Drug development struggles with high costs and time consuming processes. Hence, a need for new strategies has been accentuated by many stakeholders in drug development. This study proposes the use of pharmacometric models to rationalize drug development. Two simulated examples, within the therapeutic areas of acute stroke and type 2 diabetes, are utilized to compare a pharmacometric model–based analysis to a t-test with respect to study power of proof-of-concept (POC) trials. In all investigated examples and scenarios, the conventional statistical analysis resulted in several fold larger study sizes to achieve 80% power. For a scenario with a parallel design of one placebo group and one active dose arm, the difference between the conventional and pharmacometric approach was 4.3- and 8.4-fold, for the stroke and diabetes example, respectively. Although the model-based power depend on the model assumptions, in these scenarios, the pharmacometric model–based approach was demonstrated to permit drastic streamlining of POC trials.

Abstract. 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.

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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 modelbased 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 mixedeffects 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.

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.