To be effective as a drug, a potent molecule must reach its target in the body in sufficient concentration, and stay there in a bioactive form long enough for the expected biologic events to occur. Drug development involves assessment of absorption, distribution, metabolism and excretion (ADME) increasingly earlier in the discovery process, at a stage when considered compounds are numerous but access to the physical samples is limited. In that context, computer models constitute valid alternatives to experiments. Here, we present the new SwissADME web tool that gives free access to a pool of fast yet robust predictive models for physicochemical properties, pharmacokinetics, drug-likeness and medicinal chemistry friendliness, among which in-house proficient methods such as the BOILED-Egg, iLOGP and Bioavailability Radar. Easy efficient input and interpretation are ensured thanks to a user-friendly interface through the login-free website http://www.swissadme.ch. Specialists, but also nonexpert in cheminformatics or computational chemistry can predict rapidly key parameters for a collection of molecules to support their drug discovery endeavours.

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SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules

Drug development has a high attrition rate, with poor pharmacokinetic and safety properties a significant hurdle. Computational approaches may help minimize these risks. We have developed a novel approach (pkCSM) which uses graph-based signatures to develop predictive models of central ADMET properties for drug development. pkCSM performs as well or better than current methods. A freely accessible web server (http://structure.bioc.cam.ac.uk/pkcsm), which retains no information submitted to it, provides an integrated platform to rapidly evaluate pharmacokinetic and toxicity properties.

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pkCSM: Predicting Small-Molecule Pharmacokinetic and Toxicity Properties Using Graph-Based Signatures

DrugBank (http://www.drugbank.ca) is a comprehensive online database containing extensive biochemical and pharmacological information about drugs, their mechanisms and their targets. Since it was first described in 2006, DrugBank has rapidly evolved, both in response to user requests and in response to changing trends in drug research and development. Previous versions of DrugBank have been widely used to facilitate drug and in silico drug target discovery. The latest update, DrugBank 4.0, has been further expanded to contain data on drug metabolism, absorption, distribution, metabolism, excretion and toxicity (ADMET) and other kinds of quantitative structure activity relationships (QSAR) information. These enhancements are intended to facilitate research in xenobiotic metabolism (both prediction and characterization), pharmacokinetics, pharmacodynamics and drug design/discovery. For this release, >1200 drug metabolites (including their structures, names, activity, abundance and other detailed data) have been added along with >1300 drug metabolism reactions (including metabolizing enzymes and reaction types) and dozens of drug metabolism pathways. Another 30 predicted or measured ADMET parameters have been added to each DrugCard, bringing the average number of quantitative ADMET values for Food and Drug Administration-approved drugs close to 40. Referential nuclear magnetic resonance and MS spectra have been added for almost 400 drugs as well as spectral and mass matching tools to facilitate compound identification. This expanded collection of drug information is complemented by a number of new or improved search tools, including one that provides a simple analyses of drug-target, -enzyme and -transporter associations to provide insight on drug-drug interactions.

DrugBank 4.0: shedding new light on drug metabolism

: The unpolarized absorption and circular dichroism spectra of the fundamental vibrational transitions of the chiral molecule, 4-methyl-2-oxetanone, are calculated ab initio. Harmonic force fields are obtained using Density Functional Theory (DFT), MP2, and SCF methodologies and a 5S4P2D/3S2P (TZ2P) basis set. DFT calculations use the Local Spin Density Approximation (LSDA), BLYP, and Becke3LYP (B3LYP) density functionals. Mid-IR spectra predicted using LSDA, BLYP, and B3LYP force fields are of significantly different quality, the B3LYP force field yielding spectra in clearly superior, and overall excellent, agreement with experiment. The MP2 force field yields spectra in slightly worse agreement with experiment than the B3LYP force field. The SCF force field yields spectra in poor agreement with experiment.The basis set dependence of B3LYP force fields is also explored: the 6-31G* and TZ2P basis sets give very similar results while the 3-21G basis set yields spectra in substantially worse agreements with experiment. jg

Ab initio calculation of vibrational absorption and circular dichroism spectra using density functional force fields

C–H activation has surfaced as an increasingly powerful tool for molecular sciences, with notable applications to material sciences, crop protection, drug discovery, and pharmaceutical industries, among others. Despite major advances, the vast majority of these C–H functionalizations required precious 4d or 5d transition metal catalysts. Given the cost-effective and sustainable nature of earth-abundant first row transition metals, the development of less toxic, inexpensive 3d metal catalysts for C–H activation has gained considerable recent momentum as a significantly more environmentally-benign and economically-attractive alternative. Herein, we provide a comprehensive overview on first row transition metal catalysts for C–H activation until summer 2018.

3d Transition Metals for C-H Activation.

