ArticleFrontiers in pharmacology2023
In-silico studies to recognize repurposing therapeutics toward arginase-I inhibitors as a potential onco-immunomodulators.
Article in Frontiers in pharmacology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- In Silico Characterization and Molecular Docking of the L-Arginase Gene From Alcaligenes aquatilis BC2.Biotechnology and applied biochemistry · 2026Article
- Boron neutron capture therapy combined with immunotherapy: insights from radiotherapy, BNCT-specific evidence, and future directions.Frontiers in immunology · 2026Review
- ARG1-polyamine axis: cell-type-specific functions in disease pathogenesis and therapeutic targeting.Frontiers in immunology · 2026Review
- Identifying therapeutic target for prostate cancer: exploring Diosmetin as a CYP inhibitor.Discover oncology · 2024Article
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Rudolf Virchow was the first person to point out the important link between immune function and cancer. He did this by noticing that leukocytes were often found in tumors. Overexpression of arginase 1 (ARG1) and inducible nitric oxide synthase (iNOS) in myeloid-derived suppressor cells (MDSCs) and tumour-associated macrophages (TAMs) depletes both intracellular and extracellular arginine. TCR signalling is slowed as a result, and the same types of cells produce reactive oxygen and nitrogen species (ROS and RNS), which aggravates the situation. Human arginase I is a double-stranded manganese metalloenzyme that helps L-arginine break down into L-ornithine and urea. Thus, a quantitative structure-activity relationship (QSAR) analysis was performed to unearth the unrecognised structural aspects crucial for arginase-I inhibition. In this work, a balanced QSAR model with good prediction performance and clear mechanistic interpretation was developed using a dataset of 149 molecules encompassing a broad range of structural scaffolds and compositions. The model was made to meet OECD standards, and all of its validation parameters have values that are higher than the minimum requirements (R
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