Looking at Marine-Derived Bioactive Molecules as Upcoming Anti-Diabetic Agents: A Special Emphasis on PTP1B Inhibitors
Bibliographic record
Abstract
Diabetes mellitus (DM) is a chronic metabolic disease with high morbimortality rates. DM has two types: type 1, which is often associated with a total destruction of pancreatic beta cells, and non-insulin-dependent or type 2 diabetes mellitus (T2DM), more closely associated with obesity and old age. The main causes of T2DM are insulin resistance and/or inadequate insulin secretion. Protein-tyrosine phosphatase 1B (PTP1B) negatively regulates insulin signaling pathways and plays an important role in T2DM, as its overexpression may induce insulin resistance. Thus, since PTP1B may be a therapeutic target for both T2DM and obesity, the search for novel and promising natural inhibitors has gained much attention. Hence, several marine organisms, including macro and microalgae, sponges, marine invertebrates, sea urchins, seaweeds, soft corals, lichens, and sea grasses, have been recently evaluated as potential drug sources. This review provides an overview of the role of PTP1B in T2DM insulin signaling and treatment, and highlights the recent findings of several compounds and extracts derived from marine organisms and their relevance as upcoming PTP1B inhibitors. In this systematic literature review, more than 60 marine-derived metabolites exhibiting PTP1B inhibitory activity are listed. Their chemical classes, structural features, relative PTP1B inhibitory potency (assessed by IC50 values), and structure–activity relationships (SARs) that could be drawn from the available data are discussed. The upcoming challenge in the field of marine research—metabolomics—is also addressed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".