and advances in network medicine further indicate that AD likely shares common underlying mechanisms and intermediate pathophenotypes, or endophenotypes, with other diseases. In this review, we consider AD pathobiology, disease comorbidity, pleiotropy, and therapeutic development, and construct relevant endophenotype networks to guide future therapeutic development. Specifically, we discuss six main endophenotype hypotheses in AD: amyloidosis, tauopathy, neuroinflammation, mitochondrial dysfunction, vascular dysfunction, and lysosomal dysfunction. We further consider how this endophenotype network framework can provide advances in computational and experimental strategies for drug-repurposing and identification of new candidate therapeutic strategies for patients suffering from or at risk for AD. We highlight new opportunities for endophenotype-informed, drug discovery in AD, by exploiting multi-omics data. Integration of genomics, transcriptomics, radiomics, pharmacogenomics, and interactomics (protein-protein interactions) are essential for successful drug discovery. We describe experimental technologies for AD drug discovery including human induced pluripotent stem cells, transgenic mouse/rat models, and population-based retrospective case-control studies that may be integrated with multi-omics in a network medicine methodology. In summary, endophenotype-based network medicine methodologies will promote AD therapeutic development that will optimize the usefulness of available data and support deep phenotyping of the patient heterogeneity for personalized medicine in AD.

In silico polypharmacology of natural products.

Natural products with polypharmacological profiles have demonstrated promise as novel therapeutics for various complex diseases, including cancer. Currently, many gaps exist in our knowledge of which compounds interact with which targets, and experimentally testing all possible interactions is infeasible. Recent advances and developments of systems pharmacology and computational (in silico) approaches provide powerful tools for exploring the polypharmacological profiles of natural products. In this review, we introduce recent progresses and advances of computational tools and systems pharmacology approaches for identifying drug targets of natural products by focusing on the development of targeted cancer therapy. We survey the polypharmacological and systems immunology profiles of five representative natural products that are being considered as cancer therapies. We summarize various chemoinformatics, bioinformatics and systems biology resources for reconstructing drug-target networks of natural products. We then review currently available computational approaches and tools for prediction of drug-target interactions by focusing on five domains: target-based, ligand-based, chemogenomics-based, network-based and omics-based systems biology approaches. In addition, we describe a practical example of the application of systems pharmacology approaches by integrating the polypharmacology of natural products and large-scale cancer genomics data for the development of precision oncology under the systems biology framework. Finally, we highlight the promise of cancer immunotherapies and combination therapies that target tumor ecosystems (e.g. clones or 'selfish' sub-clones) via exploiting the immunological and inflammatory 'side' effects of natural products in the cancer post-genomics era.

Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity.

Blockade of the human ether-à-go-go-related gene (hERG) channel by small molecules induces the prolongation of the QT interval which leads to fatal cardiotoxicity and accounts for the withdrawal or severe restrictions on the use of many approved drugs. In this study, we develop a deep learning approach, termed deephERG, for prediction of hERG blockers of small molecules in drug discovery and postmarketing surveillance. In total, we assemble 7,889 compounds with well-defined experimental data on the hERG and with diverse chemical structures. We find that deephERG models built by a multitask deep neural network (DNN) algorithm outperform those built by single-task DNN, naïve Bayes (NB), support vector machine (SVM), random forest (RF), and graph convolutional neural network (GCNN). Specifically, the area under the receiver operating characteristic curve (AUC) value for the best model of deephERG is 0.967 on the validation set. Furthermore, based on 1,824 U.S. Food and Drug Administration (FDA) approved drugs, 29.6% drugs are computationally identified to have potential hERG inhibitory activities by deephERG, highlighting the importance of hERG risk assessment in early drug discovery. Finally, we showcase several novel predicted hERG blockers on approved antineoplastic agents, which are validated by clinical case reports, experimental evidence, and the literature. In summary, this study presents a powerful deep learning-based tool for risk assessment of hERG-mediated cardiotoxicities in drug discovery and postmarketing surveillance.

