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Parallel-Competitive Absorption-Presystemic Metabolism Model for Subcutaneous Bioavailability Prediction of

Mikolaj Milewski1, Maria A Cueto1, Mikhail Murashov1

  • 1Merck & Co., Inc., Rahway, New Jersey 07065, United States.

Molecular Pharmaceutics
|April 15, 2026
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Predicting subcutaneous bioavailability for monoclonal antibodies (mAbs) is crucial. A new model uses pharmacokinetic data to estimate absorption, improving predictions for these important therapeutics.

Keywords:
mathematical modelmonoclonal antibodiespresystemic metabolismsubcutaneous bioavailability

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Area of Science:

  • Pharmacology
  • Biotechnology
  • Drug Development

Background:

  • Subcutaneous administration of monoclonal antibodies (mAbs) offers patient convenience and cost benefits over intravenous delivery.
  • However, subcutaneous absorption of mAbs is frequently incomplete and highly variable, impacting therapeutic efficacy.
  • Accurate prediction of mAb bioavailability is essential for optimizing drug development and dosing.

Purpose of the Study:

  • To expand the validation of a human bioavailability prediction model for monoclonal antibodies (mAbs).
  • To incorporate both linear and nonlinear pharmacokinetic compounds into the model's validation.
  • To assess the model's performance across a wide range of mAb bioavailabilities.

Main Methods:

  • A two-compartment pharmacokinetic model was employed, integrating parallel-competitive absorption and presystemic metabolism pathways.
  • The model utilized a single absorption rate constant and compound-specific presystemic metabolism rates, linked to systemic clearance.
  • Validation was performed using a dataset of 79 monoclonal antibodies (mAbs) with diverse pharmacokinetic properties.

Main Results:

  • The prediction model achieved a root-mean-square error (RMSE) of 13.1% for human subcutaneous bioavailability.
  • The model demonstrated robust performance across a broad bioavailability range (29-100%) with a geometric mean of 66%.
  • Predictions were generated using only pharmacokinetic data obtainable from intravenous administration.

Conclusions:

  • The developed mechanistic model provides a reliable preliminary estimate of subcutaneous mAb bioavailability.
  • This predictive approach can be utilized early in drug development using readily available pharmacokinetic data.
  • The method supports informed decisions regarding subcutaneous drug formulation and administration strategies.