# Time shift in ciclosporin metabolism downregulation by inflammation in allogeneic hematopoietic cell transplantation: A time-dependent transduction modelling PK-PD approach.

David Malnoë, Tony Marchand, Pascal Le Corre

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## To cite this version:

David Malnoë, Tony Marchand, Pascal Le Corre. Time shift in ciclosporin metabolism downregulation by inflammation in allogeneic hematopoietic cell transplantation: A time-dependent transduction modelling PK-PD approach. European Journal of Pharmaceutical Sciences, 2025, 214, pp.107271. 10.1016/j.ejps.2025.107271. hal-05296249

HAL Id: hal-05296249

https://hal.science/hal-05296249v1

Submitted on 15 Nov 2025

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# Time shift in ciclosporin metabolism downregulation by inflammation in allogeneic hematopoietic cell transplantation: A time-dependent transduction modelling PK-PD approach

David Malnoë<sup>a,b,c</sup>, Tony Marchand<sup>d,e</sup>, Pascal Le Corre<sup>a,b,c,*</sup>

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## ARTICLE INFO

### Keywords:

Ciclosporin, Metabolism, Inflammation, Drug-drug interactions, Allogeneic hematopoietic stem cell transplantation, Models, Biological, Pharmacology*, Signal transduction physiology*

## ABSTRACT

Ciclosporin is a major immunosuppressant in allogeneic hematopoietic stem cell transplantation (HSCT), with a narrow therapeutic index and high interindividual variability. We previously showed that severe inflammation significantly reduces cytochrome P450 3A4 (CYP3A4)-mediated ciclosporin metabolism, as reflected by an increased concentration-to-dose (C/D) ratio. However, the temporal dynamics between inflammation and ciclosporin metabolism remain poorly understood. This study aimed to characterize the time-dependent effect of inflammation—quantified by C-reactive protein (CRP) levels—on CYP3A4-mediated ciclosporin metabolism using a signal transduction pharmacokinetic-pharmacodynamic (PK-PD) modeling approach in HSCT patients. We selected 10 HSCT patients with a single moderate-to-severe inflammatory episode (CRP > 40 mg/L) and modeled CRP kinetics alongside ciclosporin C/D ratios using a transduction model. Key parameters included the maximum effect ($E_{max}$), the CRP concentration at 50% of $E_{max}$ (EC$_{50}$), and the time delay ($\tau$) between CRP and metabolism variations. External validation was performed using published time profiles from Chen et al. 1994 and 2 patients with potential drug-drug interactions (pDDI) involving letermovir from our cohort. The model captured a 4.7-fold increase in C/D ratio at an EC$_{50}$ of 120 mg/L of CRP, with a time delay of 6 days between CRP peak and C/D ratio peak. External validation confirmed strong predictive performance. In patients with moderate-to-severe inflammation, inflammation-driven CYP3A4 downregulation appeared to outweigh the impact of letermovir-mediated DDI. Our results provide the first quantitative evidence of time-dependent inhibition due to inflammation of ciclosporin metabolism. This quantitative modeling framework may inform dose adjustment and DDI risk evaluation in inflammatory conditions, particularly in allogeneic HSCT patients.

## 1. Introduction

Graft-versus-host disease (GVHD) is a common immunologic complication of allogeneic stem cell transplantation (HSCT) characterized by an immune-mediated attack of donor-derived cells against the recipient's tissues; and a leading cause of morbidity and mortality. Standard GVHD prophylaxis typically involves calcineurin inhibitors, such as ciclosporin or tacrolimus, often combined with other immunosuppressive agents. Consensus guidelines indicate that ciclosporin and tacrolimus are equally effective for GVHD prevention, with ciclosporin being more commonly used in Europe (Penack et al., 2020). Ciclosporin

is administered twice daily, targeting whole-blood concentrations of 200–300 µg/L during the first four weeks post-transplantation.

We previously demonstrated that severe inflammation in HSCT patients significantly decreased cytochrome P450 3A4 (CYP3A4)-mediated metabolism of ciclosporin. Specifically, C-reactive protein (CRP) levels exceeding 200 mg/L were associated with an elevated concentration-to-dose (C/D) ratio, reflecting reduced ciclosporin metabolism (Malnoë et al., 2025b). Drug-drug interactions (DDIs) were not independently predictive in this context, likely because the CYP3A4-inhibitory effects of interacting drugs were partially masked by the upstream inflammation-driven downregulation of CYP3A4 activity

No funding was received for this work. The authors declared no competing interests for this work.

<footnote><sup>a</sup> Centre Hospitalier Universitaire de Rennes, Pôle Pharmacie, Secteur Pharmacotechnie et Onco-Pharmacie, 35033 Rennes, France</footnote>

<footnote><sup>b</sup> Université de Rennes, Faculté de Pharmacie, Laboratoire de Biopharmacie et Pharmacie Clinique, 35043 Rennes, France</footnote>

<footnote><sup>c</sup> Université Rennes, CHU de Rennes, Inserm, EHESP, IRSET (Institut de recherche en santé, environnement et travail) -UMR_S 1085, F-35000 Rennes, France</footnote>

<footnote><sup>d</sup> Université de Rennes, Service d'Hématologie Clinique, CHU de Rennes, INSERM U1236, 35000 Rennes, France</footnote>

<footnote>* Corresponding author at: 2, Avenue du Pr Léon Bernard, Building 5, Suite 323, 35043 Rennes, France
E-mail address: pascal.le-corre@univ-rennes1.fr (P. Le Corre).</footnote>
in these patients (Malnoë et al., 2025b). However, in patients with no-to-mild inflammation (CRP < 40 mg/L), letermovir significantly increased ciclosporin levels and C/D ratio likely through CYP3A4 inhibition, highlighting the relevance of considering inflammation leading to a complex interplay such as a disease-drug-drug interaction (D-DDI) (Malnoë et al., 2025a).

CYP450 enzymes exhibit varying sensitivity to cytokines, and the mechanisms driving their transcriptional downregulation are specific to both the gene and the particular cytokine involved (Morgan, 2024). Differences in magnitude, potency and time-course depend on the experimental animal models. CRP displays elevated expression during inflammatory conditions, cardiovascular diseases, and infections increasing up to over 500 µg/mL within 24-72 h of severe tissue damage in response to proinflammatory cytokines (Simon et al., 2004; Sproston and Ashworth, 2018). Inflammation can suppress the transcription of cytochrome P450 enzymes (CYPs), leading to decreased metabolic activity. As a result, elevated plasma CRP levels - an inflammation marker - may precede increased blood concentrations of CYP substrates due to reduced metabolism (Stanke-Labesque et al., 2020).

