# Cross-Species Extrapolation of Neonatal Fc Receptor (FcRn) Binding Affinity to Predict Monoclonal Antibody Pharmacokinetics in Humans Using Physiologically Based Pharmacokinetic Modeling (PBPK): Are We There Yet?

Salih Benamara, Carla Troisi, Florence Gattacceca, Erik Sjögren, Laurent Nguyen, Donato Teutonico

► To cite this version:

Salih Benamara, Carla Troisi, Florence Gattacceca, Erik Sjögren, Laurent Nguyen, et al. Cross-Species Extrapolation of Neonatal Fc Receptor (FcRn) Binding Affinity to Predict Monoclonal Antibody Pharmacokinetics in Humans Using Physiologically Based Pharmacokinetic Modeling (PBPK): Are We There Yet?. ACS Pharmacology & Translational Science, 2025, 8 (9), 10.1021/acspktsci.5c00356. [https://doi.org/10.1021/acspktsci.5c00356](https://doi.org/10.1021/acspktsci.5c00356)

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<figure><img src="image_1.png" /></figure>
# Cross-species extrapolation of neonatal Fc receptor (FcRn) binding affinity to predict monoclonal antibody pharmacokinetics in humans using physiologically-based pharmacokinetic modeling (PBPK): are we there yet?

<figure><img src="image_2.png" /></figure>

## Authors:

Salih Benamara¹,²*, Carla Troisi¹*, Florence Gattacceca², Erik Sjögren³, Laurent Nguyen¹, Donato Teutonico¹**

¹ Development Pharmacometrics, Translational Medicine Unit, Sanofi, 94400 Vitry-sur-Seine, France

² Computational Pharmacology and Clinical Oncology (COMPO) Unit, Inria Sophia Antipolis-Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 13385 Marseille, France

³ Pharmetus AB, Uppsala 753 19, Sweden

* These authors contributed equally to this work

** To whom correspondence should be addressed: Donato.Teutonico@sanofi.com
**Abstract**

Physiologically based pharmacokinetic (PBPK) modeling is a useful tool during drug development due to its ability to extrapolate pharmacokinetics (PK) between species and populations. For monoclonal antibodies (mAbs), these models can be used to support dose selection, especially for first-in-human (FIH) trials. In the PBPK model for biologics in the software PK-Sim®, salvaging from endosomal degradation via the neonatal fragment crystallizable receptor (FcRn) is a critical process for the systemic clearance of mAbs. However, high variability is associated with *in vitro* measurements of the dissociation constant (Kd) for FcRn ($K_d^{FcRn}$) in human. Furthermore, predicting affinity for FcRn in human presents a significant challenge due to the lack of a standardized methodology. In this paper, we evaluated different predictors of the Kd for FcRn ($K_d^{FcRn}$) in humans to enhance the precision of mAbs PK projections for FIH trials. A database comprising PK profiles for 27 mAbs was constructed across different species. Plasma concentration-time courses for each drug and species were used to develop a PBPK model for each compound, by estimating the $K_d^{FcRn}$ for each species. Cross-species correlations were established to explore extrapolation performances of $K_d^{FcRn}$ from animals to humans. As an alternative to using animal data, a direct prediction approach, based on the median of the 27 human $K_d^{FcRn}$ values, was assessed. When considering a prediction error of 100% (2-fold deviation), both the extrapolation from preclinical species and the direct approach based on the median human value accurately predict at least 80% of the $K_d^{FcRn}$ values within the prediction interval.

**Keywords**

Monoclonal antibody · Neonatal Fc receptor · Translational pharmacokinetics · PBPK · First-in human
Antibodies, also known as immunoglobulins (Igs), are large proteins used by the immune system for the precise identification and neutralization of antigens¹. These molecules belong to five classes (IgA, IgD, IgE, IgM, IgG) distinguished by their heavy chain type, with IgG dominating therapeutic applications due to its extended half-life²⁻⁵. Among these molecules, monoclonal antibodies (mAbs) are engineered B-lymphocyte products designed for specific targeted antigen binding, representing a prominent class of therapeutic antibodies⁴,⁶. The interplay between Fc and FcRn plays a crucial role in the recycling of mAbs, extending their presence within the circulation⁷. mAbs comprises a crystallizable fragment (Fc) segment, capable of binding to various cell-associated receptors, such as the neonatal fragment crystallizable receptor (FcRn) and the Fc gamma receptor (FcγR)⁷,⁸. The FcRn, also called the Brambell receptor, is expressed in diverse cell types, including vascular endothelial cells and hematopoietic cells⁹. These cells internalize mAbs that bind to FcRn within acidic endosomal compartments. Subsequently, FcRn facilitates the recycling of mAbs back into circulation, effectively extending its plasma half-life from 18 to 21 days⁹. These molecular features exert a considerable influence on the pharmacokinetics (PK) and pharmacodynamic (PD) characteristics of mAbs, distinguishing them from small chemical molecules.

The utilization of pharmacometrics modeling and simulations constitutes a pivotal practice in preclinical development of mAbs to predict the first-in-human (FIH) dose, and anticipate the outcome of the planned clinical doses¹⁰. Selecting the FIH dose is a critical step in developing new mAbs, requiring a balance between safety and efficacy. Translating preclinical PK data to humans is challenging due to the complexities of mAbs. Three main approaches are used to predict mAbs PK: allometric scaling, the Dedrick approach, and physiologically based pharmacokinetic (PBPK) modeling¹¹⁻¹³. Allometric scaling, one of the most common methods, assumes that clearance (CL) and volume of distribution scale with body weight but may require adjustments for additional factors¹². The Dedrick approach uses mathematical transformation based on the slope and intercept of the allometric relationship between species to predict human PK from animal data¹³, while PBPK models incorporate detailed physiological information for more robust predictions¹¹.

