RESUMO
Baricitinib is approved for the treatment of rheumatoid arthritis (RA) in more than 70 countries, and juvenile idiopathic arthritis (JIA) in the European Union. Population pharmacokinetic (PK) models were developed in a phase 3 trial to characterize PK in pediatric patients with JIA and identify weight-based dosing regimens. The phase 3, randomized, double-blind, placebo-controlled withdrawal, efficacy and safety trial, JUVE-BASIS, enrolled patients (aged 2 to <18 years) with polyarticular course JIA. During a safety/PK period, baricitinib concentration data from age-based dose cohorts were compared to concentrations from adult patients receiving 4-mg QD. PK data were used to develop a population PK model with allometric scaling to determine a weight-based posology in pediatric patients with JIA that matched the adult 4-mg exposure. Baricitinib plasma concentrations from 217 pediatric patients were used to characterize PK. Based on the adult model, pediatric PK was best described using a 2-compartment model with allometric scaling on clearance and volume of distribution and renal function (estimated with glomerular filtration rate [GFR], a known covariate affecting PK of baricitinib) on clearance. The PK modeling suggested the optimal dosing regimen based on weight for pediatric patients as: 2-mg QD for patients 10 to <30 kg and 4-mg QD for patients ≥30 kg. The use of a population PK model of baricitinib treatment in adult patients with RA, with the addition of allometric scaling for weight on clearance and volume terms, was useful to predict exposures and identify weight-based dosing in pediatric patients with JIA.
RESUMO
D-optimal designs for discrete-type responses have been derived using generalized linear mixed models, simulation based methods and analytical approximations for computing the fisher information matrix (FIM) of non-linear mixed effect models with homogeneous probabilities over time. In this work, D-optimal designs using an analytical approximation of the FIM for a dichotomous, non-homogeneous, Markov-chain phase advanced sleep non-linear mixed effect model was investigated. The non-linear mixed effect model consisted of transition probabilities of dichotomous sleep data estimated as logistic functions using piecewise linear functions. Theoretical linear and nonlinear dose effects were added to the transition probabilities to modify the probability of being in either sleep stage. D-optimal designs were computed by determining an analytical approximation the FIM for each Markov component (one where the previous state was awake and another where the previous state was asleep). Each Markov component FIM was weighted either equally or by the average probability of response being awake or asleep over the night and summed to derive the total FIM (FIM(total)). The reference designs were placebo, 0.1, 1-, 6-, 10- and 20-mg dosing for a 2- to 6-way crossover study in six dosing groups. Optimized design variables were dose and number of subjects in each dose group. The designs were validated using stochastic simulation/re-estimation (SSE). Contrary to expectations, the predicted parameter uncertainty obtained via FIM(total) was larger than the uncertainty in parameter estimates computed by SSE. Nevertheless, the D-optimal designs decreased the uncertainty of parameter estimates relative to the reference designs. Additionally, the improvement for the D-optimal designs were more pronounced using SSE than predicted via FIM(total). Through the use of an approximate analytic solution and weighting schemes, the FIM(total) for a non-homogeneous, dichotomous Markov-chain phase advanced sleep model was computed and provided more efficient trial designs and increased nonlinear mixed-effects modeling parameter precision.
