Wenwen Lian

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 Alzheimers 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.

Ameliorative effects of baicalein in MPTP-induced mouse model of Parkinson's disease: A microarray study

Baicalein, a flavonoid from Scutellaria baicalensis Georgi, has been shown to possess neuroprotective properties. The purpose of this study was to explore the effects of baicalein on motor behavioral deficits and gene expression in N-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-induced mice model of Parkinsons disease (PD). The behavioral results showed that baicalein significantly improves the abnormal behaviors in MPTP-induced mice model of PD, as manifested by shortening the total time for climbing down the pole, prolonging the latent periods of rotarod, and increasing the vertical movements. Using cDNA microarray and subsequent bioinformatic analyses, it was found that baicalein significantly promotes the biological processes including neurogenesis, neuroblast proliferation, neurotrophin signaling pathway, walking and locomotor behaviors, and inhibits dopamine metabolic process through regulation of gene expressions. Based on analysis of gene co-expression networks, the results indicated that the regulation of genes such as LIMK1, SNCA and GLRA1 by baicalein might play central roles in the network. Our results provide experimental evidence for the potential use of baicalein in the treatment of PD, and revealed gene expression profiles, biological processes and pathways influenced by baicalein in MPTP-treated mice.

In vitro antiviral effects and 3D QSAR study of resveratrol derivatives as potent inhibitors of influenza H1N1 neuraminidase.

The anti‐influenza virus activities of 50 resveratrol (RV: 3, 5, 4′‐trihydroxy‐trans‐stilbene) derivatives were evaluated using a neuraminidase (NA) activity assay. The results showed that 35 compounds exerted an inhibitory effect on the NA activity of the influenza virus strain A/PR/8/34 (H1N1) with 50% inhibitory concentration (IC50) values ranging from 3.56 to 186.1 μm. Next, the 35 RV derivatives were used to develop 3D quantitative structure–activity relationship (3D QSAR) models for understanding the chemical–biological interactions governing their activities against NA. The comparative molecular field analysis (CoMFA r2 = 0.973, q2 = 0.620, qtest2 = 0.661) and the comparative molecular similarity indices analysis (CoMSIA r2 = 0.956, q2 = 0.610, qtest2 = 0.531) were applied. Afterward, molecular docking was performed to study the molecular interactions between the RV derivatives and NA. Finally, a cytopathic effect (CPE) reduction assay was used to evaluate the antiviral effects of the RV derivatives in vitro. Time‐of‐addition studies demonstrated that the RV derivatives might have a direct effect on viral particle infectivity. Our results indicate that the RV derivatives are potentially useful antiviral compounds for new drug design and development for influenza treatment.

Discovery of neuroprotective compounds by machine learning approaches

Neuronal cell death from oxidative stress is a strong factor of many neurodegenerative diseases. To tackle these problems, phenotypic drug screening assays are a possible alternative strategy. The aim of this study is to develop the neuroprotective models against glutamate or H2O2-induced neurotoxicity by machine learning approaches, which helps in discovering neuroprotective compounds. Four different single classifiers (neural network, k nearest neighbors, classification tree and random forest) were constructed based on two large datasets containing 1260 and 900 known active or inactive compounds, which were integrated to develop the combined Bayesian models to obtain superior performance. Our results showed that both of the Bayesian models (combined-NB-1 and combined-NB-2) outperformed the corresponding four single classifiers. Additionally, structural fingerprint descriptors were added to improve the predictive ability of the models, resulting in the two best models NB-1-LPFP4 and NB-2-LCFP6. The best two models gave Matthews correlation coefficients of 0.972 and 0.956 for 5-fold cross validation as well as 0.953 and 0.902 for the test set, respectively. To illustrate the practical applications of the two models, NB-1-LPFP4 and NB-2-LCFP6 were used to perform virtual screening for discovering neuroprotective compounds, and 70 compounds were selected for further cell-based assay. The assay results showed that 28 compounds exhibited neuroprotective effects against glutamate-induced and H2O2-induced neurotoxicity simultaneously. Our results suggested the method that integrated single classifiers into combined Bayesian models could be feasible to predict neuroprotective compounds.

