Identification of Target Golimumab Levels in Maintenance Therapy of Crohn’s Disease and Ulcerative Colitis Associated With Mucosal Healing
Bibliographic record
Abstract
INTRODUCTION: Golimumab is approved as a therapy for ulcerative colitis (UC) patients. Recent data also demonstrate efficacy in Crohn's disease (CD); however, little is known about target drug levels to achieve endoscopic remission. METHODS: We performed a retrospective analysis of IBD patients on maintenance golimumab. Median trough levels were compared using Kruskal-Wallis test, and logistic regression was used to construct a probabilistic model to determine sensitivity and specificity of levels predicting mucosal healing. RESULTS: Fifty-eight patients on maintenance golimumab were included (n = 39 CD, n = 19 UC/IBD-unclassified [IBDU]). Forty percent (n = 23) were cotreated with an immunomodulator, 95% (n = 55) of patients were anti-TNF experienced, and 15.5% (n = 9) had 3 or more prior biologic therapies. Forty-four percent of patients achieved mucosal healing with endoscopic response in a further 26% of patients. Clinical remission was recorded in 41% of patients, and 82% had clinical response. Patients were treated with doses generally higher than the approved maintenance dose. In CD patients, median golimumab trough levels were higher in patients with mucosal healing (8.8 μg/mL vs 5.08 μg/mL, P = 0.03). After calculation of a receiver operating characteristic (ROC) curve for mucosal healing vs nonresponse, a trough level >8 μg/mL was associated with mucosal healing, with 67% sensitivity, 88% specificity, and a likelihood ratio of 3:4. CONCLUSION: Treatment with golimumab was associated with mucosal healing in 44% of all IBD patients. Higher golimumab levels were associated with mucosal healing in CD. These findings support the need for prospective studies to determine target golimumab levels in IBD, which may impact current clinical practices in relation to selection of maintenance dosing.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".