Few studies have quantified the time delay between CRP variations and altered drug metabolism intensity. For example, studies have reported a decrease in the metabolism of voriconazole, a CYP2C19 substrate, by inflammation in patients (Boglione-Kerrien et al., 2023; Hao et al., 2024; Klomp et al., 2024; Ling et al., 2024; Maeda et al., 2022; Takahashi et al., 2022; van den Born et al., 2023; van Wanrooy et al., 2014; Veringa et al., 2017). Voriconazole levels peaked around 48 h after CRP in the inflammation group as judged by mean plasma profiles (Hao et al., 2024). In one patient profile, CRP and voriconazole plasma levels paralleled with CRP levels preceding voriconazole levels with a 2-to-3 day delay (van Wanrooy et al., 2014).

The relationship between CRP and drug levels is likely drug-specific, as CRP, CYP enzyme activity, and drug concentrations re-equilibrate at different rates following inflammatory changes. Understanding this temporal shift is critical for optimizing dosing regimens of drugs with narrow therapeutic windows, such as ciclosporin, where inflammation may affect steady-state exposure days after its onset and offset.

The classical one-compartment biophase distribution model (also known as the effect-compartment model) is not always appropriate. This is especially true when the delay in drug effect is not due to distribution, but rather involves the turnover and homeostasis of endogenous compounds. In such cases, time dependent transduction system models provide a more appropriate framework, as they better capture the underlying biological processes driving the delayed response (Chae et al., 2012; Jusko, 2013; Mager and Jusko, 2001).

This study aimed to characterize the temporal relationship between plasma CRP concentrations and CYP3A4-mediated ciclosporin metabolism, estimated by the ciclosporin C/D ratio, in allogeneic hematopoietic stem cell transplantation patients with moderate-to-severe inflammation (CRP > 40 mg/L) using a time dependent transduction system modeling approach.

## 2. Materials and methods

### 2.1. Study settings

This study is an ancillary project using data from a parent study to extend knowledge in scientific areas beyond the original scope of the parent study that was approved by the Institutional Research & Ethics Committee of the Rennes Academic Medical Center (agreement n° 24.12) (Malnoë et al., 2025b). The initial study was performed as a retrospective (24 months from November 2020 to November 2022), observational, single center study of 77 adult patients hospitalized for allogeneic transplant in the Hematology Department of the Rennes Academic Medical Center.

For the current study, we retrieved data of patients with a single episode of moderate (CRP 41 to 200 mg/L) and/or severe inflammation

(CRP > 200 mg/L) as boundaries defined in adult patients with neutropenia (Manian, 1995; van Wanrooy et al., 2014). Collected drug data included daily dosing of ciclosporin and drugs likely to interact with ciclosporin through pharmacokinetic mechanisms throughout the hospital stay (either by induction or inhibition mechanism). Only drugs with the potential for DDIs of the highest severity were recorded (contra-indication and major severity according to Micromedex with excellent or good documentation were collected as previously described) (Malnoë et al., 2025b). Patients with multiple inflammatory episodes were excluded to reduce potential confounding factors (such as infection-related effects on drug absorption) and to allow for simplified modeling of CRP kinetics. Blood ciclosporin levels were obtained throughout the hospitalization and the ciclosporin concentration/dose ratio (C/D ratio) was calculated by dividing the steady-state blood ciclosporin level (ng/mL) by the daily dose (mg). The C/D ratio was used as an estimation of the ciclosporin metabolism since it is directly proportional to elimination clearance at steady state (García-Sáiz et al., 2002; Malnoë et al., 2025b; Shenoy et al., 2024). The laboratory data retrieved included CRP levels and Glomerular Filtration Rate (GFR), which was calculated according to CKD-EPI formula. The following clinical and laboratory parameters were retrieved during the hospital stay: gender, age, weight, height, hematocrit, albumin, aspartate aminotransferase (AST), CRP, GFR, antifungal treatment and disease. Potential outlier values were defined as ciclosporin concentrations > 500 ng/mL (Malnoë et al., 2025a).

### 2.2. Population-pharmacokinetic (POP-PK) model development of CRP kinetics

CRP kinetics follows the classic process observed for proteins, involving zero-order production and first-order degradation (Jusko, 2013). In an inflammatory context, hepatic CRP production increases significantly in response to IL-6, leading to elevated concentrations both at the site of inflammation and systemically (Sproston and Ashworth, 2018). To model this process, three distinct periods were empirically defined around the main inflammatory peak reflecting the typical acute-phase response in patients with a single inflammatory episode. Each peak is characterized with its own production rate: (1) Pre-inflammatory period ($k_{in0}$): basal production; (2) Inflammatory peak period ($k_{inf}$, $T_1$, $T_2$): increased production from the onset of the inflammatory peak ($T_1$) until the maximum ($T_2$); (3) Recovery period ($k_{post}$): intermediate production before returning to the basal state.

CRP elimination was assumed constant across phases and modeled using first-order kinetics ($k_e$). Overall kinetics is described by a differential equation accounting for the difference between the production function ($k_{in0}$, $k_{inf}$, or $k_{post}$ depending on the period) and the elimination function (Table 1). The initial CRP concentration at $t = 0$ is given by $C_0$.

Individual patient profiles were modeled using PKanalix2024R1 (Lixoft®). A population pharmacokinetic (popPK) model incorporating interindividual variability was then developed in MonolixR2024 (Lixoft®). The structural model was identical to the one described above. For each parameter $\theta$, taking $\mu$ as the population mean, variability was expressed by $\eta$ following a multiplicative model, where $\eta$ was normally distributed with parameters $(0, \omega^2)$.

$$ \theta i = \mu_{\theta} \times e^{n_{i0}} $$

$$ \eta_{i0} \sim N(0, \omega_{\theta}^2) \quad \text{thus} \quad \log(\theta i) \sim N(\log(\mu_{\theta}), \omega_{\theta}^2) $$

Median values of the previously described parameters were used as initial estimates. The residual error was described using a proportional model: $y = f + b\epsilon$ where $\epsilon$ follows a normal distribution.

