Intensive Loading Dose of Trastuzumab Achieves Higher-Than-Steady–State Serum Concentrations and Is Well Tolerated
Bibliographic record
Abstract
PURPOSE Pharmacokinetics (PKs) and safety results from phase II/III trials suggest that, if high trastuzumab serum concentrations are reached early during treatment for human epidermal growth factor receptor 2 (HER2)-positive breast cancer, patients will gain clinical benefit, and the synergistic effects of trastuzumab and chemotherapy will be maximized. This phase I/II study evaluated the PKs, efficacy, and safety of a novel, intensive loading regimen of trastuzumab in women with HER2-positive metastatic breast cancer (MBC). PATIENTS AND METHODS An intensive loading regimen of trastuzumab was given (6 mg/kg intravenously on days 1, 8, and 15 followed by 6 mg/kg every 3 weeks from day 22) to women age 18 years or older with HER2-positive MBC who may have received previous surgery, radiotherapy, and/or chemotherapy. Study medication was continued until disease progression or withdrawal occurred. Results All eligible women (N = 72) received at least one dose of trastuzumab. Median estimated trough concentration of trastuzumab at the end of 3 weeks of the intensive loading regimen (total of 18 mg/kg of trastuzumab administered) of cycle 1 was 119 mg/L, which is higher than steady-state trough concentrations with a conventional weekly or every-3-week regimen (64.9 or 47.3 mg/L, respectively). No new or unexpected adverse events or increased cardiotoxicity were reported during the study. In patients with measurable disease (n = 47), response rate was 23.4%. Median time to progression was 7.7 months (in all patients). CONCLUSION An intensive loading regimen of trastuzumab achieved higher-than-steady-state serum concentrations during cycle 1, was well tolerated, and had a good efficacy profile.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".