An automated mass spectrometric blood test for therapeutic drug monitoring of infliximab
Bibliographic record
Abstract
Infliximab is a monoclonal antibody therapy used to treat several chronic immune-mediated diseases, including Crohn's disease, ulcerative colitis, and rheumatoid arthritis. Infliximab acts by binding to tumor necrosis factor and, thus, inhibiting the inflammatory cascade. While it is a highly effective therapy, a subset of patients on infliximab will develop a loss of response to therapy. In these circumstances, therapeutic drug monitoring of infliximab offers a rational approach to clinical decision making and is associated with improved outcomes. While infliximab has most commonly been measured by immunometric approaches, mass spectrometric approaches offer the opportunity to improve test accuracy and reduce test costs. Herein, we describe a simple, bottom-up high performance liquid chromatography tandem mass spectrometry (LC-MS/MS) approach for quantitation of infliximab in serum. Method development included pre-digestion and digestion experiments to determine critical sample preparation steps, optimization of the workflow and selection of rapidly produced proteolytic peptide(s) for quantitation. The workflow was further improved by automating all sample preparation steps on a robotic liquid handler, facilitating implementation in routine clinical use. A method comparison was performed against a Health Canada and US Food and Drug Administration licensed enzyme-linked immunosorbent assay. Our LC-MS/MS assay accurately reported concentrations based on drug manufacturer targets and demonstrated no interference from endogenous antibodies to infliximab; immunoassay methods did not share these performance characteristics. This LC-MS/MS method provides a workflow amenable to implementation in a clinical laboratory and desired performance characteristics for guiding clinical decision making.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".