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Enhanced/prolonged cAMP signalling has been suggested as a suppressor of cancer proliferation. Interestingly, two key modulators that elevate cAMP, the A2A receptor (A2AR) and phosphodiesterase 10A (PDE10A), are differentially co-expressed in various types of non-small lung cancer (NSCLC) cell-lines. Thus, finding dual-target compounds, which are simultaneously agonists at the A2AR whilst also inhibiting PDE10A, could be a novel anti-proliferative approach. Using ligand- and structure-based modelling combined with MD simulations (which identified Val84 displacement as a novel conformational descriptor of A2AR activation), a series of known PDE10A inhibitors were shown to dock to the orthosteric site of the A2AR. Subsequent in-vitro analysis confirmed that these compounds bind to the A2AR and exhibit dual-activity at both the A2AR and PDE10A. Furthermore, many of the compounds exhibited promising anti-proliferative effects upon NSCLC cell-lines, which directly correlated with the expression of both PDE10A and the A2AR. Thus, we propose a structure-based methodology, which has been validated in in-vitro binding and functional assays, and demonstrated a promising therapeutic value.

Obesity is a complex disorder where the genome interacts with diet and environmental factors to ultimately influence body mass, composition and shape. Numerous studies have investigated how bulk lipid metabolism of adipose tissue changes with obesity, and in particular how the composition of triglycerides (TGs) changes with increased adipocyte expansion. However, reflecting the analytical challenge posed by examining non-TG lipids in extracts dominated by TGs, the glycerophospholipid (PL) composition of cell membranes has been seldom investigated. PLs contribute to a variety of cellular processes including maintaining organelle functionality, providing an optimised environment for membrane-associated proteins and as pools for metabolites (e.g. choline for one-carbon metabolism and for methylation of DNA). We have conducted a comprehensive lipidomic study of white adipose tissue in mice who become obese either through genetic modification (ob/ob), diet (high fat diet) or a combination of the two using both solid phase extraction and ion mobility to increase coverage of the lipidome. Composition changes in seven classes of lipid (free fatty acids, diglycerides, TGs, phosphatidylcholines, lyso-phosphatidylcholines, phosphatidylethanolamines, and phosphatidylserines) correlated with perturbations in one-carbon metabolism and transcriptional changes in adipose tissue. We demonstrate that changes in TGs that dominate the overall lipid composition of white adipose tissue are distinct from diet-induced alterations of PLs, the predominant components of the cell membranes. PLs correlate better with transcriptional and one-carbon metabolism changes within the cell, suggesting the compositional changes that occur in cell membranes during adipocyte expansion have far-reaching functional consequences. Data is available at MetaboLights under the submission number: MTBLS1775.

Summary Oncogenic transformation is associated with profound changes in cellular metabolism, but whether tracking these can improve disease stratification or influence therapy decision-making is largely unknown. Using the iKnife to sample the aerosol of cauterized specimens, we demonstrate a new mode of real-time diagnosis, coupling metabolic phenotype to mutant PIK3CA genotype. Oncogenic PIK3CA results in an increase in arachidonic acid and a concomitant overproduction of eicosanoids, acting to promote cell proliferation beyond a cell-autonomous manner. Mechanistically, mutant PIK3CA drives a multimodal signaling network involving mTORC2-PKCζ-mediated activation of the calcium-dependent phospholipase A2 (cPLA2). Notably, inhibiting cPLA2 synergizes with fatty acid-free diet to restore immunogenicity and selectively reduce mutant PIK3CA-induced tumorigenicity. Besides highlighting the potential for metabolic phenotyping in stratified medicine, this study reveals an important role for activated PI3K signaling in regulating arachidonic acid metabolism, uncovering a targetable metabolic vulnerability that largely depends on dietary fat restriction. Video Abstract, Graphical Abstract, Highlights • The iKnife offers near real-time diagnosis of PIK3CA mutant breast cancers • Oncogenic PIK3CA promotes enhanced arachidonic acid via mTORC2-PKCζ-cPLA2 signaling • Mutant PIK3CA regulates proliferation beyond a cell autonomous manner • cPLA2 inhibition and dietary fat restriction suppress PIK3CA-induced tumorigenicity, Metabolic fingerprinting using the iKnife offers near real-time diagnosis of PIK3CA mutant breast cancers and connects oncogenic PIK3CA with enhanced arachidonic acid metabolism. cPLA2 inhibition shows remarkable synergy with dietary fat restriction to restore tumoral immune cell infiltration and inhibit growth of mutant PIK3CA-bearing breast tumors.

