Ectonucleotidase inhibitors: an updated patent review (2017–2023)
Bibliographic record
Abstract
INTRODUCTION: The main enzymes that hydrolyzes nucleotides at the cell surface are nucleoside triphosphate diphosphohydrolases (NTPDases), ecto-nucleotide pyrophosphatases/phosphodiesterases (ENPPs), alkaline phosphatases (APs) and ecto-5'- nucleotidase (e5'NT, CD73) and by regulating the concentration of nucleotides at the cell surface, these enzymes have the potential to affect various conditions such as fibrosis, cancer metastasis, pruritus, inflammation, and autoimmune diseases. Thus, they represent a prospective therapeutic target. AREA COVERED: A number of molecules, including nucleoside/nucleotide and non-nucleoside analogues, and bicyclic compounds, have shown strong potential as ectonucleotidase inhibitors. This review covers the chemistry and clinical uses of ectonucleotidase inhibitors patented between 2017 and 2023. EXPERT OPINION: By binding to their specific P1 and P2 receptors at the cell surface, nucleosides and nucleotides regulate a number of pathophysiological events such as inflammation, fibrosis, cancer, and autoimmune diseases. Interestingly, these nucleotides can be hydrolyzed to nucleosides by several cell surface enzymes called ectonucleotidases. The development of small molecules that modulate ectonucleotidase activity is, therefore, of therapeutic value. This review provides valuable insights into recent advancements, including combination therapy and enhanced selectivity, which are poised to shape the future of ectonucleotidase inhibition through a comprehensive analysis of patents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".