Pharmacologic and pharmacometric studies of factors affecting the pharmacokinetics and pharmacodynamics of antibody drug conjugate anetumab ravtansine in patients with solid tumors.
Bibliographic record
Abstract
e15024 Background: Anetumab ravtansine (AR) is an antibody-drug conjugate (ADC) containing the microtubule inhibitor DM4. ADC drugs have high interpatient variability in pharmacokinetics and pharmacodynamics (PK/PD), which raises concerns whether current dosing based on body weight (mg/kg) is optimal. Thus, we evaluated the effects of patient characteristics, including novel biomarkers of the innate immune system (IIS), on the PK/PD of AR. Methods: Studies were performed in patients in three clinical trials of different cancers with AR as part of treatment: 1) platinum-resistant or refractory high-grade ovarian cancer treated with AR at 2.2mg/kg IV weekly plus bevacizumab(N = 19); 2) pleural mesothelioma treated with AR at 6.5 mg/kg IV every 3 weeks plus pembrolizumab(N = 28); and 3) mesothelin-positive advanced pancreatic adenocarcinoma treated with AR at 5.5-6.5 mg/kg IV every 3 weeks plus nivolumab (and ipilimumab or gemcitabine, N = 27). Intensive plasma PK sampling was performed after the first dose to calculate clearance (CL), volume of distribution (Vd), and area-under-the-plasma-concentration-time curve (AUC 0-inf ). The relationship between biomarkers of FcɣRs (CD64, CD32, CD16) on IIS blood cells, total body weight (TBW), body surface area (BSA), lean body mass (LBM), and sex with PK parameters and clinical outcomes were evaluated by pearson correlation, t-test and multivariate regression analysis. Population PK (popPK) methods further evaluated patient covariates and variability in PK. Results: Patients with higher IIS FcγR CD64 and/or higher TBW or higher BSA had higher AR ADC CL (p < 0.05). AR ADC CL was lower in females (0.030 ± 0.007 L/h) versus males (0.042 ± 0.006 L/h) (p < 0.05). Patients with stable disease and partial response had higher AR ADC AUC 0-inf versus patients with progressive disease AR ADC disposition was described by linear CL with two pathways for the release of the payload DM4, hydrolysis and the IIS. A time-varying drug-to-antibody ratio predicted the release of the DM4 payload. In the PopPK model, LBM and albumin were associated with CL of the AR ADC, LBM and IIS FcγR CD64 were associated with the Vd of the AR ADC, and LBM and age were associated with the CL of the released DM4. Conclusions: These results suggest that TBW-based dosing of AR ADC is not optimal, whereas precision dosing of antibodies and ADCs based on the use of novel metrics of body habitus, IIS biomarkers, and sex may be more appropriate to reduce PK variability and improve response.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| 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".