Comprehensive epigenomic profiling reveals the extent of disease-specific chromatin states and informs target discovery in ankylosing spondylitis.

Ankylosing spondylitis (AS) is a common, highly heritable inflammatory arthritis characterized by enthesitis of the spine and sacroiliac joints. Genome-wide association studies (GWASs) have revealed more than 100 genetic associations whose functional effects remain largely unresolved. Here, we present a comprehensive transcriptomic and epigenomic map of disease-relevant blood immune cell subsets from AS patients and healthy controls. We find that, while CD14+ monocytes and CD4+ and CD8+ T cells show disease-specific differences at the RNA level, epigenomic differences are only apparent upon multi-omics integration. The latter reveals enrichment at disease-associated loci in monocytes. We link putative functional SNPs to genes using high-resolution Capture-C at 10 loci, including PTGER4 and ETS1, and show how disease-specific functional genomic data can be integrated with GWASs to enhance therapeutic target discovery. This study combines epigenetic and transcriptional analysis with GWASs to identify disease-relevant cell types and gene regulation of likely pathogenic relevance and prioritize drug targets.

Design and synthesis of a library of chemokine antagonists.

A library of chemokine antagonists has been synthesized using a combination of solid and solution-phase chemistry. Structures of known chemokine antagonists were used to produce a pharmacophore which served to guide monomer selection. Several combinations of monomers have resulted in providing novel chemokine antagonists which in some cases display dual chemokine receptor antagonism.

Optimizing the performance of in silico ADMET general models according to local requirements: MARS approach. solubility estimations as case study.

The quality of in vitro data used to build in silico absorption, distribution, metabolism, and toxicity (ADMET) models is, in many cases, inconsistent. The paucity of data from single laboratory sources has led to the mixing of data sets with varying experimental conditions and to the coverage of restricted chemical space in models which are purported to be of general applicability. In order to overcome these shortcomings, a method, Metropolis/Monte Carlo adaptive ranking simulation (MARS) has been developed. This aims to estimate "optimal flexible threshold points" in order to achieve better correlation between any in silico ADMET model and any discrete qualitative experimental data. The MARS method covers three key factors: the predictive model, the experimental procedure for the assay, and the chemical series or scaffold. When large and general solubility data sets (>650 compounds) are analyzed against commercially available in silico models, using MARS, an improvement in kappa statistics up to 16.2% is obtained. When particular chemical series are addressed, improvements up to 46.0% are seen on kappa statistics. This coefficient then allows an investigation into the effectiveness of a classifier by assessing the improvement over chance. These improvements in ranking estimations allow more predictive decision-making for virtual libraries.

Donated chemical probes for open science.

Potent, selective and broadly characterized small molecule modulators of protein function (chemical probes) are powerful research reagents. The pharmaceutical industry has generated many high-quality chemical probes and several of these have been made available to academia. However, probe-associated data and control compounds, such as inactive structurally related molecules and their associated data, are generally not accessible. The lack of data and guidance makes it difficult for researchers to decide which chemical tools to choose. Several pharmaceutical companies (AbbVie, Bayer, Boehringer Ingelheim, Janssen, MSD, Pfizer, and Takeda) have therefore entered into a pre-competitive collaboration to make available a large number of innovative high-quality probes, including all probe-associated data, control compounds and recommendations on use (https://openscienceprobes.sgc-frankfurt.de/). Here we describe the chemical tools and target-related knowledge that have been made available, and encourage others to join the project.

Data reduction and representation in drug discovery.

Pre-clinical drug discovery relies increasingly on huge volumes of inter-related multivariate data. To make sense of these data and enable quality decision-making based on this plethora of information they must be presented in an interpretable form. Reducing the dimensionality of the data often leaves a data set that is too complex to interpret readily, so intuitive visualization methods are needed. Bioinformatics has provided much of the impetus for visualizing complex data, the cheminformatics community has been aggressive with the data-reduction problem. The increasing appreciation of the inter-related multifactorial nature of pre-clinical drug discovery makes visualization a burgeoning and active field that spans biosciences, mathematics and visual psychology.

