Development of a novel humanized anti-TSLP monoclonal antibody HZ-1127 with anti-allergic diseases and cancer potential
Bibliographic record
Abstract
Thymic stromal lymphopoietin (TSLP) is a member of the IL-2 cytokine family and has been widely recognized as a master regulator of type 2 inflammatory responses at barrier surfaces. Recent studies found dysregulation of the TSLP-TSLP receptor (TSLPR) pathway is associated with the pathogenesis of not only allergic diseases but also a wide variety of cancers including both solid tumors and hematological tumors. Thus, the blockade of TSLP represents an attractive therapeutic strategy for allergic diseases and cancer. In this study, we report the development of a novel humanized anti-TSLP monoclonal antibody (mAb) HZ-1127. Binding affinity, specificity, and ability of HZ-1127 in inhibiting TSLP were tested. HZ-1127 selectively binds to the TSLP cytokine with high affinity and specificity. Furthermore, HZ-1127 dramatically inhibits TSLP-dependent STAT5 activation and is more potent than Tezepelumab, which is an FDA-approved humanized mAb against TSLP for severe asthma treatment in inhibiting TSLP-induced CCL17 and CCL22 chemokines secretion in human peripheral blood mononuclear cells. Our pre-clinical study demonstrates that HZ-1127 may serve as a potential therapeutic agent for allergic diseases and cancer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".