Peripheral Lymphocyte Multidrug Resistance Activity as a Predictive Tool of Biological Therapeutic Response in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Multidrug resistance (MDR) transporters may be used as biomarkers to monitor disease progression in RA and as a predictive tool to establish responsiveness to biological therapy. In this multicenter clinical trial, we aimed to assess the predictive value of activity measurement of transporters MDR1, MD resistance protein (MRP)1, and breast cancer resistance protein (BCRP) for biological therapeutic response in RA before the initiation of biological therapy as well as 4 to 6 and 12 weeks after. METHODS: Peripheral blood samples were collected from 27 responders and 12 nonresponders to biological disease-modifying antirheumatic drugs (bDMARD) at the indicated timepoints as well as from 35 healthy controls. MDR activity factor (MAF) of MDR1, MRP1, and BCRP was measured in CD3+ and CD19+ cells using the Solvo MDQ Kit and cell surface staining by flow cytometry following peripheral blood mononuclear cells isolation. RESULTS: At the start of therapy, MAFC (composite MAF of MRP1 and MDR1) and MAFMDR values, and at 4 to 6 weeks of treatment, MAFC, MAFMRP, and MAFMDR values of CD3 cells were higher in nonresponders compared to responders. Receiver-operation characteristic curve analysis revealed that RA patients with MAFC values above 21.3 in CD3 cells at the start of bDMARD therapy are likely to be nonresponders. At 4 to 6 weeks of treatment, these also predict unfavorable response: MAFC values above 20.3, MAFMRP values above 6.0, and MAFMDR values above 13.9 in CD3 cells. CONCLUSION: Our results indicate that the determination of MAFC values in CD3 cells of patients with RA may be of predictive value prior to the initiation of biological therapy, to establish whether the patient will demonstrate sufficient therapeutic response.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".