The Role of Modified Fc Fragments in Treating Autoimmune Diseases: A Potential Replacement for Intravenous Immunoglobulin
Bibliographic record
Abstract
High-dose (1-2 g/kg) intravenous immunoglobulin (IVIg), and more recently, subcutaneously delivered immunoglobulin (SCIg), are used to treat a variety of autoimmune diseases; however, there are challenges associated with product production and availability. These challenges have provided incentives to develop a human recombinant fragment crystallizable region (Fc) as a more potent alternative to IVIg and SCIg for the treatment of autoimmune diseases where its mechanism has been suggested to be Fc-dependent. Various Fc multimers have been produced that show enhanced efficacy compared to IVIg for amelioration of disease in animal models, such as primary immune thrombocytopenia (ITP) and rheumatoid arthritis (RA). Recently, a recombinant human immunoglobulin (Ig) G1 Fc hexamer entitled Fc-µTP-L309C was produced by CSL Behring by fusing the 18 amino acid (aa) IgM tailpiece to the C-terminus of a variant human IgG1 Fc with a point mutation at position 309. To better understand whether Fc-µTP-L309C is good replacement therapy for IVIg/SCIg, I first examined its efficacy in a mouse model of ITP. I demonstrated that Fc-µTP-L309C is more efficacious than SCIg at ameliorating ITP and at blocking Fc gamma receptor (FcγR)-mediated phagocytosis. With the limited amount of studies performed on the therapeutic efficacy of IVIg/SCIg in RA, I decided to investigate the efficacy of IVIg and SCIg in the K/BxN serum transfer model and in the endogenous K/BxN model of RA and compare it to that of Fc-µTP-L309C. Again, I showed that Fc-µTP-L309C is more efficacious than SCIg at ameliorating RA in the K/BxN mouse models. Furthermore, I showed that Fc-µTP-L309C affected both the innate and the adaptive immune system in the K/BxN endogenous model of RA. Fc-µTP-L309C decreased auto-antibody (Ab) production by B cells and the subsequent deposition of auto-Abs on the articular cartilage. Fc-µTP-L309C also increased transforming growth factor beta (TGF-β) and forkhead box P3 (FoxP3)+ T regulatory cells (Tregs) in the joints of K/BxN mice. Fc-µTP-L309C also blocked FcγRIII on neutrophils and prevented interleukin-1 beta (IL-1β) release from neutrophils. Thus, Fc-µTP-L309C serves as a potential replacement for IVIg for Ab mediated autoimmune diseases and its mechanism is likely multifactorial and different depending on the disease.
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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) | 0.001 | 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".