A Prodrug Strategy for the In Vivo Imaging of Aldehyde Dehydrogenase Activity
Bibliographic record
Abstract
Therapy resistance is one of the biggest challenges facing clinical oncology. Despite a revolution in new anti-cancer drugs targeting multiple components of the tumour microenvironment, acquired or innate resistance frequently blunts the efficacy of these treatments. Non-invasive identification of drug-resistant tumours will enable modification of the patient treatment pathway through the selection of appropriate second-line treatments. Here, we have designed a pro-drug radiotracer for the non-invasive imaging of aldehyde dehydrogenase 1A1 (ALDH1A1) activity. Elevated ALDH1A1 activity is a marker of drug-resistant cancer cells, modelled here with matched cisplatin-sensitive and -resistant human SKOV3 ovarian cancer cells. The aromatic aldehyde of our prodrug radiotracer was intracellularly liberated by esterase cleavage of the geminal diacetate and specifically trapped by ALDH through its conversion to the charged carboxylic acid. Through this mechanism of action, ALDH-specific retention of our pro-drug radiotracer in the drug-resistant tumour cells was twice as high as the drug-sensitive cells. Acylal masking of the aldehyde afforded a modest protection from oxidation in the blood, which was substantially improved in carrier-added experiments. In vivo positron emission tomography imaging of tumour-bearing mice produced high tumour-to-background images and radiotracer uptake in high ALDH-expressing organs but was unable to differentiate between drug-sensitive and drug-resistant tumours. Alternative strategies to protect the labile aldehyde are currently under investigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".