Tofacitinib pharmacokinetics in children and adolescents with juvenile idiopathic arthritis.

These analyses characterized tofacitinib pharmacokinetics (PKs) in children and adolescents with juvenile idiopathic arthritis (JIA). Data were pooled from phase I (NCT01513902), phase III (NCT02592434), and open-label, long-term extension (NCT01500551) studies of tofacitinib tablet/solution (weight-based doses administered twice daily [b.i.d.]) in patients with JIA aged 2 to less than 18 years. Population PK modeling used a nonlinear mixed-effects approach, with covariates identified using stepwise forward-inclusion backward-deletion procedures. Simulations were performed to derive dosing recommendations for children and adolescents with JIA. Two hundred forty-six pediatric patients were included in the population PK model. A one-compartment model with first-order elimination and absorption with body weight as a covariate for oral clearance and apparent volume of distribution sufficiently described the data. Oral solution was associated with comparable average concentration (Cavg) and slightly higher (113.9%) maximum concentration (Cmax) versus tablet, which was confirmed by a subsequent randomized, open-label, bioavailability study conducted in healthy adult participants (n = 12) by demonstrating adjusted geometric mean ratios (90% confidence interval) between oral solution and tablet of 1.04 (1.00-1.09) and 1.10 (1.00-1.21) for area under the curve extrapolated to infinity and Cmax, respectively (NCT04111614). A dosing regimen of 3.2 mg b.i.d. solution in patients 10 to less than 20 kg, 4 mg b.i.d. solution in patients 20 to less than 40 kg, and 5 mg b.i.d. tablet/solution in patients greater than or equal to 40 kg, irrespective of age, was proposed to achieve constant Cavg across weight groups. In summary, population PK characterization informed a simplified tofacitinib dosing regimen that has been implemented in pediatric patients with JIA.

Development of a model-based clinical trial simulation platform to optimize the design of clinical trials for Duchenne muscular dystrophy.

Early clinical trials of therapies to treat Duchenne muscular dystrophy (DMD), a fatal genetic X-linked pediatric disease, have been designed based on the limited understanding of natural disease progression and variability in clinical measures over different stages of the continuum of the disease. The objective was to inform the design of DMD clinical trials by developing a disease progression model-based clinical trial simulation (CTS) platform based on measures commonly used in DMD trials. Data were integrated from past studies through the Duchenne Regulatory Science Consortium founded by the Critical Path Institute (15 clinical trials and studies, 1505 subjects, 27,252 observations). Using a nonlinear mixed-effects modeling approach, longitudinal dynamics of five measures were modeled (NorthStar Ambulatory Assessment, forced vital capacity, and the velocities of the following three timed functional tests: time to stand from supine, time to climb 4 stairs, and 10 meter walk-run time). The models were validated on external data sets and captured longitudinal changes in the five measures well, including both early disease when function improves as a result of growth and development and the decline in function in later stages. The models can be used in the CTS platform to perform trial simulations to optimize the selection of inclusion/exclusion criteria, selection of measures, and other trial parameters. The data sets and models have been reviewed by the US Food and Drug Administration and the European Medicines Agency; have been accepted into the Fit-for-Purpose and Qualification for Novel Methodologies pathways, respectively; and will be submitted for potential endorsement by both agencies.

Modeling of Survival and Frequency of Cardiovascular-Related Hospitalization in Patients with Transthyretin Amyloid Cardiomyopathy Treated with Tafamidis.

INTRODUCTION: ATTR-ACT (Tafamidis in Transthyretin Cardiomyopathy Clinical Trial) demonstrated the efficacy and safety of tafamidis in transthyretin amyloid cardiomyopathy (ATTR-CM). Model-based analyses from ATTR-ACT can examine predictor effects on dose-response/exposure-response relationships. METHODS: Parametric hazard distributions were developed for all-cause mortality and frequency of cardiovascular-related hospitalization. Time-to-event models were fitted to survival data, and repeated time-to-event models were fitted to hospitalization data. Disease-specific characteristics were assessed as baseline predictors of event hazards. RESULTS: There were 441 patients in this analysis. At month 30, 70.5% (tafamidis) and 57.1% (placebo) of patients were alive, with 154/441 deaths reported; 495 cardiovascular-related hospitalizations occurred. The cumulative risk of death was 42.1% (95% confidence interval [CI] 24.2-58.0) lower with tafamidis than with placebo, regardless of New York Heart Association (NYHA) class; significant predictors of decreased risk were genotype (wild-type), greater 6-Minute Walk Test (6MWT) distance, higher left ventricular ejection fraction (LVEF), and lower blood urea nitrogen (BUN) and N-terminal pro-B-type natriuretic peptide concentrations. The average cumulative risk of cardiovascular-related hospitalization up to 30 months was 40.8% (95% CI 31.0-49.7) lower with tafamidis in NYHA class I/II patients. Significant predictors of reduced risk were greater 6MWT distance, higher LVEF, and lower BUN and troponin I concentrations. CONCLUSIONS: Tafamidis reduced cumulative mortality and hospitalization risk versus placebo in patients with ATTR-CM. Baseline predictors of outcome were consistent with the cardiovascular nature of the disease and suggested that earlier treatment may improve outcomes. CLINICAL TRIALS. GOV IDENTIFIER: NCT01994889 (date of registration: November 26, 2013).