Bajijiasu Ameliorates ß-Amyloid-Triggered Endoplasmic Reticulum Stress and Related Pathologies in an Alzheimer's Disease Model.

BACKGROUND/AIMS: Alzheimer disease (AD) is a common neurodegenerative disease that is characterized by the deposition of beta-amyloid peptide and formation of intracellular neurofibrillary tangles. Due to the failure of various clinical trials of novel drugs for AD, effective drugs for AD treatment are urgently required. METHODS: In this study, we used the classic APP/PS1 mouse model to explore the neuroprotective effects of a new compound, bajijiasu, and the mechanisms involved. Behavioral tests and western blotting were performed to assess the beneficial effects of bajijiasu in APP/PS1 mice. RESULTS: Morris water maze and Y-maze test results showed that oral administration of bajijiasu (35 mg/kg/day and 70 mg/kg/day) improved learning and memory abilities in APP/PS1 mice. Bajijiasu reduced ROS and MDA levels in both the hippocampus and cortex. Moreover, western blotting results showed that bajijiasu protected neurons from apoptosis, elevated the expression levels of neurotrophic factors, and alleviated endoplasmic reticulum stress in both the hippocampus and cortex. CONCLUSION: These results indicate that the mechanisms underlying the effects of bajijiasu on AD might be related to beta-amyloid-downstream pathologies, particularly endoplasmic reticulum stress.

In Silico Pharmacoepidemiologic Evaluation of Drug-Induced Cardiovascular Complications Using Combined Classifiers.

Drug-induced cardiovascular complications are the most common adverse drug events and account for the withdrawal or severe restrictions on the use of multitudinous postmarketed drugs. In this study, we developed new in silico models for systematic identification of drug-induced cardiovascular complications in drug discovery and postmarketing surveillance. Specifically, we collected drug-induced cardiovascular complications covering the five most common types of cardiovascular outcomes (hypertension, heart block, arrhythmia, cardiac failure, and myocardial infarction) from four publicly available data resources: Comparative Toxicogenomics Database, SIDER, Offsides, and MetaADEDB. Using these databases, we developed a combined classifier framework through integration of five machine-learning algorithms: logistic regression, random forest, k-nearest neighbors, support vector machine, and neural network. The totality of models included 180 single classifiers with area under receiver operating characteristic curves (AUC) ranging from 0.647 to 0.809 on 5-fold cross-validations. To develop the combined classifiers, we then utilized a neural network algorithm to integrate the best four single classifiers for each cardiovascular outcome. The combined classifiers had higher performance with an AUC range from 0.784 to 0.842 compared to single classifiers. Furthermore, we validated our predicted cardiovascular complications for 63 anticancer agents using experimental data from clinical studies, human pluripotent stem cell-derived cardiomyocyte assays, and literature. The success rate of our combined classifiers reached 87%. In conclusion, this study presents powerful in silico tools for systematic risk assessment of drug-induced cardiovascular complications. This tool is relevant not only in early stages of drug discovery but also throughout the life of a drug including clinical trials and postmarketing surveillance.

Pharmacokinetic and Pharmacodynamic Properties of Rosmarinic Acid in Rat Cholestatic Liver Injury.

The objective of this study was to evaluate the hepatoprotective and metabolic effects of rosmarinic acid (RA) in rats. RA [100 mg/kg body weight (BW)] was intragastrically (i.g.) administered to Sprague-Dawley (SD) rats once a day for seven consecutive days. The rats were then i.g. administered α-naphthylisothiocyanate (ANIT) (80 mg/kg once on the 5th day) to induce acute intrahepatic cholestasis after the last administration of RA. Blood samples were collected at different time points (0.083 h, 0.17 h, 0.33 h, 0.5 h, 0.75 h, 1 h, 1.5 h, 3 h, 4 h, 6 h, 8 h, 12 h, 20 h) after administration, and the levels of RA were estimated by HPLC. Plasma and bile biochemical analysis, bile flow rate, and liver histopathology were measured to evaluate the hepatoprotective effect of RA. The PK-PD curves showed obviously clockwise (AST and ALT) or anticlockwise (TBA, TBIL). Pretreatment with RA at different doses significantly restrained ANIT-induced pathological changes in bile rate, TBA, TBIL, ALT, AST (p < 0.05 or p < 0.01). The relationship between RA concentration and its hepatoprotective effects on acute cholestasis responses was assessed by PK-PD modeling.

