Differential Pharmacokinetics of Ganitumab in Patients With Metastatic Pancreatic Cancer Versus Other Advanced Solid Cancers
Bibliographic record
Abstract
Ganitumab is an investigational, fully human monoclonal antibody antagonist of the insulin-like growth factor-1 receptor (IGF1R) that has shown trends towards improved progression-free survival and overall survival in a phase 2 pancreatic cancer clinical trial. To characterize ganitumab pharmacokinetics (PK) and identify factors affecting PK, ganitumab serum concentration data from three clinical trials were analyzed. The PK of ganitumab as monotherapy and in combination with gemcitabine in patients with pancreatic or non-pancreatic cancer were assessed with a non-linear mixed-effect model. We found that ganitumab exhibited linear and time-invariant kinetics. A two-compartment model adequately described data over a dose range of 1-20 mg/kg with good predictive capability. Typical clearance and central volume of distribution values were 1.7- and 1.3-fold higher, respectively, in patients with pancreatic cancer than in patients with other advanced solid cancers, resulting in lower ganitumab exposure. Covariate analysis was used to evaluate effects of cancer type, gemcitabine coadministration, clinical study, demographics, and laboratory values on ganitumab PK. Pancreatic cancer type was the most significant covariate on clearance along with weight, albumin, and serum creatinine. Gemcitabine coadministration did not affect ganitumab clearance. Thus, disease state can significantly affect PK and should be considered when selecting the clinically effective dose.
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 | 0.001 | 0.003 |
| 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 | 0.001 | 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".