Anti-FVIII Antibody and Inhibitor Development Patterns in Hemophilia A Mouse Models: A Systematic Review
Bibliographic record
Abstract
Background: Management of hemophilia A with exogenous factor VIII (FVIII) infusions can result in the development of alloantibodies that neutralize FVIII activity (inhibitors). The presence of FVIII inhibitors is associated with high morbidity and a decreased quality of life. Hemophilia A mouse models are a valuable tool for studying inhibitor development and hemophilia A. These models have been shown to develop inhibitor upon treatment with human FVIII. However, there are numerous hemophilia A mouse models, each with potentially varying patterns of inhibitor development. Objective: To identify existing hemophilia A mouse models and characterize anti-FVIII antibody and inhibitor development patterns. Methods: A systematic literature review was conducted using Ovid MEDLINE and EMBASE. The Databases were searched from inception through May 2019 for full-length publications, published abstracts, and conference proceedings that reported use of mouse models of hemophilia A to investigate total anti-FVIII antibody levels and/or inhibitor titers. The search strategy used the categorical search terms: "hemophilia A" AND "mouse" AND "animal model" AND "inhibitor" (Figure 1). Results: Our review consisted of a total of 88 studies which reported the use of 11 different hemophilia A mouse models. Out of the 88 studies, 19 studies only reported total anti-FVIII antibody data without reporting inhibitor data. In 30% of the studies that reported both total anti-FVIII antibody and inhibitor data, not all mice that developed anti-FVIII antibodies had positive inhibitor titres. Our systematic review identified the E16 - C57BL/6 model as the most frequently used model, as 27 studies (31%) reported using this model. The E16 - C57BL/6, E16 - Balb/c, E16 - C57BL6/129, E17 - C57BL/6, E17 - C57BL6/129, and FVIII total knockout (TKO) models demonstrate high immunogenicity to exogenous FVIII and were used in 65% of the studies. These mice all consistently develop anti-FVIII antibodies and inhibitors at >95% incidence following treatment with at least 4 doses of full-length recombinant human FVIII (rhFVIII). The E16 - R593C model and the E17 - HLA-DRB1*1501 model both display anti-FVIII antibody and inhibitor development patterns that are representative of the human clinical scenario (~30% inhibitor development incidence). The only model completely immunotolerant to rhFVIII is the E17 - subline E model generated by van Helden et al. (2011) which is a transgenic mouse line on the hemophilic E17 background that expresses human full-length F8 cDNA. Treatment with 8 weekly doses of native human FVIII resulted in no anti-FVIII antibody or inhibitor development in this model. Conclusions: This systematic review demonstrated that mice of the most frequently used models almost always develop inhibitors when treated with full-length rhFVIII. Humanized models may be ideal for use in studies that aim to replicate human inhibitor development patterns. In addition, immunotolerant models may be best used for in vivo studies in hemophilia A that aim to avoid both antibody and inhibitor development. Lastly, this systematic review emphasises that it is important to report both anti-FVIII antibodies and neutralizing activity in Bethesda Units since in 30% of the studies that reported both values, the incidence of inhibitor development was less than the incidence of anti-FVIII antibody development in mice. Disclosures Matino: Bioviiix: Honoraria; Roche: Research Funding; Sanofi: Honoraria; Sobi: Honoraria, Research Funding; Bayer: Honoraria, Research Funding; Pfizer: Honoraria, Research Funding.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".