Quantifying the Effect of Methotrexate on Adalimumab Response in Psoriasis by Pharmacokinetic–Pharmacodynamic Modeling
Bibliographic record
Abstract
Previously, we showed that the combination of methotrexate and adalimumab treatment leads to less antidrug antibody development. In this study, we quantify the pharmacokinetics/pharmacodynamics (PK/PD) of adalimumab and evaluate the influence of methotrexate cotreatment. A population PK-PD model was developed using prospective data from 59 patients with psoriasis (baseline PASI = 12.6) receiving adalimumab over 49 weeks. Typical PK and PD parameters and their corresponding interpatient variability were estimated. We performed a covariate analysis to assess whether interpatient variability could be explained by addition of methotrexate and other covariates. In total, 330 PASIs, 252 adalimumab serum concentrations, and 247 antidrug antibody titers were available. Presence of antidrug antibodies (adalimumab group = 46.7%, adalimumab + methotrexate group = 38.7%; P = .031) was correlated with increased adalimumab apparent clearance (P < .001). In the PD model, the use of concomitant methotrexate was borderline to significantly correlated with a decreased half-maximal inhibitory concentration (adalimumab concentration for which clinical response score is reduced by half; P < .10). On the basis of our PK-PD model, concomitant use of methotrexate indirectly increases adalimumab concentration, partially through less antidrug antibodies formation, which may result in better efficacy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 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".