Does one model fit all mAbs? An evaluation of population pharmacokinetic models
Bibliographic record
Abstract
Antibodies are extensively used in treating various diseases, with over 100 canonical monoclonal antibodies (mAbs) approved. Population pharmacokinetic (PK) models are typically developed for each individual mAb, despite their similarities in size, shape, and susceptibility to lysosomal degradation. However, sparse datasets with limited PK information pose challenges in deriving accurate parameter estimates. Here, we provide a comprehensive overview of 160 published models of 69 mAbs, administered either intravenously or subcutaneously, examining their structural, statistical, and covariate components. Median estimates for the base parameters are linear clearance (0.22 L/d), central volume (3.42 L), peripheral volume (2.68 L), intercompartmental clearance (0.54 L/d), absorption rate (0.25 L/d), and bioavailability (69%). Using these to simulate a 'generic' mAb results in plausible kinetics with a terminal half-life of 21 ds. We demonstrated that the median linear clearance was 26% lower in models that included nonlinear target-mediated kinetics, when compared to linear models (0.18 vs. 0.25 L/d). For chimeric mAbs median linear clearance was 50% higher compared to fully human and humanized mAbs. Variability in PK parameter estimates across models was comparable to the inter-individual variability, which have consistently shown to be large for mAbs PK (e.g. 55% vs. 43% for clearance and 25% vs. 30% for central volume, respectively). Our meta-analysis suggests that a priori parameter estimates derived from the large body of existing pharmacokinetic models for mAbs are representative for many mAbs and can facilitate the design of new and/or more complex pharmacokinetic models or assist in dose optimization models.
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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 | 0.040 | 0.074 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.012 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".