The first PBPK model for mAbs, introduced in 1986, considered six major tissues and successfully captured plasma and tissue IgG concentrations after intravenous (IV) injection in mice¹⁴. PBPK models have become more physiologically-relevant by incorporating the FcRn-mediated recycling
pathway, initially in skin and muscle compartments and later expanded to all tissues<sup>14,15</sup>, and by subsequently extending the model to encompass all other tissues. The structure presented in Figure 1a serves as the foundational model for many recent PBPK models for mAbs<sup>16,17</sup>. Tissue compartments are further subdivided into vascular, endosomal, and interstitial sub-compartments (Figure 1b), with the FcRn-mediated protection of mAbs, specifically IgGs, primarily described as occurring within the endosomes of vascular endothelial cells. This description often involves equilibrium or kinetic binding parameters depending on the pH conditions<sup>18</sup>. Thanks to the combination of physiological and drug specific parameters, the PBPK models are often used to perform cross-species extrapolation. Once a PBPK model accurately predicts PK in one species, it can be extrapolated to other species by incorporating species-specific physiological information<sup>16,17</sup>.

a

<figure><img src="image_3.png" /><figcaption>Figure 1. Schematic representation of the PBPK model for large molecules implemented in PK-Sim. a Whole body; b Sub-model structure including FcRn<sup>19</sup></figcaption></figure>

b

<figure><img src="image_4.png" /></figure>

In this study, PK-Sim®, an open-source PBPK modeling platform part of the Open Systems Pharmacology (OSP) suite was used<sup>19</sup>. This model integrates comprehensive physiological knowledge, including anatomical and physiological parameters, to construct virtual representations of the human body. It accounts for variations across different populations, considering factors such as age, gender, and physiological characteristics. Drug specific parameters, such as molecular weight (MW), the dissociation constant (Kd) for FcRn (Kd<sub>FcRn</sub>),
and target binding affinities, are used to predict drug concentration-time profiles. The available physiological database includes anatomical and physiological parameters for both humans and commonly used preclinical animal models (mouse, rat, minipig, dog, and monkey); the preclinical models and the human model possess the same structure, allowing for direct cross-species extrapolation of model parameters. The hydrodynamic radius and the $Kd_{FcRn}$ are key parameters for characterizing mAbs PK as the main determinants for predictions of distribution and clearance, respectively. While the hydrodynamic radius, which relates to the molecular size (a theoretical sphere) can be predicted from the drug MW$^{19}$, no means to inform $Kd_{FcRn}$ is currently available and consequently this parameter remains a hurdle for efficient predictions of mAbs PK. Among $Kd_{FcRn}$ values estimated in various species, determining which FcRn value should be used for predicting mAbs PK in humans remains challenging$^{20-22}$. The aim of the current study was to assess the ability of the $Kd_{FcRn}$ estimated from preclinical species to predict the human $Kd_{FcRn}$, which was compared with the predictivity of an average human $Kd_{FcRn}$ estimated from clinical data for various mAbs.

# Methods

## Preclinical and clinical pharmacokinetic data

The selected molecules used in the study are listed in Table 1. All concentration-time profiles were digitized from publications and studies available in the public domain. To assemble the database, combinations of the following terms were searched on Embase, PubMed and Google Scholar: pharmacokinetics, rat(s), mouse (mice), monkey(s), human(s), non-clinical, clinical, phase 1, phase 2, phase 3, phase 4 and IV administration with the name of the monoclonal antibody (mAb). The selected species included in the database were humans, monkeys, mice, and rats. The selection of these molecules was based on the availability of comprehensive PK data following IV administration, prevalence in clinical use, and the presence of full time-course data, necessary to accurately characterize the elimination phase from the PK profiles. All concentration-time profiles were digitized from figures reported in the cited publications using WebPlotDigitizer®. Based on a qualitative and visual assessment of the reported PK profiles for each mAb, concentration-time profiles showing obvious target-mediated drug disposition (TMDD) behavior were excluded for
the Kd<sub>FcRn</sub> estimation to limit confounding factors. Data processing and graph plotting were performed using R software coupled with RStudio® version 4.2.2.

