Showing papers by "Mingyue Zheng published in 2015"

In silico ADME/T modelling for rational drug design

Abstract: In recent decades, in silico absorption, distribution, metabolism, excretion (ADME), and toxicity (T) modelling as a tool for rational drug design has received considerable attention from pharmaceutical scientists, and various ADME/T-related prediction models have been reported. The high-throughput and low-cost nature of these models permits a more streamlined drug development process in which the identification of hits or their structural optimization can be guided based on a parallel investigation of bioavailability and safety, along with activity. However, the effectiveness of these tools is highly dependent on their capacity to cope with needs at different stages, e.g. their use in candidate selection has been limited due to their lack of the required predictability. For some events or endpoints involving more complex mechanisms, the current in silico approaches still need further improvement. In this review, we will briefly introduce the development of in silico models for some physicochemical parameters, ADME properties and toxicity evaluation, with an emphasis on the modelling approaches thereof, their application in drug discovery, and the potential merits or deficiencies of these models. Finally, the outlook for future ADME/T modelling based on big data analysis and systems sciences will be discussed.

Discovery and Optimization of Novel, Selective Histone Methyltransferase SET7 Inhibitors by Pharmacophore- and Docking-Based Virtual Screening

Abstract: Histone methyltransferases are involved in various biological functions, and these methylation regulating enzymes’ abnormal expression or activity has been noted in several human cancers. Within this context, SET domain-containing (lysine methyltransferase) 7 (SET7, also called KMT7, SETD7, SET9) is of increasing significance due to its diverse roles in biological functions and diseases, such as diabetes, cancers, alopecia areata, atherosclerotic vascular disease, HIV, and HCV. In this study, DC-S100, which was discovered by pharmacophore- and docking-based virtual screening, was identified as the hit compound of SET7 inhibitor. Structure–activity relationship (SAR) analysis was performed on analogs of DC-S100 and according to the putative binding mode of DC-S100, structure modifications were made to improve its activity. Of note, compounds DC-S238 and DC-S239, with IC50 values of 4.88 and 4.59 μM, respectively, displayed selectivity for DNMT1, DOT1L, EZH2, NSD1, SETD8, and G9a. Taken together, DC-S238 an...

Significant performance enhancement of a UASB reactor by using acyl homoserine lactones to facilitate the long filaments of Methanosaeta harundinacea 6Ac.

Abstract: Methanosaeta strains are frequently involved in the granule formation during methanogenic wastewater treatment. To investigate the impact of Methanosaeta on granulation and performance of upflow anaerobic sludge blanket (UASB) reactors, three 1-L working volume reactors noted as R1, R2, and R3 were operated fed with a synthetic wastewater containing sodium acetate and glucose. R1 was inoculated with 1-L activated sludge, while R2 and R3 were inoculated with 200-mL concentrated pre-grown Methanosaeta harundinacea 6Ac culture and 800 mL of activated sludge. Additionally, R3 was daily dosed with 0.5 mL/L of acetyl ether extract of 6Ac spent culture containing its quorum sensing signal carboxyl acyl homoserine lactone (AHL). Compared to R1, R2 and R3 had a higher and more constant chemical oxygen demand (COD) removal efficiency and alkaline pH (8.2) during the granulation phase, particularly, R3 maintained approximately 90 % COD removal. Moreover, R3 formed the best granules, and microscopic images showed fluorescent Methanosaeta-like filaments dominating in the R3 granules, but rod cells dominating in the R2 granules. Analysis of 16S rRNA gene libraries showed increased diversity of methanogen species like Methanosarcina and Methanospirillum in R2 and R3, and increased bacteria diversity in R3 that included the syntrophic propionate degrader Syntrophobacter. Quantitative PCR determined that 6Ac made up more than 22 % of the total prokaryotes in R3, but only 3.6 % in R2. The carboxyl AHL was detected in R3. This work indicates that AHL-facilitated filaments of Methanosaeta contribute to the granulation and performance of UASB reactors, likely through immobilizing other functional microorganisms.

