Combination Therapy With Guselkumab and Golimumab in Patients With Moderately to Severely Active Ulcerative Colitis: Pharmacokinetics, Immunogenicity and Drug–Drug Interactions
Bibliographic record
Abstract
A proof-of-concept study with the combination of guselkumab and golimumab in patients with ulcerative colitis (UC) has shown that the combination therapy resulted in greater efficacy than the individual monotherapies. The current analysis evaluated the pharmacokinetics (PK) and immunogenicity of guselkumab and golimumab in both the combination therapy and individual monotherapies. Blood samples were collected to evaluate serum concentrations and immunogenicity of guselkumab and golimumab. Population PK (PopPK) models were developed to assess the effects of combination therapy and other potential covariates on the PK of guselkumab and golimumab. The guselkumab PK was comparable between monotherapy and combination therapy, whereas golimumab concentrations were slightly higher with combination therapy. The anti-guselkumab antibody incidence was low with both monotherapy and combination therapy, and guselkumab immunogenicity did not impact the clearance. Conversely, the anti-golimumab antibody incidence with combination therapy was lower than that for monotherapy. PopPK analysis suggested that the slightly higher golimumab concentrations with combination therapy were partially due to lower immunogenicity and thus lower clearance with combination therapy. C-reactive protein (CRP) was also a significant covariate on golimumab clearance. The greater improvement of inflammation with combination therapy, as shown by reductions in CRP, may have also contributed to the higher golimumab concentrations. Combination therapy slightly decreased the clearance of golimumab, but not guselkumab clearance, in patients with UC. Lower immunogenicity and greater improvement of inflammation with combination therapy were potential mechanisms for slightly increased golimumab concentrations with combination therapy as compared with golimumab monotherapy.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".