Structure-Affinity Relationships and Structure-Kinetics Relationships of Pyrido[2,1-f]purine-2,4-dione Derivatives as Human Adenosine A(3) Receptor Antagonists

We expanded on a series of pyrido[2,1-f]purine-2,4-clione derivatives as human adenosine A(3) receptor (hA(3)R) antagonists to determine their kinetic profiles and affinities. Many compounds showed high affinities and a diverse range of kinetic profiles. We found hA(3)R antagonists with very short residence time (RT) at the receptor (2.2 min for 5) and much longer RTs (e.g., 376 min for 27 or 391 min for 31). Two representative antagonists (5 and 27) were tested in [S-35]GTP gamma S binding assays, and their RTs appeared correlated to their (in)surmountable antagonism. From a k(on)-k(off)-K-D kinetic map, we divided the antagonists into three subgroups, providing a possible direction for the further development of hA(3)R antagonists. Additionally, we performed a computational modeling study that sheds light on the crucial receptor interactions, dictating the compounds' binding kinetics. Knowledge of target binding kinetics appears useful for developing and triaging new hA(3)R antagonists in the early phase of drug discovery

Structure-Affinity Relationships and Structure-Kinetics Relationships of Pyrido[2,1-f]purine-2,4-dione Derivatives as Human Adenosine A(3) Receptor Antagonists

We expanded on a series of pyrido[2,1-f]purine-2,4-clione derivatives as human adenosine A(3) receptor (hA(3)R) antagonists to determine their kinetic profiles and affinities. Many compounds showed high affinities and a diverse range of kinetic profiles. We found hA(3)R antagonists with very short residence time (RT) at the receptor (2.2 min for 5) and much longer RTs (e.g., 376 min for 27 or 391 min for 31). Two representative antagonists (5 and 27) were tested in [S-35]GTP gamma S binding assays, and their RTs appeared correlated to their (in)surmountable antagonism. From a k(on)-k(off)-K-D kinetic map, we divided the antagonists into three subgroups, providing a possible direction for the further development of hA(3)R antagonists. Additionally, we performed a computational modeling study that sheds light on the crucial receptor interactions, dictating the compounds' binding kinetics. Knowledge of target binding kinetics appears useful for developing and triaging new hA(3)R antagonists in the early phase of drug discovery

Incidence and predictors of heart failure with reduced and preserved ejection fraction after ST-elevation myocardial infarction in the contemporary era of early percutaneous coronary intervention

Aims: The development and incidence of de-novo heart failure after ST-elevation myocardial infarction (STEMI) in the contemporary era of rapid reperfusion are largely unknown. We aimed to establish the incidence of post-STEMI heart failure, stratified by left ventricular ejection fraction (LVEF) and to find predictors for its occurrence. Furthermore, we investigated the course of left ventricular systolic and diastolic function after STEMI. Methods and results: A total of 1172 all-comer STEMI patients from the CardioLines Biobank were included. Patients were predominantly male (74.5%) and 64 ± 12 years of age. During a median follow-up of 3.7 years (2.0, 5.5) we found a total incidence of post-STEMI heart failure of 10.9%, of which 52.1% heart failure with reduced ejection fraction (HFrEF), 29.4% heart failure with mildly reduced ejection fraction and 18.5% heart failure with preserved ejection fraction (HFpEF). Independent predictors for the development of HFrEF were male sex (β = 0.97, p = 0.009), lung crepitations (β = 1.09, p = 0.001), potassium level (mmol/L, β = 0.43, p = 0.012), neutrophil count (109/L, β = 0.09, p = 0.001) and a reduced LVEF (β = 1.91, p &lt; 0.001) at baseline. Independent predictors for the development of HFpEF were female sex (β = 0.99, p = 0.029), pre-existing kidney failure (β = 1.95, p = 0.003) and greater left atrial volume index (β = 0.04, p = 0.033) at baseline. Follow-up echocardiography (median follow-up 20 months) showed an improvement in LVEF (p &lt; 0.001), whereas changes in diastolic function parameters showed both improvement and deterioration.Conclusion: In the current era of early STEMI reperfusion, still one in 10 patients develops heart failure, with approximately half of the patients with a reduced and half with a mildly reduced or normal LVEF. Predictors for the development of HFrEF were different from HFpEF.</p

Polypharmacology Modelling Using Proteochemometrics (PCM): Recent Methodological Developments, Applications to Target Families, and Future Prospects

Peer reviewe

Structure-Affinity Relationships and Structure-Kinetics Relationships of Pyrido[2,1-f]purine-2,4-dione Derivatives as Human Adenosine A(3) Receptor Antagonists

AbstractWe expanded on a series of pyrido[2,1-f]purine-2,4-dione derivatives as human adenosine A3 receptor (hA3R) antagonists to determine their kinetic profiles and affinities. Many compounds showed high affinities and a diverse range of kinetic profiles. We found hA3R antagonists with very short residence time (RT) at the receptor (2.2 min for 5) and much longer RTs (e.g., 376 min for 27 or 391 min for 31). Two representative antagonists (5 and 27) were tested in [35S]GTPγS binding assays, and their RTs appeared correlated to their (in)surmountable antagonism. From a kon-koff-KD kinetic map, we divided the antagonists into three subgroups, providing a possible direction for the further development of hA3R antagonists. Additionally, we performed a computational modeling study that sheds light on the crucial receptor interactions, dictating the compounds' binding kinetics. Knowledge of target binding kinetics appears useful for developing and triaging new hA3R antagonists in the early phase of drug discovery.<img title="Click on image to zoom" alt="An external file that holds a picture, illustration, etc. Object name is jm-2017-009505_0011.jpg" src="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5601358/bin/jm-2017-009505_0011.jpg" />Toxicolog

