Phase I Combination of Sorafenib and Erlotinib Therapy in Solid Tumors: Safety, Pharmacokinetic, and Pharmacodynamic Evaluation from an Expansion Cohort
Bibliographic record
Abstract
The aims of this study were to further define the safety of sorafenib and erlotinib, given at their full approved monotherapy doses, and to correlate pharmacokinetic and pharmacodynamic markers with clinical outcome. In addition, a novel pharmacodynamic marker based on the real-time measurement of RAF signal transduction capacity (STC) is described. Sorafenib was administered alone for a 1-week run-in period, and then both drugs were given together continuously. RAF STC was assessed in peripheral blood monocytes prior to erlotinib initiation. Epidermal growth factor receptor (EGFR) expression and K-RAS mutations were measured in archival tumor samples. Changes in pERK and CD31 were determined in fresh tumor biopsies obtained pretreatment, prior to erlotinib dosing, and during the administration of both drugs. In addition, positron emission tomography-computed tomography scans and pharmacokinetic assessments were done. Eleven patients received a total of 57 cycles (median, 5; range, 1-10). Only four patients received full doses of both drugs for the entire study course, with elevation of liver enzymes being the main reason for dose reductions and delays. Among 10 patients evaluable for response, 8 experienced tumor stabilization of >or=4 cycles. Pharmacokinetic analysis revealed no significant interaction of erlotinib with sorafenib. Sorafenib-induced decrease in RAF-STC showed statistically significant correlation with time-to-progression in seven patients. Other pharmacodynamic markers did not correlate with clinical outcome. This drug combination resulted in promising clinical activity in solid tumor patients although significant toxicity warrants close monitoring. RAF-STC deserves further study as a predictive marker for sorafenib.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".