Population Pharmacokinetics of Monalizumab in Patients With Advanced Solid Tumors
Bibliographic record
Abstract
Monalizumab is a novel, first-in-class humanized immunoglobulin G4 monoclonal antibody immune checkpoint inhibitor that targets the inhibitory CD94/NKG2A receptors. The objectives of this analysis were to develop a population pharmacokinetic (PK) model of monalizumab, evaluate the impact of clinically relevant covariates on monalizumab PK, and provide dose justification for clinical trials. We developed a monalizumab population PK model to characterize the PK properties of monalizumab in patients with advanced solid tumors or head and neck squamous cell carcinoma. Data from clinical studies D419NC00001 (NCT02671435) and IPH2201-203 (NCT02643550) were pooled for the analysis, resulting in a data set of 3066 PK samples derived from 507 subjects. The PK of monalizumab were reasonably described by a 2-compartment model with first-order elimination. Monalizumab generally exhibited linear PK over a dose range of 22.5-750 mg or 10 mg/kg every 2 weeks. The estimate of clearance was ≈0.255 L/day, and apparent volume of distribution was 6.36 L for a typical individual, consistent with previous findings for endogenous immunoglobulin Gs and other therapeutic monoclonal antibodies. Baseline albumin and body weight were identified as significant covariates of clearance; body weight, sex, and smoking status had a significant impact on volume of distribution; and none of these covariates had impact on peripheral volume of distribution. Although these covariates were identified as statistically significant, they are considered to be not clinically meaningful, as changes in monalizumab exposure were <30%. Therefore, no dose adjustments of monalizumab based on patient or disease characteristics are recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 teacher head, 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".