Tinzaparin Pharmacokinetics in Patients with Cancer: A Comparative Modeling Study
Bibliographic record
Abstract
Abstract Cancer-associated thrombosis (CAT) is common and a leading cause of mortality in patients with cancer. In specific CAT scenarios, low-molecular-weight heparins (LMWHs), including tinzaparin, are preferred over direct oral anticoagulants. Despite the importance of understanding LMWH pharmacokinetics (PK) in cancer for optimizing CAT management, available data remain limited. To compare tinzaparin PK in cancer and non-cancer patients by developing a population PK model. This prospective, multicenter, case–control trial enrolled patients receiving once-daily subcutaneous tinzaparin at a therapeutic dose of 175 IU·kg−1, including matched cancer and non-cancer patients. Plasma anti-Xa activity was measured at multiple time points and analyzed using a non-linear mixed-effect modeling. A PK model was developed, and covariate effects were assessed for parameters of the model. The impact of cancer on tinzaparin PK was evaluated by incorporating cancer status as a categorical covariate. A total of 333 patients (including 46 matched cancer and non-cancer patients) were included in the analysis. A monocompartmental model with first-order absorption best described tinzaparin PK. The volume of distribution was associated with body weight, while clearance and anti-Xa activity were associated with creatinine clearance. No significant differences were observed between matched cancer and non-cancer patients in anti-Xa activity exposure at day 1 and steady state. PK profiles were comparable between cancer and non-cancer patients. Additionally, further studies should clarify the role of renal function in guiding tinzaparin dosing.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".