The apelin receptor is a potential target in the treatment of heart failure and pulmonary arterial hypertension where levels of endogenous apelin peptides are reduced but significant receptor levels remain. Our aim was to characterise the pharmacology of a modified peptide agonist, MM202, designed to have high affinity for the apelin receptor and resistance to peptidase degradation and linked to an anti-serum albumin domain antibody (AlbudAb) to extend half-life in the blood. In competition, binding experiments in human heart MM202-AlbudAb (pKi = 9.39 ± 0.09) bound with similar high affinity as the endogenous peptides [Pyr1 ]apelin-13 (pKi = 8.83 ± 0.06) and apelin-17 (pKi = 9.57 ± 0.08). [Pyr1 ]apelin-13 was tenfold more potent in the cAMP (pD2 = 9.52 ± 0.05) compared to the β-arrestin (pD2 = 8.53 ± 0.03) assay, whereas apelin-17 (pD2 = 10.31 ± 0.28; pD2 = 10.15 ± 0.13, respectively) and MM202-AlbudAb (pD2 = 9.15 ± 0.12; pD2 = 9.26 ± 0.03, respectively) were equipotent in both assays, with MM202-AlbudAb tenfold less potent than apelin-17. MM202-AlbudAb bound to immobilised human serum albumin with high affinity (pKD = 9.02). In anaesthetised, male Sprague Dawley rats, MM202-AlbudAb (5 nmol, n = 15) significantly reduced left ventricular systolic pressure by 6.61 ± 1.46 mm Hg and systolic arterial pressure by 14.12 ± 3.35 mm Hg and significantly increased cardiac contractility by 533 ± 170 mm Hg/s, cardiac output by 1277 ± 190 RVU/min, stroke volume by 3.09 ± 0.47 RVU and heart rate by 4.64 ± 2.24 bpm. This study demonstrates that conjugating an apelin mimetic peptide to the AlbudAb structure retains receptor and in vivo activity and may be a new strategy for development of apelin peptides as therapeutic agents.

The predicted protein encoded by the APJ gene discovered in 1993 was originally classified as a class A G protein coupled orphan receptor but was subseqently paired with a novel peptide ligand, apelin-36 in 1998. Substantial research identified a family of shorter peptides activating the apelin receptor including apelin -17, apelin-13 and [Pyr1]apelin-13, with the latter peptide predominating in human plasma and cardiovascular system. A range of pharmacological tools have been developed including radiolabelled ligands, analogues with improved plasma stability, peptides and small molecules including biased agonists and antagonists, leading to the recommendation that the APJ gene be renamed APLNR and encodes the apelin receptor protein. Recently, a second endogenous ligand has been identified and called Elabela/Toddler, a 54 amino acid peptide originally identified in the genomes of fish and humans but misclassified as non-coding. This precursor is also able to be cleaved to shorter sequences (32, 21 and 11 amino acids), and all are able to activate the apelin receptor and are blocked by apelin receptor antagonists. This review summarises the pharmacology of these ligands and the apelin receptor, highlights the emerging physiological and pathophysiological roles in a number of diseases and recommends that Elabela/Toddler is a second endogenous peptide ligand of the apelin receptor protein., British Heart Foundation [TAF 03, ; FS/14/59/31282, CR, FS/17/61/33473]; Medical Research Council [MC_PC_14116]; Wellcome Trust [WT107715/Z/15/Z ], Wellcome Trust Programme in Metabolic and Cardiovascular Disease [096822/Z/11/Z , 203814/Z/16/A]; CRUK [C33616/A27229 ], Cambridge Biomedical Research Centre Biomedical Resources Grant [RG85315], University of Cambridge.

International audience; Background Metabolomics is the comprehensive study of a multitude of small molecules to gain insight into an organism's metabolism. The research field is dynamic and expanding with applications across biomedical, biotechnological, and many other applied biological domains. Its computationally intensive nature has driven requirements for open data formats, data repositories, and data analysis tools. However, the rapid progress has resulted in a mosaic of independent, and sometimes incompatible, analysis methods that are difficult to connect into a useful and complete data analysis solution. Findings PhenoMeNal (Phenome and Metabolome aNalysis) is an advanced and complete solution to set up Infrastructure-as-a-Service (IaaS) that brings workflow-oriented, interoperable metabolomics data analysis platforms into the cloud. PhenoMeNal seamlessly integrates a wide array of existing open-source tools that are tested and packaged as Docker containers through the project's continuous integration process and deployed based on a kubernetes orchestration framework. It also provides a number of standardized, automated, and published analysis workflows in the user interfaces Galaxy, Jupyter, Luigi, and Pachyderm. Conclusions PhenoMeNal constitutes a keystone solution in cloud e-infrastructures available for metabolomics. PhenoMeNal is a unique and complete solution for setting up cloud e-infrastructures through easy-to-use web interfaces that can be scaled to any custom public and private cloud environment. By harmonizing and automating software installation and configuration and through ready-to-use scientific workflow user interfaces, PhenoMeNal has succeeded in providing scientists with workflow-driven, reproducible, and shareable metabolomics data analysis platforms that are interfaced through standard data formats, representative datasets, versioned, and have been tested for reproducibility and interoperability. The elastic implementation of PhenoMeNal further allows easy adaptation of the infrastructure to other application areas and omics research domains.

