Hypoxia Activated Prodrugs: Factors Influencing Design and Development
Bibliographic record
Abstract
Hypoxia in tumor cells is characterized by a lack of oxygen resulting from reduced blood supply to the surrounding tissue, and is a common characteristic of solid tumors as a consequence of rapid cell growth. Hypoxia in tumors is a predictor of both resistance to chemotherapy and of a metastatic/aggressive form of cancer, and as a result, development of cancer therapies which target hypoxia is of vital importance. One such targeting strategy is the development of hypoxia-activated prodrugs (HAP) which can preferentially release chemotherapeutic agents within hypoxic tumor regions. This targeting strategy is accomplished by attaching a hypoxia activated trigger to a chemotherapeutic agent and under oxygen-poor conditions, the agent (effector) is released into the tumor, while remaining intact in normal tissue, and leaving non-hypoxic cells undamaged. Overall, this strategy can achieve an increased therapeutic benefit over conventional small molecule chemotherapeutic treatments by concentrating the drugs within hypoxic tumor environments, while simultaneously reducing the side-effects and toxicity issues that surround the systemic distribution of traditional drugs on normoxic cells. In this review, we will describe the factors that should be considered when designing an effective HAP, such as the mechanism of prodrug action, the elements that influence the rational design of HAP (i.e. reduction potential), and the activating enzymes of HAP. As part of this description, we will utilize select examples from the literature to reinforce these factors, and make a case for the intelligent design of new HAPs, leading to the development of novel hypoxia targeting chemotherapeutic agents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".