### 2.3. PK-PD model development

The PK-PD (pharmacokinetic pharmacodynamic modeling)
<table><caption>**Table 1**
Structural equations of the PK/PD model describing CRP kinetics and its transduction-mediated effect on ciclosporin C/D ratios.</caption><thead><tr><th colspan="3">Parameters = {k<sub>in0</sub>, k<sub>inf</sub>, k<sub>post</sub>, k<sub>e</sub>, T<sub>1</sub>, T<sub>2</sub>, C<sub>0</sub>, τ, E<sub>max</sub>, EC<sub>50</sub>, E<sub>0</sub>, γ}</th></tr></thead><tbody><tr><td rowspan="3">CRP<br>pharmacokinetic</td><td>If t &lt; T<sub>1</sub>: input = k<sub>in0</sub>, output = -k<sub>e</sub> &times; CRP</td><td rowspan="3">dCRP/dt = input - output<br>with CRP<sub>t=0</sub> = C<sub>0</sub></td></tr><tr><td>If T<sub>1</sub> &le; t &lt; T<sub>2</sub>: input = k<sub>inf</sub>, output = -k<sub>e</sub> &times; CRP</td></tr><tr><td>If t &gt; T<sub>2</sub>: input = k<sub>post</sub>, output = -k<sub>e</sub> &times; CRP</td></tr><tr><td rowspan="2">Ciclosporin C/D<br>ratio</td><td>dZ<sub>1</sub>/dt = (1/τ) &times; (E<sub>max</sub> &times; CRP / (EC<sub>50</sub> + CRP) - Z<sub>1</sub>) with Z<sub>1</sub><sub>t=0</sub> = 0</td><td rowspan="2"></td></tr><tr><td>dZ<sub>2</sub>/dt = (1/τ) &times; (Z<sub>1</sub><sup>γ</sup> - Z<sub>2</sub>) with Z<sub>2</sub><sub>t=0</sub> = E<sub>0</sub></td></tr></tbody></table>

relationship between inflammation—estimated by CRP kinetics—and its effect on ciclosporin metabolism—estimated by the C/D ratio—was modeled using the same population approach in MonolixR2024 (Lixoft®). In the final model (Fig. 1), the measured effect (the C/D ratio) is represented by a compartment Z₂ with an initial value E₀. This C/D ratio is modulated over time by an intermediate effector Z₁, which reflects changes in the inhibition of hepatic metabolism mediated by CYP3A4 under the influence of inflammation (as given by the CRP kinetic). Z₁ acts as a latent inflammation signal that translates CRP variations into a biological effect on drug metabolism, while Z₂ represents the observable pharmacokinetic consequence (the C/D ratio).

The popPK model for CRP was used to fix the parameters of inflammatory kinetics. The estimated pharmacodynamic parameters included τ (time delay between the CRP and the C/D ratio peak), Eₘₐₓ (maximum effect), EC₅₀ (CRP concentration leading to 50% of the maximum effect), E₀ (initial C/D ratio), and γ (amplification factor). Thus, the overall kinetics of CRP and the C/D ratio are described using three differential equations (Table 1), which are solved in Monolix2024R1 (Lixoft®). The parameters were specified according to a multiplicative model with a proportional residual error, in the same manner as for the initial popPK approach describing the CRP kinetic alone. Graphical representations were generated using a fixed interval of 1 hour between simulated points. Simulation data were exported from Monolix2024R1 (Lixoft®) and visualized using GraphPad Prism version 10.2.1 (GraphPad Software, Boston, MA, USA).

## 2.4. Model validation

Both population approaches were validated graphically using Visual Predictive Checks (VPC) and through bootstrap validation with 200 replicates (non-parametric case resampling, using final estimates as initial values). Patients without DDI were used to build the training set, whereas patients with DDI involving letermovir served as the internal validation dataset. Additionally, external datasets with individual ciclosporin blood levels and CRP levels over time in the clinical context of HSCT were searched in the literature, and an external validation dataset was constructed using data from Chen et al (see the flowchart in Fig. 2). Due to multiple inflammatory episodes or ciclosporin interruptions, only two patients out of six from the original study could be included (Chen et al., 1994). Model performance was evaluated through graphical analyses (Visual Predictive Checks (VPC) and percentage of points outside the 90% prediction interval), internal validation by bootstrap as well as percentage of error (PE), PE greater than 30% (PE₃₀) and root mean square error (RMSE). For a complete validation strategy, see Supplementary Material S1: Detailed Validation Method for the Population Approach.

## 2.5. Statistics

Continuous data were assessed for normality using the Shapiro-Wilk test. Normally distributed data are presented as mean ± standard

<figure><img src="image_5.png" /><figcaption>**Fig. 1.** Diagram of the PK/PD transduction model describing the effect of inflammation, represented by CRP levels, on ciclosporin metabolism through changes in the C/D ratio.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>**Fig. 2.** Flowchart of the study population including selection of the external validation dataset from Chen et al. 1994.</figcaption></figure>

deviation (SD), while non-normally distributed data are reported as median (interquartile range, IQR). Intergroup comparisons were performed using the Wilcoxon signed-rank test. Statistical analyses were performed using GraphPad Prism version 10.2.1 (GraphPad Software, Boston, MA, USA). Non-parametric tests were applied when normality assumptions were not met. A P-value < 0.05 was considered significant.

nature of the study, using data from the hospital clinical data warehouse (eHOP) developed at the Rennes Academic Medical Center, a consent waiver was granted. The principles of ethical research, such as confidentiality and anonymity, were strictly followed. The extracted data did not contain any nominative data, and were de-identified to ensure confidentiality.

## 2.6. Ethics statement

This study was approved by the Institutional Research & Ethics Committee of the Rennes Academic Medical Center (agreement n° 24.12). This study is an ancillary project using data from a parent study to extend knowledge in scientific areas beyond the original scope of the parent study that (Malnoë et al., 2025b). All aspects of the study were conducted in accordance with the ethical standards set forth in the 1964 Declaration of Helsinki and its subsequent amendments, or comparable ethical standards. Due to the retrospective and non-interventional

## 3. Results

### 3.1. Subject demographics

Thirteen patients met the inclusion criteria based on inflammation, however, of the initial cohort of 77 patients, the study finally included 12 patients (9 men and 3 women) aged 18 to 69 years with moderate to severe inflammation (CRP > 40 mg/L). This included two patients who were treated with letermovir. One patient was excluded due to insufficient data for CRP kinetic characterization (3 data points over the
hospitalization period) (see the flowchart in Fig. 2). Full demographic characteristics are presented in Table 2. Among two suspicious values identified, only one (>1,000 ng/mL) was considered an outlier and excluded; the other (901 ng/mL) was retained based on consistency with surrounding measurements.

The patients did not have renal failure (median GFR of 114 mL/min at admission) or evidence of hepatic dysfunction (albumin and AST within normal ranges) (Table 2), however they experienced a significant decrease in renal function at discharge (p = 0.0005, Wilcoxon signed-rank test). This was likely related to one or more complications, as evidenced by a major inflammatory event. Nonetheless, renal function at discharge remained acceptable for most patients. Median peak CRP levels (224 mg/L) indicated that most of the patients (8 out of 12) experienced episodes of severe inflammation (CRP > 200 mg/L) (van Wanrooy et al., 2014) with a median observed CRP time-to-peak of 11 days after transplantation. The median observed ciclosporin time-to-peak occurred 18 days after transplantation indicating a median time shift of around 7 days.