Selective modulators of the γ-amino butyric acid (GABAA) family of receptors have the potential to treat a range of disease states related to cognition, pain, and anxiety. While the development of various α subunit-selective modulators is currently underway for the treatment of anxiety disorders, a mechanistic understanding of the correlation between their bioactivity and efficacy, based on ligand-target interactions, is currently still lacking. In order to alleviate this situation, in the current study we have analyzed, using ligand- and structure-based methods, a data set of 5440 GABAA modulators. The Spearman correlation (ρ) between binding activity and efficacy of compounds was calculated to be 0.008 and 0.31 against the α1 and α2 subunits of GABA receptor, respectively; in other words, the compounds had little diversity in structure and bioactivity, but they differed significantly in efficacy. Two compounds were selected as a case study for detailed interaction analysis due to the small difference in their structures and affinities (ΔpKi(comp1_α1 - comp2_α1) = 0.45 log units, ΔpKi(comp1_α2 - comp2_α2) = 0 log units) as compared to larger relative efficacies (ΔRE(comp1_α1 - comp2_α1) = 1.03, ΔRE(comp1_α2 - comp2_α2) = 0.21). Docking analysis suggested that His-101 is involved in a characteristic interaction of the α1 receptor with both compounds 1 and 2. Residues such as Phe-77, Thr-142, Asn-60, and Arg-144 of the γ chain of the α1γ2 complex also showed interactions with heterocyclic rings of both compounds 1 and 2, but these interactions were disturbed in the case of α2γ2 complex docking results. Binding pocket stability analysis based on molecular dynamics identified three substitutions in the loop C region of the α2 subunit, namely, G200E, I201T, and V202I, causing a reduction in the flexibility of α2 compared to α1. These amino acids in α2, as compared to α1, were also observed to decrease the vibrational and dihedral entropy and to increase the hydrogen bond content in α2 in the apo state. However, freezing of both α1 and α2 was observed in the ligand-bound state, with an increased number of internal hydrogen bonds and increased entropy. Therefore, we hypothesize that the amino acid differences in the loop C region of α2 are responsible for conformational changes in the protein structure compared to α1, as well as for the binding modes of compounds and hence their functional signaling.

Dimeric calpains constitute a promising therapeutic target for many diseases such as cardiovascular, neurodegenerative and ischaemic disease. The discovery of selective calpain inhibitors, however, has been extremely challenging. Previously, allosteric inhibitors of calpains, such as PD150606, which included a specific α-mercaptoacrylic acid sub-structure, were reported to bind to the penta-EF hand calcium binding domain, PEF(S) of calpain. Although these are selective to calpains over other cysteine proteases, their mode of action has remained elusive due to their ability to inhibit the active site domain with and without the presence of PEF(S), with similar potency. These findings have led to the question of whether the inhibitory response can be attributed to an allosteric mode of action or alternatively to inhibition at the active site. In order to address this problem, we report a structure-based virtual screening protocol as a novel approach for the discovery of PEF(S) binders that populate a novel chemical space. We have identified compound 1, Vidupiprant, which is shown to bind to the PEF(S) domain by the TNS displacement method, and it exhibited specificity in its allosteric mode of inhibition. Compound 1 inhibited the full-length calpain-1 complex with a higher potency (IC50 = 7.5 μM) than the selective, cell-permeable non-peptide calpain inhibitor, PD150606 (IC50 = 19.3 μM), where the latter also inhibited the active site domain in the absence of PEF(S) (IC50 = 17.8 μM). Hence the method presented here has identified known compounds with a novel allosteric mechanism for the inhibition of calpain-1. We show for the first time that the inhibition of enzyme activity can be attributed to an allosteric mode of action, which may offer improved selectivity and a reduced side-effects profile.

BACKGROUND: Gut microbiota composition and function are symbiotically linked with host health and altered in metabolic, inflammatory and neurodegenerative disorders. Three recognised mechanisms exist by which the microbiome influences the gut-brain axis: modification of autonomic/sensorimotor connections, immune activation, and neuroendocrine pathway regulation. We hypothesised interactions between circulating gut-derived microbial metabolites, and the blood-brain barrier (BBB) also contribute to the gut-brain axis. Propionate, produced from dietary substrates by colonic bacteria, stimulates intestinal gluconeogenesis and is associated with reduced stress behaviours, but its potential endocrine role has not been addressed. RESULTS: After demonstrating expression of the propionate receptor FFAR3 on human brain endothelium, we examined the impact of a physiologically relevant propionate concentration (1 μM) on BBB properties in vitro. Propionate inhibited pathways associated with non-specific microbial infections via a CD14-dependent mechanism, suppressed expression of LRP-1 and protected the BBB from oxidative stress via NRF2 (NFE2L2) signalling. CONCLUSIONS: Together, these results suggest gut-derived microbial metabolites interact with the BBB, representing a fourth facet of the gut-brain axis that warrants further attention.

Graphical abstract, Apelin peptide analogues displaying bias towards G protein signalling pathways have beneficial cardiovascular actions compared with the native peptide in humans in vivo. Our aim was to determine whether small molecule agonists could retain G protein bias. We have identified a biased small molecule, CMF-019, and characterised it in vitro and in vivo. In competition radioligand binding experiments in heart homogenates, CMF-019 bound to the human, rat and mouse apelin receptor with high affinity (pKi = 8.58 ± 0.04, 8.49 ± 0.04 and 8.71 ± 0.06 respectively). In cell-based functional assays, whereas, CMF-019 showed similar potency for the Gαi pathway to the endogenous agonist [Pyr1]apelin-13 (pD2 = 10.00 ± 0.13 vs 9.34 ± 0.15), in β-arrestin and internalisation assays it was less potent (pD2 = 6.65 ± 0.15 vs 8.65 ± 0.10 and pD2 = 6.16 ± 0.21 vs 9.28 ± 0.10 respectively). Analysis of these data demonstrated a bias of ∼400 for the Gαi over the β-arrestin pathway and ∼6000 over receptor internalisation. CMF-019 was tested for in vivo activity using intravenous injections into anaesthetised male Sprague–Dawley rats fitted with a pressure-volume catheter in the left ventricle. CMF-019 caused a significant increase in cardiac contractility of 606 ± 112 mmHg/s (p

