Humanisation of a claudin-1-specific monoclonal antibody for clinical prevention and cure of HCV infection without escape
Bibliographic record
Abstract
OBJECTIVE: HCV infection is a leading cause of chronic liver disease and a major indication for liver transplantation. Although direct-acting antivirals (DAAs) have much improved the treatment of chronic HCV infection, alternative strategies are needed for patients with treatment failure. As an essential HCV entry factor, the tight junction protein claudin-1 (CLDN1) is a promising antiviral target. However, genotype-dependent escape via CLDN6 and CLDN9 has been described in some cell lines as a possible limitation facing CLDN1-targeted therapies. Here, we evaluated the clinical potential of therapeutic strategies targeting CLDN1. DESIGN: We generated a humanised anti-CLDN1 monoclonal antibody (mAb) (H3L3) suitable for clinical development and characterised its anti-HCV activity using cell culture models, a large panel of primary human hepatocytes (PHH) from 12 different donors, and human liver chimeric mice. RESULTS: H3L3 pan-genotypically inhibited HCV pseudoparticle entry into PHH, irrespective of donor. Escape was likely precluded by low surface expression of CLDN6 and CLDN9 on PHH. Co-treatment of a panel of PHH with a CLDN6-specific mAb did not enhance the antiviral effect of H3L3, confirming that CLDN6 does not function as an entry factor in PHH from multiple donors. H3L3 also inhibited DAA-resistant strains of HCV and synergised with current DAAs. Finally, H3L3 cured persistent HCV infection in human-liver chimeric uPA-SCID mice in monotherapy. CONCLUSIONS: Overall, these findings underscore the clinical potential of CLDN1-targeted therapies and describe the functional characterisation of a humanised anti-CLDN1 antibody suitable for further clinical development to complement existing therapeutic strategies for HCV.
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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 | 0.001 | 0.001 |
| 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".