## 3.2. Pharmacokinetic modeling of CRP

The popPK modeling approach for CRP was built using the ten patients with no potential interaction with letermovir, allowing for relatively good parameter estimation (Relative Standard Error, RSE < 50% and parameter estimation within the 95% confidence interval of the bootstrap simulation; Supplementary Table S1) and kinetic profiles consistent with the observed data (Supplementary Figure S1, S2 and S3).

## 3.3. Pharmacokinetic-pharmacodynamic (PK-PD) relationship between CRP and ciclosporin metabolism

An anti-clockwise (or counter-clockwise) hysteresis loop was observed (Fig. 3, see Supplementary Material S2 for details on the graphical generation method), indicating a clear time lag between CRP pharmacokinetics and the pharmacodynamic effect measured by the C/D ratio. The anti-clockwise hysteresis loop reflects a time delay between CRP concentrations and C/D ratio of ciclosporin meaning that for the same CRP concentration, the observed effect (i.e., inhibition of ciclosporin metabolism by downregulation) is greater on the descending part of the CRP concentration-time curve than on the ascending part. With time, C/D ratio of ciclosporin increases up to CRP values of around 150mg/L. Then although CRP concentrations fall, the inhibition of

<figure><img src="image_7.png" /><figcaption>**Fig. 3.** Anti-clockwise hysteresis loop (see Supplementary Material S2 for details on the graphical generation method), indicating a clear time lag between CRP pharmacokinetics and the pharmacodynamic effect measured by the C/D ratio.</figcaption></figure>

ciclosporin metabolism persists and increases because the metabolic downregulation reaches a maximum and then decreases quite sharply (for low CRP values). This finding justified using a PK-PD modeling-based methodology to study the effect. Since the observed delay is due to the turnover and homeostasis of endogenous compounds, here involving CYP3A4, a time dependent transduction system model is more appropriate for capturing these complex dynamics involving one or more intermediates (Chae et al., 2012; Jusko, 2013; Mager and Jusko, 2001).

Several pharmacodynamic models (linear, log-linear, $E_{max}$, and sigmoidal $E_{max}$) were tested in order to describe the relationship between inflammation (as estimated by CRP) and the measured pharmacological effect (the ciclosporin C/D ratio). The classical $E_{max}$ model provided the best performance and/or biological consistency and was therefore selected. Adding a third compartment in the transduction cascade did not improve model performance. Details of the models tested, along with the modeling strategy, are provided in the appendix

<table><caption>**Table 2**

Demographic characteristic of the study population (n = 12) at HSCT day.</caption><thead><tr><th>Sex</th><th>M</th><th>M</th><th>M</th><th>F</th><th>M</th><th>M</th><th>M</th><th>F</th><th>M</th><th>F</th><th>M</th><th>Median</th><th>Interquartile</th></tr></thead><tbody><tr><td>Age (years)</td><td>41</td><td>47</td><td>18</td><td>61</td><td>42</td><td>53</td><td>55</td><td>58</td><td>25</td><td>53</td><td>58</td><td>69</td><td>53</td><td>41.8 - 58</td></tr><tr><td>Weight (kg)</td><td>87</td><td>68</td><td>53</td><td>78</td><td>-</td><td>71</td><td>96</td><td>77</td><td>74</td><td>64</td><td>114</td><td>78</td><td>77</td><td>69.5 - 82.5</td></tr><tr><td>Height (m)</td><td>1.78</td><td>1.7</td><td>1.7</td><td>1.6</td><td>-</td><td>-</td><td>1.8</td><td>1.8</td><td>1.7</td><td>1.8</td><td>1.68</td><td>1.75</td><td>1.73</td><td>1.7 - 1.8</td></tr><tr><td>BMI (kg/m²)</td><td>27.5</td><td>24.7</td><td>18.3</td><td>29.4</td><td>-</td><td>-</td><td>29.3</td><td>24.9</td><td>27.2</td><td>19.8</td><td>40.4</td><td>35.5</td><td>27</td><td>24.8 - 29.4</td></tr><tr><td>Haematocrit (%)</td><td>31.1</td><td>20.0</td><td>31.7</td><td>33.6</td><td>38.0</td><td>39.5</td><td>30.9</td><td>22.4</td><td>22.2</td><td>35.3</td><td>30.5</td><td>25.9</td><td>31</td><td>25 - 34</td></tr><tr><td>Albumin (g/L)</td><td>37.4</td><td>30.7</td><td>40.1</td><td>43.3</td><td>37.7</td><td>41.5</td><td>34.5</td><td>38.9</td><td>46.5</td><td>34.7</td><td>32.4</td><td>33.2</td><td>38</td><td>34.2 - 40.5</td></tr><tr><td>AST (U/L)</td><td>48</td><td>19</td><td>22</td><td>13</td><td>12</td><td>20</td><td>10</td><td>10</td><td>9</td><td>16</td><td>37</td><td>30</td><td>18</td><td>11.5 - 24</td></tr><tr><td>GFR (CKD-EPI) at HSCT</td><td>133</td><td>113</td><td>153</td><td>114</td><td>119</td><td>114</td><td>107</td><td>94</td><td>139</td><td>132</td><td>55</td><td>91</td><td>114</td><td>103.8 - 132.3</td></tr><tr><td>GFR (CKD-EPI) End of stay</td><td>65</td><td>94</td><td>134</td><td>110</td><td>78</td><td>100</td><td>48</td><td>73</td><td>138</td><td>73</td><td>30</td><td>58</td><td>76</td><td>63.3 - 102.5</td></tr><tr><td>CRP peak (mg/L)</td><td>323</td><td>249</td><td>236</td><td>341</td><td>211</td><td>251</td><td>156</td><td>94</td><td>205</td><td>158</td><td>191</td><td>237</td><td>224</td><td>182.8 - 249.5</td></tr><tr><td>Clinical diagnoses</td><td>MML</td><td>AML</td><td>AML</td><td>AML</td><td>ALL</td><td>HL</td><td>Other</td><td>Other</td><td>AML</td><td>AML</td><td>cPTCL</td><td>Other</td><td>-</td><td>-</td></tr><tr><td>Ongoing medication with potential drug-drug interactions</td><td>FLC</td><td>FLC</td><td>FLC</td><td>FLC</td><td>FLC</td><td>FLC</td><td>FLC</td><td>FLC</td><td>FLC</td><td>FLC</td><td>LET</td><td>FLC</td><td>-</td><td>-</td></tr></tbody></table>

Data are presented as individual values and as a summary of the population, with the median and interquartile range (IQR) shown in bold.