<table><caption>Table 1. Preclinical and clinical PK data for 27 mAbs used for model development</caption><thead><tr><th>Drug</th><th>MW (Da)</th><th>Species</th><th>Doses</th><th>References</th></tr></thead><tbody><tr><td rowspan="3">Adalimumab</td><td rowspan="3">144188</td><td>human</td><td>0.25, 0.5, 1, 3, 5, 10 mg/kg</td><td>23,24</td></tr><tr><td>monkey</td><td>5 mg/kg</td><td>25</td></tr><tr><td>mouse</td><td>5 mg/kg</td><td>26</td></tr><tr><td rowspan="2">Atezolizumab</td><td rowspan="2">144611</td><td>human</td><td>1, 3, 10, 15, 20 mg/kg, 1200 mg</td><td>27</td></tr><tr><td>monkey</td><td>5, 20 mg/kg</td><td>28</td></tr><tr><td rowspan="2">Belimumab</td><td rowspan="2">147000</td><td>human</td><td>1, 4, 10, 20 mg/kg</td><td>29,30</td></tr><tr><td>monkey</td><td>5, 15, 50, 150 mg/kg</td><td>29,31</td></tr><tr><td rowspan="3">Bevacizumab</td><td rowspan="3">149000</td><td>human</td><td>5, 15 mg/kg</td><td>29,32</td></tr><tr><td>monkey</td><td>10 mg/kg</td><td>33</td></tr><tr><td>rat</td><td>10 mg/kg</td><td>34</td></tr><tr><td rowspan="3">Canakinumab</td><td rowspan="3">145200</td><td>human</td><td>1, 3 mg/kg, 600 mg</td><td>29,35</td></tr><tr><td>monkey</td><td>2 mg/kg</td><td>29,35</td></tr><tr><td>mouse</td><td>10 mg/kg</td><td>29,35</td></tr><tr><td rowspan="4">Cetuximab</td><td rowspan="4">145782</td><td>human</td><td>740 mg</td><td>36</td></tr><tr><td>monkey</td><td>7.5, 24, 75 mg/kg</td><td>37</td></tr><tr><td>mouse</td><td>0.04, 0.25, 1 mg</td><td>38</td></tr><tr><td>rat</td><td>5 mg/kg</td><td>39</td></tr><tr><td rowspan="3">Concizumab</td><td rowspan="3">NA<br>(150000)</td><td>human</td><td>3, 9 mg/kg</td><td>40</td></tr><tr><td>monkey</td><td>200 mg/kg</td><td>41</td></tr><tr><td>rat</td><td>2 mg/kg</td><td>42</td></tr><tr><td rowspan="4">Dacetuzumab</td><td rowspan="4">145100</td><td>human</td><td>1, 2, 4 mg/kg</td><td>43</td></tr><tr><td>monkey</td><td>1, 10 mg/kg</td><td>29,44</td></tr><tr><td>mouse</td><td>1, 10 mg/kg</td><td>29,44</td></tr><tr><td>rat</td><td>1, 10 mg/kg</td><td>29,44</td></tr><tr><td rowspan="2">Daclizumab</td><td rowspan="2">142612</td><td>human</td><td>2, 4, 6, 8 mg/kg, 200, 400 mg</td><td>45,46</td></tr><tr><td>rat</td><td>2 mg/kg</td><td>47</td></tr><tr><td rowspan="4">Drozitumab</td><td rowspan="4">NA<br>(150000)</td><td>human</td><td>4, 10, 15, 20 mg/kg</td><td>48</td></tr><tr><td>monkey</td><td>5 mg/kg</td><td>49</td></tr><tr><td>mouse</td><td>0.2, 0.5, 1, 2, 5, 10, 15 mg/kg</td><td>49</td></tr><tr><td>rat</td><td>0.5, 2, 5, 15 mg/kg</td><td>49</td></tr><tr><td rowspan="2">Efalizumab</td><td rowspan="2">146170</td><td>human</td><td>1, 3, 10 mg/kg</td><td>50</td></tr><tr><td>mouse</td><td>2 mg/kg</td><td>26</td></tr><tr><td>Felvizumab</td><td>146000</td><td>human</td><td>1.25, 5, 10 mg/kg</td><td>29,51</td></tr></tbody></table>
<table><tbody><tr><td></td><td></td><td>monkey</td><td>1 mg/kg</td><td>29,52</td></tr><tr><td></td><td></td><td>mouse</td><td>5 mg/kg</td><td>29,53</td></tr><tr><td></td><td></td><td>rat</td><td>1 mg/kg</td><td>29,52</td></tr><tr><td rowspan="2">Figitumumab</td><td rowspan="2">146006</td><td>human</td><td>6, 10, 20 mg/kg</td><td>54</td></tr><tr><td>monkey</td><td>3, 5, 15, 30, 100 mg/kg</td><td>55,56</td></tr><tr><td rowspan="3">Gedivumab</td><td rowspan="3">146000</td><td>human</td><td>1.5, 5, 15, 45, 100 mg/kg, 8400, 10800 mg</td><td>57,58</td></tr><tr><td>monkey</td><td>5, 100 mg/kg</td><td>58</td></tr><tr><td>mouse</td><td>5 mg/kg</td><td>58</td></tr><tr><td rowspan="2">Infliximab</td><td rowspan="2">144200</td><td>human</td><td>5, 10, 20 mg/kg, 5mg</td><td>59–61</td></tr><tr><td>rat</td><td>10, 50 mg/kg</td><td>62</td></tr><tr><td rowspan="4">Lutikizumab</td><td rowspan="4">NA<br>(150000)</td><td>human</td><td>0.3, 1, 3, 10 mg/kg</td><td>63</td></tr><tr><td>monkey</td><td>5 mg/kg</td><td>64</td></tr><tr><td>mouse</td><td>5 mg/kg</td><td>64</td></tr><tr><td>rat</td><td>4 mg/kg</td><td>64</td></tr><tr><td rowspan="2">Mepolizumab</td><td rowspan="2">149000</td><td>human</td><td>0.03, 0.05, 0.5, 1, 2.5, 3, 9, 10 mg/kg, 250, 750 mg</td><td>65,66</td></tr><tr><td>monkey</td><td>0.05, 0.5, 1, 5, 50, 100 mg/kg</td><td>67,68</td></tr><tr><td rowspan="3">Onartuzumab</td><td rowspan="3">99147</td><td>human</td><td>1, 4, 10, 15, 20, 30 mg/kg</td><td>69</td></tr><tr><td>monkey</td><td>0.5, 3, 10, 30 mg/kg</td><td>50,70</td></tr><tr><td>mouse</td><td>3, 10, 30 mg/kg</td><td>70</td></tr><tr><td rowspan="2">Rilotumumab</td><td rowspan="2">145200</td><td>human</td><td>1, 5, 20 mg/kg</td><td>29,71</td></tr><tr><td>monkey</td><td>5, 25, 75, 150 mg/kg</td><td>29,72</td></tr><tr><td rowspan="4">Rituximab</td><td rowspan="4">143860</td><td>human</td><td>500, 1000 mg</td><td>29,73</td></tr><tr><td>monkey</td><td>20 mg/kg</td><td>74</td></tr><tr><td>mouse</td><td>0.1, 1, 10 mg/kg</td><td>29</td></tr><tr><td>rat</td><td>1, 10 mg/kg</td><td>29,75</td></tr><tr><td rowspan="2">SB249417</td><td rowspan="2">150000</td><td>human</td><td>0.1, 0.3, 0.6, 0.9, 1 mg/kg</td><td>29,59</td></tr><tr><td>monkey</td><td>1, 3, 10 mg/kg</td><td>29,76</td></tr><tr><td rowspan="3">Stamulumab</td><td rowspan="3">137501</td><td>human</td><td>0.3, 1, 3, 7.5, 11.25 mg/kg</td><td>77</td></tr><tr><td>mouse</td><td>5, 20, 100 mg/kg</td><td>77</td></tr><tr><td>rat</td><td>2, 10, 30, 50, 100 mg/kg</td><td>77</td></tr><tr><td rowspan="3">Tocilizumab</td><td rowspan="3">144985</td><td>human</td><td>8 mg/kg</td><td>78</td></tr><tr><td>monkey</td><td>1, 10 mg/kg</td><td>79</td></tr><tr><td>mouse</td><td>1 mg/kg</td><td>80</td></tr><tr><td rowspan="3">Trastuzumab</td><td rowspan="3">145230</td><td>human</td><td>1, 2, 4, 8 mg/kg</td><td>50</td></tr><tr><td>monkey</td><td>0.3, 3, 20 mg/kg</td><td>81</td></tr><tr><td>rat</td><td>10, 30 mg/kg</td><td>81</td></tr><tr><td rowspan="2">Trastuzumab emtansine</td><td rowspan="2">148500</td><td>human</td><td>3.6 mg/kg</td><td>82</td></tr><tr><td>monkey</td><td>10, 30 mg/kg</td><td>81</td></tr></tbody></table>
<table><tbody><tr><td rowspan="3">Trebananib</td><td rowspan="3">148500</td><td>rat</td><td>0.3, 3, 20 mg/kg</td><td>81</td></tr><tr><td>human</td><td>0.3, 1, 3, 10, 10 mg/kg</td><td>29,83</td></tr><tr><td>mouse</td><td>3, 10 mg/kg</td><td>29,84</td></tr><tr><td rowspan="4">Vesencumab</td><td rowspan="4">144973</td><td>rat</td><td>3, 10 mg/kg</td><td>29,84</td></tr><tr><td>human</td><td>2, 5, 10, 15, 20, 30, 40 mg/kg</td><td>85</td></tr><tr><td>monkey</td><td>0.5, 3, 10, 15, 30, 50, 100 mg/kg</td><td>86</td></tr><tr><td>mouse</td><td>0.65, 10, 100 mg/kg</td><td>87</td></tr><tr><td></td><td></td><td>rat</td><td>2, 10, 50 mg/kg</td><td>87</td></tr></tbody></table>

NA: Not available. 150000 Da used to estimate the Kd<sub>FcRn</sub><sup>88</sup>

## PBPK modeling

The default PBPK model for large molecules available in the software PK-Sim®, part of the OSP suite version 11.1, was used for model development, with default parameter values unless otherwise stated. When simulating human PK profiles, the demographics of the study population were matched as closely as possible based on available information. The only drug-specific input parameter that differs across individual mAbs is the MW of the molecules. Information on drug-specific MW was used when available, otherwise, a default value of 150000 Da was applied<sup>88</sup>. The IV plasma concentration-time data for each mAb were used to estimate the binding affinity to the FcRn receptor (Kd<sub>FcRn</sub>) and build a specific PBPK model. In order to allow the comparison of the Kd<sub>FcRn</sub> parameter values, the same and default model for distribution and elimination was used among species, with consistent parameter values related to the mechanistic FcRn pattern, while modifying the species specific anatomical and physiological parameters<sup>19</sup>.

