# Semi-mechanistic modelling to show the impact of structural modifications on radiolabeled-dendrimers pharmacokinetics

Jessica Ou, Béatrice Louis, Vincent Nail, Laure Balasse, Ahlem Bouhlel, Adrien Chabert, Tom Roussel, Ling Peng, Philippe Garrigue, Benjamin Guillet, et al.

► To cite this version:

Jessica Ou, Béatrice Louis, Vincent Nail, Laure Balasse, Ahlem Bouhlel, et al. Semi-mechanistic modelling to show the impact of structural modifications on radiolabeled-dendrimers pharmacokinetics. DMDG/GMP/SPS, Oct 2022, Amsterdam, Netherlands. [hal-03930671](https://hal.science/hal-03930671)

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Submitted on 9 Jan 2023

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# Semi-mechanistic modelling shows the impact of structural modifications on the pharmacokinetics of radiolabeled dendrimers

Jessica OU[1],[2], Béatrice LOUIS[3],[4], Vincent NAIL[3],[4], Laure BALASSE[3],[4], Ahlem BOUHLEL[3],[4], Adrien CHABERT[3],[4], Tom ROUSSEL[5], Ling PENG[5], Philippe GARRIGUE[3],[4], Benjamin GUILLET[3],[4], Florence GATTACCECA[1],[2]

[1] Inria-Inserm COMPO team, Centre Inria Sophia Antipolis - Méditerranée, Inserm U1068-CNRS UMR7258, Aix-Marseille University UM105

[2] CRCM, CNRS UMR7258-INSERM U1068- Institut Paoli-Calmettes UMR7258, Aix-Marseille University UM105, Marseille, France

[3] CERIMED, CNRS, Aix-Marseille University, Marseille, France

[4] C2VN, INSERM 1263, INRAE 1260, Aix-Marseille University, Marseille, France

[5] CINaM, Aix-Marseille University UMR7325 / UPR3118, Centre National de la Recherche Scientifique UMR7325 / UPR3118

## Introduction

### 1. Development of a nanosystem for PET imaging of tumors

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### 2. Reduce liver retention by chemical structure optimization

<figure><img src="image_3.png" /></figure>

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Selection of the best version through PK studies

### 3. Functionalization for a specific tumor targeting

Addition of RGD at 2 ratios:

* 1 dendrimer for 3 RGD (1:3)

* 3 dendrimers for 1 RGD (3:1)

* 2 functionalized dendrimers

7 dendrimers studied

Dendrimers PK profiles vary due to structural modifications

<figure><img src="image_5.png" /></figure>

**Aim of the project:**
Understand the impact of structural modifications on dendrimers biodistribution with PK modelling

## Results

### Non-compartmental analysis

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No significant difference

<figure><img src="image_7.png" /></figure>

No significant difference

### 1. Selection of C₄F₉-C11 for functionalization

### 2. Similar PK parameters between C₄F₉-C11 +/- RGD

One-way ANOVA post-hoc Tukey's test, *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001, n=6 except RGD 1:3 3:1 n=3

<figure><img src="image_8.png" /></figure>

most exposed organs

2h post injection

Two-way ANOVA, *p ≤ 0.05, **p ≤ 0.01, ***p ≤ 0.001, ****p ≤ 0.0001, n=6 per dendrimer except for (C4F9)-C11 n=5 and RGD 1:3 3:1 n=3

