Population pharmacokinetics of orally administered letermovir in real world allogeneic hematopoietic cell transplantation recipients
Abstract
Population pharmacokinetics of orally administered letermovir in real world allogeneic hematopoietic cell transplantation recipients
Chrisoula Tahtsidou (1), Sabrina Kraus (2), Nora Isberner (2), Oliver Scherf-Clavel (1)
Institution: (1) Department of Pharmacy, Ludwig-Maximilians-Universität München, (2) Department of Internal Medicine II, University Hospital Würzburg
Introduction:
Cytomegalovirus (CMV) reactivation remains a major infectious complication after allogeneic hematopoietic stem cell transplantation (allo-HSCT), contributing to significant morbidity and mortality [1]. Letermovir (LTV) is approved for CMV prophylaxis but breakthrough infections and exposure variability persist [2], [3]. The impact of drug exposure on efficacy and interactions with comedications, particularly in vulnerable subpopulations such as patients with gastrointestinal graft-versus-host disease (GI GvHD), remains unclear. Therefore, the objective of this study was to assess factors affecting LTV pharmacokinetics through development of a population pharmacokinetic (popPK) model using real-world data.
Methods:
LTV concentration data were obtained through a retrospective study including patients treated with LTV for CMV prophylaxis after allo-HSCT or received off-label LTV for treatment of CMV reactivation via oral administration. PopPK analysis of LTV was performed using a non-linear mixed effect modeling approach implemented in Monolix 2024R1 (Lixoft SAS, a Simulations Plus company, USA), via the stochastic approximation expectation maximization algorithm and was based on the published popPK model by Prohn et al. [4]. The initial two-compartment model included linear elimination, absorption with a lag and differential clearance (CL) and bioavailability (F) depending on the co-administration of cyclosporine A (CsA), interindividual variability (IIV) on CL, F, V2 and ka, as well as interoccasion variability (IOV) on F, fixed to the published values [4]. F in HSCT patients was reported as 35% without CsA and 85% with CsA, compared to 100% in healthy individuals [4]. Additionally, the effect of covariates was investigated, including demographic factors, the occurrence of GI and liver GvHD, and comedication. Covariates were evaluated using a stepwise approach with forward inclusion (p < 0.05) and backward
elimination (p < 0.01). The final model was chosen based on reduced estimated log-likelihood (OFV) and corrected Bayesian Information Criteria (cBIC), parameters’ relative standard error (RSE, <50 %), goodness-of-fit plots and visual predictive check using prediction-corrected concentrations (pcVPC) [5].
Results:
The analysis comprised 51 adult allo-HSCT recipients, including four patients with GI GvHD. External validation of the published popPK model using our data revealed that real-life concentrations were substantially higher than simulated concentrations [4]. To account for the difference in trough concentrations (Ctrough) compared to those of the published model, Frel was fixed to 100% in patients without CsA comedication, while retaining the effect of CsA. The population estimate of V1/F was 6.81 L (19.7 % RSE; 95 % CI, 4.68-9.91 L) and improved the model fit significantly (ΔOFV: -13.21, ΔcBIC: -7.81). Residual population parameters and IIV as well as IOV were fixed to values from literature [4]. Covariate search showed that Frel was reduced by 71.2 % (8.36% RSE; 95% CI, 82.9-59.5%) in patients with GI GvHD compared to patients without GI GvHD, significantly improving the model fit (ΔOFV:- 10.45; Wald test: p < 2.2E-16 ). Further, posaconazole comedication resulted in a reduction of CL/F by 23.3 % (15.4 % RSE; 95 % CI, 30.3-16.3 %) and was retained in the final model (ΔOFV: - 10.22; Wald test: p = 8.7E-11).
Conclusion:
The modified popPK model describes the real-world PK data adequately. Substantial differences in Ctrough could be attributed to varying bioavailability in our dataset. Unlike clinical trials with structured sampling, our real-world data rely on variable, less frequent TDM sampling, potentially affecting parameter estimation. Two statistically significant covariates were identified during stepwise covariate modeling: GI GvHD on Frel and posaconazole on CL. However, covariate effects should be interpreted with care due to the low number of patients with GI GvHD and the use of fixed IIV values from literature which may have limited the model's ability to fully capture variability. Further research should investigate the effect of GI GvHD and concomitant medications on LTV exposure to optimize therapeutic efficacy.
References:
[1] M. L. Green et al., “Cytomegalovirus viral load and mortality after haemopoietic stem cell transplantation in the era of pre-emptive therapy: a retrospective cohort study,” Lancet Haematol., vol. 3, no. 3, pp. e119–e127, Mar. 2016, doi: 10.1016/S2352-3026(15)00289-6.
[2] “219104s000lbl.pdf.” Accessed: Mar. 06, 2025. [Online]. Available: https://www.accessdata.fda.gov/drugsatfda_docs/label/2024/219104s000lbl.pdf
[3] Y. Qiu et al., “Role of letermovir therapeutic drug monitoring for cytomegalovirus prophylaxis in allogeneic hematopoietic stem cell transplantation recipients: a prospective study,” Eur. J. Clin. Microbiol. Infect. Dis., vol. 44, no. 1, pp. 71–82, Jan. 2025, doi: 10.1007/s10096-024-04977-7.
[4] M. Prohn et al., “Population pharmacokinetics of letermovir following oral and intravenous administration in healthy participants and allogeneic hematopoietic cell transplantation recipients,” CPT Pharmacomet. Syst. Pharmacol., vol. 10, no. 3, pp. 255–267, 2021, doi: 10.1002/psp4.12593.
[5] D. R. Mould and R. N. Upton, “Basic Concepts in Population Modeling, Simulation, and Model-Based Drug Development—Part 2: Introduction to Pharmacokinetic Modeling Methods,” CPT Pharmacomet. Syst. Pharmacol., vol. 2, no. 4, p. e38, Apr. 2013, doi: 10.1038/psp.2013.14.
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