Recent Advances on the Development of Early Transition Metal Complexes for Photo‐Assisted Cytotoxicity Applications
Bibliographic record
Abstract
Abstract For targeted therapy, the strategic use of an inactive precursor or “prodrug” could be an attractive strategy. Photo‐assisted chemotherapy offers a smart opportunity for developing new inorganic prodrugs for selective and temporal regulation on the chemotherapeutic activity of the prodrugs with light. The 3d transition metal complexes with versatile coordination number and geometry, redox, thermodynamic and kinetic properties, the presence of low‐energy metal‐centered electronic transitions, and the ability to exhibit light‐assisted chemical reactions have emerged as strategic tools for photo‐chemotherapeutic applications. Transition metal complexes are typically characterized by metal‐centered, ligand to metal, metal to ligand or intra‐ligand electronic transitions, and photo‐activated electronic states of the transition metal complexes potentially exhibit a wide range of chemical reactions viz. intramolecular oxido‐reduction reactions, intramolecular rearrangements, ligand exchange reactions or energy transfer those cascades into the generation of ROS (⋅OH, O 2 − ⋅, O 2 2− , 1 O 2 ), small molecules like H 2 , N 2 , CO or NO, and alkyl or aryl radicals. Applications of photo‐activable transition metal complexes for generating cytotoxic ions or radicals in the presence of light have emerged as an attractive strategy for photo‐activated chemotherapy (PACT). Here in, we reviewed the photophysical and photochemical aspects of the early transition metal complexes exhibiting photo‐ activated chemotherapy.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".