Commentary: detection of infliximab levels and anti‐infliximab antibodies
Bibliographic record
Abstract
Clinicians treating inflammatory bowel disease frequently face the dilemma of loss of response to anti-TNF agents.1 Previous studies have demonstrated that poor clinical outcomes relate to a lack of circulating drug and the secondary development of antibodies to monoclonal antibodies including infliximab (ATI).2, 3 However, there is variability and a lack of standardisation in existing infliximab drug level and antidrug antibody assays. The nicely written manuscript by Vande Casteele et al.,4 addresses these concerns by setting up a robust round robin experiment evaluating both serum samples and spiked control samples to compare three different European assays from the Netherlands, Belgium, and France. While the authors conclude that there was a good correlation of infliximab and ATI measurements between the assays, of concern, the commercially available Biomedical Diagnostics (BMD) kit from Paris, France, detected false positive infliximab levels in nearly a fifth of the samples. This has implications for the recently published retrospective study by Pariente et al. who utilised the BMD kit and concluded in contrast to existing literature and, perhaps incorrectly, that antibodies to infliximab may not predict response to intensification of infliximab therapy in patients with IBD.5 With the recent interest in therapeutic monitoring of anti-TNF therapy to optimise clinical outcomes for patients, there has been an explosion in the number and type of assays reported in the medical literature ranging from solid phase to fluid phase assays, different platforms, including radio-immunoassays and enzyme-linked immunosorbent assays, and differences in the antigenic targets e.g. double-antigen vs. antilambda chain assays.6-11 In the absence of a gold standard assay, most importantly, this article highlights the need for stringent assessment, standardisation and validation of any future assays before they become commercially available and used for clinical decision making. Declaration of personal and funding interests: None.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.063 | 0.042 |
| Insufficient payload (model declined to judge) | 0.009 | 0.014 |
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".