Compounds designed to display polypharmacology may have utility in treating complex diseases, where activity at multiple targets is required to produce a clinical efect. In particular, suitable compounds may be useful in treating neurodegenerative diseases by promoting neuronal survival in a synergistic manner via their multi-target activity at the adenosine A1 and A2A receptors (A1R and A2AR) and phosphodiesterase 10A (PDE10A), which modulate intracellular cAMP levels. Hence, in this work we describe a computational method for the design of synthetically feasible ligands that bind to A1 and A2A receptors and inhibit phosphodiesterase 10A (PDE10A), involving a retrosynthetic approach employing in silico target prediction and docking, which may be generally applicable to multi-target compound design at several target classes. This approach has identifed 2-aminopyridine-3-carbonitriles as the frst multi-target ligands at A1R, A2AR and PDE10A, by showing agreement between the ligand and structure based predictions at these targets. The series were synthesized via an efcient one-pot scheme and validated pharmacologically as A1R/A2AR–PDE10A ligands, with IC50 values of 2.4–10.0 μM at PDE10A and Ki values of 34–294 nM at A1R and/or A2AR. Furthermore, selectivity profling of the synthesized 2-amino-pyridin-3-carbonitriles against other subtypes of both protein families showed that the multi-target ligand 8 exhibited a minimum of twofold selectivity over all tested oftargets. In addition, both compounds 8 and 16 exhibited the desired multi-target profle, which could be considered for further functional efcacy assessment, analog modifcation for the improvement of selectivity towards A1R, A2AR and PDE10A collectively, and evaluation of their potential synergy in modulating cAMP levels LK thanks the IDB Cambridge International Scholarship for support. This work was fnancially supported by the ERC Starting Grant to AB (No. 336159), the Consellería de Cultura, Educación e Ordenación Universitaria of the Galician Government: (Grant: GPC2014/03), Centro singular de investigación de Galicia accreditation 2016–2019 (ED431G/09) and the European Regional Development Fund (ERDF) SI

Glioblastomas are aggressive brain tumours that contain a subpopulation of highly plastic self-renewing cancer cells. Harford-Wright et al. show that the vasoactive peptide apelin, secreted by brain endothelial cells, regulates glioblastoma patient-derived cells with stem-like properties. Pharmacological blockade of apelin hampers glioblastoma cell expansion and improves survival in xenografted mice., Glioblastoma are highly aggressive brain tumours that are associated with an extremely poor prognosis. Within these tumours exists a subpopulation of highly plastic self-renewing cancer cells that retain the ability to expand ex vivo as tumourspheres, induce tumour growth in mice, and have been implicated in radio- and chemo-resistance. Although their identity and fate are regulated by external cues emanating from endothelial cells, the nature of such signals remains unknown. Here, we used a mass spectrometry proteomic approach to characterize the factors released by brain endothelial cells. We report the identification of the vasoactive peptide apelin as a central regulator for endothelial-mediated maintenance of glioblastoma patient-derived cells with stem-like properties. Genetic and pharmacological targeting of apelin cognate receptor abrogates apelin- and endothelial-mediated expansion of glioblastoma patient-derived cells with stem-like properties in vitro and suppresses tumour growth in vivo. Functionally, selective competitive antagonists of apelin receptor were shown to be safe and effective in reducing tumour expansion and lengthening the survival of intracranially xenografted mice. Therefore, the apelin/apelin receptor signalling nexus may operate as a paracrine signal that sustains tumour cell expansion and progression, suggesting that apelin is a druggable factor in glioblastoma.

Summary Live imaging studies give unparalleled insight into dynamic single cell behaviours and fate decisions. However, the challenge of reliably tracking single cells over long periods of time limits both the throughput and ease with which such studies can be performed. Here, we present NucliTrack, a cross platform solution for automatically segmenting, tracking and extracting features from fluorescently labelled nuclei. NucliTrack performs similarly to other state-of-the-art cell tracking algorithms, but NucliTrack’s interactive, graphical interface makes it significantly more user friendly. Availability and implementation NucliTrack is available as a free, cross platform application and open source Python package. Installation details and documentation are at: http://nuclitrack.readthedocs.io/en/latest/ A video guide can be viewed online: https://www.youtube.com/watch?v=J6e0D9F-qSU Source code is available through Github: https://github.com/samocooper/nuclitrack. A Matlab toolbox is also available at: https://uk.mathworks.com/matlabcentral/fileexchange/61479-samocooper-nuclitrack-matlab. Supplementary information Supplementary data are available at Bioinformatics online.

