Benjamin Guiastrennec1, Henning Schmidt1, Maria Sunzel2, Giulia Ghibellini2, Frank Schneider3, Mark Gordon2, Rajendra Pratap Singh2
1IntiQuan GmbH, Basel, Switzerland; 2Teva Branded Pharmaceuticals, West Chester, PA USA; 3Teva Pharmaceutical Industries, Ratiopharm GmbH, Ulm, Germany.
Objectives: Tourette syndrome (TS) is a neurodevelopmental disorder characterized by multiple motor and phonic tics that frequently co-occurs with a variety of behavioral and psychiatric problems. AUSTEDO (deutetrabenazine), a deuterated modification of tetrabenazine, a VMAT2 inhibitor, has been studied as a potential treatment for Tourette syndrome. In the phase 2/3 (ARTIST 1) and phase 3 (ARTIST 2) studies of children and adolescents with TS, the primary endpoint of a reduction in motor and phonic tics assessed by the Total Tic Score of the Yale Global Tic Severity Scale (YGTSS-TTS) was not met. In this work we aimed to characterize the pharmacokinetics (PK) and to predict individual steady state PK exposure parameters of deutetrabenazine (parent) and its 2 active metabolites (α-HTBZ and β-HTBZ) from data in children and adolescents with TS through a non-linear mixed-effects (NLME) modeling approach.
Methods: Deutetrabenazine, α-HTBZ, and β-HTBZ PK concentrations from 2 sparsely sampled Phase 2/3 studies in children and adolescents (6 to 16 years of age) with TS were pooled with richly sampled data from 9 Phase 1/2 studies in healthy adults. Population PK models for deutetrabenazine, α-HTBZ, and β-HTBZ were developed and qualified based on data from extensively sampled adult studies. The predictive performances of these models were evaluated on the TS patient’s data before and after parameter re-estimation on the pooled data. Qualified models were used to predict the steady state minimal (Cmin,ss), maximal (Cmax,ss), and average (Cavg,ss) TEV-50717, α-HTBZ and β-HTBZ concentrations. Bias and precision of individual PK exposure parameters were evaluated through a simulation and re-estimation process. Population PK models were developed in NONMEM 7.4.3 using the stochastic approximation expectation maximization algorithm (SAEM).
Results: The population PK models for deutetrabenazine, α-HTBZ, and β-HTBZ developed using healthy adults data featured 2 parallel first-order absorption processes with lag times, mono- (deutetrabenazine) or bi- (metabolites) phasic distributions and linear PK. Clinically relevant effects of body weight were implemented on clearances and volumes parameters only for metabolites models. Due to limited data for deutetrabenazine, no statistically significant effect of body weight was identified. All 3 models were qualified on the healthy adult data. When applied to the pediatric TS patient data, the α-HTBZ and β-HTBZ adult models were predictive. However, the deutetrabenazine model had a tendency to under predict the observed data over time. The healthy adult models were re-estimated on the pooled data (i.e., healthy adult and TS patients) and all displayed good predictive performances. The models qualified on the pooled data predicted the individual Cmax,ss, and Cavg,ss with acceptable bias and precision for TS pediatric patients. For Cmin,ss, a systematic bias toward over-prediction (+25%) and loss of precision was expected in the individually predicted values.
Conclusions: Through an NLME approach, deutetrabenazine, α-HTBZ, and β-HTBZ population PK models were developed using data from healthy adults. The adult models appropriately predicted data from TS pediatric patients.
Reference: PAGE 29 (2021) Abstr 9814 [www.page-meeting.org/?abstract=9814]
Poster: Drug/Disease Modelling - Paediatrics