Quantitative and Systems Pharmacology. 1. In Silico Prediction of Drug-Target Interactions of Natural Products Enables New Targeted Cancer Therapy.

Natural products with diverse chemical scaffolds have been recognized as an invaluable source of compounds in drug discovery and development. However, systematic identification of drug targets for natural products at the human proteome level via various experimental assays is highly expensive and time-consuming. In this study, we proposed a systems pharmacology infrastructure to predict new drug targets and anticancer indications of natural products. Specifically, we reconstructed a global drug-target network with 7,314 interactions connecting 751 targets and 2,388 natural products and built predictive network models via a balanced substructure-drug-target network-based inference approach. A high area under receiver operating characteristic curve of 0.96 was yielded for predicting new targets of natural products during cross-validation. The newly predicted targets of natural products (e.g., resveratrol, genistein, and kaempferol) with high scores were validated by various literature studies. We further built the statistical network models for identification of new anticancer indications of natural products through integration of both experimentally validated and computationally predicted drug-target interactions of natural products with known cancer proteins. We showed that the significantly predicted anticancer indications of multiple natural products (e.g., naringenin, disulfiram, and metformin) with new mechanism-of-action were validated by various published experimental evidence. In summary, this study offers powerful computational systems pharmacology approaches and tools for the development of novel targeted cancer therapies by exploiting the polypharmacology of natural products.

DL0410 Ameliorates Memory and Cognitive Impairments Induced by Scopolamine via Increasing Cholinergic Neurotransmission in Mice.

Deficiency of the cholinergic system is thought to play a vital role in cognitive impairment of dementia. DL0410 was discovered as a dual inhibitor of acetylcholinesterase (AChE) and butyrylcholinestease (BuChE), with potent efficiency in in-vitro experiments, but its in vivo effect on the cholinergic model has not been evaluated, and its action mechanism has also not been illustrated. In the present study, the capability of DL0410 in ameliorating the amnesia induced by scopolamine was investigated, and its effect on the cholinergic system in the hippocampus and its binding mode in the active site of AChE was also explored. Mice were administrated DL0410 (3 mg/kg, 10 mg/kg, and 30 mg/kg), and mice treated with donepezil were used as a positive control. The Morris water maze, escape learning task, and passive avoidance task were used as behavioral tests. The test results indicated that DL0410 could significantly improve the learning and memory impairments induced by scopolamine, with 10 mg/kg performing best. Further, DL0410 inhibited the AChE activity and increased acetylcholine (ACh) levels in a dose-dependent manner, and interacted with the active site of AChE in a similar manner as donepezil. However, no difference in the activity of BuChE was found in this study. All of the evidence indicated that its AChE inhibition is an important mechanism in the anti-amnesia effect. In conclusion, DL0410 could be an effective therapeutic drug for the treatment of dementia, especially Alzheimer's disease.

Discovery of Influenza A virus neuraminidase inhibitors using support vector machine and Naïve Bayesian models.

