Management of loss of response to anti-TNF drugs: Change the dose or change the drug?
Bibliographic record
Abstract
The advent of biological therapy has dramatically changed our concept of treating refractory inflammatory bowel disease (IBD). Chimeric and more humanized anti-TNF Abs have shown to be highly efficacious in these illnesses, but all issues have not been resolved. Indeed, still 20–30% of patients with refractory Crohn's disease 1 – 6 and 30–40% 7 of those with refractory ulcerative colitis do not respond to anti-TNF treatment. Moreover, the long-term use of anti-TNF monoclonal antibodies is associated with immunogenicity, which interferes with efficacy, and with the risk of infectious complications. Secondary loss of response to monoclonal antibodies is a reality and clinicians should be prepared to optimize therapy. A rapidly declining response to a given drug in patients responding to the first doses is usually called tachyphylaxis. Underlying reasons for a rapid loss of response can be very diverse. First, the human body usually reacts to external activators of endogenous proteins by decreasing the expression of the target protein or by internalizing membrane bound receptors. Second, alternative pathways can be recruited to restore the bioactivity targeted by a given drug. Third, the bioavailability and/or pharmacokinetics of therapeutic compounds are highly variable among individuals and liable to dramatic changes over time. The last mechanism can also induce a gradual loss of response, which is more commonly observed with anti-TNF agents. We will focus on loss of response with the use of anti-TNF antibodies in IBD and suggest strategies to optimize therapy in these patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".