Phytol Promotes the Formation of Slow-Twitch Muscle Fibers through PGC-1α/miRNA but Not Mitochondria Oxidation.

Phytol is a side chain of chlorophyll belonging to the side-chain double terpenoid. When animals consume food rich in chlorophyll, phytol can be broken down to phytanic acid after digestion. It was reported that feeding animals with different varieties and levels of forage could significant improve pH and marbling score of steer and lamb carcasses, but the internal mechanism for this is still not reported. The marbling score and pH of muscle was mainly determined by skeletal muscle fiber type, which is due to expression of different myosin heavy-chain (MHC) isoforms. Here, we provide evidence that phytol can indeed affect the diversity of muscle fiber types both in vitro and in vivo and demonstrate that phytol can increase the expression of MHC I (p < 0.05), likely by upgrading the expression of PPARδ, PGC-1α, and related miRNAs. This fiber-type transformation process may not be caused by activated mitochondrial metabolism but by the structural changes in muscle fiber types.

Identification and biochemical characterization of DC07090 as a novel potent small molecule inhibitor against human enterovirus 71 3C protease by structure-based virtual screening.

Hand, foot and mouth disease (HFMD) is a serious, highly contagious disease. HFMD caused by Enterovirus 71 (EV71), results in severe complications and even death. The pivotal role of EV71 3Cpro in the viral life cycle makes it an attractive target for drug discovery and development to treat HFMD. In this study, we identified novel EV71 3Cpro inhibitors by docking-based virtual screening. Totally 50 compounds were selected to test their inhibitory activity against EV71 3Cpro. The best inhibitor DC07090 exhibited the inhibition potency with an IC50 value of 21.72 ± 0.95 µM without apparent toxicity (CC50 > 200 µM). To explore structure-activity relationship of DC07090, 15 new derivatives were designed, synthesized and evaluated in vitro enzyme assay accordingly. Interestingly, four compounds showed inhibitory activities against EV71 3Cpro and only DC07090 inhibited EV71 replication with an EC50 value of 22.09 ± 1.07 µM. Enzyme inhibition kinetic experiments showed that the compound was a reversible and competitive inhibitor. The Ki value was determined to be 23.29 ± 12.08 µM. Further molecular docking, MD simulation and mutagenesis studies confirmed the binding mode of DC07090 and EV71 3Cpro. Besides, DC07090 could also inhibit coxsackievirus A16 (CVA16) replication with an EC50 value of 27.76 ± 0.88 µM. Therefore, DC07090 represents a new non-peptidyl small molecule inhibitor for further development of antiviral therapy against EV71 or other picornaviruses.

Wedelolactone metabolism in rats through regioselective glucuronidation catalyzed by uridine diphosphate-glucuronosyltransferases 1As (UGT1As).

BACKGROUND: Wedelolactone (WEL), a medicinal plant-derived coumestan, has been reported to exhibit a diverse range of pharmacological activities. However, the metabolism and disposition of WEL remain unexplored. PURPOSE: The present study aims to investigate the metabolism of WEL in rats and identify the enzymes responsible for forming major WEL metabolites. METHODS: Plasma, urine, feces, and bile samples were collected before and after 50 mg/kg WEL was orally administered to rats. Metabolites were profiled by ultrahigh performance liquid chromatography/quadrupole time-of-flight mass spectrometry and identified by high-performance liquid chromatography-solid-phase extraction-nuclear magnetic resonance spectroscopy. The in vitro WEL glucuronidation activities of human liver microsomes, human kidney microsomes, human intestine microsomes, and 12 recombinant human uridine diphosphate-glucuronosyltransferase (UGT) isoforms were screened. Molecular docking simulation of the interaction between WEL and UGT1A9 was conducted. RESULTS: WEL underwent extensive metabolism, and 17 metabolites were identified. The major metabolic pathways observed were glucuronidation and methylation. Glucuronic acid was preferentially introduced into 5-OH, whereas no obvious regioselectivity was observed in the methylation of 11-OH and 12-OH. Multiple UGTs, including UGT1A1, UGT1A3, UGT1A6, UGT1A7, UGT1A8, UGT1A9, and UGT1A10, were involved in forming WEL glucuronides and O-methylated WEL glucuronides. CONCLUSION: The extensive glucuronidation and methylation is responsible for the low oral bioavailability of WEL in rats. UGT1A1 and UGT1A9 were the major enzymes involved in the glucuronidation of WEL and O-methylated WEL. Molecular docking studies revealed that 5-OH was accessible to the catalytic domain of UGT1As; therefore, 5-OH exhibited a high probability of glucuronidation.

