Fast, facile, base-free microwave-assisted metallation of bacteriochlorophylls and corresponding high yield synthesis of TOOKAD
Bibliographic record
Abstract
Naturally-derived metallo-bacteriochlorophylls have attracted much attention since their clinical approval for cancer photodynamic therapy. Their therapeutic properties are rooted in the metal complexation of bacteriochlorophylls, which endows them with optical properties favourable for biophotonic and biomedical applications, including near-infrared light-activated reactive oxygen species generation at therapeutic levels. Despite these advantages, the utility of these chromophores has been limited by synthetic challenges associated with bacteriochlorophyll metallation; specifically, a slow reaction rate and necessity of complex purification procedures remain barriers towards metalated bacteriochlorophyll synthesis. Here, these limitations are overcome through the development of a new fast, facile, efficient, base-free microwave heating metallation method for the synthesis of a series of metallo (Pd, Cu, Zn, Cd, Sn, In, Mn, Co) bacteriopyropheophorbides. The preparation and structural and optical spectral characterization of these complexes are presented. This microwave-enabled synthetic method is then applied to generate the clinical photosensitizer agent Pd-bacteriopheophorbide (TOOKAD) effectively and efficiently, followed by validation of its metallation-enhanced ROS generation.
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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".