Tarini Singh 1, Meemansa Sood 2, Letao Li 1, Sarah Zohar 1, Moreno Ursino 1, Marina Savelieva 2
1 Inserm, Université Paris Cité, Inria, HeKA, F-75015 Paris, France (, ), 2 Novartis Pharma AG, Basel, Switzerland (, )
Objectives:
INVENTS project (https://invents-he.eu/) aims to assess and improve modelling and simulation approaches and their regulatory acceptance to expedite drug development in rare diseases including paediatrics. As part of this initiative, Novartis Pharma AG provided access to anonymised data from four Phase III clinical trials of Fingolimod in adult and paediatric patients with Relapsing-Remitting Multiple Sclerosis (RRMS). Paediatric Multiple Sclerosis (MS) is rare and represents only 3-5% of all MS cases worldwide, predominantly of the RRMS subtype [1]. The rarity of the disease and its paediatric manifestation make drug development challenging.
This work focuses on assessing whether population PK and sequential population PKPD models developed using anonymised adult data in combination with external evidence can be refined and qualified to capture the dynamics of paediatric patients.
Methods:
The methodology strategy was aligned with the ICH M15 guideline [2] and the framework proposed by Musuamba et al [3]. The anonymised dataset comprised 1856 adults (median age 38 years, 71.7% female, 93.1% Caucasian) and 106 paediatrics (median age 16 years, 66.03% female, 93.40% Caucasian). The patients were administered a once-daily dose of Fingolimod ranging from 0.25 to 1.25 mg. Anonymisation resulted in global suppression of BMI, ethnicity, height and time of first visit, while race was generalised to three categories and MS duration was generalised to 3-year intervals. To evaluate the impact of data anonymisation, all the results were tested for their consistency with previous PK analyses [4, 5]. Emphasis was placed on ensuring similar estimates for parameters and covariate effects and their statistical significance. A linear model for steady-state PK concentrations [4] as well as non-linear mixed effects (NLME) 2-compartment population PK model with first-order absorption and linear elimination from the prior published model in adult healthy volunteers [5] was tested using adult data only. Based on this, the choice was made to adapt the NLME model with time lag, absorption, clearance, volume(s) of distribution and inter-compartmental clearance to enable full longitudinal analysis. The predicted concentrations from the PK model were then used to evaluate the time course of absolute lymphocyte counts (ALC), a key biomarker for RRMS. Model adequacy was assessed using goodness-of-fit plots, relative standard errors (RSE), and shrinkage. Covariate and error model selection followed the Stochastic Approximation for Model Building Algorithm [6]. In order to verify the suitability of the adult model built for paediatric extrapolation, the PK parameters derived from the adult cohort were used to simulate drug concentrations for 1000 paediatric individuals (using the observed paediatric demographic data). Furthermore, the parameter estimates from the adult PD model was used to simulate the lymphocyte response for the (simulated) 1000 individuals. The model was then refitted to the observed paediatric data, with the quality of fit evaluated using goodness of fit plots.
Results:
As a result of credibility assessments, the 2-compartment population PK model and a direct inhibitory Emax population PKPD lymphocyte model were finally selected. The anonymised dataset yielded PK and PKPD predictions consistent with results from previously developed models for adults and paediatrics prior to anonymisation. Covariate for the PK model included adjustment of Clearance for race, age and body weight at baseline. PKPD lymphocyte model comprised Imax adjusted for age, prior MS treatment and sex; IC50 for prior MS treatment, and baseline lymphocyte count for age and body weight. The PK and sequential PKPD model yielded RSE <30% for all parameters and covariate effects. Simulation-based verification for both PK and PD showed that most of the observed paediatric data was within 5-95% prediction intervals, supporting the suitability of the model use for extrapolation.
Conclusions:
We presented here first steps in credibility evaluation of PK and PKPD models for paediatric patients with MS. Future work will focus on refining extrapolation and extending the analysis to additional efficacy endpoints to assess their suitability to generate virtual control arms for prospective RRMS trials. Incorporating established model‑credibility frameworks and harmonised MIDD principles ensures that the modelling approach is scientifically robust and aligned with evolving regulatory expectations.
References:
1. Dahlke F, Arnold DL, Aarden P, Ganjgahi H, Häring DA, Čuklina J, Nichols TE, Gardiner S, Bermel R, Wiendl H. Characterisation of MS phenotypes across the age span using a novel data set integrating 34 clinical trials (NO.MS cohort): Age is a key contributor to presentation. Mult Scler.2021;27(13):2062‑2076. doi:10.1177/1352458520988637.
2. International Council for Harmonisation (ICH). ICH M15: General principles on model‑informed drug development. Draft Guideline. ICH; 2024.
3. Musuamba FT, Skottheim Rusten I, Bursi R, Emili L, Wangorsch G, Karlsson KE, et al. Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility. CPT Pharmacometrics Syst Pharmacol. 2021;10(9):997‑1010.
4. Population pharmacokinetic and exposure-lymphocyte count analysis of FTY720 (Fingolimod/Gilenya) in pediatric patients with multiple sclerosis, Mita M Thapar , Colm Farrell , Gordon Graham , Olivier Petricoul, https://www.page-meeting.org/wp-content/uploads/pdf_abstracts/8203-Poster_FTY720_v3_17May2018.pdf
5. Wu K, Mercier F, David OJ, Schmouder RL, Looby M. Population pharmacokinetics of fingolimod phosphate in healthy participants. J Clin Pharmacol. 2012;52(7):1054‑1068. doi:10.1177/0091270011409229.
6. Prague M, Lavielle M. SAMBA: A novel method for fast automatic model building in nonlinear mixed‑effects models. CPT Pharmacometrics Syst Pharmacol. 2022;11(2):161‑172. doi:10.1002/psp4.12742.
Reference: PAGE 34 (2026) Abstr 12296 [www.page-meeting.org/?abstract=12296]
Poster: Drug/Disease Modelling - Paediatrics