IgG1 heavy chain-coding gene polymorphism (G1m allotypes) and development of antibodies-to-infliximab
Bibliographic record
Abstract
OBJECTIVE: The chimeric anti-tumor necrosis factor-alpha antibody infliximab is known to induce antibodies-to-infliximab (ATI) in some treated patients. Immunogenicity in murine variable domains is expected; however, constant domains of its human heavy gamma1 chain may also be implicated as it expresses G1m1 and G1m17 allotypes. This allelic form may be immunogenic in patients that are homozygous for the G1m3 allotype commonly expressed in Caucasoid populations. METHODS: As G1m allotypic divergence may explain the presence of ATI or may influence their concentration, a genotyping method was developed and validated to determine antithetical (i.e. mutually exclusive) G1m3 and G1m17 allotypes (amino acid 120 of CH1 according to the international ImMunoGeneTics information system unique numbering) at the IGHG1 gene level (CH1 359g/a nucleotide polymorphism). Two hundred forty-five blood donors and 118 previously described patients suffering from Crohn's disease, treated with infliximab, and having developed ATI in 73 of them, were genotyped. RESULTS: The IGHG1 CH1 359g/a polymorphism does not depart from the Hardy-Weinberg equilibrium in the control population, and allele frequencies were similar in controls and patients. No association was found between the patient G1m allotypes and the presence of ATI or their concentration. It remains possible that anti-Gm1 antibodies are not well detected by the enzyme-linked immunosorbent assays used for ATI detection and/or that the G1m allotypes are minor antigens on IgG1. CONCLUSION: The IGHG1 polymorphism does not seem to play a major role in the induction of ATI. Further analyses will be required to determine whether it is also the case for humanized or fully human antibodies bearing the same G1m allotypes.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".