Prediction of Monoclonal Antibodies Pharmacokinetics in Human: Identification of a Reference Neonatal Fc Receptor (FcRn) Binding Affinity Using Physiologically Based Pharmacokinetic (PBPK) Modeling

Prediction of monoclonal antibody (mAb) pharmacokinetics (PK) in drug development remains challenging due to the lack of a standardized method for predicting elimination based on mechanistic pathways. Among the processes implemented in the physiologically based pharmacokinetic (PBPK) models for large molecules, FcRn-mediated recycling constitutes the predominant mechanism influencing the elimination of mAbs. In the present study, we assessed the predictivity of a generic value for the dissociation constant (<i>K</i>d) for FcRn (<i>K</i>d<sub>FcRn</sub>) in humans, identified based on clinical data, to provide means for mechanism-based PK projections for mAbs in first-in-human (FIH) trials. We compiled a database of digitalized linear PK profiles for 50 mAbs administered intravenously in humans. Subsequently, the database was randomly divided into a training and a test data set, using a 7:3 ratio. For each drug in the training data set, a generic PBPK model was set up in PK-Sim, and a drug-specific <i>K</i>d<sub>FcRn</sub> parameter was estimated through data fitting. The median of estimated drug-specific <i>K</i>d<sub>FcRn</sub> was 1.05 μM and was used for naïve predictions of the PK of the drugs in the test data set. Plasma exposure (AUC) and terminal half-life were accurately predicted for 80% and 60% of the drugs in the test data set, respectively, with a prediction error within the 0.80-1.25-fold range. Additionally, 100% of the test data set showed prediction errors within the 0.50-2.00-fold range for both plasma exposure and half-life. The median of the estimated drug-specific <i>K</i>d<sub>FcRn</sub> determined using the whole database with 50 mAbs was 1.07 μM and was retained after evaluation as a more accurate default <i>K</i>d<sub>FcRn</sub> value. The reported results provide a large database of mAbs PBPK models with estimated <i>K</i>d<sub>FcRn</sub> values using PK-Sim, and a validated default <i>K</i>d<sub>FcRn</sub> value of 1.07 μM to perform naïve predictions of mAbs linear PK in the context of FIH trials.

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Journal
ACS Pharmacology & Translational Science
Published
2025-12-22
DOI
https://doi.org/10.1021/acsptsci.5c00674
Primary Topic
Monoclonal and Polyclonal Antibodies Research
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article
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article

Prediction of Monoclonal Antibodies Pharmacokinetics in Human: Identification of a Reference Neonatal Fc Receptor (FcRn) Binding Affinity Using Physiologically Based Pharmacokinetic (PBPK) Modeling

Antoine Deslandes, Donato Teutonico, Salih Benamara, Marylore Chenel et al.
ACS Pharmacology & Translational Science
Monoclonal and Polyclonal Antibodies Research
article

Prediction of Monoclonal Antibodies Pharmacokinetics in Human: Identification of a Reference Neonatal Fc Receptor (FcRn) Binding Affinity Using Physiologically Based Pharmacokinetic (PBPK) Modeling

Antoine Deslandes, Donato Teutonico, Salih Benamara, Marylore Chenel, Laurent Nguyen, Erik Sjögren, Florence Gattacceca
article en

Abstract

Prediction of monoclonal antibody (mAb) pharmacokinetics (PK) in drug development remains challenging due to the lack of a standardized method for predicting elimination based on mechanistic pathways. Among the processes implemented in the physiologically based pharmacokinetic (PBPK) models for large molecules, FcRn-mediated recycling constitutes the predominant mechanism influencing the elimination of mAbs. In the present study, we assessed the predictivity of a generic value for the dissociation constant (<i>K</i>d) for FcRn (<i>K</i>d<sub>FcRn</sub>) in humans, identified based on clinical data, to provide means for mechanism-based PK projections for mAbs in first-in-human (FIH) trials. We compiled a database of digitalized linear PK profiles for 50 mAbs administered intravenously in humans. Subsequently, the database was randomly divided into a training and a test data set, using a 7:3 ratio. For each drug in the training data set, a generic PBPK model was set up in PK-Sim, and a drug-specific <i>K</i>d<sub>FcRn</sub> parameter was estimated through data fitting. The median of estimated drug-specific <i>K</i>d<sub>FcRn</sub> was 1.05 μM and was used for naïve predictions of the PK of the drugs in the test data set. Plasma exposure (AUC) and terminal half-life were accurately predicted for 80% and 60% of the drugs in the test data set, respectively, with a prediction error within the 0.80-1.25-fold range. Additionally, 100% of the test data set showed prediction errors within the 0.50-2.00-fold range for both plasma exposure and half-life. The median of the estimated drug-specific <i>K</i>d<sub>FcRn</sub> determined using the whole database with 50 mAbs was 1.07 μM and was retained after evaluation as a more accurate default <i>K</i>d<sub>FcRn</sub> value. The reported results provide a large database of mAbs PBPK models with estimated <i>K</i>d<sub>FcRn</sub> values using PK-Sim, and a validated default <i>K</i>d<sub>FcRn</sub> value of 1.07 μM to perform naïve predictions of mAbs linear PK in the context of FIH trials.

ACS Pharmacology & Translational ScienceVol. 9(1)
Aix-Marseille Université (FR), Quantitative BioSciences (US), Centre Inria d'Université Côte d'Azur (FR)
Good health and well-being
Openalex Percentile: Top 32%
Monoclonal and Polyclonal Antibodies Research
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