Amelioration of PXR-mediated CYP3A4 induction by mGluR2 modulators.

This work describes the rational amelioration of Cytochrome P450 4/5 (CYP3A4/5) induction through the Pregnane-X Receptor (PXR) pathway in a series of compounds that modulate the metabotropic glutamate Receptor 2 (mGluR2) via an allosteric mechanism. The compounds were initially shown to induce CYP3A4/5 via the gold-standard induction assay measured in primary human hepatocytes. This was followed up by testing the compounds in a PXR assay which correlated well with the assay in primary cells. Further, one of the compounds was crystallized with PXR (pdb code 6DUP). Analysis of this co-crystal structure, together with previously published PXR co-crystal structures, lead to modification ideas. The compounds synthesized based on these ideas were shown not to be CYP3A4/5 inducers. The mGluR2 activity of the resulting compounds was maintained.

Dependence on the Pyrimidine Biosynthetic Enzyme DHODH Is a Synthetic Lethal Vulnerability in Mutant KRAS-Driven Cancers.

Activating KRAS mutations are major oncogenic drivers in multiple tumor types. Synthetic lethal screens have previously been used to identify targets critical for the survival of KRAS mutant cells, but their application to drug discovery has proven challenging, possibly due in part to a failure of monolayer cultures to model tumor biology. Here, we report the results of a high-throughput synthetic lethal screen for small molecules that selectively inhibit the growth of KRAS mutant cell lines in soft agar. Chemoproteomic profiling identifies the target of the most KRAS-selective chemical series as dihydroorotate dehydrogenase (DHODH). DHODH inhibition is shown to perturb multiple metabolic pathways. In vivo preclinical studies demonstrate strong antitumor activity upon DHODH inhibition in a pancreatic tumor xenograft model.

A NMDA-receptor calcium influx assay sensitive to stimulation by glutamate and glycine/D-serine.

N-methyl-D-aspartate-receptors (NMDARs) are ionotropic glutamate receptors that function in synaptic transmission, plasticity and cognition. Malfunction of NMDARs has been implicated in a variety of nervous system disorders, making them attractive therapeutic targets. Overexpression of functional NMDAR in non-neuronal cells results in cell death by excitotoxicity, hindering the development of cell-based assays for NMDAR drug discovery. Here we report a plate-based, high-throughput approach to study NMDAR function. Our assay enables the functional study of NMDARs with different subunit composition after activation by glycine/D-serine or glutamate and hence presents the first plate-based, high throughput assay that allows for the measurement of NMDAR function in glycine/D-serine and/or glutamate sensitive modes. This allows to investigate the effect of small molecule modulators on the activation of NMDARs at different concentrations or combinations of the co-ligands. The reported assay system faithfully replicates the pharmacology of the receptor in response to known agonists, antagonists, positive and negative allosteric modulators, as well as the receptor's sensitivity to magnesium and zinc. We believe that the ability to study the biology of NMDARs rapidly and in large scale screens will enable the identification of novel therapeutics whose discovery has otherwise been hindered by the limitations of existing cell based approaches.

Screenit: Visual Analysis of Cellular Screens.

High-throughput and high-content screening enables large scale, cost-effective experiments in which cell cultures are exposed to a wide spectrum of drugs. The resulting multivariate data sets have a large but shallow hierarchical structure. The deepest level of this structure describes cells in terms of numeric features that are derived from image data. The subsequent level describes enveloping cell cultures in terms of imposed experiment conditions (exposure to drugs). We present Screenit, a visual analysis approach designed in close collaboration with screening experts. Screenit enables the navigation and analysis of multivariate data at multiple hierarchy levels and at multiple levels of detail. Screenit integrates the interactive modeling of cell physical states (phenotypes) and the effects of drugs on cell cultures (hits). In addition, quality control is enabled via the detection of anomalies that indicate low-quality data, while providing an interface that is designed to match workflows of screening experts. We demonstrate analyses for a real-world data set, CellMorph, with 6 million cells across 20,000 cell cultures.