MetaSite: understanding metabolism in human cytochromes from the perspective of the chemist.

Identification of metabolic biotransformations can significantly affect the drug discovery process. Since bioavailability, activity, toxicity, distribution, and final elimination all depend on metabolic biotransformations, it would be extremely advantageous if this information could be produced early in the discovery phase. Once obtained, this information can help chemists to judge whether a potential candidate should be eliminated from the pipeline or modified to improve chemical stability or safety of new compounds. The use of in silico methods to predict the site of metabolism in phase I cytochrome-mediated reactions is a starting point in any metabolic pathway prediction. This paper presents a new method, specifically designed for chemists, that provides the cytochrome involved and the site of metabolism for any human cytochrome P450 (CYP) mediated reaction acting on new substrates. The methodology can be applied automatically to all the cytochromes for which 3D structure is known and can be used by chemists to detect positions that should be protected in order to avoid metabolic degradation or to check the suitability of a new scaffold or prodrug. The fully automated procedure is also a valuable new tool in early ADME-Tox assays (absorption, distribution, metabolism, and excretion toxicity assays), where drug safety and metabolic profile patterns must be evaluated as soon, and as early, as possible.

CGH2466, a combined adenosine receptor antagonist, p38 mitogen-activated protein kinase and phosphodiesterase type 4 inhibitor with potent in vitro and in vivo anti-inflammatory activities.

Theophylline, a phosphodiesterase inhibitor and adenosine receptor antagonist, is used in asthma and chronic obstructive pulmonary disease (COPD) treatment. However, the relatively low effectiveness of theophylline have recently led to reduced usage. The goal of the present study was to identify a theophylline-like compound with improved effectiveness. We discovered CGH2466, which not only antagonised the adenosine A1, A2b and A3 receptors with IC50 values of 19 +/- 4, 21 +/- 3 and 80 +/- 14 nM, respectively, but also inhibited the p38 mitogen-activated protein (MAP) kinases alpha and beta and the phosphodiesterase 4D (PDE4D) isoenzyme with IC50 values of 187 +/- 18, 400 +/- 38 and 22 +/- 5 nM, respectively. Despite similar potencies on individual targets, CGH2466 inhibited the production of cytokines and oxygen radicals by human peripheral blood leucocytes in vitro, more potently (IC50 values between 30 and 50 nM) than the standard p38 MAP kinase inhibitor SB203580 (30 nM to >1 microM), the PDE4 inhibitor cilomilast (120-400 nM) and the broad spectrum adenosine receptor antagonist CGS15943 (>10 microM). When given either orally or locally into the lungs, CGH2466 (3 to 10 mg kg(-1)) inhibited the ovalbumin- or lipopolysaccharide-induced airway inflammation in mice more potently than the single receptor antagonists or enzyme inhibitors used alone. In conclusion, CGH2466 through its combined activities at multiple targets exerted a powerful anti-inflammatory effect and therefore may have beneficial therapeutic value in diseases such as asthma and COPD.

New highly potent and selective adenosine A(3) receptor antagonists.

A class of potent, selective adenosine A(3) receptor antagonists was obtained via optimisation of the screening hit N-[4-(4-methoxyphenyl)-thiazol-2-yl]-acetamide. Structural modifications of this hit revealed very quickly that a 5-(pyridin-4-yl) substituent on the 2-aminothiazole ring was optimal for high potency at the adenosine A(3) receptor. Structure activity relationship studies led to both potent and selective A(3) receptor antagonists, including N-[5-pyridin-4-yl-4-(3,4,5-trimethoxyphenyl)-thiazol-2-yl]-acetamide, a highly potent aden-osine A(3) receptor antagonist with greater than 100- fold selectivity against the related adenosine receptors. As well as demonstrating selective in vitro binding on the human A(3) adenosine receptor, this compound was also shown to selectively block the rat A(3) receptor in vivo. This important new compound can be readily synthesised in four steps from commercially available starting materials.

