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

Development of Covalent Ligands for G Protein-Coupled Receptors: A Case for the Human Adenosine A3 Receptor

The development of covalent ligands for G protein-coupled receptors (GPCRs) is not a trivial process. Here, we report a streamlined workflow thereto from synthesis to validation, exemplified by the discovery of a covalent antagonist for the human adenosine A3 receptor (hA3AR). Based on the 1H,3H-pyrido[2,1-f]purine-2,4-dione scaffold, a series of ligands bearing a fluorosulfonyl warhead and a varying linker was synthesized. This series was subjected to an affinity screen, revealing compound 17b as the most potent antagonist. In addition, a nonreactive methylsulfonyl derivative 19 was developed as a reversible control compound. A series of assays, comprising time-dependent affinity determination, washout experiments, and [35S]GTPγS binding assays, then validated 17b as the covalent antagonist. A combined in silico hA3AR-homology model and site-directed mutagenesis study was performed to demonstrate that amino acid residue Y2657.36 was the unique anchor point of the covalent interaction. This workflow might be applied to other GPCRs to guide the discovery of covalent ligands.Medicinal Chemistr

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

Successive statistical and structure-based modeling to identify chemically novel kinase inhibitors

Kinases are frequently studied in the context of anticancer drugs. Their involvement in cell responses, such as proliferation, differentiation, and apoptosis, makes them interesting subjects in multitarget drug design. In this study, a workflow is presented that models the bioactivity spectra for two panels of kinases: (1) inhibition of RET, BRAF, SRC, and S6K, while avoiding inhibition of MKNK1, TTK, ERK8, PDK1, and PAK3, and (2) inhibition of AURKA, PAK1, FGFR1, and LKB1, while avoiding inhibition of PAK3, TAK1, and PIK3CA. Both statistical and structure-based models were included, which were thoroughly benchmarked and optimized. A virtual screening was performed to test the workflow for one of the main targets, RET kinase. This resulted in 5 novel and chemically dissimilar RET inhibitors with remaining RET activity of 50 value of 5.1 for the most active compound. The experimental validation of inhibitors for RET strongly indicates that the multitarget workflow is able to detect novel inhibitors for kinases, and hence, this workflow can potentially be applied in polypharmacology modeling. We conclude that this approach can identify new chemical matter for existing targets. Moreover, this workflow can easily be applied to other targets as well.Toxicolog

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

Beyond the Hype: Deep Neural Networks Outperform Established Methods Using A ChEMBL Bioactivity Benchmark Set

This dataset contains the (standardized) data used in the experiments, alongside the scripts used to perform Deep Neural Nets (DNN_Scripts), and the other machine learning methods in both Pipeline Pilot (PP_protocols) and Python/Scikit-Learn (PY_scripts