The mechanistic FcRn pattern implemented within the whole-body PBPK model for large molecules was used in this analysis<sup>19</sup>. Briefly, the whole model consists of 15 compartments representing organs or tissues connected by blood and lymph flows. It accounts for the disposition of mAbs in the vascular, endothelial, and interstitial layers within each tissue compartment (Figure 1b). The passive distribution process in this model is characterized using the two-pores formalism<sup>23</sup>, which accounts for the movement of proteins across the vascular endothelium. According to this theory, the barrier between plasma and interstitial space is described as a membrane consisting of two types of pores: large and small. A multitude of small pores (radius
~4.5 nm) is present in the capillary, accompanied by a smaller quantity of large pores (radius ~25 nm)⁸⁹. mAbs can move through the large pores via convection and diffusion but are unable to cross the membrane through the small pores. The transport of the drug into lymph vessels is modeled via reflection coefficients, representing convection. The drug uptake into endothelial cells occurs through pinocytosis. Once inside the endosomes of endothelial cells, the free drug undergoes degradation, which is characterized by a fixed degradation rate constant¹⁹. However, the drug can be protected from endosomal degradation by binding to the FcRn. This binding is characterized by the Kd<sub>FcRn</sub>.

## Estimation of the Kd<sub>FcRn</sub> from pharmacokinetic data

Parameter identification was performed for each mAb and each species to estimate the Kd<sub>FcRn</sub> value that allows the best description of the observations, and it was the only parameter estimated for each molecule¹⁹. Depending on the available PK data following IV administration, one or more doses were used, and one specific Kd<sub>FcRn</sub> was estimated for each mAb. If TMDD was suspected, drug profiles exhibiting such behavior were excluded from the Kd<sub>FcRn</sub> estimation. This approach assumes linear clearance to minimize confounding factors affecting elimination, with Kd<sub>FcRn</sub> considered the primary parameter influencing clearance.

## Prediction of human Kd<sub>FcRn</sub>

Four methods to predict human Kd<sub>FcRn</sub> were evaluated: human Kd<sub>FcRn</sub> was either set equal to the median human Kd<sub>FcRn</sub> values of all mAbs or extrapolated from each animal species (monkey, rat, mouse) based on linear regression, performed using R software coupled with RStudio® version 4.2.2. In the regression models, the Kd<sub>FcRn</sub> values from the preclinical species (monkey, rat, mouse) were used as the independent variable (X-axis), and the corresponding human Kd<sub>FcRn</sub> values were used as the dependent variable (Y-axis).

To limit the influence of extreme values on the regression analysis, a predefined exclusion criterion was applied. Any individual Kd<sub>FcRn</sub> value that deviated by more than ±10-fold from the species-
specific median was considered an outlier and excluded from the dataset. This threshold was chosen to minimize the influence of atypical data points that could disproportionately affect the analysis.

The predictive performance of each method was evaluated by comparison of the predicted human Kd<sub>FcRn</sub> with the Kd<sub>FcRn</sub> estimated from PK data, using MSE (mean squared error), RMSE (root mean squared error) and RPE (relative prediction error) as performance criteria, computed as follows:

$$MSE = \frac{1}{n} \sum_{i=1}^{n} (Human \ Kd \ FcRn^{Predicted} - Human \ Kd \ FcRn^{Estimated})^2 \quad (1)$$

$$RMSE = MSE^{0.5} \qquad (2)$$

$$RPE = \frac{1}{n} \sum_{i=1}^{n} \left[ \frac{(Kd \ FcRn^{Predicted} - Kd \ FcRn^{Estimated})}{Kd \ FcRn^{Estimated}} \right] \times 100 \quad (3)$$

n: number of mAbs considered

# Results

## Preclinical and clinical pharmacokinetic data

The literature-derived database included 27 mAbs administered intravenously: data were available in humans for all the 27 mAbs, in monkeys for 23, in rats for 15 and in mice for 15 mAbs. Among the 27 mAbs, 9 were fully human, 13 were humanized, 3 were chimeric, 1 was a peptibody, and 1 was an antibody-drug conjugate (ADC). Furthermore, 23 of them belonged to the IgG1 subclass, 2 were IgG2, 1 was IgG4, and 1 was a dual variable domain Ig (DVD), designed as a bispecific tetravalent IgG-like molecule engineered to block two targets. These data were used to fit the model and estimate the Kd<sub>FcRn</sub> for each mAb. Simulations performed with the PBPK model after Kd<sub>FcRn</sub> estimation are shown in Figure 2 for the 7 mAbs reported with available data across all four species.
<figure><img src="image_5.png" /><figcaption>Figure 2. PBPK model simulated (lines) concentration versus time profiles after IV administration compared to observations (dots) in: a Human; b Monkey; c Mouse; d Rat, for the 7 mAbs with available data across all four species</figcaption></figure>
Estimation of the Kd<sub>FcRn</sub> from *in vivo* pharmacokinetic data

Table 2 displays the Kd<sub>FcRn</sub> values for each mAb and species. For human Kd<sub>FcRn</sub>, the median of the estimated values was 1.52 µM with individual values ranging from 0.71 µM (canakinumab) to 24.40 µM (SB249417). For monkey Kd<sub>FcRn</sub>, the median of the estimated values was 0.43 µM with individual values ranging from 0.12 µM (concizumab) to 2.17 µM (dacetuzumab). For mouse Kd<sub>FcRn</sub>, the median of the estimated values was 1.67 µM with individual values ranging from 0.45 µM (tocilizumab) to 30 µM (vesencumab). For rat Kd<sub>FcRn</sub>, the median of the estimated values was 3.66 µM with individual values ranging from 1.99 µM (infliximab) to 20 µM (trebananib).