A comparison of ligand based virtual screening methods and application to corticotropin releasing factor 1 receptor.

Ligand based virtual screening approaches were applied to the CRF1 receptor. We compared ECFP6 fingerprints, FTrees, Topomers, Cresset FieldScreen, ROCS OpenEye shape Tanimoto, OpenEye combo-score and OpenEye electrostatics. The 3D methods OpenEye Shape Tanimoto, combo-score and Topomers performed the best at separating actives from inactives in retrospective experiments. By virtue of their higher enrichment the same methods identified more active scaffolds. However, amongst a given number of active compounds the Cresset and OpenEye electrostatic methods contained more scaffolds and returned ranked compounds with greater diversity. A selection of the methods were employed to recommend compounds for screening in a prospective experiment. New CRF1 actives antagonists were found. The new actives contained different underlying chemical architecture to the query molecules, results indicative of successful scaffold-hopping.

Novel approach for chemotype hopping based on annotated databases of chemically feasible fragments and a prospective case study: new melanin concentrating hormone antagonists.

A novel strategy for chemotype hopping, based on annotated databases of chemically feasible fragments and their oriented functionalization, is presented. A three-dimensional (3D) similarity analysis of project-oriented functionalized scaffolds provides a prioritized proposal for synthesis with the most appropriate linkers and optimal regiochemistry on R-groups. This strategy maximizes the potential of proprietary and commercially available compounds. A retrospective and prospective case study, on melanin concentrating hormone (MCH) antagonists, showing the impact on the drug discovery process of this new strategy by maintaining primary activity and improving key ADME/Tox property while enhancing intellectual property (IP) position is demonstrated.

Assessment of additive/nonadditive effects in structure-activity relationships: implications for iterative drug design.

Free-Wilson (FW) analysis is common practice in medicinal chemistry and is based on the assumption that the contributions to activity made by substituents at different substitution positions are additive. We analyze eight near complete combinatorial libraries assayed on several different biological response(s) (GPCR, ion channel, kinase and P450 targets) and show that only half-exhibit clear additive behavior, which leads us to question the concept of additivity that is widely taken for granted in drug discovery. Next, we report a series of retrospective experiments in which subsets are extracted from the libraries for FW analysis to determine the minimum attributes (size, distribution of substituents, and activity range) necessary to reach the same conclusion about additive/nonadditive effects. These attributes can provide guidelines on when it is appropriate to apply FW analysis as well as for library design, and they also have important implications for further steps in iterative drug design.

Application of non-parametric regression to quantitative structure-activity relationships.

Several non-parametric regressors have been applied to modelling quantitative structure-activity relationship (QSAR) data. Performances were benchmarked against multilinear regression and the nonlinear method of smoothing splines. Variable selection was explored through systematic combinations of different variables and combinations of principal components. For the training set examined--539 inhibitors of the tyrosine kinase, Syk--the best two-descriptor model had a 5-fold cross-validated q2 of 0.43. This was generated by a multi-variate Nadaraya-Watson kernel estimator. A subsequent, independent, test set of 371 similar chemical entities showed the model had some predictive power. Other approaches did not perform as well. A modest increase in predictive ability can be achieved with three descriptors, but the resulting model is less easy to visualise. We conclude that non-parametric regression offers a potentially powerful approach to identifying predictive, low-dimensional QSARs.

8-Aryl xanthines potent inhibitors of phosphodiesterase 5.

In clinical studies, several inhibitors of phosphodiesterase 5 (PDE5) have demonstrated utility in the treatment of erectile dysfunction. We describe herein a series of 8-aryl xanthine derivatives which function as potent PDE5 inhibitors with, in many cases, high levels of selectivity versus other PDE isoforms.