Effect of pH on types of acidogenic fermentation of fruit and vegetable wastes

Abstract: The effect of pH (4.0, 5.0, and 6.0) on acidification and fermentation of fruit and vegetable wastes was investigated using batch and semi-continuous experiments under mesophilic condition. Results showed that fermentation types change with pH variation. The pH of acidification system containing fruit and vegetable wastes could automatically decrease to 3.0 ~ 4.0. At this pH range, a stable ethanol production was observed, at which ethanol-type fermentation was obtained. Based on the results, the fermentation types were classified into ethanol-type, mixed acid-type, propionic acid-type, and butyric acid-type fermentations, which occurred at pH 4.0 ~ 4.5, 4.5 ~ 5.0, 5.0 ~ 5.5, and 5.5 ~ 6.5, respectively.

TarPred: a web application for predicting therapeutic and side effect targets of chemical compounds

Abstract: Motivation Discovering the relevant therapeutic targets for drug-like molecules, or their unintended 'off-targets' that predict adverse drug reactions, is a daunting task by experimental approaches alone. There is thus a high demand to develop computational methods capable of detecting these potential interacting targets efficiently. Results As biologically annotated chemical data are becoming increasingly available, it becomes feasible to explore such existing knowledge to identify potential ligand-target interactions. Here, we introduce an online implementation of a recently published computational model for target prediction, TarPred, based on a reference library containing 533 individual targets with 179 807 active ligands. TarPred accepts interactive graphical input or input in the chemical file format of SMILES. Given a query compound structure, it provides the top ranked 30 interacting targets. For each of them, TarPred not only shows the structures of three most similar ligands that are known to interact with the target but also highlights the disease indications associated with the target. This information is useful for understanding the mechanisms of action and toxicities of active compounds and can provide drug repositioning opportunities. Availability and implementation TarPred is available at: http://www.dddc.ac.cn/tarpred.

Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors

Abstract: The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson’s correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors.

A Genetic Algorithm Based Support Vector Machine Model for Blood-Brain Barrier Penetration Prediction.

Abstract: Blood-brain barrier (BBB) is a highly complex physical barrier determining what substances are allowed to enter the brain. Support vector machine (SVM) is a kernel-based machine learning method that is widely used in QSAR study. For a successful SVM model, the kernel parameters for SVM and feature subset selection are the most important factors affecting prediction accuracy. In most studies, they are treated as two independent problems, but it has been proven that they could affect each other. We designed and implemented genetic algorithm (GA) to optimize kernel parameters and feature subset selection for SVM regression and applied it to the BBB penetration prediction. The results show that our GA/SVM model is more accurate than other currently available log BB models. Therefore, to optimize both SVM parameters and feature subset simultaneously with genetic algorithm is a better approach than other methods that treat the two problems separately. Analysis of our log BB model suggests that carboxylic acid group, polar surface area (PSA)/hydrogen-bonding ability, lipophilicity, and molecular charge play important role in BBB penetration. Among those properties relevant to BBB penetration, lipophilicity could enhance the BBB penetration while all the others are negatively correlated with BBB penetration.

High-rate hydrogenotrophic methanogenesis for biogas upgrading: the role of anaerobic granules

Abstract: Hydrogenotrophic methanogenesis has been proved to be a feasible biological method for biogas upgrading. To improve its performance, the feasibility of typical anaerobic granules as the inoculum was investigated in both batch and continuous experiments. The results from batch experiments showed that glucose-acclimated granules seemed to perform better than granules acclimated to acidified products (AP, i.e. acetate, propionate and ethanol) in in situ biogas upgrading systems and a slightly higher H2 consumption rate (1.5 mmol H2 g VSS−1 h−1) was obtained for glucose-acclimated granules. For AP-acclimated granules, the inhibition on anaerobic digestion and pH increase (up to 9.55±0.16) took place, and the upgrading performance was adversely affected. In contrast, better performance for AP-acclimated granules was observed in ex situ systems, possibly due to their higher hydrogenotrophic methanogenic activities (HMA). Moreover, when gas–liquid mass transfer limitations were alleviated, the upgrading performa...

The Use of Chemical-Chemical Interaction and Chemical Structure to Identify New Candidate Chemicals Related to Lung Cancer.

