Synthesis of SHIP1‐Activating Analogs of the Sponge Meroterpenoid Pelorol
Bibliographic record
Abstract
Abstract Two biomimetic approaches have been used to synthesize analogs of the SHIP1‐activating sponge meroterpenoid pelorol (1). One approach started from the chiral pool plant natural product sclareolide, which has the same absolute configuration as pelorol. The second approach utilized an enantioselective polyene cyclization to efficiently access both absolute configurations of the pelorol meroterpenoid skeleton and to prepare A‐ring functionalized compounds. Selected analogs have been evaluated for water solubility and biological activity. It was found that the undesirable catechol and ester functionalities in 1 could be removed to give MN100 (3), without a decrease in SHIP1‐activating ability. A further refinement led to the resorcinol analog 18, which is the most effective SHIP1‐activating pelorol analog made to date. The HCl salt of ent‐28, a C‐3 amino analog of 18, is about 500,000‐fold more soluble in water than MN100 (3). (±)‐28·HCl activates SHIP1 in vitro, inhibits Akt phosphorylation in stimulated MOLT‐4 (SHIP+) cells, and is active in a dose‐dependent manner in a mouse model of inflammation when administered by oral gavage (ED50 ≈ 0.1 mg/kg). Pelorol analogs ent‐28 or (±)‐28 are promising chemical tools for further preclinical in vivo evaluation of the potential of SHIP1 activators as therapeutics for treating hematopoietic diseases involving aberrant activation of PI3K cell signaling.
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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) | 0.003 | 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".