<table><caption>Table 2. Estimated values for Kd<sub>FcRn</sub> after parameter identification using PK-Sim®</caption><thead><tr><th>Drug</th><th>Sequence source</th><th>Isotype</th><th>Human Kd<sub>FcRn</sub> (µM)</th><th>Monkey Kd<sub>FcRn</sub> (µM)</th><th>Mouse Kd<sub>FcRn</sub> (µM)</th><th>Rat Kd<sub>FcRn</sub> (µM)</th></tr></thead><tbody><tr><td>Adalimumab</td><td>Human</td><td>IgG1</td><td>1.52</td><td>0.49</td><td>2.02</td><td></td></tr><tr><td>Atezolizumab</td><td>Humanized</td><td>IgG1</td><td>0.94</td><td>0.35</td><td></td><td></td></tr><tr><td>Belimumab</td><td>Human</td><td>IgG1</td><td>2.08</td><td>0.40</td><td></td><td></td></tr><tr><td>Bevacizumab</td><td>Humanized</td><td>IgG1</td><td>1.43</td><td>0.25</td><td></td><td>2.42</td></tr><tr><td>Canakinumab</td><td>Human</td><td>IgG1</td><td>0.71</td><td>0.29</td><td>0.70</td><td></td></tr><tr><td>Cetuximab</td><td>Chimeric</td><td>IgG1</td><td>3.91</td><td>0.78</td><td>5.25</td><td>2.70</td></tr><tr><td>Concizumab</td><td>Humanized</td><td>IgG4</td><td>1.99</td><td>0.12</td><td></td><td>3.84</td></tr><tr><td>Dacetuzumab</td><td>Humanized</td><td>IgG1</td><td>5.57</td><td>2.17</td><td>2.67</td><td>3.82</td></tr><tr><td>Daclizumab</td><td>Humanized</td><td>IgG1</td><td>1.03</td><td></td><td></td><td>2.49</td></tr><tr><td>Drozitumab</td><td>Human</td><td>IgG1</td><td>1.35</td><td>0.53</td><td>1.62</td><td>4.14</td></tr><tr><td>Efalizumab</td><td>Humanized</td><td>IgG1</td><td>2.86</td><td></td><td>0.50</td><td></td></tr><tr><td>Felvizumab</td><td>Humanized</td><td>IgG1</td><td>0.87</td><td>0.19</td><td>0.91</td><td>3.27</td></tr><tr><td>Figitumumab</td><td>Human</td><td>IgG2</td><td>0.84</td><td>0.58</td><td></td><td></td></tr><tr><td>Gedivumab</td><td>Human</td><td>IgG1</td><td>0.77</td><td>0.29</td><td>1.26</td><td></td></tr><tr><td>Infliximab</td><td>Chimeric</td><td>IgG1</td><td>1.34</td><td></td><td></td><td>1.99</td></tr><tr><td>Lutikizumab</td><td>Humanized</td><td>DVD</td><td>1.50</td><td>0.43</td><td>1.67</td><td>2.83</td></tr><tr><td>Mepolizumab</td><td>Humanized</td><td>IgG1</td><td>0.94</td><td>0.29</td><td></td><td></td></tr><tr><td>Onartuzumab</td><td>Humanized</td><td>IgG1</td><td>2.84</td><td>1.78</td><td>3.69</td><td></td></tr><tr><td>Rilotumumab</td><td>Human</td><td>IgG2</td><td>1.28</td><td>1.05</td><td></td><td></td></tr><tr><td>Rituximab</td><td>Chimeric</td><td>IgG1</td><td>1.01</td><td>0.75</td><td>1.92</td><td>2.53</td></tr><tr><td>SB249417</td><td>Humanized</td><td>IgG1</td><td>24.40</td><td>2.00</td><td></td><td></td></tr><tr><td>Stamulumab</td><td>Human</td><td>IgG1</td><td>2.55</td><td>0.23</td><td>1.12</td><td>3.88</td></tr><tr><td>Tocilizumab</td><td>Humanized</td><td>IgG1</td><td>2.49</td><td>1.09</td><td>0.45</td><td></td></tr><tr><td>Trastuzumab</td><td>Humanized</td><td>IgG1</td><td>1.95</td><td>0.38</td><td></td><td>3.66</td></tr></tbody></table>
<table><tr><td>Trastuzumab (emtansine)</td><td>ADC</td><td>IgG1</td><td>4.37</td><td>1.14</td><td>7.43</td></tr><tr><td>Trebananib</td><td>Peptibody</td><td>IgG1</td><td>9.81</td><td></td><td>20.00</td></tr><tr><td>Vesencumab</td><td>Human</td><td>IgG1</td><td>4.41</td><td>0.26</td><td>11.90</td></tr></table>

DVD: dual-variable domain

ADC: anti-body conjugates

IgG: immunoglobulin

The distribution of the individual $Kd_{FcRn}$ values for each mAb and species, along with the medians, are illustrated in Figure 3. Based on the predefined exclusion criterion (±10-fold deviation from the species-specific median), two data points were identified as outliers and excluded from the linear regression analysis: the human $Kd_{FcRn}$ for SB249417, and the mouse $Kd_{FcRn}$ for vesencumab. These values of 24.4 µM and 30 µM, respectively exceeded the species-specific median values (1.52 µM for human and 1.67 µM for mouse) by more than 10-fold and were therefore not considered for the regression analysis.

<figure><img src="image_6.png" /><figcaption>Figure 3. Distribution (dots) and median (dashed red line) of estimated $Kd_{FcRn}$ values for each species</figcaption></figure>

Graphical presentation of human $Kd_{FcRn}$ versus $Kd_{FcRn}$ in other species shows a general trend, as displayed in Figure 4. Specifically, human $Kd_{FcRn}$ estimates are higher than those in monkeys but smaller than those in mice and rats, as indicated by the medians.
<figure><img src="image_7.png" /><figcaption>Figure 4. Comparison between human Kd<sub>FcRn</sub> and animal Kd<sub>FcRn</sub> for monkey, mouse and rat, for all mAbs with available data in each species. Trend of the median is shown as a red dashed line</figcaption></figure>

Extrapolation of human Kd<sub>FcRn</sub> from animal Kd<sub>FcRn</sub>

In order to evaluate the predictivity of the preclinical species (monkey, rat and mice) for the human Kd<sub>FcRn</sub>, all the individual estimated values for each preclinical species were used to perform a linear regression with the corresponding human values. Since the number of data available was different for each species, the number of data points used to perform the regression and calculate the coefficient of determination was different for each species (Figure 5). The linear regression resulted in one equation expressing the human Kd<sub>FcRn</sub> as a function of the animal Kd<sub>FcRn</sub> for each species (Eq. 4-6).

$$Human Kd FcRn^{From Monkey} = 1.557 \times Kd FcRn^{Monkey} + 1.080 \quad (4)$$

$$Human Kd FcRn^{From Mouse} = 0.891 \times Kd FcRn^{Mouse} + 0.983 \quad (5)$$

$$Human Kd FcRn^{From Rat} = 0.431 \times Kd FcRn^{Rat} + 0.662 \quad (6)$$
<figure><img src="image_8.png" /><figcaption>Figure 5. Correlation of human Kd<sub>FcRn</sub> vs animals Kd<sub>FcRn</sub> for monkeys, mice, and rats. Dot = individual mAb value</figcaption></figure>

Performance of human Kd<sub>FcRn</sub> prediction methods

The Kd<sub>FcRn</sub> values in humans were either considered as equal to the median of the 27 human Kd<sub>FcRn</sub> values (1.52 µM) or extrapolated from Kd<sub>FcRn</sub> in each animal species using the equations obtained after linear regression (Eq. 4-6).

