Population Pharmacokinetics and Exposure–Response Modeling of Golimumab in Adults With Moderately to Severely Active Ulcerative Colitis
Bibliographic record
Abstract
PURPOSE: Golimumab is a fully human monoclonal antibody to tumor necrosis factor-α and is indicated for the treatment of moderately to severely active ulcerative colitis (UC). This study analyzed the population pharmacokinetic (PK) properties of golimumab and exposure-response for efficacy and safety, using data from combined Phase II/III UC studies. METHODS: Data on serum golimumab concentration following IV and subcutaneous (SC) administration were fitted simultaneously using nonlinear mixed-effects modeling for the development of a population PK model. Logistic regression models were used for assessing relationships between serum golimumab concentrations and clinical efficacy outcomes in SC induction and maintenance studies. The percentages of patients developing infections, serious infections, and serious adverse events were assessed by golimumab exposure metric quartiles. FINDINGS: was 10.5 days; bioavailability following SC administration was 52.2%. Body weight, anti-golimumab antibodies, serum albumin, C-reactive protein, and alkaline phosphatase affected golimumab disposition. A positive exposure-response relationship was established between golimumab concentration and efficacy outcomes. No apparent correlation between golimumab exposure and rate of infections, serious infections, or serious adverse events was observed in patients receiving golimumab 50 or 100 mg SC every 4 weeks through 1 year. IMPLICATIONS: Body weight, serum albumin, and anti-golimumab antibodies explain some of the variability observed in the PK properties of golimumab, and exposure-response findings support the recommended posology of golimumab in UC. ClinicalTrials.gov identifiers: NCT00488774, NCT00487539, and NCT00488631.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".