Activatable Photosensitizers: From Fundamental Principles to Advanced Designs
Bibliographic record
Abstract
Photodynamic therapy (PDT) is a promising treatment that uses light to excite photosensitizers in target tissue, producing reactive oxygen species and localized cell death. It is recognized as a minimally invasive, clinically approved cancer therapy with additional preclinical applications in arthritis, atherosclerosis, and infection control. A hallmark of ideal PDT is delivering disease-specific cytotoxicity while sparing healthy tissue. However, conventional photosensitizers often suffer from non-specific photoactivation, causing off-target toxicity. Activatable photosensitizers (aPS) have emerged as more precise alternatives, offering controlled activation. Unlike traditional photosensitizers, they remain inert and photoinactive during circulation and off-target accumulation, minimizing collateral damage. These photosensitizers are designed to "turn on" in response to disease-specific biostimuli, enhancing therapeutic selectivity and reducing off-target effects. This review explores the principles of aPS, including quenching mechanisms stemming from activatable fluorescent probes and applied to activatable photosensitizers (RET, PeT, ICT, ACQ, AIE), as well as pathological biostimuli (pH, enzymes, redox conditions, cellular internalization), and bioresponsive constructs enabling quenching and activation. We also provide a critical assessment of unresolved challenges in aPS development, including limitations in targeting precision, selectivity under real-world conditions, and potential solutions to persistent issues (dual-lock, targeting moieties, biorthogonal chemistry and artificial receptors). Additionally, it provides an in-depth discussion of essential research design considerations needed to develop translationally relevant aPS with improved therapeutic outcomes and specificity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".