Abstract: Lung cancer causes over one million deaths every year worldwide. However, prevention and treatment methods for this serious disease are limited. The identification of new chemicals related to lung cancer may aid in disease prevention and the design of more effective treatments. This study employed a weighted network, constructed using chemical-chemical interaction information, to identify new chemicals related to two types of lung cancer: non-small lung cancer and small-cell lung cancer. Then, a randomization test as well as chemical-chemical interaction and chemical structure information were utilized to make further selections. A final analysis of these new chemicals in the context of the current literature indicates that several chemicals are strongly linked to lung cancer.

Combinatorial Pharmacophore Modeling of Multidrug and Toxin Extrusion Transporter 1 Inhibitors: a Theoretical Perspective for Understanding Multiple Inhibitory Mechanisms

Abstract: A combinatorial pharmacophore (CP) model for Multidrug and toxin extrusion 1 (MATE1/SLC47A1) inhibitors was developed based on a data set including 881 compounds. The CP model comprises four individual pharmacophore hypotheses, HHR1, DRR, HHR2 and AAAP, which can successfully identify the MATE1 inhibitors with an overall accuracy around 75%. The model emphasizes the importance of aromatic ring and hydrophobicity as two important structural determinants for MATE1 inhibition. Compared with the pharmacophore model of Organic Cation Transporter 2 (OCT2/ SLC22A2), a functional related transporter of MATE1, the hypotheses of AAAP and PRR5 are suggested to be responsible for their ligand selectivity, while HHR a common recognition pattern for their dual inhibition. A series of analysis including molecular sizes of inhibitors matching different hypotheses, matching of representative MATE1 inhibitors and molecular docking indicated that the small inhibitors matching HHR1 and DRR involve in competitive inhibition, while the relatively large inhibitors matching AAAP are responsible for the noncompetitive inhibition by locking the conformation changing of MATE1. In light of the results, a hypothetical model for inhibiting transporting mediated by MATE1 was proposed.

Prediction of Drug Indications Based on Chemical Interactions and Chemical Similarities

Abstract: Discovering potential indications of novel or approved drugs is a key step in drug development. Previous computational approaches could be categorized into disease-centric and drug-centric based on the starting point of the issues or small-scaled application and large-scale application according to the diversity of the datasets. Here, a classifier has been constructed to predict the indications of a drug based on the assumption that interactive/associated drugs or drugs with similar structures are more likely to target the same diseases using a large drug indication dataset. To examine the classifier, it was conducted on a dataset with 1,573 drugs retrieved from Comprehensive Medicinal Chemistry database for five times, evaluated by 5-fold cross-validation, yielding five 1st order prediction accuracies that were all approximately 51.48%. Meanwhile, the model yielded an accuracy rate of 50.00% for the 1st order prediction by independent test on a dataset with 32 other drugs in which drug repositioning has been confirmed. Interestingly, some clinically repurposed drug indications that were not included in the datasets are successfully identified by our method. These results suggest that our method may become a useful tool to associate novel molecules with new indications or alternative indications with existing drugs.

[Diversity-oriented synthesis and its application in drug discovery]

Abstract: Diversity-oriented synthesis (DOS) aims to efficiently generate collections of small molecules with diverse appendages, functional groups, stereochemistry and skeletons, thus yielding diverse biological activities capable of modulating a wide variety of biological processes. In this review, we discussed the common strategies employed in DOS with specific examples from recent literature, including reagent-based approach, substrate-based approach, build-couple-pair strategy and privileged substructure-based DOS. The application of some DOS libraries in drug discovery is also presented.

Correction to Anthraquinone Derivatives as Potent Inhibitors of c-Met Kinase and the Extracellular Signaling Pathway.

Abstract: We synthesized compounds 1a–1m and 2a–2h according to the synthetic procedure reported by Younis Baqi and Christa E. Muller. We added the related two papers as references in the Supporting Information and sentences “Compounds 1a–1m were synthesized according to synthetic procedure1,2″ and “Compounds 2a–2h were synthesized according to synthetic procedure1,2″ were also added before the related experimental procedures.