Analogs of Cyclic Peptide Mortiamide‐D From Marine Fungi Have Improved Membrane Permeability and Kill Drug‐Resistant Melanoma Cells
Bibliographic record
Abstract
ABSTRACT Targeted melanoma therapies, including treatment with the small molecule drug dabrafenib, can become ineffective due to acquired drug resistance. Dabrafenib targets BRAF‐V600E, a mutation that is present in more than half of melanoma cancers. Therefore, drug discovery efforts need to explore alternative candidate molecules that selectively target and kill melanoma cells via mechanisms different to those of current drugs. Marine fungi are an underexplored resource for bioactive molecules. Mortiamide‐D, a seven amino acid cyclic peptide from Mortierella sp, is an example molecule with desirable features for drug development. We synthesized mortiamide‐D and three rationally designed analogs and observed modest micromolar activity against HT144 melanoma cells that are sensitive or resistant to dabrafenib. By contrast, mortiamide‐D and analogs did not kill noncancer HaCaT cells at these concentrations. Substitution of D‐Ile at position 7 with D‐Arg improved membrane permeability and enhanced potency against HT144 cells via a mode‐of‐action that includes perturbation of mitochondrial membrane potential. These studies suggest the potential of mortiamides as modifiable scaffolds for developing a new class of molecule for targeting melanoma cells.
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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".