Free fatty acid receptor 1 (FFAR1), a member of class A in G-protein-coupled receptors (GPCRs), is a promising antidiabetic target. The crystal structure of FFAR1 revealed that one agonist (MK-8666) binds to the extracellular vestibule of this receptor, while another (AP8) occupies the surface pocket between transmembrane (TM) helices TM4 and TM5. In this study, we performed 1 μs unbiased molecular dynamics (MD) simulation on each of five systems, to uncover why two ligands in completely different sites both serve as agonists and how they exert a positive synergistic effect together. They are two agonist-bound systems (FFAR1_MK-8666 and FFAR1_AP8), a ternary complex system FFAR1_MK-8666_AP8, an antagonist-bound system (FFAR1_15i), and an unliganded (apo) system, among which the antagonist 15i-bound and apo systems were used as controls. The results showed that Y913.37 played a pivotal role in the activation process of FFAR1. The agonist could disrupt the Y913.37-centered residue interaction network within protein, whereas the antagonist could stabilize the network. Furthermore, our simulations revealed that the hydrophobic layer amino acid residues next to the transmission switch (CWXP) formed a gate and could open only upon agonist activation, which might exert an important role in the formation of water pathway. These results would be helpful for elucidating the molecular activation mechanism of FFAR1 and provide insights into the design and discovery of novel allosteric agonists of FFAR1 for the treatment of type 2 diabetes mellitus (T2DM).

Computational Insights into Molecular Activation and Positive Cooperative Mechanisms of FFAR1 Modulators.

The traditional Chinese medicines (TCMs) have been used to treat diseases over a long history, but it is still a great challenge to uncover the underlying mechanisms for their therapeutic effects due to the complexity of their ingredients. Based on a novel network pharmacology-based approach, we explored in this study the potential therapeutic targets of Liuwei Dihuang (LWDH) decoction in its neuroendocrine immunomodulation (NIM) function. We not only collected the known targets of the compounds in LWDH but also predicted the targets for these compounds using the balanced substructure-drug-target network-based inference (bSDTNBI), which is a target prediction method based on network inferring developed by our laboratory. A "target-(pathway)-target" (TPT) network, in which targets of LWDH were connected by relevant pathways, was constructed and divided into several separate modules with strong internal connections. Then the target module that contributes the most to NIM function was determined through a contribution scoring algorithm. Finally, the targets with the highest contribution score to NIM-related diseases in this target module were recommended as potential therapeutic targets of LWDH.

Insights into the Molecular Mechanisms of Liuwei Dihuang Decoction via Network Pharmacology.

The neuroprotective effects of (-)-S.R-daurisoline on glutamate-induced neurotoxicity were studied in rat primary cortical cultures. The inhibitory effects of (-)-S.R-daurisoline on glutamate-elicited free intracellular Ca2+ increase were also studied in freshly dissociated single brain cells isolated from new born rat using AR-CM-MIC Cation Measurement System. Our experimental results demonstrated that (-)-S.R-daurisoline could obviously inhibit the neurotoxicity induced by glutamate and significantly increased cell viability in dose-dependent manner. In inhibiting glutamate-induced neurotoxicity, the IC50 value of (-)-S.R-daurisoline was found to be 3.4 mumol.L-1. (-)-S.R-daurisoline was also shown to markedly inhibit glutamate-elicited increase of cytosolic free Ca2+ concentration in dose-dependent manner with IC50 value of 2.0 mumol.L-1. Our results showed that (-)-S.R-daurisoline has an obvious protective effect on glutamate-induced neurotoxicity in primary cortical cultures. The protective mechanism of (-)-S.R-daurisoline may be relevant to inhibit Ca2+ influx into cells via glutamate-mediated ligand-gated ion channels.

[Protective effects of (-)-S.R-daurisoline on neuronal injury in rat primary cortical cultures].

Estrogen-related receptor α (ERRα), a member of nuclear receptors (NRs), plays a role in the regulation of cellular energy metabolism and is reported to be a novel potential target for type 2 diabetes therapy. To date, only a few agonists of ERRα have been identified to improve insulin sensitivity and decrease blood glucose levels. Herein, the discovery of novel potent agonists of ERRα determined using a combined virtual screening approach is described. Molecular dynamics (MD) simulations were used to obtain structural ensembles that can consider receptor flexibility. Then, an efficient virtual screening strategy with a combination of similarity search and ensemble docking was performed against the Enamine, SPECS, and Drugbank databases to identify potent ERRα agonists. Finally, a total of 66 compounds were purchased for experimental testing. Biological investigation of promising candidates identified seven compounds that have activity against ERRα with EC50 values ranging from 1.11 to 21.70 μM, with novel scaffolds different from known ERRα agonists until now. Additionally, the molecule GX66 showed micromolar inverse activity against ERRα with an IC50 of 0.82 μM. The predicted binding modes showed that these compounds were anchored in ERRα-LBP via interactions with several residues of ERRα. Overall, this study not only identified the novel potent ERRα agonists or an inverse agonist that would be the promising starting point for further exploration but also demonstrated a successful molecular dynamics-guided approach applicable in virtual screening for ERRα agonists.

Guixia Liu

Papers

Computational Insights into Molecular Activation and Positive Cooperative Mechanisms of FFAR1 Modulators.

Insights into the Molecular Mechanisms of Liuwei Dihuang Decoction via Network Pharmacology.

[Protective effects of (-)-S.R-daurisoline on neuronal injury in rat primary cortical cultures].

Discovery of New Estrogen-Related Receptor α Agonists via a Combination Strategy Based on Shape Screening and Ensemble Docking

Ru-Catalyzed Site-Selective Aliphatic C–H Bond Silylation of Amides and Carbamides