Drug Discovery of Host CLK1 Inhibitors for Influenza Treatment

The rapid evolution of influenza virus makes antiviral drugs less effective, which is considered to be a major bottleneck in antiviral therapy. The key proteins in the host cells, which are related with the replication cycle of influenza virus, are regarded as potential drug targets due to their distinct advantage of lack of evolution and drug resistance. Cdc2-like kinase 1 (CLK1) in the host cells is responsible for alternative splicing of the M2 gene of influenza virus during influenza infection and replication. In this study, we carried out baculovirus-mediated expression and purification of CLK1 and established a reliable screening assay for CLK1 inhibitors. After a virtual screening of CLK1 inhibitors was performed, the activities of the selected compounds were evaluated. Finally, several compounds with strong inhibitory activity against CLK1 were discovered and their in vitro anti-influenza virus activities were validated using a cytopathic effect (CPE) reduction assay. The assay results showed that clypearin, corilagin, and pinosylvine were the most potential anti-influenza virus compounds as CLK1 inhibitors among the compounds tested. These findings will provide important information for new drug design and development in influenza treatment, and CLK1 may be a potent drug target for anti-influenza drug screening and discovery.

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.

AlzhCPI: A knowledge base for predicting chemical-protein interactions towards Alzheimer’s disease

Alzheimers disease (AD) is a complicated progressive neurodegeneration disorder. To confront AD, scientists are searching for multi-target-directed ligands (MTDLs) to delay disease progression. The in silico prediction of chemical-protein interactions (CPI) can accelerate target identification and drug discovery. Previously, we developed 100 binary classifiers to predict the CPI for 25 key targets against AD using the multi-target quantitative structure-activity relationship (mt-QSAR) method. In this investigation, we aimed to apply the mt-QSAR method to enlarge the model library to predict CPI towards AD. Another 104 binary classifiers were further constructed to predict the CPI for 26 preclinical AD targets based on the naive Bayesian (NB) and recursive partitioning (RP) algorithms. The internal 5-fold cross-validation and external test set validation were applied to evaluate the performance of the training sets and test set, respectively. The area under the receiver operating characteristic curve (ROC) for the test sets ranged from 0.629 to 1.0, with an average of 0.903. In addition, we developed a web server named AlzhCPI to integrate the comprehensive information of approximately 204 binary classifiers, which has potential applications in network pharmacology and drug repositioning. AlzhCPI is available online at http://rcidm.org/AlzhCPI/index.html. To illustrate the applicability of AlzhCPI, the developed system was employed for the systems pharmacology-based investigation of shichangpu against AD to enhance the understanding of the mechanisms of action of shichangpu from a holistic perspective.

Discovery of VEGFR2 inhibitors by integrating naïve Bayesian classification, molecular docking and drug screening approaches

The high morbidity and mortality of cancer make it one of the leading causes of global death, thus it is an urgent need to develop effective drugs for cancer therapy. Vascular endothelial growth factor receptor-2 (VEGFR2) acts as a central modulator of angiogenesis, and is therefore an important pharmaceutical target for developing anti-angiogenic agents. In this study, ligand-based naive Bayesian (NB) models and structure-based molecular docking were combined to develop a virtual screening (VS) pipeline for identifying potential VEGFR2 inhibitors from FDA-approved drugs. The best validated naive Bayesian model (NB-c) gave Matthews correlation coefficients of 0.966 and 0.951 for the test set and external validation set, respectively. 1841 FDA-approved drugs were sequentially screened by the optimal model NB-c and molecular docking module LibDock. By analyzing the results of VS, 9 top ranked drugs with EstPGood value ≥ 0.6 and LibDock Score ≥ 120 were chosen for biological validation. VEGFR2 kinase assay results demonstrated that flubendazole, rilpivirine and papaverine showed VEGFR2 inhibitory activities with IC50 values ranging from 0.47 to 6.29 μM. Binding mode analysis with CDOCKER revealed the action mechanism of the 3 hit drugs binding to VEGFR2. In summary, we not only proposed an integrated VS pipeline for potential VEGFR2 inhibitors screening, but also identified 3 FDA-approved drugs as novel VEGFR2 inhibitors, which could be used to design and develop new antiangiogenic agents.

Design and one-pot synthesis of 2-thiazolylhydrazone derivatives as influenza neuraminidase inhibitors

Two series of novel 2-thiazolylhydrazone derivatives were designed and synthesized via one-pot reaction of benzaldehyde derivatives,