A novel small-molecule compound targeting CD147 inhibits the motility and invasion of hepatocellular carcinoma cells.

CD147, a type I transmembrane glycoprotein, is highly expressed in various cancer types and plays important roles in tumor progression, especially by promoting the motility and invasion of hepatocellular carcinoma (HCC) cells. These crucial roles make CD147 an attractive target for therapeutic intervention in HCC, but no small-molecule inhibitors of CD147 have been developed to date. To identify a candidate inhibitor, we used a pharmacophore model derived from the structure of CD147 to virtually screen over 300,000 compounds. The 100 highest-ranked compounds were subjected to biological assays, and the most potent one, dubbed AC-73 (ID number: AN-465/42834501), was studied further. We confirmed that AC-73 targeted CD147 and further demonstrated it can specifically disrupt CD147 dimerization. Moreover, molecular docking and mutagenesis experiments showed that the possible binding sites of AC-73 on CD147 included Glu64 and Glu73 in the N-terminal IgC2 domain, which two residues are located in the dimer interface of CD147. Functional assays revealed that AC-73 inhibited the motility and invasion of typical HCC cells, but not HCC cells that lacked the CD147 gene, demonstrating on-target action. Further, AC-73 reduced HCC metastasis by suppressing matrix metalloproteinase (MMP)-2 via down-regulation of the CD147/ERK1/2/signal transducer and activator of transcription 3 (STAT3) signaling pathway. Finally, AC-73 attenuated progression in an orthotopic nude mouse model of liver metastasis, suggesting that AC-73 or its derivatives have potential for use in HCC intervention. We conclude that the novel small-molecule inhibitor AC-73 inhibits HCC mobility and invasion, probably by disrupting CD147 dimerization and thereby mainly suppressing the CD147/ERK1/2/STAT3/MMP-2 pathways, which are crucial for cancer progression.

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.

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

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.

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

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.

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

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.

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.

Differential gene expression pattern in hypothalamus of chickens during fasting-induced metabolic reprogramming: functions of glucose and lipid metabolism in the feed intake of chickens.

Fasting-induced hypothalamic metabolic reprogramming is involved in regulating energy homeostasis and appetite in mammals, but this phenomenon remains unclear in poultry. In this study, the expression patterns of a panel of genes related to neuropeptides, glucose, and lipid metabolism enzymes in the hypothalamus of chickens during fasting and refeeding were characterized by microarray analysis and quantitative PCR. Results showed that 48 h of fasting upregulated (P < 0.05) the mRNA expressions of orexigenic neuropeptide Y and agouti-related protein but downregulated (P < 0.05) that of anorexigenic neuropeptide pro-opiomelanocortin; growth hormone-releasing hormone; islet amyloid polypeptide; thyroid-stimulating hormone, ß; and glycoprotein hormones, α polypeptide. After 48 h of fasting, the mRNA expression of fatty acid ß-oxidation [peroxisome proliferator-activated receptor α (PPARα), carnitine palmitoyltransferase 1A, and forkhead box O1], energy sensor protein [sirtuin 1 (SIRT1) and forkhead box O1], and glycolysis inhibitor (pyruvate dehydrogenase kinase, isozyme 4) were enhanced, but that of fatty acid synthesis and transport associated genes (acetyl-CoA carboxylase α, fatty acid synthase, apolipoprotein A-I, endothelial lipase, and fatty acid binding protein 7) were suppressed. Liver and muscle also demonstrated similar expression patterns of genes related to glucose and lipid metabolism with hypothalamus, except for that of acetyl-CoA carboxylase α, acyl-CoA synthetase long-chain family member 4, and apolipoprotein A-I. The results of intracerebroventricular (ICV) injection experiments confirmed that α-lipoic acid (ALA, pyruvate dehydrogenase kinase, isozyme 4 inhibitor, 0.10 µmol) and NADH (SIRT1 inhibitor, 0.80 µmol) significantly suppressed the appetite of chickens, whereas 2-deoxy-d-glucose (glycolytic inhibitor, 0.12 to 1.20 µmol) and NAD(+) (SIRT1 activator, 0.08 to 0.80 µmol) increased feed intake in chickens. The orexigenic effect of NAD(+) was also blocked by cotreatment with NADH. However, ICV injection of either GW7647 (PPARα agonist) or GW6471 (PPARα antagonist) showed no effects on feed intake. Results suggested that hypothalamic glycolysis (inhibited by ALA and promoted by 2-deoxy-d-glucose) and SIRT1 (inhibited by NADH and promoted by NAD(+)), not PPARα, were probably involved in feed intake regulation in chickens.

