Integration of Ontogeny Into a Physiologically Based Pharmacokinetic Model for Monoclonal Antibodies in Premature Infants
Why this work is in the frame
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Bibliographic record
Abstract
Abstract An understanding of pediatric pharmacokinetics (PK) is essential for first‐in‐pediatric dose selection and clinical trial design. At present, there is no reliable way to scale the PK of monoclonal antibodies and immunoglobulin G drug products from adults to young children or to premature infants—a vulnerable population with a rapidly growing drug development pipeline. In this work, pediatric physiologically based PK models are constructed in PK‐Sim and Mobi to explore the PK of pagibaximab, palivizumab, MEDI8897, and intravenous immunoglobulin in preterm infants. In addition to considering ontogeny in pediatric organ volumes, organ composition, blood flow rates, and hematocrit, advanced ontogeny is applied for 3 key parameters: capillary surface area, hematopoietic cell concentration, and lymph flow rate. The role and importance of each parameter for determining pediatric clearance (CL) and volume of distribution at steady state (V SS ) are quantitatively assessed with a local sensitivity analysis. In addition, the uncertainty around parameters with limited information in pediatrics is addressed (eg, free neonatal Fc receptor concentration). The full ontogeny parameterization yields pediatric PK predictions that are within 1.5‐fold prediction error >90% of the time for preterm infants, with an absolute average fold error of 1.05. This result suggests that many of the key factors related to ontogeny are appropriately addressed. Overall, this study makes a first step toward developing a platform pediatric physiologically based PK model for monoclonal antibodies and immunoglobulin G drug products by solidifying existing parameterizations, integrating new concepts, and drawing attention to unmet needs for physiologic knowledge in children.
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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 | 0.003 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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