Exposure and Tumor Fn14 Expression as Determinants of Pharmacodynamics of the Anti-TWEAK Monoclonal Antibody RG7212 in Patients with Fn14-Positive Solid Tumors
Bibliographic record
Abstract
PURPOSE: The TWEAK-Fn14 pathway represents a novel anticancer target that is being actively investigated. Understanding the relationship between pharmacokinetics of anti-TWEAK therapeutics and tumor pharmacodynamics is critical. We investigated exposure-response relationships of RG7212, an anti-TWEAK mAb, in patients with Fn14-expressing tumors. EXPERIMENTAL DESIGN: Patients with Fn14-positive tumors (IHC ≥ 1+) treated in a phase I first-in-human study with ascending doses of RG7212 were the basis for this analysis. Pharmacokinetics of RG7212 and dynamics of TWEAK were determined, as were changes in tumor TWEAK-Fn14 signaling in paired pre- and posttreatment tumor biopsies. The objectives of the analysis were to define exposure-response relationships and the relationship between pretreatment tumor Fn14 expression and pharmacodynamic effect. Associations between changes in TWEAK-Fn14 signaling and clinical outcome were explored. RESULTS: Thirty-six patients were included in the analysis. RG7212 reduced plasma TWEAK to undetectable levels at all observed RG7212 exposures. In contrast, reductions in tumor Fn14 and TRAF1 protein expression were observed only at higher exposure (≥ 300 mg*h/mL). Significant reductions in tumor Ki-67 expression and early changes in serum concentrations of CCL-2 and MMP-9 were observed exclusively in patients with higher drug exposure who had high pretreatment tumor Fn14 expression. Pretreatment tumor Fn14 expression was not associated with outcome, but a trend toward longer time on study was observed with high versus low RG7212 exposure. CONCLUSIONS: RG7212 reduced tumor TWEAK-Fn14 signaling in a systemic exposure-dependent manner. In addition to higher exposure, relatively high Fn14 expression might be required for pharmacodynamic effect of anti-TWEAK monoclonal antibodies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".