Transition Metal Complexes and Photodynamic Therapy from a Tumor-Centered Approach: Challenges, Opportunities, and Highlights from the Development of TLD1433
Bibliographic record
Abstract
Transition metal complexes are of increasing interest as photosensitizers in photodynamic therapy (PDT) and, more recently, for photochemotherapy (PCT). In recent years, Ru(II) polypyridyl complexes have emerged as promising systems for both PDT and PCT. Their rich photochemical and photophysical properties derive from a variety of excited-state electronic configurations accessible with visible and near-infrared light, and these properties can be exploited for both energy- and electron-transfer processes that can yield highly potent oxygen-dependent and/or oxygen-independent photobiological activity. Selected examples highlight the use of rational design in coordination chemistry to control the lowest-energy triplet excited-state configurations for eliciting a particular type of photoreactivity for PDT and/or PCT effects. These principles are also discussed in the context of the development of TLD1433, the first Ru(II)-based photosensitizer for PDT to enter a human clinical trial. The design of TLD1433 arose from a tumor-centered approach, as part of a complete PDT package that includes the light component and the protocol for treating non-muscle invasive bladder cancer. Briefly, this review summarizes the challenges to bringing PDT into mainstream cancer therapy. It considers the chemical and photophysical solutions that transition metal complexes offer, and it puts into context the multidisciplinary effort needed to bring a new drug to clinical trial.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".