Dataset Information

Adenosine Receptor Antagonists to Combat Cancer and to Boost Anti-Cancer Chemotherapy and Immunotherapy.

ABSTRACT: Extracellular adenosine accumulates in the environment of numerous tumors. For years, this fact has fueled preclinical research to determine whether adenosine receptors (ARs) could be the target to fight cancer. The four ARs discovered so far, A1, A2A, A2B and A3, belong to the class A family of G protein-coupled receptors (GPCRs) and all four have been involved in one way or another in regulating tumor progression. Prompted by the successful anti-cancer immunotherapy, the focus was placed on the ARs more involved in regulation of immune cell differentiation and activation and that are able to establish molecular and functional interactions. This review focuses on the potential of A2A and A2B receptor antagonists in cancer control and in boosting anti-cancer chemotherapy and immunotherapy. The article also overviews the ongoing clinical trials in which A2AR and A2BR ligands are being tested in anti-cancer therapy.

SUBMITTER: Franco R

PROVIDER: S-EPMC8616521 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature

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Adenosine Receptor Antagonists to Combat Cancer and to Boost Anti-Cancer Chemotherapy and Immunotherapy.

Franco Rafael R Rivas-Santisteban Rafael R Navarro Gemma G Reyes-Resina Irene I

Cells 20211021 11

Extracellular adenosine accumulates in the environment of numerous tumors. For years, this fact has fueled preclinical research to determine whether adenosine receptors (ARs) could be the target to fight cancer. The four ARs discovered so far, A1, A2A, A2B and A3, belong to the class A family of G protein-coupled receptors (GPCRs) and all four have been involved in one way or another in regulating tumor progression. Prompted by the successful anti-canc ...[more]

PMID: 34831054

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Project description:Prostate cancer (PCa) is a major leading cause of mortality of cancer among males. There have been numerous studies to develop antagonists against androgen receptor (AR), a crucial therapeutic target for PCa. This study is a systematic cheminformatic analysis and machine learning modeling to study the chemical space, scaffolds, structure-activity relationship, and landscape of human AR antagonists. There are 1678 molecules as final data sets. Chemical space visualization by physicochemical property visualization has demonstrated that molecules from the potent/active class generally have a mildly smaller molecular weight (MW), octanol-water partition coefficient (log P), number of hydrogen-bond acceptors (nHA), number of rotatable bonds (nRot), and topological polar surface area (TPSA) than molecules from intermediate/inactive class. The chemical space visualization in the principal component analysis (PCA) plot shows significant overlapping distributions between potent/active class molecules and intermediate/inactive class molecules; potent/active class molecules are intensively distributed, while intermediate/inactive class molecules are widely and sparsely distributed. Murcko scaffold analysis has shown low scaffold diversity in general, and scaffold diversity of potent/active class molecules is even lower than intermediate/inactive class molecules, indicating the necessity for developing molecules with novel scaffolds. Furthermore, scaffold visualization has identified 16 representative Murcko scaffolds. Among them, scaffolds 1, 2, 3, 4, 7, 8, 10, 11, 15, and 16 are highly favorable scaffolds due to their high scaffold enrichment factor values. Based on scaffold analysis, their local structure-activity relationships (SARs) were investigated and summarized. In addition, the global SAR landscape was explored by quantitative structure-activity relationship (QSAR) modelings and structure-activity landscape visualization. A QSAR classification model incorporating all of the 1678 molecules stands out as the best model from a total of 12 candidate models for AR antagonists (built on PubChem fingerprint, extra trees algorithm, accuracy for training set: 0.935, 10-fold cross-validation set: 0.735 and test set: 0.756). Deeper insights into the structure-activity landscape highlighted a total of seven significant activity cliff (AC) generators (ChEMBL molecule IDs: 160257, 418198, 4082265, 348918, 390728, 4080698, and 6530), which provide valuable SAR information for medicinal chemistry. The findings in this study provide new insights and guidelines for hit identification and lead optimization for the development of novel AR antagonists.

Dataset Information

Adenosine Receptor Antagonists to Combat Cancer and to Boost Anti-Cancer Chemotherapy and Immunotherapy.

Publications

Adenosine Receptor Antagonists to Combat Cancer and to Boost Anti-Cancer Chemotherapy and Immunotherapy.

Similar Datasets

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