Neuraminidase (NA) is a critical enzyme in the life cycle of influenza virus, which is known as a successful paradigm in the design of anti-influenza agents. However, to date there are no classification models for the virtual screening of NA inhibitors. In this work, we built support vector machine and Naïve Bayesian models of NA inhibitors and non-inhibitors, with different ratios of active-to-inactive compounds in the training set and different molecular descriptors. Four models with sensitivity or Matthews correlation coefficients greater than 0.9 were chosen to predict the NA inhibitory activities of 15,600 compounds in our in-house database. We combined the results of four optimal models and selected 60 representative compounds to assess their NA inhibitory profiles in vitro. Nine NA inhibitors were identified, five of which were oseltamivir derivatives with large C-5 substituents exhibiting potent inhibition against H1N1 NA with IC50 values in the range of 12.9-185.0 nM, and against H3N2 NA with IC50 values between 18.9 and 366.1 nM. The other four active compounds belonged to novel scaffolds, with IC50 values ranging 39.5-63.8 µM against H1N1 NA and 44.5-114.1 µM against H3N2 NA. This is the first time that classification models of NA inhibitors and non-inhibitors are built and their prediction results validated experimentally using in vitro assays.

[Network pharmacology study of effective constituents of traditional Chinese medicine for Alzheimer's disease treatment].

This study aims to investigate the network pharmacology of Chinese medicinal formulae for treatment of Alzheimer's disease.Machine learning algorithms were applied to construct classifiers in predicting the active molecules against 25 key targets toward Alzheimer's disease(AD).By extensive data profiling, we compiled 13 classical traditional Chinese medicine(TCM) formulas with clinical efficacy for AD. There were 7 Chinese herbs with a frequency of 5 or higher in our study. Based on the predicted results, we built constituent-target, and further construct target-target interaction network by STRING(Search Tool for the Retrieval of Interacting Genes/Proteins) and target-disease network by DAVID(Database for Annotation,Visualization and Integrated Discovery) and gene disease database to study the synergistic mechanism of the herbal constituents in the Chinese traditional patent medicine. By prediction of blood-brain penetration and validation by TCMsp (traditional Chinese medicine systems pharmacology) and Drugbank, we found 7 typical multi-target constituents which have diverse structure. The mechanism uncovered by this study may offer a deep insight into the action mechanism of TCMs for AD. The predicted inhibitors for the AD-related targets may provide a good source of new lead constituents against AD.

Discovery of multitarget-directed ligands against Alzheimer's disease through systematic prediction of chemical-protein interactions.

To determine chemical-protein interactions (CPI) is costly, time-consuming, and labor-intensive. In silico prediction of CPI can facilitate the target identification and drug discovery. Although many in silico target prediction tools have been developed, few of them could predict active molecules against multitarget for a single disease. In this investigation, naive Bayesian (NB) and recursive partitioning (RP) algorithms were applied to construct classifiers for predicting the active molecules against 25 key targets toward Alzheimer's disease (AD) using the multitarget-quantitative structure-activity relationships (mt-QSAR) method. Each molecule was initially represented with two kinds of fingerprint descriptors (ECFP6 and MACCS). One hundred classifiers were constructed, and their performance was evaluated and verified with internally 5-fold cross-validation and external test set validation. The range of the area under the receiver operating characteristic curve (ROC) for the test sets was from 0.741 to 1.0, with an average of 0.965. In addition, the important fragments for multitarget against AD given by NB classifiers were also analyzed. Finally, the validated models were employed to systematically predict the potential targets for six approved anti-AD drugs and 19 known active compounds related to AD. The prediction results were confirmed by reported bioactivity data and our in vitro experimental validation, resulting in several multitarget-directed ligands (MTDLs) against AD, including seven acetylcholinesterase (AChE) inhibitors ranging from 0.442 to 72.26 µM and four histamine receptor 3 (H3R) antagonists ranging from 0.308 to 58.6 µM. To be exciting, the best MTDL DL0410 was identified as an dual cholinesterase inhibitor with IC50 values of 0.442 µM (AChE) and 3.57 µM (BuChE) as well as a H3R antagonist with an IC50 of 0.308 µM. This investigation is the first report using mt-QASR approach to predict chemical-protein interaction for a single disease and discovering highly potent MTDLs. This protocol may be useful for in silico multitarget prediction of other diseases.