<scp>TRUST</scp> trial: <scp>BAY</scp> 86‐6150 use in haemophilia with inhibitors and assessment for immunogenicity
Bibliographic record
Abstract
Introduction The most serious and challenging complication of haemophilia treatment is development of inhibitors to replacement factors VIII or IX . Innovative therapies currently being explored for patients with haemophilia and inhibitors include BAY 86‐6150, a modified recombinant activated factor VII ( FVII a). Immunogenicity remains a substantial barrier in this endeavour. Aim To present safety and efficacy results of the BAY 86‐6150 study in patients with inhibitors and report detailed analysis of epitope mapping in a patient who developed anti‐ BAY 86‐6150 antibodies. Methods Patients aged 12–62 years with moderate or severe haemophilia A or B were eligible for the phase 3 TRUST trial if they had a history of high‐titre inhibitors. Four escalating doses of BAY 86‐6150 (6.5, 20, 50, 90 μg kg −1 ) were planned with ≥10 patients per dose level. Bleeding episodes were treated with BAY 86‐6150. Development of anti‐ BAY 86‐6150 antibodies was considered a serious adverse event. Results TRUST was discontinued after one patient in the 6.5‐μg kg −1 cohort developed anti‐ BAY 86‐6150 neutralizing antibodies following three exposures. The anti‐ BAY 86‐6150 antibodies cross‐reacted with and neutralized wild‐type FVII a ( WT ‐ FVII a). Post hoc epitope mapping using peripheral blood mononuclear cells from the responding patient found that none of the 14 peptides unique to BAY 86‐6150 were recognized by the patient's T cells, but strong responses were detected against 2 WT ‐ FVII a peptides. Conclusion In the single patient with haemophilia A who developed anti‐ BAY 86‐6150 antibodies, results of T‐cell epitope mapping indicated BAY 86‐6150 was no more immunogenic than WT ‐ FVII a.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".