Specific Inhibition of HIF Activity: Can Peptides Lead the Way?
Bibliographic record
Abstract
Reduced oxygen availability (hypoxia) is a characteristic of many disorders including cancer. Central components of the systemic and cellular response to hypoxia are the Hypoxia Inducible Factors (HIFs), a small family of heterodimeric transcription factors that directly or indirectly regulate the expression of hundreds of genes, the products of which mediate adaptive changes in processes that include metabolism, erythropoiesis, and angiogenesis. The overexpression of HIFs has been linked to the pathogenesis and progression of cancer. Moreover, evidence from cellular and animal models have convincingly shown that targeting HIFs represents a valid approach to treat hypoxia-related disorders. However, targeting transcription factors with small molecules is a very demanding task and development of HIF inhibitors with specificity and therapeutic potential has largely remained an unattainable challenge. Another promising approach to inhibit HIFs is to use peptides modelled after HIF subunit domains known to be involved in protein-protein interactions that are critical for HIF function. Introduction of these peptides into cells can inhibit, through competition, the activity of endogenous HIFs in a sequence and, therefore also isoform, specific manner. This review summarizes the involvement of HIFs in cancer and the approaches for targeting them, with a special focus on the development of peptide HIF inhibitors and their prospects as highly-specific pharmacological agents.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".