Mingyue Zheng

Site of metabolism prediction for six biotransformations mediated by cytochromes P450

MOTIVATION One goal of metabolomics is to define and monitor the entire metabolite complement of a cell, while it is still far from reach since systematic and rapid approaches for determining the biotransformations of newly discovered metabolites are lacking. For drug development, such metabolic biotransformation of a new chemical entity (NCE) is of more interest because it may profoundly affect its bioavailability, activity and toxicity profile. The use of in silico methods to predict the site of metabolism (SOM) in phase I cytochromes P450-mediated reactions is usually a starting point of metabolic pathway studies, which may also assist in the process of drug/lead optimization. RESULTS This article reports the Cytochromes P450 (CYP450)-mediated SOM prediction for the six most important metabolic reactions by incorporating the use of machine learning and semi-empirical quantum chemical calculations. Non-local models were developed on the basis of a large dataset comprising 1858 metabolic reactions extracted from 1034 heterogeneous chemicals. For validation, the overall accuracies of all six reaction types are higher than 0.81, four of which exceed 0.90. In further receiver operating characteristic (ROC) analyses, each of the SOM model gave a significant area under curve (AUC) value over 0.86, indicating a good predicting power. An external test was made on a previously published dataset, of which 80% of the experimentally observed SOMs can be correctly identified by applying the full set of our SOM models. AVAILABILITY The program package SOME_v1.0 (Site Of Metabolism Estimator) developed based on our models is available at http://www.dddc.ac.cn/adme/myzheng/SOME_1_0.tar.gz.

Identifying Novel Selective Non-Nucleoside DNA Methyltransferase 1 Inhibitors through Docking-Based Virtual Screening

The DNA methyltransferases (DNMTs) found in mammals include DNMT1, DNMT3A, and DNMT3B and are attractive targets in cancer chemotherapy. DNMT1 was the first among the DNMTs to be characterized, and it is responsible for maintaining DNA methylation patterns. A number of DNMT inhibitors have been reported, but most of them are nucleoside analogs that can lead to toxic side effects and lack specificity. By combining docking-based virtual screening with biochemical analyses, we identified a novel compound, DC_05. DC_05 is a non-nucleoside DNMT1 inhibitor with low micromolar IC50 values and significant selectivity toward other AdoMet-dependent protein methyltransferases. Through a process of similarity-based analog searching, compounds DC_501 and DC_517 were found to be more potent than DC_05. These three potent compounds significantly inhibited cancer cell proliferation. The structure-activity relationship (SAR) and binding modes of these inhibitors were also analyzed to assist in the future development of more potent and more specific DNMT1 inhibitors.

In silico ADME/T modelling for rational drug design

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.

Recent Advances in Neuraminidase Inhibitor Development as Anti-influenza Drugs

The recent emergence of the highly pathogenic H5N1 subtype of avian influenza virus (AIV) and of the new type of human influenza A (H1N1) have emphasized the need for the development of effective anti‐influenza drugs. Presently, neuraminidase (NA) inhibitors are widely used in the treatment and prophylaxis of human influenza virus infection, and tremendous efforts have been made to develop more potent NA inhibitors to combat resistance and new influenza viruses. In this review, we discuss the structural characteristics of NA catalytic domains and the recent developments of new NA inhibitors using structure‐based drug design strategies. These drugs include analogues of zanamivir, analogues of oseltamivir, analogues of peramivir, and analogues of aromatic carboxylic acid and present promising options for therapeutics or leads for further development of NA inhibitors that may be useful in the event of a future influenza pandemic.

Molecular Basis of NDM-1, a New Antibiotic Resistance Determinant

The New Delhi Metallo-β-lactamase (NDM-1) was first reported in 2009 in a Swedish patient. A recent study reported that Klebsiella pneumonia NDM-1 positive strain or Escherichia coli NDM-1 positive strain was highly resistant to all antibiotics tested except tigecycline and colistin. These can no longer be relied on to treat infections and therefore, NDM-1 now becomes potentially a major global health threat. In this study, we performed modeling studies to obtain its 3D structure and NDM-1/antibiotics complex. It revealed that the hydrolytic mechanisms are highly conserved. In addition, the detailed analysis indicates that the more flexible and hydrophobic loop1, together with the evolution of more positive-charged loop2 leads to NDM-1 positive strain more potent and extensive in antibiotics resistance compared with other MBLs. Furthermore, through biological experiments, we revealed the molecular basis for antibiotics catalysis of NDM-1 on the enzymatic level. We found that NDM-1 enzyme was highly potent to degrade carbapenem antibiotics, while mostly susceptible to tigecycline, which had the ability to slow down the hydrolysis velocity of meropenem by NDM-1. Meanwhile, the mutagenesis experiments, including D124A, C208A, K211A and K211E, which displayed down-regulation on meropenem catalysis, proved the accuracy of our model. At present, there are no effective antibiotics against NDM-1 positive pathogen. Our study will provide clues to investigate the molecular basis of extended antibiotics resistance of NDM-1 and then accelerate the search for new antibiotics against NDM-1 positive strain in clinical studies.

Computational Screening for Active Compounds Targeting Protein Sequences: Methodology and Experimental Validation

The three-dimensional (3D) structures of most protein targets have not been determined so far, with many of them not even having a known ligand, a truly general method to predict ligand-protein interactions in the absence of three-dimensional information would be of great potential value in drug discovery. Using the support vector machine (SVM) approach, we constructed a model for predicting ligand-protein interaction based only on the primary sequence of proteins and the structural features of small molecules. The model, trained by using 15,000 ligand-protein interactions between 626 proteins and over 10,000 active compounds, was successfully used in discovering nine novel active compounds for four pharmacologically important targets (i.e., GPR40, SIRT1, p38, and GSK-3β). To our knowledge, this is the first example of a successful sequence-based virtual screening campaign, demonstrating that our approach has the potential to discover, with a single model, active ligands for any protein.