Identification of optimum computational protocols for modeling the aryl hydrocarbon receptor (AHR) and its interaction with ligands.

The aryl hydrocarbon receptor (AHR) is one of the principal xenobiotic receptors in living organisms and is responsible for interacting with several drugs and environmental toxins, most notably tetrachlorodibenzodioxin (TCDD). Binding of diverse agonists to AHR initiates an extensive set of downstream gene expression responses and thus identifies AHR among a key set of proteins responsible for mediating interactions between living organisms and foreign molecules. While extensive biochemical investigations on the interaction of AHR with ligands have been carried out, studies comparing the abilities of specific computational algorithms in explaining the potency of known AHR ligands are lacking. In this study we use molecular dynamics simulations to identify a physically realistic conformation of the AHR that is relevant to ligand binding. We then use two sets of existing data on known AHR ligands to evaluate the performance of several docking and scoring protocols in rationalizing the potencies of these ligands. The results identify an optimum set of protocols that could prove useful in future AHR ligand discovery and design as a target or anti-target. Exploration of the details of these protocols sheds light on factors operating in modeling AHR ligand binding.

The challenges of in silico contributions to drug metabolism in lead optimization.

IMPORTANCE OF THE FIELD: The site of metabolism (SOM) predictions by CYP 3A4 are extremely important during the drug discovery process especially during the lead discovery or library design phases. With the ability to rapidly characterize metabolites from these enzymes, the challenges facing in silico contribution change during the drug optimization phase. Some of the challenges are addressed in this article. Some aspects of the SOM prediction software and methodology are discussed in this opinion article and examples of software utility in overcoming metabolic instability in drug optimization are shown. AREAS COVERED IN THIS REVIEW: SOM prediction by various approaches is discussed. Two ways of overcoming metabolic instability, blocking the metabolic softspots and rational modification of the instable molecule to avoid interaction with the CYP pocket, are discussed. The contribution plot in MetaSite and its use are discussed. WHAT THE READER WILL GAIN: The reader will gain an understanding of possible approaches to either blocking the metabolic softspot or rationally modifying the molecule using MetaSite software or docking approaches. Blocking metabolism using fluorination has risks especially introducing multifluorinated benzene rings in the molecule. TAKE HOME MESSAGE: During the lead optimization phase of drug discovery, when metabolic instability is an issue in a series, in silico approaches can be used to modify the molecule in order to decrease clearance due to metabolism, even that due to CYP3A4.

A novel computational method for comparing vibrational circular dichroism spectra.

A novel method, SimIR/VCD, for comparing experimental and calculated VCD (vibrational circular dichroism) spectra is developed, based on newly defined spectra similarities. With computationally optimized frequency scaling and shifting, a calculated spectrum can be easily identified to match an observed spectrum, which leads to an unbiased molecular chirality assignment. The time-consuming manual band-fitting work is greatly reduced. With (1S)-(-)-alpha-pinene as an example, it demonstrates that the calculated VCD similarity is correlated with VCD spectra matching quality and has enough sensitivity to identify variations in the spectra. The study also compares spectra calculated using different DFT methods and basis sets. Using this method should facilitate the spectra matching, reduce human error and provide a confidence measure in the chiral assignment using VCD spectroscopy.

On the mechanism of microsomal prostaglandin E synthase type-2--a theoretical study of endoperoxide reaction with MeS(-).

The reaction pathways of deprotonation versus nucleophilic substitution involving mPGES-2 enzyme catalysis were investigated by ab initio molecular orbital theory calculations for the reaction of methylthiolate with the endoperoxide core of PGH(2) and by the combined quantum mechanical molecular mechanical methods. The calculations showed that deprotonation mechanism is energetically more favorable than the nucleophilic substitution pathway.

Discovery of covalent inhibitors for MIF tautomerase via cocrystal structures with phantom hits from virtual screening.

Biochemical and X-ray crystallographic studies confirmed that hydroxyquinoline derivatives identified by virtual screening were actually covalent inhibitors of the MIF tautomerase. Adducts were formed by N-alkylation of the Pro-1 at the catalytic site with a loss of an amino group of the inhibitor.