Aims: Apelin is a predicted substrate for ACE2, a novel therapeutic target. Our aim was to demonstrate the endogenous presence of the putative ACE2 product [Pyr(1)]apelin-13(1-12) in human cardiovascular tissues and to confirm it retains significant biological activity for the apelin receptor in vitro and in vivo. The minimum active apelin fragment was also investigated. Methods and Results: [Pyr(1)]apelin-13 incubated with recombinant human ACE2 resulted in de novo generation of [Pyr(1)]apelin-13(1-12) identified by mass spectrometry. Endogenous [Pyr(1)]apelin-13(1-12) was detected by immunostaining in human heart and lung localized to the endothelium. Expression was undetectable in lung from patients with pulmonary arterial hypertension. In human heart [Pyr(1)]apelin-13(1-12) (pKi = 8.04 ± 0.06) and apelin-13(F13A) (pKi = 8.07 ± 0.24) competed with [(125)I]apelin-13 binding with nanomolar affinity, 4-fold lower than for [Pyr(1)]apelin-13 (pKi = 8.83 ± 0.06) whereas apelin-17 exhibited highest affinity (pKi = 9.63 ± 0.17). The rank order of potency of peptides to inhibit forskolin-stimulated cAMP was apelin-17 (pD2 = 10.31 ± 0.28) > [Pyr(1)]apelin-13 (pD2 = 9.67 ± 0.04) ≥ apelin-13(F13A) (pD2 = 9.54 ± 0.05) > [Pyr(1)]apelin-13(1-12) (pD2 = 9.30 ± 0.06). The truncated peptide apelin-13(R10M) retained nanomolar potency (pD2 = 8.70 ± 0.04) but shorter fragments exhibited low micromolar potency. In a β-arrestin recruitment assay the rank order of potency was apelin-17 (pD2 = 10.26 ± 0.09) > [Pyr(1)]apelin-13 (pD2 = 8.43 ± 0.08) > apelin-13(R10M) (pD2 = 8.26 ± 0.17) > apelin-13(F13A) (pD2 = 7.98 ± 0.04) ≥ [Pyr(1)]apelin-13(1-12) (pD2 = 7.84 ± 0.06) > shorter fragments (pD2 < 6). [Pyr(1)]apelin-13(1-12) and apelin-13(F13A) contracted human saphenous vein with similar sub-nanomolar potencies and [Pyr(1)]apelin-13(1-12) was a potent inotrope in paced mouse right ventricle and human atria. [Pyr(1)]apelin-13(1-12) elicited a dose-dependent decrease in blood pressure in anesthetized rat and dose-dependent increase in forearm blood flow in human volunteers. Conclusions: We provide evidence that ACE2 cleaves [Pyr(1)]apelin-13 to [Pyr(1)]apelin-13(1-12) and this cleavage product is expressed in human cardiovascular tissues. We have demonstrated biological activity of [Pyr(1)]apelin-13(1-12) at the human and rodent apelin receptor in vitro and in vivo. Our data show that reported enhanced ACE2 activity in cardiovascular disease should not significantly compromise the beneficial effects of apelin based therapies for example in PAH.

BACKGROUND: -Elabela/Toddler (ELA) is a critical cardiac developmental peptide that acts through the G protein-coupled apelin receptor, despite lack of sequence similarity to the established ligand apelin. Our aim was to investigate the receptor pharmacology, expression pattern and in vivo function of ELA peptides in the adult cardiovascular system, to seek evidence for alteration in pulmonary arterial hypertension (PAH) in which apelin signaling is down-regulated, and to demonstrate attenuation of PAH severity with exogenous administration of ELA in a rat model. METHODS: -In silico docking analysis, competition binding experiments and down-stream assays were used to characterize ELA receptor binding in human heart and signaling in cells expressing the apelin receptor. ELA expression in human cardiovascular tissues and plasma was determined using RT-qPCR, dual-labelling immunofluorescent staining and immunoassays. Acute cardiac effects of ELA-32 and [Pyr(1)]apelin-13 were assessed by magnet resonance imaging and cardiac catheterization in anesthetized rats. Cardiopulmonary human and rat tissues from PAH patients and monocrotaline (MCT) and Sugen/hypoxia exposed rats were used to show changes in ELA expression in PAH. The effect of ELA treatment on cardiopulmonary remodeling in PAH was investigated in the MCT rat model. RESULTS: -ELA competed for binding of apelin in human heart with overlap for the two peptides indicated by in silico modeling. ELA activated G protein- and Β-arrestin-dependent pathways. We detected ELA expression in human vascular endothelium and plasma. Comparable to apelin, ELA increased cardiac contractility, ejection fraction, cardiac output and elicited vasodilatation in rat in vivo ELA expression was reduced in cardiopulmonary tissues from PAH patients and PAH rat models, respectively. ELA treatment significantly attenuated elevation of right ventricular systolic pressure and right ventricular hypertrophy and pulmonary vascular remodeling in MCT exposed rats. CONCLUSIONS: -These results show ELA is an endogenous agonist of the human apelin receptor, exhibits a cardiovascular profile comparable to apelin, is down-regulated in human disease and rodent PAH models and exogenous peptide can reduce the severity of cardiopulmonary remodeling and function in PAH in rats. This study provides additional proof of principle that an apelin receptor agonist may be of therapeutic use in PAH in man.