Computational methods for drug design and discovery: focus on China

In the past decades, Chinas computational drug design and discovery research has experienced fast development through various novel methodologies. Application of these methods spans a wide range, from drug target identification to hit discovery and lead optimization. In this review, we firstly provide an overview of Chinas status in this field and briefly analyze the possible reasons for this rapid advancement. The methodology development is then outlined. For each selected method, a short background precedes an assessment of the method with respect to the needs of drug discovery, and, in particular, work from China is highlighted. Furthermore, several successful applications of these methods are illustrated. Finally, we conclude with a discussion of current major challenges and future directions of the field.

Catalytic Mechanism Investigation of Lysine-Specific Demethylase 1 (LSD1): A Computational Study

Lysine-specific demethylase 1 (LSD1), the first identified histone demethylase, is a flavin-dependent amine oxidase which specifically demethylates mono- or dimethylated H3K4 and H3K9 via a redox process. It participates in a broad spectrum of biological processes and is of high importance in cell proliferation, adipogenesis, spermatogenesis, chromosome segregation and embryonic development. To date, as a potential drug target for discovering anti-tumor drugs, the medical significance of LSD1 has been greatly appreciated. However, the catalytic mechanism for the rate-limiting reductive half-reaction in demethylation remains controversial. By employing a combined computational approach including molecular modeling, molecular dynamics (MD) simulations and quantum mechanics/molecular mechanics (QM/MM) calculations, the catalytic mechanism of dimethylated H3K4 demethylation by LSD1 was characterized in details. The three-dimensional (3D) model of the complex was composed of LSD1, CoREST, and histone substrate. A 30-ns MD simulation of the model highlights the pivotal role of the conserved Tyr761 and lysine-water-flavin motif in properly orienting flavin adenine dinucleotide (FAD) with respect to substrate. The synergy of the two factors effectively stabilizes the catalytic environment and facilitated the demethylation reaction. On the basis of the reasonable consistence between simulation results and available mutagenesis data, QM/MM strategy was further employed to probe the catalytic mechanism of the reductive half-reaction in demethylation. The characteristics of the demethylation pathway determined by the potential energy surface and charge distribution analysis indicates that this reaction belongs to the direct hydride transfer mechanism. Our study provides insights into the LSD1 mechanism of reductive half-reaction in demethylation and has important implications for the discovery of regulators against LSD1 enzymes.

Astemizole arrests the proliferation of cancer cells by disrupting the EZH2-EED interaction of polycomb repressive complex 2.

Polycomb Repressive Complex 2 (PRC2) modulates the chromatin structure and transcriptional repression by trimethylation lysine 27 of histone H3 (H3K27me3), a process that necessitates the protein-protein interaction (PPI) between the catalytic subunit EZH2 and EED. Deregulated PRC2 is intimately involved in tumorigenesis and progression, making it an invaluable target for epigenetic cancer therapy. However, until now, there have been no reported small molecule compounds targeting the EZH2-EED interactions. In the present study, we identified astemizole, an FDA-approved drug, as a small molecule inhibitor of the EZH2-EED interaction of PRC2. The disruption of the EZH2-EED interaction by astemizole destabilizes the PRC2 complex and inhibits its methyltransferase activity in cancer cells. Multiple lines of evidence have demonstrated that astemizole arrests the proliferation of PRC2-driven lymphomas primarily by disabling the PRC2 complex. Our findings demonstrate the chemical tractability of the difficult PPI target by a small molecule compound, highlighting the therapeutic promise for PRC2-driven human cancers via targeted destruction of the EZH2-EED complex.

AST1306, A Novel Irreversible Inhibitor of the Epidermal Growth Factor Receptor 1 and 2, Exhibits Antitumor Activity Both In Vitro and In Vivo

Despite the initial response to the reversible, ATP-competitive quinazoline inhibitors that target ErbB-family, such a subset of cancer patients almost invariably develop resistance. Recent studies have provided compelling evidence that irreversible ErbB inhibitors have the potential to override this resistance. Here, we found that AST1306, a novel anilino-quinazoline compound, inhibited the enzymatic activities of wild-type epidermal growth factor receptor (EGFR) and ErbB2 as well as EGFR resistant mutant in both cell-free and cell-based systems. Importantly, AST1306 functions as an irreversible inhibitor, most likely through covalent interaction with Cys797 and Cys805 in the catalytic domains of EGFR and ErbB2, respectively. Further studies showed that AST1306 inactivated pathways downstream of these receptors and thereby inhibited the proliferation of a panel of cancer cell lines. Although the activities of EGFR and ErbB2 were similarly sensitive to AST1306, ErbB2-overexpressing cell lines consistently exhibited more sensitivity to AST1306 antiproliferative effects. Consistent with this, knockdown of ErbB2, but not EGFR, decreased the sensitivity of SK-OV-3 cells to AST1306. In vivo, AST1306 potently suppressed tumor growth in ErbB2-overexpressing adenocarcinoma xenograft and FVB-2/Nneu transgenic breast cancer mouse models, but weakly inhibited the growth of EGFR-overexpressing tumor xenografts. Tumor growth inhibition induced by a single dose of AST1306 in the SK-OV-3 xenograft model was accompanied by a rapid (within 2 h) and sustained (≥24 h) inhibition of both EGFR and ErbB2, consistent with an irreversible inhibition mechanism. Taken together, these results establish AST1306 as a selective, irreversible ErbB2 and EGFR inhibitor whose growth-inhibitory effects are more potent in ErbB2-overexpressing cells.