Advancing porphyrin's biomedical utility via supramolecular chemistry
Bibliographic record
Abstract
Porphyrins are organic heterocyclic macrocycles with photophysical properties well-suited for clinical phototherapy and cancer imaging. However, their wider application in the clinical management of disease is barred by poor aqueous solubility, bioavailability, tumour accumulation and skin phototoxicity. These limitations instigated the development of supramolecular platforms that improved porphyrin pharmacokinetics and tumour-homing. The supramolecular formulation of porphyrins also facilitates single agent-mediated deeper tissue photoactivation, extended imaging and theranostic multimodality, and synergistic application of multiple therapies. Supramolecular porphyrin structures can overcome additional limitations of porphyrin-mediated photodynamic therapy (PDT), including low depths of tissue penetration that restrict PDT to superficial lesions, inability to treat hypoxic tumours, and incomplete tumour damage. In this review, we discuss the photophysical properties of porphyrins, and overview the clinically-relevant advantages and challenges arising from their incorporation within supramolecular platforms. Specifically, fundamentals underlying the ability of these platforms to ameliorate passive and active porphyrin delivery to tumours, achieve deeper tissue PDT via red-shifted porphyrin Q-bands, energy transfer and sonodynamic effects, and enable new porphyrin-mediated theranostics and synergistic therapeutic capabilities will be explained and exemplified with seminal and cutting-edge in vivo studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".