The ASK1 inhibitor selonsertib in patients with nonalcoholic steatohepatitis: A randomized, phase 2 trial
Bibliographic record
Abstract
Inhibition of apoptosis signal-regulating kinase 1, a serine/threonine kinase, leads to improvement in inflammation and fibrosis in animal models of nonalcoholic steatohepatitis. We evaluated the safety and efficacy of selonsertib, a selective inhibitor of apoptosis signal-regulating kinase 1, alone or in combination with simtuzumab, in patients with nonalcoholic steatohepatitis and stage 2 or 3 liver fibrosis. In this multicenter phase 2 trial, 72 patients were randomized to receive 24 weeks of open-label treatment with either 6 or 18 mg of selonsertib orally once daily with or without once-weekly injections of 125 mg of simtuzumab or simtuzumab alone. The effect of treatment was assessed by paired pretreatment and posttreatment liver biopsies, magnetic resonance elastography, magnetic resonance imaging-estimated proton density fat fraction, quantitative collagen content, and noninvasive markers of liver injury. Due to the lack of effect of simtuzumab on histology or selonsertib pharmacokinetics, selonsertib groups with and without simtuzumab were pooled. After 24 weeks of treatment, the proportion of patients with a one or more stage reduction in fibrosis in the 18-mg selonsertib group was 13 of 30 (43%; 95% confidence interval, 26-63); in the 6-mg selonsertib group, 8 of 27 (30%; 95% confidence interval, 14-50); and in the simtuzumab-alone group, 2 of 10 (20%; 95% confidence interval, 3-56). Improvement in fibrosis was associated with reductions in liver stiffness on magnetic resonance elastography, collagen content and lobular inflammation on liver biopsy, as well as improvements in serum biomarkers of apoptosis and necrosis. There were no significant differences in adverse events between the treatment groups. Conclusion: These findings suggest that selonsertib may reduce liver fibrosis in patients with nonalcoholic steatohepatitis and stage 2-3 fibrosis. (Hepatology 2018;67:549-559).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".