Evaluating Dosage Optimality for Tofacitinib, an Oral Janus Kinase Inhibitor, in Plaque Psoriasis, and the Influence of Body Weight.

Tofacitinib is an oral Janus kinase inhibitor. An integrated analysis was conducted to evaluate dosage optimality for tofacitinib in patients with moderate-to-severe plaque psoriasis and the impact of body weight on optimality in this patient population. Data were pooled from one phase IIb trial (2, 5, and 15 mg twice daily (b.i.d.)) and four phase III trials (5 and 10 mg b.i.d.). A longitudinal exposure-response model for Psoriasis Area and Severity Index (PASI) improvement (percent change from baseline) was established. Body weight influenced potency; heavier subjects require higher doses to achieve comparable benefit to lighter subjects. Disease severity, sex, and prior biologic usage were also predictive of response. The 10 and 5 mg doses were predicted to achieve 81% and 65%, respectively, of the maximum effect based on a 75% improvement in PASI. The greater efficacy of 10 mg over 5 mg was clinically meaningful.

Model-Informed Development and Registration of a Once-Daily Regimen of Extended-Release Tofacitinib.

Extended-release (XR) formulations enable less frequent dosing vs. conventional (e.g., immediate release (IR)) formulations. Regulatory registration of such formulations typically requires pharmacokinetic (PK) and clinical efficacy data. Here we illustrate a model-informed, exposure-response (E-R) approach to translate controlled trial data from one formulation to another without a phase III trial, using a tofacitinib case study. Tofacitinib is an oral Janus kinase (JAK) inhibitor for the treatment of rheumatoid arthritis (RA). E-R analyses were conducted using validated clinical endpoints from phase II dose-response and nonclinical dose fractionation studies of the IR formulation. Consistent with the delay in clinical response dynamics relative to PK, average concentration was established as the relevant PK parameter for tofacitinib efficacy and supported pharmacodynamic similarity. These evaluations, alongside demonstrated equivalence in total systemic exposure between IR and XR formulations, provided the basis for the regulatory approval of tofacitinib XR once daily by the US Food and Drug Administration.

Population Exposure-Response Modeling of Naloxegol in Patients With Noncancer-Related Pain and Opioid-Induced Constipation.

Naloxegol is a polyethylene glycol derivative of naloxone approved in the US as a once-daily oral treatment for opioid-induced constipation (OIC) in adults with chronic noncancer pain. Population exposure-response models were constructed based on data from two phase III studies comprising 1,331 adults with noncancer pain and OIC. In order to characterize the protocol-defined naloxegol responder rate, the number of daily spontaneous bowel movements (SBMs) was characterized by a longitudinal ordinal nonlinear mixed-effects logistic regression dose-response model, and the incidence of diary entry discontinuation was described by a time-to-event model. The mean number of SBMs per week increased with increasing naloxegol dose. The predicted placebo-adjusted responder rates (90% confidence interval) were 10.4% (4.6-13.4%) and 11.1% (4.8-14.4%) for naloxegol 12.5 and 25 mg/day, respectively. Model-predicted response to naloxegol was influenced by the baseline SBM frequency and characteristics of the opioid treatment.

Evaluating the Use of Linear Mixed-Effect Models for Inference of the Concentration-QTc Slope Estimate as a Surrogate for a Biological QTc Model.

In concentration-QTc modeling, oscillatory functions have been used to characterize biological rhythms in QTc profiles. Fitting such functions is not always feasible because it requires sufficient electrocardiograph sampling. In this study, drug concentration and QTc data were simulated using a published biological QTc model (oscillatory functions). Then, linear mixed-effect models and the biological model were fitted and evaluated in terms of biases, precisions, and qualities of inferences. The simpler linear mixed-effect model with day and time as a factor variables provided similar accuracy of the concentration-QTc slope estimates to the complex biological model and was able to accurately predict the drug-induced QTc prolongation with less than 1 ms bias, despite its empirical nature to account for biological rhythm. The current study may guide a concentration-QTc modeling strategy that can be easily prespecified, does not suffer from poor convergence, and achieves little bias in drug-induced QTc estimates.

Modeling of Functional Assessment Questionnaire (FAQ) as continuous bounded data from the ADNI database.

An assessment of abilities to function independently in daily life is an important clinical endpoint for all Alzheimer's disease (AD) patients and caregivers. A mathematical model was developed to describe the natural history of change of the Functional Assessment Questionnaire (FAQ) from data obtained in normal elderly, mild cognitive impairment, and mild AD in the AD neuroimaging initiative (ADNI) study. FAQ is a bounded outcome (ranging from 0 to 30), with 0 scored as "no impairment" and 30 as "severely impaired". Since many normal elderly patients had 0 scores and some AD patients had scores of 30 in the ADNI database, a censored approach for handling the boundary data was compared with a standard approach, which ignores the bounded nature of the data. Baseline severity, ApoE4 genotype, age, sex, and imaging biomarkers were tested as covariates. The censored approach greatly improved the predictability of the disease progression in FAQ scores. The basic method for handling boundary data used in this analysis is also applicable to handle boundary observations for numerous other endpoints.

