Metabolic profile and in vivo stability of a peptide-conjugated chlorin-type photosensitiser targeting neuropilin-1 : Interest of pseudopeptides.
Bibliographic record
Abstract
Destruction of the neovasculature is essential for efficient tumour eradication by photodynamic therapy (PDT). Since the over-expression of Vascular Endothelial Growth Factor (VEGF) receptors is correlated with tumour angiogenesis and growth, we conjugated a photosensitiser (5-4-carboxyphenyl)-10,15,20-triphenyl-chlorin, TPC) via a spacer (6-aminohexanoic acid, Ahx) to a neuropilin-1 (NRP-1) specific homing heptapeptide (ATWLPPR) targeting tumour vasculature (Tirand et al. 2006). The intratumoural localisation of the photosensitiser, its stability and its metabolic profile have been studied on a model of nude mice xenografted with U87 human malignant glioma cells (Tirand et al. 2007). By fluorescence microscopy, we evidenced a selective accumulation of the conjugated photosensitiser in the endothelial cells bordering the tumour vessels compared with unconjugated photosensitiser. TPC-Ahx-ATWLPPR accumulated at high levels in the tumour and tissues of the reticuloendothelial system, particularly by liver and spleen. TPC-Ahx-ATWLPPR was stable in vitro in plasma for at least 24 h at 37°C. In vivo, the peptide moiety was progressively degraded from 2h post injection, resulting in the formation of a metabolic product, TPC-Ahx-A (Tirand, et al. 2007). In order to improve the heptapeptide stability towards proteases (Adessi and Soto 2002) and to avoid any non-selective accumulation of the metabolic product, this photosensitiser has been coupled to pseudopeptides on solid support. We have, for the first time, studied the affinity of these pseudopeptides towards the different VEGF165 (isoform 165 of VEGF) receptors (NRP-1, NRP-2, Flt-1 and KDR) by competition experiments. .
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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 | 0.000 |
| 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".