Xiaocong Pang

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.

DL0410 can reverse cognitive impairment, synaptic loss and reduce plaque load in APP/PS1 transgenic mice.

Cholinesterase inhibitors are first-line therapy for Alzheimers disease (AD). DL0410 is an AChE/BuChE dual inhibitor with a novel new structural scaffold. It has been demonstrated that DL0410 could improve memory deficits in both Aβ1-42-induced and scopolamine-induced amnesia in mice. In the present study, the therapeutic effect of DL0410 and its action mechanism were investigated in APP/PS1 transgenic mice. Six-month old APP/PS1 transgenic mice were orally administered with DL0410 (3, 10, 30 mg/kg/day). After 60 days, several behavioural tests, including the Morris water maze and step-down tests, were used to investigate the effects of DL0410 on mice behaviours. All the behavioural experimental results showed that DL0410 significantly ameliorated memory deficits. Meanwhile, DL0410 attenuated neural cell damage and reduced senile plaques significantly in the hippocampus of APP/PS1 transgenic mice. In addition, DL0410 significantly decreased Aβ plaques, while increasing the number of synapses and the thickness of PSD in the hippocampus. We also found DL0410 decreased the expression of APP, NMDAR1B and the phosphorylation level of NMDAR2B, and increased the phosphorylation level of CAMKII and the expression of PSD-95. In this study, the results of behavioural tests demonstrated for the first time that DL0410 could improve learning and memory dysfunction in APP/PS1 transgenic mice. The mechanism of its beneficial effects might be related to cholinesterase inhibition, Aβ plaques inhibition, improvement of synapse loss by regulating of expression of proteins related to synapses. As a result, DL0410 could be considered as a candidate drug for the therapy of AD.

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.

Effects of the Nrf2 Protein Modulator Salvianolic Acid A Alone or Combined with Metformin on Diabetes-associated Macrovascular and Renal Injury

Nuclear factor E2-related factor 2 (Nrf2) is considered a promising target against diabetic complications such as cardiovascular diseases and diabetic nephropathy. Herein, we investigated the effects of a potential Nrf2 modulator, salvianolic acid A (SAA), which is a natural polyphenol, on diabetes-associated macrovascular and renal injuries in streptozotocin-induced diabetic mice. Given that lowering glucose is the first objective of diabetic patients, we also examined the effects of SAA combined with metformin (MET) on both complications. Our results showed that SAA significantly increased the macrovascular relaxation response to acetylcholine and sodium nitroprusside in diabetic mice. Interestingly, treatment with SAA alone only provided minor protection against renal injury, as reflected by minor improvements in impaired renal function and structure, despite significantly reduced oxidative stress observed in the diabetic kidney. We demonstrated that decreased oxidative stress and NF-κB p65 expression were associated with SAA-induced expression of Nrf2-responsive antioxidant enzymes heme oxygenase-1 (HO-1), NAD(P)H dehydrogenase (quinone) 1 (NQO-1), and glutathione peroxidase-1 (GPx-1) in vivo or in vitro, which suggested that SAA was a potential Nrf2 modulator. More significantly, compared with treatment with either SAA or MET alone, we found that their combination provided further protection against the macrovascular and renal injury, which was at least partly due to therapeutic activation of both MET-mediated AMP-activated protein kinase and SAA-mediated Nrf2/antioxidant-response element pathways. These findings suggested that polyphenol Nrf2 modulators, especially combined with drugs activating AMP-activated protein kinase, including hypoglycemic drugs, are worthy of further investigation to combat diabetic complications.

Members of FOX family could be drug targets of cancers

ABSTRACT FOX families play important roles in biological processes, including metabolism, development, differentiation, proliferation, apoptosis, migration, invasion and longevity. Here we are focusing on roles of FOX members in cancers, FOX members and drug resistance, FOX members and stem cells. Finally, FOX members as drug targets of cancer treatment were discussed. Future perspectives of FOXC1 research were described in the end.

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.

Evaluation of Novel Dual Acetyl- and Butyrylcholinesterase Inhibitors as Potential Anti-Alzheimer’s Disease Agents Using Pharmacophore, 3D-QSAR, and Molecular Docking Approaches

DL0410, containing biphenyl and piperidine skeletons, was identified as an acetylcholinesterase (AChE) and butyrylcholinesterase (BuChE) inhibitor through high-throughput screening assays, and further studies affirmed its efficacy and safety for Alzheimer’s disease treatment. In our study, a series of novel DL0410 derivatives were evaluated for inhibitory activities towards AChE and BuChE. Among these derivatives, compounds 6-1 and 7-6 showed stronger AChE and BuChE inhibitory activities than DL0410. Then, pharmacophore modeling and three-dimensional quantitative structure activity relationship (3D-QSAR) models were performed. The R2 of AChE and BuChE 3D-QSAR models for training set were found to be 0.925 and 0.883, while that of the test set were 0.850 and 0.881, respectively. Next, molecular docking methods were utilized to explore the putative binding modes. Compounds 6-1 and 7-6 could interact with the amino acid residues in the catalytic anionic site (CAS) and peripheral anionic site (PAS) of AChE/BuChE, which was similar with DL0410. Kinetics studies also suggested that the three compounds were all mixed-types of inhibitors. In addition, compound 6-1 showed better absorption and blood brain barrier permeability. These studies provide better insight into the inhibitory behaviors of DL0410 derivatives, which is beneficial for rational design of AChE and BuChE inhibitors in the future.

Effects of P-Glycoprotein on the Transport of DL0410, a Potential Multifunctional Anti-Alzheimer Agent

In our study, we attempted to investigate the influences of P-glycoprotein (P-gp) on DL0410, a novel synthetic molecule for Alzheimer’s disease (AD) treatment, for intestinal absorption and blood-brain barrier permeability in vitro and related binding mechanisms in silico. Caco-2, MDCK, and MDCK-MDR1 cells were utilized for transport studies, and homology modelling of human P-gp was built for further docking study to uncover the binding mode of DL0410. The results showed that the apparent permeability (Papp) value of DL0410 was approximately 1 × 10−6 cm/s, indicating the low permeability of DL0410. With the presence of verapamil, the directional transport of DL0410 disappeared in Caco-2 and MDCK-MDR1 cells, suggesting that DL0410 should be a substrate of P-gp, which was also confirmed by P-gp ATPase assay. In addition, DL0410 could competitively inhibit the transport of Rho123, a P-gp known substrate. According to molecular docking, we also found that DL0410 could bind to the drug binding pocket (DBP), but not the nucleotide binding domain (NBD). In conclusion, DL0410 was a substrate as well as a competitive inhibitor of P-gp, and P-gp had a remarkable impact on the intestine and brain permeability of DL0410, which is of significance for drug research and development.