MSE and RMSE were calculated for all mAbs with available values in each species, namely 26 for human median (excluding SB249417), 22 for monkeys, 14 for mice (excluding vesencumab), and 15 for rats (Eq. 1-3). The results, detailed in Table 3, reveal that for all 26 mAbs, the lowest RMSE values are associated with Kd<sub>FcRn</sub> extrapolated from monkeys and rats, with values of 1.08 and 1.10 µM, respectively. Notably, this pattern changes when focusing on the seven mAbs with data available for all species (cetuximab, dacetuzumab, drozitumab, felvizumab, lutikizumab, rituximab and stamulumab), where the lowest RMSE is similarly associated with Kd<sub>FcRn</sub> extrapolated from monkeys (1.02 µM), but extrapolation from rat (1.62 µM) is less predictive than extrapolation from mice (1.42 µM). Using the median human Kd<sub>FcRn</sub> was the less performant methods, for 7 mAbs or for all mAbs, with a RMSE of 1.85 µM and 4.87 µM respectively.

<table><caption>Table 3. Comparison of the four methods to predict Human Kd<sub>FcRn</sub></caption><thead><tr><th>From Median</th><th>From Monkey</th><th>From Mouse</th><th>From Rat</th></tr></thead></table>
<table><tr><td>RMSE 7 mAbs (µM)</td><td>1.85</td><td>1.02</td><td>1.42</td><td>1.62</td></tr><tr><td>Median RPE 7 mAbs (%)</td><td>0</td><td>17.1</td><td>35.6</td><td>40.4</td></tr><tr><td>RMSE all mAbs (µM)</td><td>4.87</td><td>1.08</td><td>2.07</td><td>1.20</td></tr><tr><td>Median RPE all mAbs (%)</td><td>0.01</td><td>18.7</td><td>57.4</td><td>16.5</td></tr></table>

The seven mAbs for which data were available in all four species were: cetuximab, dacetuzumab, drozitumab, felvizumab, lutikizumab, rituximab and stamulumab

RMSE and RPE (all mAbs) were calculated from data of the 26 mAbs studied, excluding SB249417, with 26, 22, 14 and 15 values for respectively human median, monkey, mouse, and rat
From median: $Kd_{FcRn}$ was set to 1.52 µM, the human median $Kd_{FcRn}$ calculated from 27 mAbs
From monkey/mouse/rat: $Kd_{FcRn}$ was extrapolated from the regression equation in monkeys, mice, or rats

Conversely, analyzing the distribution of the RPE (using all mAbs, except SB249417) in Figure 6 reveals that the median RPE associated with using the median human $Kd_{FcRn}$ value of 1.52 µM is very low, with a value of 0.01%. This indicates that this method is less biased than methods based on extrapolation from animals, while extrapolation from mouse was the most biased method with a median RPE greater than 50%, to be compared with 18.71% for monkey, and 16.54% for rat. During the development of mAbs, a different level of precision may be acceptable and animal data may or may not be available, especially in the early stages. To consider that, the four prediction methods for human $Kd_{FcRn}$ were compared in terms of percentage of values with an acceptable RPE for the 26 mAbs as a function of the acceptable RPE (Figure 7).

<figure><img src="image_9.png" /></figure>
<figure><img src="image_10.png" /><figcaption>Figure 7. Percentage of values with an acceptable RPE as a function of the acceptable RPE (%) for the four human Kd<sub>FcRn</sub> prediction methods</figcaption></figure>

Median human method: solid red line

Monkey method: green dotted line

Mouse method: blue dot dash line

Rat method: purple dashed

At an acceptable RPE of 10%, the most reliable methods are extrapolating Kd<sub>FcRn</sub> from the rat or the mouse regression line, with respectively 50% or 45% of the predictions falling within 10% of the true value. Notably, when tolerating a prediction error of 100%, corresponding to the 2-fold error commonly used in PBPK modeling, all four methods exhibit similar performance. Remarkably, the method extrapolating from the rat regression line and the method fixing the human Kd<sub>FcRn</sub> at 1.52 µM (median human Kd<sub>FcRn</sub>) exceed 90% of success. This finding holds significant promise, particularly when non-clinical data are limited, offering potential perspectives to reduce reliance on animal experiments<sup>90</sup>.

Discussion

In the present study, we explored various methods to predict human Kd<sub>FcRn</sub>, a critical parameter influencing mAbs PK, either from preclinical data or by considering simply a default median value
in human. A dataset of PK profiles for 27 mAbs administered IV in humans, monkeys, rats, and mice was compiled. The first step of the assessment was to estimate human $K_d_{FcRn}$ from human and animal data using the default large molecules model implemented in the PBPK software PK-Sim®. The median of the estimated values for human $K_d_{FcRn}$, was 1.52 µM. For comparison with *in vitro* measurements, human endogenous IgG was reported to have a $K_d_{FcRn}$ of 0.90 µM⁹¹,⁹² using a microscale thermophoresis assay or ranging between 0.78 and 1.30 µM using a surface plasmon resonance assay at pH 6⁹³,⁹⁴. These findings indicate that the median of the estimated values for human $K_d_{FcRn}$ obtained with our approach is close to the reported ranges of IgG affinities for FcRn. Subsequently, we performed a linear regression between human $K_d_{FcRn}$ and $K_d_{FcRn}$ for each preclinical species to assess which species was the most predictive of human $K_d_{FcRn}$. All $K_d_{FcRn}$ values exceeding the species-specific median by more than ±10-fold were removed from the regression analysis to exclude extreme outliers. Based on this criterion, two data points were excluded: vesencumab in mice and SB249417 in humans. Their $K_d_{FcRn}$ values, 30 µM for vesencumab in mice and 24.4 µM for SB249417 in humans, were more than ten times higher than the median values for their respective species, namely 1.67 µM for mouse and 1.52 µM for human. Consequently, these data were excluded since such deviations may reflect unique molecular characteristics or alternative clearance mechanisms unrelated to FcRn-mediated recycling. For instance, vesencumab exhibited non-linear PK in mice, suggesting a target-mediated clearance⁸⁷ and impairing an accurate fitting of the PK profile with the linear model displayed in PK-Sim. This phenomenon was attributed to saturable binding sites in lungs, liver, and kidneys, as observed in mouse tissue distribution studies⁷⁶