**Abbreviations:** AST, aspartate aminotransferase; BMI, body mass index; CRP, C-reactive protein; FLC, fluconazole; GFR (CKD-EPI), glomerular filtration rate estimated by the CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equation; HSCT, Hematopoietic Stem Cell Transplant; LET, letermovir; Sex: F, female; M, male. **Clinical diagnoses (in alphabetical order):** ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; cPTCL, cutaneous peripheral T-cell lymphoma; HL, Hodgkin's lymphoma; MML, myelomonocytic leukemia; Other, other lymphoma-related condition.
(Supplementary Table S2).

The model built using the patients without potential interaction with letermovir reproduces the observed data very satisfactorily, with fewer than 10% of the individual conditional distributions lying outside the 90% prediction interval (8.9%, see Fig. 4). The distribution of normalized prediction distribution errors (NPDE) is also satisfactory and aligns with the assumption of normality for the residual error (Appendices Fig. A and Fig. B). Bootstrap sampling does not indicate any instability in the parameter estimates, as all parameters are well-defined (Table 3 and Fig. 5) with bias between the mean bootstrap and reference estimates below 10% for all fixed-effect parameters, and ranging from -8.5% to -13.2% for random-effect parameters. Finally, visual predictive checks (VPC) definitively validate the model, which proves reliable and robust despite the limited number of individuals (Fig. 6).

The estimated parameters are presented in Table 3. The initial effect $E_0$ is estimated at 0.34 in the population, with interindividual variability. The maximum effect $E_{max}$ represents an 8.4-fold increase from the initial effect, again with significant interindividual variability. The effect concentration $EC_{50}$ that produces 50% of $E_{max}$ is estimated at 120 mg/L, with no variability and precise confidence intervals (95% CI: 109–132 mg/L). This $EC_{50}$ concentration of 120 mg/L results in a 4.7-fold increase in C/D ratio. The observed time delay $\tau$ (Tau) of 6 days (95% CI: 4.0–8.9 days) is accompanied by notable interindividual variability. RMSE for population predictions was relatively high (56.3%), indicating substantial variability. However, the median prediction error (PE) was low at -2%, with just over half of the population predictions exceeding the 30% error threshold ($PE_{30} = 56.2\%$). In

contrast, individual predictions showed much better performance, with an RMSE below 30% (24.2%), a median PE of 2%, and only 10.5% of errors exceeding 30% ($PE_{30}$). All performance metrics are summarized in Table 4.

### 3.4. Model validation

Data from the Chen et al. cohort (Chen et al., 1994) showed relatively good individual prediction performance ($PE_{30} = 35.5\%$, RMSE = 69.2%, $PE_{median} = 2.4\%$), with most PE values falling within the [-30%, 30%] interval (Table 4, Fig. 7A). Simulated individual predictions adequately fit the observed data, particularly for patient 2 (Fig. 7B). However, population predictions were less accurate ($PE_{30} = 90.3\%$, RMSE = 78.9%), with a median PE ($PE_{median} = 78.9\%$) outside the [-30%, 30%] interval (Table 4, Fig. 7B).

Data from the DDI patients in our cohort demonstrated excellent individual prediction performance ($PE_{30} = 0.0\%$, RMSE = 12.8%, $PE_{median} = 6.4\%$, Table 4). Population prediction metrics were less satisfactory ($PE_{30} = 61.1\%$, RMSE = 38.6%, $PE_{median} = 26.0\%$), though the $PE_{median}$ for both individual and population predictions remained within the [-30%, 30%] interval (Table 4, Fig. 7C). Both individual and population simulated curves adequately fit the observed data, providing a reasonable estimation of the overall course of the C/D ratio (Fig. 7D).

## 4. Discussion

The popPK modeling approach for CRP was built using patients with

<figure><img src="image_8.png" /><figcaption>**Fig. 4.** Individual and population goodness-of-fit diagnostics. (A) Time profiles of ciclosporin C/D ratio observations with conditional individual mode by empirical bayes estimates (red line) and population (blue dotted line) predictions; (B) Observed vs. predicted concentrations (population predictions); (C) Observed vs. predicted concentrations (individual conditional predictions).</figcaption></figure>
<table><caption>**Table 3**

Parameter estimates of the final transduction model.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">VALUE</th><th colspan="4">ESTIMATION</th><th colspan="4">BOOTSTRAP (N=200)</th></tr><tr><th>S.E.</th><th>R.S.E.(%)</th><th>CI2.5</th><th>CI97.5</th><th>CI2.5</th><th>MEDIAN</th><th>CI97.5</th><th>BIAS (%)</th></tr></thead><tbody><tr><td>Fixed effect</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>tau_pop</td><td>5.99</td><td>1.25</td><td>20.8</td><td>4.03</td><td>8.89</td><td>4.00</td><td>5.89</td><td>8.67</td><td>-0.020</td></tr><tr><td>Emax_pop</td><td>2.84</td><td>0.46</td><td>16.1</td><td>2.08</td><td>3.87</td><td>1.47</td><td>2.88</td><td>4.52</td><td>2.93</td></tr><tr><td>EC<sub>50</sub>_pop</td><td>120</td><td>5.73</td><td>4.78</td><td>109</td><td>132</td><td>43.2</td><td>131</td><td>221</td><td>8.55</td></tr><tr><td>E0_pop</td><td>0.34</td><td>0.029</td><td>8.40</td><td>0.29</td><td>0.40</td><td>0.28</td><td>0.34</td><td>0.43</td><td>0.33</td></tr><tr><td>gamma_pop</td><td>1.95</td><td>0.0068</td><td>0.349</td><td>1.93</td><td>1.96</td><td>1.14</td><td>1.93</td><td>3.1</td><td>1.20</td></tr><tr><td>Random effects</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>omega_tau</td><td>0.57</td><td>0.16</td><td>28.7</td><td>0.33</td><td>0.97</td><td>0.17</td><td>0.54</td><td>0.79</td><td>-8.50</td></tr><tr><td>omega_Emax</td><td>0.45</td><td>0.12</td><td>25.8</td><td>0.28</td><td>0.73</td><td>0.15</td><td>0.43</td><td>0.62</td><td>-6.36</td></tr><tr><td>omega_E0</td><td>0.21</td><td>0.072</td><td>34.1</td><td>0.11</td><td>0.39</td><td>0.031</td><td>0.19</td><td>0.30</td><td>-13.23</td></tr><tr><td>Residual error</td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>b</td><td>0.22</td><td>0.019</td><td>8.66</td><td>0.19</td><td>0.26</td><td>0.17</td><td>0.22</td><td>0.28</td><td>-1.00</td></tr></tbody></table>

All parameters are reported as the estimated value with associated standard error (S.E.), relative standard error (R.S.E.), and 95% confidence interval (CI) of the estimation. Bootstrap validation was conducted using 200 nonparametric resampling iterations, each based on a dataset of 10 patients. Results are presented as the median and 95% CI of the bootstrap estimates, along with the bias (%) between the mean bootstrap estimate and the reference estimate.

