Note of Caution for the Aqueous Behaviour of Metal‐Based Drug Candidates
Bibliographic record
Abstract
Abstract Poor aqueous solubility is one of the recurrent drawbacks of many compounds in medicinal chemistry. To overcome this limitation, the dilution of drug candidates from stock solutions of an organic solvent is common practice. However, the precise characterisation of these compounds in aqueous solutions is often neglected, leading to some uncertainties regarding the nature of the actual active species. In this communication, we demonstrate that two ruthenium complexes previously reported by our group for their chemotherapeutic potential against cancer, namely [Ru(DIP) 2 (sq)](PF 6 ) and [Ru(DIP) 2 (3‐methoxysq)](PF 6 ), where DIP is 4,7‐diphenyl‐1,10‐phenanthroline, sq=semiquinonate and 3‐methoxysq=3‐methoxysemiquinonate, form colloids in water‐DMSO (1 % v/v) mixtures that are invisible to the naked eyes. [Ru(DIP) 2 (3‐methoxysq)](PF 6 ) was found to form a highly stable and monodispersed colloid with nanoaggregates of ∼25 nm. In contrast, [Ru(DIP) 2 (sq)](PF 6 ) was found to form large reticulates of mostly spherical aggregates which size was found to increase over time. The difference in size and shape distribution of drug candidates is of tremendous significance as the study of their biological activity might be severely affected. Overall, we strongly believe that these observations should be taken into account by the scientific community working on the development of metal‐based drugs with poor water solubility.
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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 | 0.020 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.009 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 0.008 |
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".