### Semi-mechanistic modelling

### 1. Final model

<figure><img src="image_9.png" /></figure>

RSE < 30% except for βkᵢ RSE = 41.7%

<table><thead><tr><th>Parameter (unit)</th><th>Tissue</th><th>Value</th><th>IIV value (%)</th><th>Covariate value (β)</th></tr></thead><tbody><tr><td>k12 (h<sup>-1</sup>)</td><td rowspan="2">Heart</td><td>6.55</td><td>45</td><td rowspan="2"></td></tr><tr><td>k21 (h<sup>-1</sup>)</td><td>54.56</td><td>50</td></tr><tr><td>k13 (h<sup>-1</sup>)</td><td rowspan="2">Brain</td><td>0.33</td><td>55</td><td rowspan="2"></td></tr><tr><td>k31 (h<sup>-1</sup>)</td><td>18.14</td><td>34</td></tr><tr><td>k14 (h<sup>-1</sup>)</td><td rowspan="3">Liver</td><td>13.39</td><td>58</td><td rowspan="3">0.79</td></tr><tr><td>k41 (h<sup>-1</sup>)</td><td>42.04</td><td>53</td></tr><tr><td>kl (h<sup>-1</sup>)</td><td>0.39</td><td>69</td></tr><tr><td>k15 (h<sup>-1</sup>)</td><td rowspan="2">Muscle</td><td>0.032</td><td>59</td><td rowspan="2"></td></tr><tr><td>k51 (h<sup>-1</sup>)</td><td>5.5</td><td>22</td></tr><tr><td>k16 (h<sup>-1</sup>)</td><td rowspan="2">Lungs</td><td>2.19</td><td>72</td><td rowspan="2"></td></tr><tr><td>k61 (h<sup>-1</sup>)</td><td>38.17</td><td>51</td></tr><tr><td>k17 (h<sup>-1</sup>)</td><td rowspan="2">Kidneys</td><td>4.64</td><td>62</td><td rowspan="2">-0.72</td></tr><tr><td>k71 (h<sup>-1</sup>)</td><td>29.72</td><td>54</td></tr><tr><td>k78 (h<sup>-1</sup>)</td><td rowspan="2">Peripheral kidneys</td><td>0.51</td><td>113</td><td rowspan="2"></td></tr><tr><td>k87 (h<sup>-1</sup>)</td><td>0.92</td><td>124</td></tr><tr><td>k79 (h<sup>-1</sup>)</td><td rowspan="2">Bladder</td><td>1.94</td><td>38</td><td rowspan="2">0.74</td></tr><tr><td>k91 (h<sup>-1</sup>)</td><td>1.15</td><td>82</td></tr><tr><td>k110 (h<sup>-1</sup>)</td><td rowspan="2">Peripheral blood</td><td>4.31</td><td>43</td><td rowspan="2"></td></tr><tr><td>k101 (h<sup>-1</sup>)</td><td>4.30</td><td>16</td></tr><tr><td>V (mL)</td><td>Central</td><td>1.71</td><td>9.3</td><td></td></tr></tbody></table>

### 2. Covariates

<figure><img src="image_10.png" /></figure>

<table><thead><tr><th>Parameter affected by covariates</th><th>% of IIV explained</th></tr></thead><tbody><tr><td>k<sub>l</sub></td><td>22</td></tr><tr><td>k<sub>71</sub></td><td>21</td></tr><tr><td>k<sub>79</sub></td><td>31</td></tr><tr><td>k<sub>91</sub></td><td>42</td></tr></tbody></table>

### 3. Model validation

Individual fits

<figure><img src="image_11.png" /></figure>

Good description of the observed data in the liver and other organs (data not shown)

EXCEPTION

Slight deviation for the heart → non-specific PET signal

OBS vs PRED - KIDNEYS

<figure><img src="image_12.png" /></figure>

Underprediction caused by the most weighted group (reference)

<figure><img src="image_13.png" /></figure>

No bias for the other organs (data not shown)

## Conclusion

* **Selection of C₄F₉-C11** for functionalization

* **Description** of dendrimers **biodistribution** with the semi-mechanistic model

* **Functionalization (RGD)** → **Higher liver elimination**

* **Shorter carbon chain** → **Higher renal elimination**

* **No impact of fluorination** on dendrimers PK

## Perspective

PBPK model tailored to nanoparticles (NPs) to predict PK profiles *in vivo* based on NP structural characteristics

→ NP optimization

## References

Garrigue P. et al., Proc. Natl. Acad. Sci. 115, 11454–11459 (2018)

## Acknowledgments

This work was supported by Ecole Doctorale 62 – Sciences de la vie, Aix-Marseille University and initiated by CINaM and CERIMED/C2VN teams.

## Abbreviations

C: carbon

ID: injected dose

IIV: interindividual variability

RGD: Arginyl-glycyl-aspartic acid

RSE: relative standard error

## Correspondence

Jessica OU, PhD student
COMPO Inria-Inserm
Aix-Marseille University
jessica.ou@etu.univ-amu.fr

## LinkedIn

## Online version

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