<figure><img src="image_9.png" /><figcaption>**Fig. 5.** Bootstrap validation of the final transduction model. Bootstrap analysis was performed using 200 nonparametric resampling iterations, each based on a dataset of 10 patients. Results are presented as boxplots showing the median (mean as a + sign), first and third quartiles (boxes), and the 5th and 95th percentiles (whiskers), as well as with the 95% confidence interval of the bootstrap simulation results.</figcaption></figure>

no potential interaction with letermovir, allowing for relatively good parameter estimation (supplementary Table S1). The fixed-effect parameters were estimated with acceptable relative standard errors (RSE < 50%) for all parameters, except for C₀. The kinetic profiles were consistent with the observed data, and the observed-versus-predicted plot was satisfactory, with 5% of the conditional means lying outside the 90% prediction interval (Supplementary Figure S1). The significant variability for C₀, kᵢₐ₀, and kᵢₙf can be explained by the substantial differences in inflammatory statuses among patients at the start of their

<figure><img src="image_10.png" /><figcaption>**Fig. 6.** Visual predictive check (VPC) of the final transduction model. A total of 1,000 simulations were performed to generate 90% prediction intervals. The empirical percentiles of the observed data (5th, 50th, and 95th) were compared with the corresponding percentiles from the simulations and the 90% prediction intervals. For visualization, ten bins were defined using the least-squares method implemented in Monolix®, and values between bins were interpolated linearly.</figcaption></figure>

<table><caption>**Table 4**

Performance metrics of the final time-dependent transduction PK-PD model in the training and validation datasets.</caption><thead><tr><th rowspan="2">Dataset</th><th rowspan="2">n</th><th colspan="3">Population Predictions</th><th colspan="3">Conditional Individual Mode Predictions (EBEs)</th></tr><tr><th>PE30 (%)</th><th>PEmed (%)</th><th>RMSE (%)</th><th>PE30 (%)</th><th>PEmed (%)</th><th>RMSE (%)</th></tr></thead><tbody><tr><td>Training Set</td><td>10</td><td>56.2</td><td>-2.0</td><td>56.3</td><td>10.5</td><td>2.0</td><td>24.2</td></tr><tr><td>Internal Validation Cohort</td><td>2</td><td>61.1</td><td>26.0</td><td>38.6</td><td>0.0</td><td>6.4</td><td>12.8</td></tr><tr><td>External Validation Cohort (Chen et al. 1994)</td><td>2</td><td>90.3</td><td>78.9</td><td>75.8</td><td>35.5</td><td>2.4</td><td>69.2</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>**Fig. 7.** External validation of developed popPK-PD model of inflammation impact (as CRP) on ciclosporin metabolism (as C/D ratio) on Chen et al. 1994 with percentage of error for conditional individual mode (indivPred) by Empirical Bayes Estimates (EBEs) and population (popPred) predictions (A) as well as time profiles of C/D ratios observations with individual (red line) and population (blue dotted line) predictions (B); and on Malnoë et al. DDI patients with percentage of error for conditional individual mode (indivPred) by EBEs and population (popPred) predictions (C) as well as time profiles of C/D ratios observations with individual (red line) and population (blue dotted line) predictions (D).</figcaption></figure>

hospital stays. Including a correlation between T1 (onset) and T2 (peak) improved model performance, lowering the objective function by 10 points. However, the bootstrap analysis revealed a lack of robustness for this parameter, as the confidence interval included zero, likely due to the influence of possible outliers (Supplementary Figure S2). As the objective of this study was not to precisely model the CRP kinetic profile, the chosen model, while simplified, sufficiently captured the high variability of the profiles. Therefore, it was validated in its current form for use in the subsequent modeling approach.

The frequency of CRP and ciclosporin monitoring strongly impacts modeling results. Because of the retrospective design of this study, sampling could not be standardized. For future prospective investigations, an optimal strategy would involve joint measurement of CRP and C/D ratio every two days, intensified to daily sampling during the CRP peak and maintained for one week after resolution of the inflammatory episode, consistent with the observed six-day delay between CRP peak and maximal effect. In routine clinical practice, a more pragmatic approach would consist of biweekly joint assessments of CRP and the C/D ratio, intensified to every two days during inflammatory episodes. This strategy, balancing feasibility with clinical utility, would limit the total number of blood draws while ensuring that ciclosporin exposure is adequately assessed at steady state after dose adjustments.

The EC₅₀ of 120 mg/L (Table 3) corresponds to moderate inflammation, and is consistent with the previously conducted multivariate analysis (Malnoë et al., 2025b). Moreover, it has been demonstrated that a severe inflammation, ranging from CRP values of 150 to 250 mg/L, is associated with a reduced CYP3A4 activity of 53% in the context of SARS-CoV-2 infection (Le Carpentier et al., 2022). Even though our findings seem to show lower CRP values resulting in 50% of the inhibitory effect towards CYP3A4, it should be noted that the pathological context of our patients as well as the drugs they were taking were different (midazolam in the previous study vs. ciclosporin in the current

study). However, both studies indicate a strong CYP3A4 repression induced by severe inflammation. The observed time shift (τ) of 6 days and the 4.7-fold increase in the ciclosporin C/D ratio at an EC₅₀ CRP concentration of 120 mg/L (Table 3) are consistent with early findings of Chen et al., who reported a 3.6-fold increase in blood ciclosporin levels 4.8 days after an IL-6 inflammatory peak (Chen et al., 1994). The slight difference in time shift, may reflect the different kinetics of the inflammatory biomarkers. Indeed, IL-6 as an early marker, rises within hours during inflammation, whereas CRP levels typically increase within 24-72 hours in response to proinflammatory cytokines (Simon et al., 2004; Sproston and Ashworth, 2018). These findings support the mechanistic hypothesis of inflammation-mediated suppression of hepatic CYP3A4 activity via IL-6.

