Uttroside B, a US-FDA-Designated Orphan Drug Against Hepatocellular Carcinoma (HCC), Impedes Non-alcoholic Steatohepatitis (NASH) and NASH -Induced HCC
Bibliographic record
Abstract
Abstract Introduction Non-alcoholic steatohepatitis (NASH) is characterized by excessive accumulation of fat, accompanied by inflammation and liver injury. NASH can lead to chronic conditions like fibrosis and cirrhosis, and has an elevated risk of progressing to hepatocellular carcinoma (HCC). Currently there are no FDA-approved drugs for the treatment of NASH. Objectives Our discovery of Uttroside B (Utt-B), a phytosaponin isolated from Solanum nigrum Linn., which exhibits remarkable anti-HCC potential, has gained global recognition and is currently a US-FDA-designated ‘orphan drug’ against HCC. The present study highlights Utt-B as an anti-NASH molecule, by utilizing a High-Fat-Diet murine model, and as an inhibitor to the progression of NASH to HCC, using a streptozotocin-induced steatohepatitis-derived HCC animal model, thereby warranting its further validation as a propitious candidate drug molecule against NASH and NASH-induced HCC. Methods High fat diet-induced NASH and streptozotocin-induced steatohepatitis-derived HCC were developed in C57BL/6 mice. Utt-B was administered intraperitoneally. q-PCR, immunoblotting and staining techniques such as Haematoxylin and eosin, Oil Red O, Sirius Red and Masson’s Trichrome, were performed to assess the therapeutic potency of Utt-B against NASH. Nanostring n-Counter analysis was conducted to verify the anti-fibrotic potential of Utt-B in NASH-induced HCC mouse model. Results Utt-B ameliorates the pathological features such as, steatosis, hepatocyte ballooning and inflammation associated with NASH. Utt-B up-regulates the expression of autophagy markers ATG7, Beclin-1 and LC-III and down-regulates the expression of α-SMA, the indicator protein for the activation of hepatic stellate cells. Utt-B hinders the development of fibrosis and halts the progression of NASH to HCC in NASH-induced HCC mouse model. Conclusion Our investigation reveals that Utt-B effectively alleviates NASH and abrogates its progression to HCC. As no treatment options are currently available against NASH, our findings are very relevant and strengthen the prospect of developing Utt-B as a potent drug for the treatment of NASH and NASH-induced HCC. Graphical Abstract
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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) | 0.002 | 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".