Incidence and predictors of heart failure with reduced and preserved ejection fraction after ST-elevation myocardial infarction in the contemporary era of early percutaneous coronary intervention

Aims: The development and incidence of de-novo heart failure after ST-elevation myocardial infarction (STEMI) in the contemporary era of rapid reperfusion are largely unknown. We aimed to establish the incidence of post-STEMI heart failure, stratified by left ventricular ejection fraction (LVEF) and to find predictors for its occurrence. Furthermore, we investigated the course of left ventricular systolic and diastolic function after STEMI. Methods and results: A total of 1172 all-comer STEMI patients from the CardioLines Biobank were included. Patients were predominantly male (74.5%) and 64 ± 12 years of age. During a median follow-up of 3.7 years (2.0, 5.5) we found a total incidence of post-STEMI heart failure of 10.9%, of which 52.1% heart failure with reduced ejection fraction (HFrEF), 29.4% heart failure with mildly reduced ejection fraction and 18.5% heart failure with preserved ejection fraction (HFpEF). Independent predictors for the development of HFrEF were male sex (β = 0.97, p = 0.009), lung crepitations (β = 1.09, p = 0.001), potassium level (mmol/L, β = 0.43, p = 0.012), neutrophil count (109/L, β = 0.09, p = 0.001) and a reduced LVEF (β = 1.91, p < 0.001) at baseline. Independent predictors for the development of HFpEF were female sex (β = 0.99, p = 0.029), pre-existing kidney failure (β = 1.95, p = 0.003) and greater left atrial volume index (β = 0.04, p = 0.033) at baseline. Follow-up echocardiography (median follow-up 20 months) showed an improvement in LVEF (p < 0.001), whereas changes in diastolic function parameters showed both improvement and deterioration. Conclusion: In the current era of early STEMI reperfusion, still one in 10 patients develops heart failure, with approximately half of the patients with a reduced and half with a mildly reduced or normal LVEF. Predictors for the development of HFrEF were different from HFpEF

Advances in GPCR modeling evaluated by the GPCR Dock 2013 assessment: Meeting new challenges

© 2014 Elsevier Ltd All rights reserved. Despite tremendous successes of GPCR crystallography, the receptors with available structures represent only a small fraction of human GPCRs. An important role of the modeling community is to maximize structural insights for the remaining receptors and complexes. The community-wide GPCR Dock assessment was established to stimulate and monitor the progress in molecular modeling and ligand docking for GPCRs. The four targets in the present third assessment round presented new and diverse challenges for modelers, including prediction of allosteric ligand interaction and activation states in 5-hydroxytryptamine receptors 1B and 2B, and modeling by extremely distant homology for smoothened receptor. Forty-four modeling groups participated in the assessment. State-of-the-art modeling approaches achieved close-to-experimental accuracy for small rigid orthosteric ligands and models built by close homology, and they correctly predicted protein fold for distant homology targets. Predictions of long loops and GPCR activation states remain unsolved problems

Improving virtual screening of G protein-coupled receptors via ligand-directed modeling

G protein-coupled receptors (GPCRs) play crucial roles in cell physiology and pathophysiology. There is increasing interest in using structural information for virtual screening (VS) of libraries and for structure-based drug design to identify novel agonist or antagonist leads. However, the sparse availability of experimentally determined GPCR/ligand complex structures with diverse ligands impedes the application of structure-based drug design (SBDD) programs directed to identifying new molecules with a select pharmacology. In this study, we apply ligand-directed modeling (LDM) to available GPCR X-ray structures to improve VS performance and selectivity towards molecules of specific pharmacological profile. The described method refines a GPCR binding pocket conformation using a single known ligand for that GPCR. The LDM method is a computationally efficient, iterative workflow consisting of protein sampling and ligand docking. We developed an extensive benchmark comparing LDM-refined binding pockets to GPCR X-ray crystal structures across seven different GPCRs bound to a range of ligands of different chemotypes and pharmacological profiles. LDM-refined models showed improvement in VS performance over origin X-ray crystal structures in 21 out of 24 cases. In all cases, the LDM-refined models had superior performance in enriching for the chemotype of the refinement ligand. This likely contributes to the LDM success in all cases of inhibitor-bound to agonist-bound binding pocket refinement, a key task for GPCR SBDD programs. Indeed, agonist ligands are required for a plethora of GPCRs for therapeutic intervention, however GPCR X-ray structures are mostly restricted to their inactive inhibitor-bound state

Artificial intelligence in biological activity prediction

Artificial intelligence has become an indispensable resource in chemoinformatics. Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. These approaches enable the automation of compound discovery to find biologically active molecules with important properties. Here, we present a review of some of the main machine learning studies in biological activity prediction of compounds, in particular for sweetness prediction. We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks.This study was supported by the European Commission through project SHIKIFACTORY100 - Modular cell factories for the production of 100 compounds from the shikimate pathway (Reference 814408), and by the Portuguese FCT under the scope of the strategic funding of UID/BIO/04469/2019 unit and BioTecNorte operation (NORTE-01-0145-FEDER-000004) funded by the European Regional Development Fund under the scope of Norte2020.info:eu-repo/semantics/publishedVersio

Predicting binding affinities for GPCR ligands using free-energy perturbation

Medicinal ChemistryDrug Delivery Technolog