Population Pharmacokinetics of Tofacitinib in Patients With Moderate to Severe Ulcerative Colitis.

Tofacitinib is an oral, small molecule Janus kinase inhibitor for the treatment of ulcerative colitis (UC). We characterized tofacitinib pharmacokinetics in patients with moderate to severe UC, and the effects of covariates on variability in pharmacokinetic parameter estimates. Data were pooled from 1 8-week phase 2 and 2 8-week phase 3 induction studies, and a 52-week phase 3 maintenance study (N = 1096). Population pharmacokinetic analysis was conducted using nonlinear mixed-effects modeling. Potential predictors of apparent oral clearance (CL/F) and volume of distribution (V/F) were evaluated. The PK was described by a 1-compartment model parameterized in terms of CL/F (26.3 L/hour [h]) and V/F (115.8 L), with first-order absorption (Ka ; 9.85 h-1 ) and lag time (0.236 h). The derived elimination half-life was approximately 3.05 h. In the final model, baseline creatinine clearance, sex, and race (Asian vs non-Asian) were significant covariates for CL/F; significant covariates for V/F were age, sex, and body weight; baseline albumin and baseline Mayo score were not significant covariates. CL/F between-patient variability was estimated at 22%. Tofacitinib exposure did not change significantly over the duration of induction/maintenance treatment in patients with UC. Although statistically significant covariate effects on CL/F and V/F were observed, the magnitude of the effects are not clinically significant. Therefore, dose adjustment/restrictions for age, body weight, sex, race, or baseline disease severity are not required during tofacitinib treatment. ClinicalTrials.gov numbers: NCT00787202, NCT01465763, NCT01458951, NCT01458574.

Towards regulatory endorsement of drug development tools to promote the application of model-informed drug development in Duchenne muscular dystrophy.

Drug development for rare diseases is challenged by small populations and limited data. This makes development of clinical trial protocols difficult and contributes to the uncertainty around whether or not a potential therapy is efficacious. The use of data standards to aggregate data from multiple sources, and the use of such integrated databases to develop statistical models can inform protocol development and reduce the risks in developing new therapies. Achieving regulatory endorsement of such models through defined pathways at the US Food and Drug Administration and European Medicines Authority allows such tools to be used by the drug development community for defined contexts of use without further need for discussion of the underlying model(s). The Duchenne Regulatory Science Consortium (D-RSC) has brought together multiple stakeholders to develop a clinical trial simulation tool for Duchenne muscular dystrophy using such an approach. Here we describe the work of D-RSC as an example of how such an approach may be effective at reducing uncertainty in drug development for rare diseases, and thus bringing effective therapies to patients faster.

Rapid sample size calculations for a defined likelihood ratio test-based power in mixed-effects models.

Efficient power calculation methods have previously been suggested for Wald test-based inference in mixed-effects models but the only available alternative for Likelihood ratio test-based hypothesis testing has been to perform computer-intensive multiple simulations and re-estimations. The proposed Monte Carlo Mapped Power (MCMP) method is based on the use of the difference in individual objective function values (ΔiOFV) derived from a large dataset simulated from a full model and subsequently re-estimated with the full and reduced models. The ΔiOFV is sampled and summed (∑ΔiOFVs) for each study at each sample size of interest to study, and the percentage of ∑ΔiOFVs greater than the significance criterion is taken as the power. The power versus sample size relationship established via the MCMP method was compared to traditional assessment of model-based power for six different pharmacokinetic and pharmacodynamic models and designs. In each case, 1,000 simulated datasets were analysed with the full and reduced models. There was concordance in power between the traditional and MCMP methods such that for 90% power, the difference in required sample size was in most investigated cases less than 10%. The MCMP method was able to provide relevant power information for a representative pharmacometric model at less than 1% of the run-time of an SSE. The suggested MCMP method provides a fast and accurate prediction of the power and sample size relationship.