Visual inspection of tumour tissues does not reveal the complex metabolic changes that differentiate cancer and its sub-types from healthy tissues. Mass spectrometry imaging, which quantifies the underlying chemistry, represents a powerful tool for the molecular exploration of tumour tissues. A 3-dimensional topological description of the chemical properties of the tumour permits the formulation of hypotheses about the biological composition and interactions and the possible causes of its heterogeneous structure. The large amount of information contained in such datasets requires powerful tools for its analysis, visualisation and interpretation. Linear methods for unsupervised dimensionality reduction, such as PCA, are inadequate to capture the complex non-linear relationships present in these data. For this reason, a deep unsupervised neural network based technique, parametric t-SNE, is adopted to map a 3D-DESI-MS dataset from a human colorectal adenocarcinoma biopsy onto a 2-dimensional manifold. This technique allows the identification of clusters not visible with linear methods. The unsupervised clustering of the tumour tissue results in the identification of sub-regions characterised by the abundance of identified metabolites, making possible the formulation of hypotheses to account for their significance and the underlying biological heterogeneity in the tumour.

This work was completed through funding provided by the BBSRC [BB/L005077/1 and BB/M019985/1] and Wellcome Trust [202952/Z/16/Z].

Early phase drug discovery is a multi-parameter optimisation process. Finding drugable targets, discovering starting points for lead optimisation and creating novel structures with new biological properties within these constraints is challenging. As an example of a drug optimisation strategy, recent work on 5-HT1B antagonists will be described. This is put in the context of the drugability of the target, the desired physicochemical properties of the desired molecules and approaches to compound design to create high affinity, selective molecules that are optimised to have low Central Nervous System (CNS) penetration.

Metabolism of xenobiotics remains a central challenge for the discovery and development of drugs, cosmetics, nutritional supplements, and agrochemicals. Metabolic transformations are frequently related to the incidence of toxic effects that may result from the emergence of reactive species, the systemic accumulation of metabolites, or by induction of metabolic pathways. Experimental investigation of the metabolism of small organic molecules is particularly resource demanding; hence, computational methods are of considerable interest to complement experimental approaches. This review provides a broad overview of structure- and ligand-based computational methods for the prediction of xenobiotic metabolism. Current computational approaches to address xenobiotic metabolism are discussed from three major perspectives: (i) prediction of sites of metabolism (SOMs), (ii) elucidation of potential metabolites and their chemical structures, and (iii) prediction of direct and indirect effects of xenobiotics on metabolizing enzymes, where the focus is on the cytochrome P450 (CYP) superfamily of enzymes, the cardinal xenobiotics metabolizing enzymes. For each of these domains, a variety of approaches and their applications are systematically reviewed, including expert systems, data mining approaches, quantitative structure-activity relationships (QSARs), and machine learning-based methods, pharmacophore-based algorithms, shape-focused techniques, molecular interaction fields (MIFs), reactivity-focused techniques, protein-ligand docking, molecular dynamics (MD) simulations, and combinations of methods. Predictive metabolism is a developing area, and there is still enormous potential for improvement. However, it is clear that the combination of rapidly increasing amounts of available ligand- and structure-related experimental data (in particular, quantitative data) with novel and diverse simulation and modeling approaches is accelerating the development of effective tools for prediction of in vivo metabolism, which is reflected by the diverse and comprehensive data sources and methods for metabolism prediction reviewed here. This review attempts to survey the range and scope of computational methods applied to metabolism prediction and also to compare and contrast their applicability and performance.

Background In silico predictive models have proved to be valuable for the optimisation of compound potency, selectivity and safety profiles in the drug discovery process. Results camb is an R package that provides an environment for the rapid generation of quantitative Structure-Property and Structure-Activity models for small molecules (including QSAR, QSPR, QSAM, PCM) and is aimed at both advanced and beginner R users. camb's capabilities include the standardisation of chemical structure representation, computation of 905 one-dimensional and 14 fingerprint type descriptors for small molecules, 8 types of amino acid descriptors, 13 whole protein sequence descriptors, filtering methods for feature selection, generation of predictive models (using an interface to the R package caret), as well as techniques to create model ensembles using techniques from the R package caretEnsemble). Results can be visualised through high-quality, customisable plots (R package ggplot2). Conclusions Overall, camb constitutes an open-source framework to perform the following steps: (1) compound standardisation, (2) molecular and protein descriptor calculation, (3) descriptor pre-processing and model training, visualisation and validation, and (4) bioactivity/property prediction for new molecules. camb aims to speed model generation, in order to provide reproducibility and tests of robustness. QSPR and proteochemometric case studies are included which demonstrate camb's application.Graphical abstractFrom compounds and data to models: a complete model building workflow in one package. Electronic supplementary material The online version of this article (doi:10.1186/s13321-015-0086-2) contains supplementary material, which is available to authorized users.

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.