Prediction of the 3D structure and dynamics of human DP G-protein coupled receptor bound to an agonist and an antagonist.

Prostanoids play important physiological roles in the cardiovascular and immune systems and in pain sensation in peripheral systems through their interactions with eight G-protein coupled receptors. These receptors are important drug targets, but development of subtype specific agonists and antagonists has been hampered by the lack of 3D structures for these receptors. We report here the 3D structure for the human DP G-protein coupled receptor (GPCR) predicted by the MembStruk computational method. To validate this structure, we use the HierDock computational method to predict the binding mode for the endogenous agonist (PGD2) to DP. Based on our structure, we predicted the binding of different antagonists and optimized them. We find that PGD2 binds vertically to DP in the TM1237 region with the alpha chain toward the extracellular (EC) region and the omega chain toward the middle of the membrane. This structure explains the selectivity of the DP receptor and the residues involved in the predicted binding site correlate very well with available mutation experiments on DP, IP, TP, FP, and EP subtypes. We report molecular dynamics of DP in explicit lipid and water and find that the binding of the PGD2 agonist leads to correlated rotations of helices of TM3 and TM7, whereas binding of antagonist leads to no such rotations. Thus, these motions may be related to the mechanism of activation.

Similarity searching of chemical databases using atom environment descriptors (MOLPRINT 2D): evaluation of performance.

A molecular similarity searching technique based on atom environments, information-gain-based feature selection, and the naive Bayesian classifier has been applied to a series of diverse datasets and its performance compared to those of alternative searching methods. Atom environments are count vectors of heavy atoms present at a topological distance from each heavy atom of a molecular structure. In this application, using a recently published dataset of more than 100000 molecules from the MDL Drug Data Report database, the atom environment approach appears to outperform fusion of ranking scores as well as binary kernel discrimination, which are both used in combination with Unity fingerprints. Overall retrieval rates among the top 5% of the sorted library are nearly 10% better (more than 14% better in relative numbers) than those of the second best method, Unity fingerprints and binary kernel discrimination. In 10 out of 11 sets of active compounds the combination of atom environments and the naive Bayesian classifier appears to be the superior method, while in the remaining dataset, data fusion and binary kernel discrimination in combination with Unity fingerprints is the method of choice. Binary kernel discrimination in combination with Unity fingerprints generally comes second in performance overall. The difference in performance can largely be attributed to the different molecular descriptors used. Atom environments outperform Unity fingerprints by a large margin if the combination of these descriptors with the Tanimoto coefficient is compared. The naive Bayesian classifier in combination with information-gain-based feature selection and selection of a sensible number of features performs about as well as binary kernel discrimination in experiments where these classification methods are compared. When used on a monoaminooxidase dataset, atom environments and the naive Bayesian classifier perform as well as binary kernel discrimination in the case of a 50/50 split of training and test compounds. In the case of sparse training data, binary kernel discrimination is found to be superior on this particular dataset. On a third dataset, the atom environment descriptor shows higher retrieval rates than other 2D fingerprints tested here when used in combination with the Tanimoto similarity coefficient. Feature selection is shown to be a crucial step in determining the performance of the algorithm. The representation of molecules by atom environments is found to be more effective than Unity fingerprints for the type of biological receptor similarity calculations examined here. Combining information prior to scoring and including information about inactive compounds, as in the Bayesian classifier and binary kernel discrimination, is found to be superior to posterior data fusion (in the datasets tested here).

Molecular similarity searching using atom environments, information-based feature selection, and a naïve Bayesian classifier.

A novel technique for similarity searching is introduced. Molecules are represented by atom environments, which are fed into an information-gain-based feature selection. A naïve Bayesian classifier is then employed for compound classification. The new method is tested by its ability to retrieve five sets of active molecules seeded in the MDL Drug Data Report (MDDR). In comparison experiments, the algorithm outperforms all current retrieval methods assessed here using two- and three-dimensional descriptors and offers insight into the significance of structural components for binding.