Our analysis in Figure 4 revealed a trend between human $K_d_{FcRn}$ estimations and those derived from other animal species (monkey, mouse, rat). This trend was not explained by factors such as the type of mAb (Figure S1), IgG subtype (data not shown), the targeted disease (Figure S2), or target type (Figure S3). It is important to acknowledge that the type of mAb can influence binding to FcRn. Indeed, chimeric mAbs are generally cleared faster than humanized or fully human mAbs due to their higher immunogenicity, but also to their potentially lower FcRn binding affinity.⁹⁵. Although the distribution of mAb classes in our study was not uniform (9 fully human, 13 humanized, 3 chimeric, 1 peptibody, and 1 antibody-drug conjugate), our analysis in Figure S1 (Supplementary Material) suggested that the mAb class itself did not influence the observed
interspecies differences in $Kd_{FcRn}$ values. Since therapeutic mAbs compete with endogenous IgG for FcRn recycling, the disease state could potentially influence FcRn binding⁹⁶. To investigate the potential influence of disease on $Kd_{FcRn}$ estimation, we examined the human populations in the clinical data used to estimate $Kd_{FcRn}$. Most human studies involved diseased patients (19 studies), with only a few involving only healthy participants (6 studies) or both (1 study). Monkeys and rats were generally healthy animals, while mice studies included both wild-type and diseased mice, carrying tumors or other diseases, including athymic mice. Since PK profiles identified as potentially involving TMDD were excluded, the mAb concentrations were too low to saturate the target and the presence of tumors likely did not affect $Kd_{FcRn}$ estimates. However, autoimmune diseases can alter endogenous IgG levels, potentially impacting $Kd_{FcRn}$ estimation. Interestingly, our data in Figure S2 suggest that the disease state does not influence the observed interspecies differences. This finding, despite the unequal distribution of healthy versus diseased subjects, suggests that TMDD was either effectively excluded during $Kd_{FcRn}$ estimation or had minimal impact on the estimates. No significant effect of IgG subclass was observed (data not shown), but the analysis was limited by the unequal distribution of mAbs across subclasses: 23 IgG1, 2 IgG2, 1 IgG4 and 1 DVD (mostly IgG1 subtype). Similarly, the target type analyzed in Figure S3 did not appear to explain the interspecies trends in $Kd_{FcRn}$.

We analyzed the difference between the estimated $Kd_{FcRn}$ in human and in each of the three animal species, using the ratio: Estimated animal $Kd_{FcRn}$ / Estimated human $Kd_{FcRn}$, as illustrated in Figure S4. Mouse data points (green) are predominantly distributed near the identity line. Specifically, 60% of mouse $Kd_{FcRn}$ values fall within a 0.50–2.00-fold error range of the human values, with 27% within a 0.80–1.25-fold difference. Rat data points (blue) exhibit similar behavior, with 60% falling within the 2-fold range, but none within the 0.80–1.25-fold error range. In contrast, monkey data points (red) display a broader spread. Only 17% of monkey $Kd_{FcRn}$ values fall within the 0.50–2.00-fold error range, and just 4% fall within the 0.80–1.25-fold error range, indicating a higher degree of variability compared to human $Kd_{FcRn}$ values. These results suggest that mouse and rat $Kd_{FcRn}$ values are more often comparable to human values. Conversely, monkey $Kd_{FcRn}$ values show a wider variation, with a significant proportion deviating beyond the 0.50–2.00-fold error range, consistently exhibiting lower values compared to human $Kd_{FcRn}$ values.
To assess the predictive utility of $Kd_{FcRn}$ values from different species, we compared human $Kd_{FcRn}$ estimates with those from monkeys, mice, and rats and performed a regression analysis. The R-squared values for these regressions indicate the proportion of human $Kd_{FcRn}$ variability explained by the corresponding animal species. Monkey $Kd_{FcRn}$ estimates exhibited a moderate correlation ($R^2 = 0.36$) with human values, suggesting they provide some insight but may not be fully reliable predictors. Mouse $Kd_{FcRn}$ estimates displayed a weaker correlation ($R^2 = 0.24$), indicating their limited accuracy in predicting human $Kd_{FcRn}$. Interestingly, rat $Kd_{FcRn}$ estimates showed the highest correlation in our dataset ($R^2 = 0.74$) with human values, suggesting that rats might be a reliable animal model for predicting human $Kd_{FcRn}$ in this context.

PBPK modeling approach, which integrates physiological parameters and drug properties, allowed for a robust estimation of the $Kd_{FcRn}$ values using preclinical and clinical data. Our approach aims to assess a potential way to reduce animal use in preclinical safety testing. It suggests a more efficient and ethical pathway for drug development⁹⁰. Recently, due to the limitations of animal testing as well as ethical concerns about animals testing, the Food and Drug Administration (FDA) highlighted the need to reduce animal testing for mAbs and pave the way for more ethical, innovative, and efficient alternatives such as quantitative modeling solutions, including PBPK modeling ⁹⁷ which can offer accurate predictions.

It is important to acknowledge that the conclusions drawn may be specific to the mAbs analyzed and might not be generalizable to all mAbs. External validity, which refers to the generalizability of the findings to a broader population of mAbs, is primarily dependent on the representativeness of the sample. In our study, while efforts were made to randomly select mAbs, a limitation exists due to our reliance on available literature references, which could potentially introduce bias. Nevertheless, the mAbs included in this study are expected to provide a reasonably representative sample of those currently available on the market. A notable challenge in the statistical analysis was the unequal distribution of species (humans, monkeys, mice, and rats) within the dataset. Comparing samples of disparate sizes presents difficulties, prompting the use of RMSE. The analysis involved calculating these parameters using mAbs with $Kd_{FcRn}$ estimates across all four species and subsequently using all available $Kd_{FcRn}$ estimates to address missing data concerns. However, as indicated in Table 3, the data gaps do influence the calculation of RMSE, introducing
a limitation in the confidence that can be placed in the obtained results. This underscores the need
for cautious interpretation of the statistical findings, acknowledging the impact of data
completeness on the reliability of the outcomes.