Interindividual variability was noted for both τ and Emax, which may be influenced by covariates such as age, sex, and conditioning regimen (Table 3). Additionally, while external validations were acceptable, especially for individual prediction, population predictions showed poorer performance in both external datasets (Table 4, Fig. 7). Although this was expected, it highlighted significant interindividual variability. This may be better explained by incorporating additional clinical covariates, which could not be explored here due to the limited sample size. Similarly, the relatively low predictive accuracy may be attributed to the small sample size used for both model construction and validation. Additionally, the developed model aimed to support and confirm the mechanistic hypothesis of CYP3A4 inhibition driven by inflammation impacting ciclosporin metabolism, as demonstrated through time-dependent transduction modeling, rather than predict the C/D ratio. The model's performance was deemed acceptable, given the restricted dataset available for development.

External validation confirmed that the population PK-PD model adequately described the data of two patients who were initially excluded due to a potential drug-drug interaction (pDDI) involving
letermovir. Individual predictions fit the data better fit than the population curve did (Fig. 7D), because they accounted for interindividual variability. However, the general shape of the population curve remained consistent. The individual predictions fell within the variability observed in the original population, suggesting that the pDDI with letermovir contributed minimally to the overall variability. Moreover, as previously shown in HSCT patients with moderate-to-severe inflammation (CRP > 40 mg/L), no significant pDDI effect on the ciclosporin concentration-to-dose (C/D) ratio was observed (Malnoë et al., 2025b). However, in HSCT patients with no-to-mild inflammation (CRP < 40 mg/L), our prior findings revealed that letermovir increased ciclosporin blood levels by 1.8-fold and concentration-to-dose (C/D) ratios by 2.3-fold (Malnoë et al., 2025a). Therefore, we hypothesized that in these two patients, inflammation-driven downregulation of CYP3A4 masked the letermovir pDDI, rendering its effect on ciclosporin negligible compared to the dominant and upstream influence of inflammation. Additionally, inflammation also represses transporter expression, including organic anion-transporting polypeptides (OATPs) (Vee et al., 2009). As letermovir is an OATP1B1/1B3 substrate (Menzel et al., 2021), reduced hepatic uptake during severe inflammation may limit its availability for CYP3A4 inhibition. This effect, combined with the upstream downregulation of CYP3A4 activity by inflammation, likely explains the absence of additional CYP3A4 inhibition by the letermovir pDDI. Health care professionals should be aware of this point because the letermovir inhibition on ciclosporin metabolism that may lead to changes in ciclosporin dosing regimen may be less relevant in patients with severe inflammation.

These findings have implications for therapeutic drug monitoring (TDM) strategies of ciclosporin. Inflammation acts like a time-dependent CYP3A4 inhibitor so that a systematic co-monitoring of inflammation markers (daily CRP levels or every 48 h) should be considered. In HSCT patients, ciclosporin exposure should be interpreted in light of inflammation status. In patients with moderate-to-severe inflammation (CRP > 40 mg/L, reduced CYP3A4 is expected with higher ciclosporin exposure for a given dose and potential toxicity (renal, neurological, hypertension) should be considered. In situations with high or rapidly changing CRP (e.g. CRP > EC₅₀ of 120 mg/L), ciclosporin TDM every two days may be recommended, until stable. In patients with no-to-mild inflammation (CRP < 40 mg/L) standard ciclosporin TDM applies (twice a week).

For a more precise dose adjustment strategy, CRP trends should be analyzed. When CRP rises (e.g., infection), increased exposure should be anticipated and pre-emptive dose reduction (10–20%) could be considered if levels approach upper target. When CRP falls, a rebound increase in metabolism should be anticipated and exposure may drop suddenly. Hence TDM frequency should be increased and clinicians should be ready for dose escalation to prevent underexposure.

Similarly, TDM of ciclosporin during letermovir co-administration must be interpreted in the context of the patient's inflammatory status; and CRP levels must be co-monitored. When CRP levels are high, inflammation dominates and letermovir effect on ciclosporin levels is negligible as shown for the patient of the validation cohort with DDI (Fig. 7 C and D). For low CRP levels, classical letermovir–ciclosporin DDI is evident (up to 2-fold exposure) (Malnoë et al., 2025a) and ciclosporin dosing should be adjusted accordingly. However, in the transition phase (CRP falling), there is a highest risk of overexposure that require intensifying TDM of ciclosporin.

An inflammation-informed TDM model may be incorporated into real-time clinical decision support (CDS) by linking ciclosporin concentration data with routinely available biomarkers such as CRP. Automated algorithms could flag situations where elevated CRP is likely to attenuate drug metabolism or mask drug–drug interactions, thereby preventing premature dose adjustments. Such events may arise when CRP levels are elevated with half of the maximum effect arising at CRP levels of 120 mg/L and with a delay of around 6 days. Conversely, when CRP decreases, the CDS could alert clinicians to the risk of rebound in

metabolism or unmasking DDIs and recommend intensified monitoring or proactive dose modifications. Embedding such dynamic, biomarker-driven rules into electronic prescribing or TDM platforms would allow individualized, adaptive dosing strategies in patients experiencing fluctuating inflammatory states.

The extent of inflammation-induced downregulation observed with ciclosporin in HSCT patients should not be extrapolated to other drugs since it is dependent on the metabolic pathways involved. Inflammation downregulates CYP activities in an isoform-specific manner. For example, in type 2 diabetic patients that display chronic inflammation, albeit low-grade, the mean metabolic activity of CYP3A4, CYP2C19, and CYP2B6 was reduced by 1.5-2 fold while CYP1A2 and CYP2C9 activities slightly increased, and no difference was observed for CYP2D6 or CYP2E1 activities (Gravel et al., 2019). Considering the limited data available in the literature on the time shift between inflammation and drug metabolism in HSCT patients, voriconazole, an antifungal agent frequently used in HSCT patients for the treatment of invasive aspergillosis, is one of the only studied drugs. It is metabolized by CYP2C19 and to a lesser extent by CYP3A and CYP2C9 so that our modeling strategy could be applied to estimate the time shift between plasma CRP levels and the voriconazole metabolism (C/D ratio). The time shift between CRP and the voriconazole levels reported was shorter (around 48 h) compared to the one that we described for ciclosporin (Hao et al., 2024). The differences might occur since CYP2C19 protein and voriconazole levels re-equilibrate on different time scales following onset of inflammation. Particularly, peak voriconazole levels may re-equilibrate more rapidly as its apparent elimination t₁/₂ is smaller (around 9 h in heterozygous metabolizer) (Scholz et al., 2009) compared to ciclosporin (ranging from 6 to 14 h) (Duncan et al., 2010; Kim et al., 2015). However, complexity might occur because of the non-linear kinetics of voriconazole leading to a terminal half-life of elimination which is dose-dependent. Additionally, CYP2C19 genotype might be considered even though its impact on the terminal elimination half-life is modest, increasing up to 20-30 % in homozygous poor metabolizer (Scholz et al., 2009). Indeed, it has been showed that inflammation-induced changes in CYP450-mediated drug metabolism are influenced by an individual's CYP metabolizer genotype. In patients with invasive fungal infections the effect of inflammation was influenced by the CYP2C19 genotype: the metabolic ratio of voriconazole to its metabolite was reduced more significantly by inflammation in CYP2C19 ultrarapid metabolizers compared to CYP2C19 intermediate metabolizers (Veringa et al., 2017). The modulation of the impact of inflammation on other CYP isoforms remain to be investigated. Indeed, genetic variability in patients might be a factor influencing the extent of inflammation-induced phenoc conversion, the mismatch between the genotype-based prediction of drug metabolism and the true capacity of an individual to metabolize drugs.