In Silico target fishing: addressing a "Big Data" problem by ligand-based similarity rankings with data fusion.

BACKGROUND: Ligand-based in silico target fishing can be used to identify the potential interacting target of bioactive ligands, which is useful for understanding the polypharmacology and safety profile of existing drugs. The underlying principle of the approach is that known bioactive ligands can be used as reference to predict the targets for a new compound. RESULTS: We tested a pipeline enabling large-scale target fishing and drug repositioning, based on simple fingerprint similarity rankings with data fusion. A large library containing 533 drug relevant targets with 179,807 active ligands was compiled, where each target was defined by its ligand set. For a given query molecule, its target profile is generated by similarity searching against the ligand sets assigned to each target, for which individual searches utilizing multiple reference structures are then fused into a single ranking list representing the potential target interaction profile of the query compound. The proposed approach was validated by 10-fold cross validation and two external tests using data from DrugBank and Therapeutic Target Database (TTD). The use of the approach was further demonstrated with some examples concerning the drug repositioning and drug side-effects prediction. The promising results suggest that the proposed method is useful for not only finding promiscuous drugs for their new usages, but also predicting some important toxic liabilities. CONCLUSIONS: With the rapid increasing volume and diversity of data concerning drug related targets and their ligands, the simple ligand-based target fishing approach would play an important role in assisting future drug design and discovery.

Estimation of acute oral toxicity in rat using local lazy learning.

BACKGROUND: Acute toxicity means the ability of a substance to cause adverse effects within a short period following dosing or exposure, which is usually the first step in the toxicological investigations of unknown substances. The median lethal dose, LD50, is frequently used as a general indicator of a substance's acute toxicity, and there is a high demand on developing non-animal-based prediction of LD50. Unfortunately, it is difficult to accurately predict compound LD50 using a single QSAR model, because the acute toxicity may involve complex mechanisms and multiple biochemical processes. RESULTS: In this study, we reported the use of local lazy learning (LLL) methods, which could capture subtle local structure-toxicity relationships around each query compound, to develop LD50 prediction models: (a) local lazy regression (LLR): a linear regression model built using k neighbors; (b) SA: the arithmetical mean of the activities of k nearest neighbors; (c) SR: the weighted mean of the activities of k nearest neighbors; (d) GP: the projection point of the compound on the line defined by its two nearest neighbors. We defined the applicability domain (AD) to decide to what an extent and under what circumstances the prediction is reliable. In the end, we developed a consensus model based on the predicted values of individual LLL models, yielding correlation coefficients R(2) of 0.712 on a test set containing 2,896 compounds. CONCLUSION: Encouraged by the promising results, we expect that our consensus LLL model of LD50 would become a useful tool for predicting acute toxicity. All models developed in this study are available via http://www.dddc.ac.cn/admetus.

In silico site of metabolism prediction for human UGT-catalyzed reactions.

MOTIVATION: The human uridine diphosphate-glucuronosyltransferase enzyme family catalyzes the glucuronidation of the glycosyl group of a nucleotide sugar to an acceptor compound (substrate), which is the most common conjugation pathway that serves to protect the organism from the potential toxicity of xenobiotics. Moreover, it could affect the pharmacological profile of a drug. Therefore, it is important to identify the metabolically labile sites for glucuronidation. RESULTS: In the present study, we developed four in silico models to predict sites of glucuronidation, for four major sites of metabolism functional groups, i.e. aliphatic hydroxyl, aromatic hydroxyl, carboxylic acid or amino nitrogen, respectively. According to the mechanism of glucuronidation, a series of 'local' and 'global' molecular descriptors characterizing the atomic reactivity, bonding strength and physical-chemical properties were calculated and selected with a genetic algorithm-based feature selection approach. The constructed support vector machine classification models show good prediction performance, with the balanced accuracy ranging from 0.88 to 0.96 on test set. For further validation, our models can successfully identify 84% of experimentally observed sites of metabolisms for an external test set containing 54 molecules. AVAILABILITY AND IMPLEMENTATION: The software somugt based on our models is available at www.dddc.ac.cn/adme/jlpeng/somugt_win32.zip.