Does one model fit all mAbs? An evaluation of population pharmacokinetic models
Why this work is in the frame
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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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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 | 0.004 | 0.012 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it