[Pyr(1)]apelin-13 is an endogenous vasodilator and inotrope but is downregulated in pulmonary hypertension and heart failure, making the apelin receptor an attractive therapeutic target. Agonists acting at the same G-protein-coupled receptor can be engineered to stabilize different conformational states and function as biased ligands, selectively stimulating either G-protein or β-arrestin pathways. We used molecular dynamics simulations of apelin/receptor interactions to design cyclic analogues and identified MM07 as a biased agonist. In β-arrestin and internalization assays (G-protein-independent), MM07 was 2 orders of magnitude less potent than [Pyr(1)]apelin-13. In a G-protein-dependent saphenous vein contraction assay, both peptides had comparable potency (pD2:[Pyr(1)]apelin-13 9.93±0.24; MM07 9.54±0.42) and maximum responses with a resulting bias for MM07 of ≈350- to 1300-fold for the G-protein pathway. In rats, systemic infusions of MM07 (10-100nmol) caused a dose-dependent increase in cardiac output that was significantly greater than the response to [Pyr(1)]apelin-13. Similarly, in human volunteers, MM07 produced a significant dose-dependent increase in forearm blood flow with a maximum dilatation double that is seen with [Pyr(1)]apelin-13. Additionally, repeated doses of MM07 produced reproducible increases in forearm blood flow. These responses are consistent with a more efficacious action of the biased agonist. In human hand vein, both peptides reversed an established norepinephrine constrictor response and significantly increased venous flow. Our results suggest that MM07 acting as a biased agonist at the apelin receptor can preferentially stimulate the G-protein pathway, which could translate to improved efficacy in the clinic by selectively stimulating vasodilatation and inotropic actions but avoiding activating detrimental β-arrestin-dependent pathways.

The Centre for Molecular Informatics, formerly Unilever Centre for Molecular Science Informatics (UCMSI), at the University of Cambridge is a world-leading driving force in the field of cheminformatics. Since its opening in 2000 more than 300 scientific articles have fundamentally changed the field of molecular informatics. The Centre has been a key player in promoting open chemical data and semantic access. Though mainly focussing on basic research, close collaborations with industrial partners ensured real world feedback and access to high quality molecular data. A variety of tools and standard protocols have been developed and are ubiquitous in the daily practice of cheminformatics. Here, we present a retrospective of cheminformatics research performed at the UCMSI, thereby highlighting historical and recent trends in the field as well as indicating future directions.

According to Cobanoglu et al and Murphy, it is now widely acknowledged that the single target paradigm (one protein or target, one disease, one drug) that has been the dominant premise in drug development in the recent past is untenable. More often than not, a drug-like compound (ligand) can be promiscuous - that is, it can interact with more than one target protein. In recent years, in in silico target prediction methods the promiscuity issue has been approached computationally in different ways. In this study we confine attention to the so-called ligand-based target prediction machine learning approaches, commonly referred to as target-fishing. With a few exceptions, the target-fishing approaches that are currently ubiquitous in cheminformatics literature can be essentially viewed as single-label multi-classification schemes; these approaches inherently bank on the single target paradigm assumption that a ligand can home in on one specific target. In order to address the ligand promiscuity issue, one might be able to cast target-fishing as a multi-label multi-class classification problem. For illustrative and comparison purposes, single-label and multi-label Naive Bayes classification models (denoted here by SMM and MMM, respectively) for target-fishing were implemented. The models were constructed and tested on 65,587 compounds and 308 targets retrieved from the ChEMBL17 database. SMM and MMM performed differently: for 16,344 test compounds, the MMM model returned recall and precision values of 0.8058 and 0.6622, respectively; the corresponding recall and precision values yielded by the SMM model were 0.7805 and 0.7596, respectively. However, at a significance level of 0.05 and one degree of freedom McNemar test performed on the target prediction results returned by SMM and MMM for the 16,344 test ligands gave a chi-squared value of 15.656, in favour of the MMM approach.

Background The prediction of sites and products of metabolism in xenobiotic compounds is key to the development of new chemical entities, where screening potential metabolites for toxicity or unwanted side-effects is of crucial importance. In this work 2D topological fingerprints are used to encode atomic sites and three probabilistic machine learning methods are applied: Parzen-Rosenblatt Window (PRW), Naive Bayesian (NB) and a novel approach called RASCAL (Random Attribute Subsampling Classification ALgorithm). These are implemented by randomly subsampling descriptor space to alleviate the problem often suffered by data mining methods of having to exactly match fingerprints, and in the case of PRW by measuring a distance between feature vectors rather than exact matching. The classifiers have been implemented in CUDA/C++ to exploit the parallel architecture of graphical processing units (GPUs) and is freely available in a public repository. Results It is shown that for PRW a SoM (Site of Metabolism) is identified in the top two predictions for 85%, 91% and 88% of the CYP 3A4, 2D6 and 2C9 data sets respectively, with RASCAL giving similar performance of 83%, 91% and 88%, respectively. These results put PRW and RASCAL performance ahead of NB which gave a much lower classification performance of 51%, 73% and 74%, respectively. Conclusions 2D topological fingerprints calculated to a bond depth of 4-6 contain sufficient information to allow the identification of SoMs using classifiers based on relatively small data sets. Thus, the machine learning methods outlined in this paper are conceptually simpler and more efficient than other methods tested and the use of simple topological descriptors derived from 2D structure give results competitive with other approaches using more expensive quantum chemical descriptors. The descriptor space subsampling approach and ensemble methodology allow the methods to be applied to molecules more distant from the training data where data mining would be more likely to fail due to the lack of common fingerprints. The RASCAL algorithm is shown to give equivalent classification performance to PRW but at lower computational expense allowing it to be applied more efficiently in the ensemble scheme., Journal of Cheminformatics, 6, ISSN:1758-2946

