Gegen Qinlian Tablets attenuate immune-related adverse events in NSCLC patients: A multi-center randomized controlled trial in China
Bibliographic record
Abstract
BACKGROUND: Immune related adverse events (irAEs) significantly compromise patients' quality of life and limit the application of immunotherapy. Gegen Qinlian Tablets (GQT), a classical Chinese herbal formula, have shown potential in mitigating irAEs. However, clinical evidence supporting the use of GQT as a synergy therapy for immune checkpoint inhibitor (ICI) therapy in advanced non-small cell lung cancer (NSCLC) remains insufficient. PURPOSE: To evaluate the efficacy and safety of GQT in reducing the incidence of irAEs in NSCLC with ICI treatment. STUDY DESIGN: A multi-center, open-label, randomized controlled trial. METHODS: Eligible patients were randomly assigned (1:1) to receive ICI plus chemotherapy plus GQT (GQT group) or ICI plus chemotherapy alone (control group). The primary outcome was the incidence and severity of irAEs. Secondary outcomes included objective response rate (ORR) and disease control rate (DCR). Serum levels of interleukin-6 (IL-6) and tumor necrosis factor α (TNF-α) were assessed at baseline and after treatment. RESULTS: From October 2022 to August 2024, 94 participants were randomized to the GQT group (n = 47) or the control group (n = 47) at four hospitals in China. The incidence of irAEs was significantly lower in the GQT group than in the control group [31.91 % (15/47) vs. 59.57 % (28/47), p = 0.007, 95 % CI: 0.137-0.741]. No patient in the GQT group experienced multi-systemic toxicity, whereas 3 patients in the control group did (3/28, 10.7 %). The median onset time of irAEs in the GQT group was 14.9 weeks, and was 8.7 weeks in the control group (p = 0.807). Among response-evaluable patients, the ORR was 48.9 % (0 CR, 23 PR) in the GQT group (n = 45), and 36.2 % (1 CR, 16 PR) in the control group (n = 43) (p = 0.211, 95 % CI: 0.686-3.725). IL-6 level significantly decreased after 3 treatment cycles in the GQT group (p < 0.001), potentially contributing to the reduced incidence of irAEs. CONCLUSION: GQT significantly reduced the incidence of irAEs and prolonged the median onset time of irAEs in patients with advanced NSCLC receiving ICI therapy. The observed effects may be associated with the downregulation of IL-6. GQT showed promise as a synergistic treatment that mitigated irAEs and might enhance the therapeutic efficacy of ICIs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".