This study provides insights into the interspecies extrapolation of Kd<sub>FcRn</sub>, a key parameter mAb
pharmacokinetics. Although no preclinical species showed a sufficiently strong correlation to
allow robust prediction of human Kd<sub>FcRn</sub>, the analysis identified the rat as the most aligned species,
which may guide species selection in early-stage PK studies. Furthermore, the use of a fixed
median human Kd<sub>FcRn</sub> value emerged as a practical alternative when animal data are unavailable.
This approach is particularly relevant in early development phases or in FIH simulations using
PBPK modeling, where rapid, data-sparse predictions are often needed. While this method does
not account for molecule-specific variability, it offers a reasonable compromise to support early
decision-making, species selection, and modeling assumptions during monoclonal antibody
development.

**Conclusion**

To conclude, this study assembled a dataset comprising PK profiles of 27 mAbs administered intravenously across humans, monkeys, rats, and mice, with Kd<sub>FcRn</sub> estimation using PK-Sim. The pivotal parameter for PK, Kd<sub>FcRn</sub>, was extrapolated from animals to humans through regression equations, revealing rat as the most relevant species. An alternative approach, using only a median human Kd<sub>FcRn</sub> of 1.52 µM, was of interest, particularly in early development phases in the absence of animal data. However, among the preclinical species evaluated in this analysis, there was none showing a sufficiently strong correlation to support a robust prediction of the Kd<sub>FcRn</sub> in human. On the other side, the use of a median default value in human could represent a reasonable compromise to support prediction of human PK using PBPK modeling. Future perspectives will include an extension of the database focusing on human data to explore the use of a global human value for the Kd<sub>FcRn</sub>.
List of abbreviations

<table><tr><td>ADC</td><td>: Antibody-drug conjugate</td></tr><tr><td>CL</td><td>: Clearance</td></tr><tr><td>Da</td><td>: Dalton</td></tr><tr><td>DVD</td><td>: Dual variable domain Ig</td></tr><tr><td>Fc</td><td>: Crystallizable fragment</td></tr><tr><td>FcRn</td><td>: Neonatal fragment crystallizable receptor</td></tr><tr><td>FDA</td><td>: Food and Drug Administration</td></tr><tr><td>FIH</td><td>: First-in-human</td></tr><tr><td>IgA</td><td>: Immunoglobulins A</td></tr><tr><td>IgD</td><td>: Immunoglobulins D</td></tr><tr><td>IgE</td><td>: Immunoglobulins E</td></tr><tr><td>IgG</td><td>: Immunoglobulins G</td></tr><tr><td>IgM</td><td>: Immunoglobulins M</td></tr><tr><td>Igs</td><td>: Immunoglobulins</td></tr><tr><td>IV</td><td>: Intravenous</td></tr><tr><td>Kd<sub>FcRn</sub></td><td>: Dissociation constant for FcRn</td></tr><tr><td>mAb</td><td>: Monoclonal antibody</td></tr><tr><td>mAbs</td><td>: Monoclonal antibodies</td></tr><tr><td>MSE</td><td>: Mean squared error</td></tr><tr><td>MW</td><td>: Molecular weight</td></tr><tr><td>OSP</td><td>: Open Systems Pharmacology</td></tr><tr><td>PBPK</td><td>: Physiologically based pharmacokinetic</td></tr><tr><td>PD</td><td>: Pharmacodynamic</td></tr><tr><td>PK</td><td>: Pharmacokinetics</td></tr><tr><td>RMSE</td><td>: Root mean squared error</td></tr><tr><td>RPE</td><td>: Relative prediction error</td></tr><tr><td>TMDD</td><td>: Target-mediated drug disposition</td></tr><tr><td>ADC</td><td>: Antibody-drug conjugate</td></tr><tr><td>CL</td><td>: Clearance</td></tr><tr><td>Da</td><td>: Dalton</td></tr><tr><td>DVD</td><td>: Dual variable domain Ig</td></tr></table>

Corresponding author

Donato Teutonico

Development Pharmacometrics, Translational Medicine Unit, Sanofi

13 Quai Jules Guesde,

94400 Vitry-sur-Seine, France

Donato.teutonico@sanofi.com
**Conflict of interest**

SB, LN and DT are Sanofi employees and may hold shares and/or stock options in the company.

FG, ES and CT have nothing to disclose. ES and DT are members of the OSP Management Team.

**Author contribution**

Data analysis: S.B. and C.T.; investigation: C.T., S.B., and D.T.; project supervision: D.T., F.G., and L,N. writing original draft: S.B.; writing—review and editing: S.B., C.T., D.T., F.G., E.S., L,N. All authors have read and agreed to the published version of the manuscript.

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## Supporting information

# Cross-species extrapolation of neonatal Fc receptor (FcRn) binding affinity to predict monoclonal antibody pharmacokinetics in humans using physiologically-based pharmacokinetic (PBPK) modeling: are we there yet ?

Salih Benamara¹,²*, Carla Troisi¹*, Florence Gattacceca², Erik Sjögren³, Laurent Nguyen¹, Donato Teutonico¹**

¹ Development Pharmacometrics, Translational Medicine Unit, Sanofi, 94400 Vitry-sur-Seine, France

² Computational Pharmacology and Clinical Oncology (COMPO) Unit, Inria Sophia Antipolis-Méditerranée, Cancer Research Center of Marseille, Inserm UMR1068, CNRS UMR7258, Aix Marseille University UM105, 13385 Marseille, France

³ Pharmetus AB, Uppsala 753 19, Sweden

* These authors contributed equally to this work

** To whom correspondence should be addressed: Donato.Teutonico@sanofi.com

## Table of contents

Figure S1...................................................................................................................page S2

Figure S2...................................................................................................................page S3

Figure S3...................................................................................................................page S4

Figure S4...................................................................................................................page S5
<figure><img src="image_11.png" /><figcaption>**Figure S1.** Comparison between human $K_{d_{FcRn}}$ and animal $K_{d_{FcRn}}$ for monkey, mouse and rat, stratified on the type of mAb (ADC, chimeric, human, humanized, or peptibody)</figcaption></figure>
<figure><img src="image_12.png" /><figcaption>**Figure S2.** Comparison between human Kd<sub>FcRn</sub> and animal Kd<sub>FcRn</sub> for monkey, mouse and rat, stratified on the presence or absence of disease</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure S3.** Comparison between human Kd<sub>FCRn</sub> and animal Kd<sub>FCRn</sub> for monkey, mouse and rat, stratified on the target location</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>**Figure S4.** Comparison of the ratio of Kd<sub>FcRn</sub> (Animal/Human) between three animal species

Monkey ratio: red circles
Mouse ratio: green triangles
Rat ratio: blue squares</figcaption></figure>