Comparison of QTc data analysis methods recommended by the ICH E14 guidance and exposure-response analysis: case study of a thorough QT study of asenapine.

An assessment of the effects of asenapine on QTc interval in patients with schizophrenia revealed a discrepancy between the results obtained by two different methods: an intersection-union test (IUT) (as recommended in the International Conference on Harmonisation E14 guidance) and an exposure-response (E-R) analysis. Simulations were performed in order to understand and reconcile this discrepancy. Although estimates of the time-matched, placebo-corrected mean change in QTc from baseline (ddQTc) at peak plasma concentrations from the E-R analysis ranged from 2 to 5 ms per dose level, the IUT applied to simulated data from the E-R model yielded maximum ddQTc estimates of 7-10 ms for the various doses of asenapine. These results indicate that the IUT can produce biased estimates that may induce a high false-positive rate in individual thorough QTc trials. In such cases, simulations from an E-R model can aid in reconciling the results from the two methods and may support the use of E-R results as a basis for labeling.

Modeling and simulation to support dose selection and clinical development of SC-75416, a selective COX-2 inhibitor for the treatment of acute and chronic pain.

Pharmacokinetic/pharmacodynamic (PK/PD) models were developed and clinical trial simulations were conducted to recommend a study design to test the hypothesis that a dose of SC-75416, a selective cyclooxygenase-2 inhibitor, can be identified that achieves superior pain relief (PR) compared to 400 mg ibuprofen in a post-oral surgery pain model. PK/PD models were developed for SC-75416, rofecoxib, valdecoxib, and ibuprofen relating plasma concentrations to PR scores using a nonlinear logistic-normal model. Clinical trial simulations conducted using these models suggested that 360 mg SC-75416 could achieve superior PR compared to 400 mg ibuprofen. A placebo- and positive-controlled parallel-group post-oral surgery pain study was conducted evaluating placebo, 60, 180, and 360 mg SC-75416 oral solution, and 400 mg ibuprofen. The study results confirmed the hypothesis that 360 mg SC-75416 achieved superior PR relative to 400 mg ibuprofen (DeltaTOTPAR6=3.3, P<0.05) and demonstrated the predictive performance of the PK/PD models.

Model-based drug development.

The low productivity and escalating costs of drug development have been well documented over the past several years. Less than 10% of new compounds that enter clinical trials ultimately make it to the market, and many more fail in the preclinical stages of development. These challenges in the "critical path" of drug development are discussed in a 2004 publication by the US Food and Drug Administration. The document emphasizes new tools and various opportunities to improve drug development. One of the opportunities recommended is the application of "model-based drug development (MBDD)." This paper discusses what constitutes the key elements of MBDD and how these elements should fit together to inform drug development strategy and decision-making.

Efficient screening of covariates in population models using Wald's approximation to the likelihood ratio test.

We propose an efficient algorithm for screening covariates in population model building using Wald's approximation to the likelihood ratio test (LRT) statistic in conjunction with Schwarz's Bayesian criterion. The algorithm can be applied to a full model fit of k covariate parameters to calculate the approximate LRT for all 2k - 1 possible restricted models. The algorithm's efficiency also permits internal validation of the model selection process via bootstrap methods. We illustrate the use of this algorithm for both model selection and validation with data from a Daypro pediatric study. The algorithm is easily implemented using standard statistical software such as SAS/IML and S-Plus. A SAS/IML macro to perform the algorithm is provided.

Design evaluation for a population pharmacokinetic study using clinical trial simulations: a case study.

Clinical trial simulations were conducted to assess power and sample size requirements for a population pharmacokinetic (PK) substudy of a phase III clinical trial. The simulations were based on a population PK model developed from phase I healthy volunteer data. A sparse sampling design was employed taking into account the practical considerations regarding the desire not to keep patients at the study sites for extended periods of time for blood sampling. It was expected that the sparse sampling design would not support fitting the same model developed in healthy volunteers due to the narrow range of sampling times. Therefore, a model with fewer parameters and variance components was fit to simulated data from the proposed design to assess the bias in the estimates of the population mean PK parameters and variance components. Results indicate that the proposed design employing the simple model can provide accurate mean estimates of oral drug clearance (CL) and the apparent steady-state volume of distribution (V(ss)). However, the simulation results also suggest that the size and power of the likelihood ratio test for subpopulation differences in CL are inflated when using the simple model.