Polymorphism of drugs has been the subject of intense interest in the pharmaceutical industry for over forty years. Although identical in chemical composition, polymorphs differ in bioavailability, solubility, dissolution rate, chemical and physical stability, melting point, colour, filterability, density, flow properties, and many other properties. The difference in solubility is particularly important for pharmaceuticals, as it can affect drug efficacy, bioavailability and safety. Despite significant investment in processes to find all the possible polymorphs of active pharmaceutical ingredients (APIs), new polymorphs can suddenly appear without warning. Polymorphs tend to convert spontaneously from less stable to more stable forms, and, therefore, it is best to discover and characterize the stable form as early as possible. Ideally the most stable polymorph will be found while the drug candidate is still in the discovery process, so that this is the form used for subsequent testing. The most stable polymorph will be the least soluble and solubility may be a limiting factor in the efficacy of the API. Despite the huge importance of polymorphism in the properties of materials, however, there is no method that can produce all the stable polymorphs of a compound, or even one that can provide confidence that the most stable polymorph has been obtained. Here we describe a new method, `potentiometric cycling for polymorph creation (PC)2', which is able to generate the most stable polymorph in aqueous solution. This new method has been applied to sulindac, a non-steroidal anti-inflammatory drug, which also shows promise in anticancer treatment, producing two polymorphs of this API, including a new more stable one. By adjusting the conditions, this method is able to produce either polymorph exclusively.

BACKGROUND: An in silico mechanism-of-action analysis protocol was developed, comprising molecule bioactivity profiling, annotation of predicted targets with pathways and calculation of enrichment factors to highlight targets and pathways more likely to be implicated in the studied phenotype. RESULTS: The method was applied to a cytotoxicity phenotypic endpoint, with enriched targets/pathways found to be statistically significant when compared with 100 random datasets. Application on a smaller apoptotic set (10 molecules) did not allowed to obtain statistically relevant results, suggesting that the protocol requires modification such as analysis of the most frequently predicted targets/annotated pathways. CONCLUSION: Pathway annotations improved the mechanism-of-action information gained by target prediction alone, allowing a better interpretation of the predictions and providing better mapping of targets onto pathways.

The mechanism of phospholipidosis is still not well understood. Numerous different mechanisms have been proposed, varying from direct inhibition of the breakdown of phospholipids to the binding of a drug compound to the phospholipid, preventing breakdown. We have used a probabilistic method, the Parzen-Rosenblatt Window approach, to build a model from the ChEMBL dataset which can predict from a compound's structure both its primary pharmaceutical target and other targets with which it forms off-target, usually weaker, interactions. Using a small dataset of 182 phospholipidosis-inducing and non-inducing compounds, we predict their off-target activity against targets which could relate to phospholipidosis as a side-effect of a drug. We link these targets to specific mechanisms of inducing this lysosomal build-up of phospholipids in cells. Thus, we show that the induction of phospholipidosis is likely to occur by separate mechanisms when triggered by different cationic amphiphilic drugs. We find that both inhibition of phospholipase activity and enhanced cholesterol biosynthesis are likely to be important mechanisms. Furthermore, we provide evidence suggesting four specific protein targets. Sphingomyelin phosphodiesterase, phospholipase A2 and lysosomal phospholipase A1 are shown to be likely targets for the induction of phospholipidosis by inhibition of phospholipase activity, while lanosterol synthase is predicted to be associated with phospholipidosis being induced by enhanced cholesterol biosynthesis. This analysis provides the impetus for further experimental tests of these hypotheses. Publisher PDF

Background Predicting metabolic sites is important in the drug discovery process to aid in rapid compound optimisation. No interactive tool exists and most of the useful tools are quite expensive. Results Here a fast and reliable method to analyse ligands and visualise potential metabolic sites is presented which is based on annotated metabolic data, described by circular fingerprints. The method is available via the graphical workbench Bioclipse, which is equipped with advanced features in cheminformatics. Conclusions Due to the speed of predictions (less than 50 ms per molecule), scientists can get real time decision support when editing chemical structures. Bioclipse is a rich client, which means that all calculations are performed on the local computer and do not require network connection. Bioclipse and MetaPrint2D are free for all users, released under open source licenses, and available from http://www.bioclipse.net.

Understanding the metabolic fate of small organic molecules is of fundamental importance to the successful design and development of drugs, nutritional supplements, cosmetics and agrochemicals [1,2]. In the current study we investigated how the products of metabolism differ from their parent molecules by analysing a large dataset of experimentally determined metabolic transformations (Figure ​(Figure1).1). This dataset was split into three specific chemical domains representing approved drug molecules, human metabolites and molecules from traditional Chinese medicines to allow individual analysis. We also quantified the impact of individual Phase I and Phase II metabolic reactions on calculated chemical descriptors using MetaPrint2D [3] and suggest new approaches to utilise metabolism for the design of drugs and cosmetics. The last section of this study investigates the properties of metabolites found in the bile, faeces and urine and analyses their commonalities and differences. Figure 1 Four important questions pertinent to the design and development of new molecules with favourable ADME properties addressed in this work. d, approved drugs; h, human metabolites; t, molecules from traditional Chinese medicines; MW, molecular weight.