PK-PD approaches are often applied in inflammation modeling, typically modeling inflammation as the response variable to drug intervention. These models, including indirect response and time-dependent transduction models, have been instrumental in capturing the dynamics of anti-inflammatory drug effects on biomarkers and disease progression (Lon et al., 2012). In contrast, our study introduces a novel application of PK-PD time-dependent transduction modeling by conceptualizing inflammation as the effector variable that modulates ciclosporin metabolism. Rather than modeling the pharmacodynamic effects of a drug on inflammation, we reverse the paradigm by treating inflammation as an upstream modulator of drug metabolism. This approach provides a mechanistic framework to investigate how physiological stress responses, such as immune activation, alter pharmacokinetic behavior. By enabling the quantification of these modulatory effects, this time-dependent transduction modeling strategy offers valuable insights into the influence of homeostatic and signaling cascades on drug disposition. Ultimately, this methodology may support the development of more personalized and adaptive therapeutic strategies, particularly in complex clinical contexts involving dynamic
physiological perturbations. For example, beyond inflammation, other stress responses such as hypothermia have been shown to significantly reduce cytochrome P450-mediated metabolic clearance, underscoring the relevance of modeling physiological states as modulators of drug kinetics (Tortorici et al., 2007).

Our findings highlight the importance of considering inflammation as a covariate in Model-Informed Drug Development (MIDD). In early-phase trials (i.e., phase I), populations rarely display inflammation, whereas later-phase populations may exhibit inflammation due to underlying disease or comorbidities. Integrating biomarkers such as CRP or IL-6 into PK-PD models can help separate inflammation-driven changes in metabolism from classical DDI, improving parameter estimation and exposure-response characterization. This strategy may also refine safety margin definitions by accounting for transient reductions in metabolic capacity, support robust dose selection, and inform regulatory submissions with mechanistic justification for dose adjustments in patients with high inflammatory burden (e.g., HSCT or Intensive Care Unit (ICU) settings).

However, several limitations must be acknowledged. First, our cohort included only allogeneic HSCT patients, limiting extrapolation to solid organ transplants or diseases treated by ciclosporin Indeed, HSCT patients that represent a highly specific population characterized by intensive polypharmacy, profound immunosuppression, and frequent infectious complications. The interaction between inflammation and drug metabolism in this context may not apply directly to other patient groups. However, our observations raise the hypothesis that similar mechanisms could be relevant in ICU patients, where severe systemic inflammation is common. Future studies in ICU populations are warranted to assess whether inflammation similarly impacts metabolism of narrow therapeutic drugs, and modulates DDI's.

Second, CRP, which is typically used in clinical settings, is less specific than IL-6, and the C/D ratio only served as a proxy for CYP3A4 activity. The earlier and more sensitive IL-6 signal could help in developing a more precise model. A joint model incorporating both IL-6 and CRP may better capture the relationship between inflammation and metabolism, while improving transposability to clinical settings.

Finally, the sample size was constrained by strict inclusion criteria, that selected only patients with a single inflammatory episode, which allowed for simplified CRP trajectory modeling. Additionally, inter occasion (intra-individual) variability could not be evaluated. Future models that accommodate multiple inflammatory episodes could enable the inclusion of a larger patient population and allow for a covariate analysis.

## 5. Conclusion

The impact of inflammation on ciclosporin metabolism, as assessed by the C/D ratio, was successfully captured using a time-dependent transduction PK-PD modeling approach in HSCT patients with moderate-to-severe inflammation (CRP > 40 mg/L). The study

demonstrated a 4.7-fold increase in the C/D ratio at an EC₅₀ of 120 mg/L of CRP, with a time shift of 6 days between the CRP peak and the C/D ratio peak. To our knowledge, this constitutes the first quantitative assessment of the upstream and time-dependent impact of inflammation on ciclosporin metabolism. External validation using published time profiles supports strong confidence in the model, and successful external validation on patients with letermovir pDDI from our initial cohort indicates that inflammation-driven CYP3A4 downregulation can outweigh classical DDI effects in HSCT patients with moderate-to-severe inflammation. Further studies with larger cohorts are warranted to ensure robustness and generalizability.

Overall, our findings underscore the critical need to integrate inflammation biomarkers into ciclosporin TDM and PK/PD modeling, especially in vulnerable populations, such as HSCT recipients and ICU patients where elevated inflammation-driven CYP3A4 downregulation may dominate over conventional DDI effects. Incorporating such dynamic, biomarker-driven approaches into MIDD could enable individualized, adaptive dosing, improve clinical management, and enhance early-phase trial predictions. By conceptualizing inflammation as a time-dependent upstream modulator, this PK-PD framework may provide a foundation for improved DDI prediction, dose adjustment, and individualized therapy in clinical settings marked by immunological or metabolic stress.

## Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

## CRediT authorship contribution statement

**David Malnoë:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Tony Marchand:** Writing – review & editing, Formal analysis. **Pascal Le Corre:** Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declared no competing interests for this work.

## Acknowledgements

The authors extend their gratitude to all the patients who participated in the study. Open access publishing facilitated by Université de Rennes, as part of the Couperin - Université de Rennes agreement. Graphical abstract was created in BioRender. MALNOE, D. (2025) https://BioRender.com/d6oz9ge.

## Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ejps.2025.107271.
Appendices

<figure><img src="image_12.png" /><figcaption>Fig. A. Distribution of Individual Weighted Residuals (iWRES) and Normalized Prediction Distribution Errors (NPDE) for the final transduction model: density and probability functions.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Fig. B.** Scatter Plots of Individual Weighted Residuals (iWRES) and Normalized Prediction Distribution Errors (NPDE) for the final transduction model: versus time and individual/population predictions.</figcaption></figure>

## Data availability

The authors do not have permission to share data. Indeed, individual participant-level data cannot be adequately anonymized as there is a reasonable likelihood that individual participants could be re-identified.

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