Is there an optimal sequence of biologic therapies for inflammatory bowel disease?
Bibliographic record
Abstract
Over the past two decades, 11 biologic agents have been approved for use in most countries for the treatment of moderate-to-severe inflammatory bowel disease (IBD). Antitumor necrosis factor α (anti-TNF) agents are commonly used as the first biologic in clinical practice, and nearly all pivotal studies of induction therapy enrolled patients with and without prior use of anti-TNF therapy. This narrative review presents a reasonable approach to devising treatment sequences, examining the magnitude of benefit for each drug versus placebo or active comparator and then considering how that benefit changes with prior anti-TNF treatment. Data from ULTRA 2, GEMINI 1, VARSITY, and True North in patients with ulcerative colitis indicate that induction adalimumab, vedolizumab, and ozanimod showed lower clinical remission rates after anti-TNF therapy, while UNIFI, OCTAVE 1&2, and U-ACHIEVE/U-ACCOMPLISH show ustekinumab, tofacitinib, and upadacitinib did not. In patients with Crohn’s disease, endoscopic remission or mucosal healing after induction therapy rather than clinical remission as well as assessment of persistent endoscopic remission are good measures of long-term disease outcomes. Considering the drugs for which data on endoscopic remission rates are available, EXTEND and GEMINI 2&3 show adalimumab and vedolizumab with persistently lower endoscopic remission rates after prior anti-TNF therapy, while IM-UNIFI, SEAVUE, and FORTIFY show ustekinumab and risankizumab did not. Data from the multicenter retrospective EVOLVE study indicate that the effectiveness of anti-TNF therapy does not seem to be significantly impacted by prior vedolizumab therapy, and may further suggest the benefit of using vedolizumab as a first-line biologic. As adverse event rates remain low across all treatments, the magnitude of harm from untreated or poorly treated disease far outweighs harm from any individual therapy. Regardless of the treatment sequence, careful monitoring for early signs of treatment nonresponse and switching to another potentially highly active therapy are critical to effective management of IBD.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".