José Pinheiro

Model‐based dose finding under model uncertainty using general parametric models

The statistical methodology for the design and analysis of clinical Phase II dose-response studies, with related software implementation, is well developed for the case of a normally distributed, homoscedastic response considered for a single timepoint in parallel group study designs. In practice, however, binary, count, or time-to-event endpoints are encountered, typically measured repeatedly over time and sometimes in more complex settings like crossover study designs. In this paper, we develop an overarching methodology to perform efficient multiple comparisons and modeling for dose finding, under uncertainty about the dose-response shape, using general parametric models. The framework described here is quite broad and can be utilized in situations involving for example generalized nonlinear models, linear and nonlinear mixed effects models, Cox proportional hazards models, with the main restriction being that a univariate dose-response relationship is modeled, that is, both dose and response correspond to univariate measurements. In addition to the core framework, we also develop a general purpose methodology to fit dose-response data in a computationally and statistically efficient way. Several examples illustrate the breadth of applicability of the results. For the analyses, we developed the R add-on package DoseFinding, which provides a convenient interface to the general approach adopted here.

Designing Phase 2 Trials Based on Program-Level Considerations: A Case Study for Neuropathic Pain

Traditionally, sample size considerations for phase 2 trials are based on the desired properties of the design and response information from the trials. In this article, we propose to design phase 2 trials based on program-level optimization. We present a framework to evaluate the impact that several phase 2 design features have on the probability of phase 3 success and the expected net present value of the product. These factors include the phase 2 sample size, decision rules to select a dose for phase 3 trials, and the sample size for phase 3 trials. Using neuropathic pain as an example, we use simulations to illustrate the framework and show the benefit of including these factors in the overall decision process.

Modeling and simulation for medical product development and evaluation: highlights from the FDA-C-Path-ISOP 2013 workshop

Medical-product development has become increasingly challenging and resource-intensive. In 2004, the Food and Drug Administration (FDA) described critical challenges facing medical-product development by establishing the critical path initiative [1]. Priorities identified included the need for improved modeling and simulation tools, further emphasized in FDA’s 2011 Strategic Plan for Regulatory Science [Appendix]. In an effort to support and advance model-informed medical-product development (MIMPD), the Critical Path Institute (C-Path) [www.c-path.org], FDA, and International Society of Pharmacometrics [www.go-isop.org] co-sponsored a workshop in Washington, D.C. on September 26, 2013, to examine integrated approaches to developing and applying model- MIMPD. The workshop brought together an international group of scientists from industry, academia, FDA, and the European Medicines Agency to discuss MIMPD strategies and their applications. A commentary on the proceedings of that workshop is presented here.

Optimizing Drug Development Programs Type 2 Diabetes Case Study

Recently, consideration was given to the impact of dose selection strategies in phase IIb on the overall success of drug development programs. A natural next step is to simultaneously optimize design aspects of both phase IIB and phase III. We used type 2 diabetes as an example, including realistic regulatory and commercial scenarios for this indication. The expected net present value (eNPV) has been selected as the primary outcome because it naturally accommodates optimization, providing an explicit trade-off between the probability of success (PoS) and time delays and trial costs. Our findings are that larger studies and/or implementation of an adaptive design over a fixed design in phase IIb provide more precise dose selection and reduce the bias of treatment effects and uncertainty in the estimated eNPV within the range of sample sizes that we examined. Developers also have to ensure that dose selection criteria are consistent with development strategy and objectives.

Adaptive Clinical Trials: Overview of Early-Phase Designs and Challenges

In this paper, the authors describe developments in adaptive design methodology and discuss implementation strategies and operational challenges in early-phase adaptive clinical trials. The BATTLE trial—the first completed biomarker-based Bayesian adaptive randomized study in lung cancer—is presented as a case study to illustrate main ideas and share learnings.

Designing Multiregional Trials Under the Discrete Random Effects Model

A discrete random effects model (Lan and Pinheiro, 2012) was proposed recently for multiregional clinical trials for continuous responses. This article elucidates further the application of this model to time-to-event and binary responses. We provide some guidelines on how to design multiregional trials and also show how the same model lends itself naturally to meta-analysis.

Dynamic population pharmacokinetic–pharmacodynamic modelling and simulation supports similar efficacy in glycosylated haemoglobin response with once or twice‐daily dosing of canagliflozin

Aim Canagliflozin is an SGLT2 inhibitor approved for the treatment of type‐2 diabetes. A dynamic population pharmacokinetic–pharmacodynamic (PK/PD) model relating 24‐h canagliflozin exposure profiles to effects on glycosylated haemoglobin was developed to compare the efficacy of once‐daily and twice‐daily dosing. Methods Data from two clinical studies, one with once‐daily, and the other with twice‐daily dosing of canagliflozin as add‐on to metformin were used (n = 1347). An established population PK model was used to predict full 24‐h profiles from measured trough concentrations and/or baseline covariates. The dynamic PK/PD model incorporated an Emax relationship between 24‐h canagliflozin exposure and HbA1c‐lowering with baseline HbA1c affecting the efficacy. Results Internal and external model validation demonstrated that the model adequately predicted HbA1c‐lowering for canagliflozin once‐daily and twice‐daily dosing regimens. The differences in HbA1c reduction between the twice‐daily and daily mean profiles were minimal (at most 0.023% for 100 mg total daily dose [TDD] and 0.011% for 300 mg TDD, up to week 26, increasing with time and decreasing with TDD) and not considered clinically meaningful. Conclusions Simulations using this model demonstrated the absence of clinically meaningful between‐regimen differences in efficacy, supported the regulatory approval of a canagliflozin‐metformin immediate release fixed‐dose combination tablet and alleviated the need for an additional clinical study.

Further Evaluation of Covariate Analysis using Empirical Bayes Estimates in Population Pharmacokinetics: the Perception of Shrinkage and Likelihood Ratio Test.

Covariate analysis based on population pharmacokinetics (PPK) is used to identify clinically relevant factors. The likelihood ratio test (LRT) based on nonlinear mixed effect model fits is currently recommended for covariate identification, whereas individual empirical Bayesian estimates (EBEs) are considered unreliable due to the presence of shrinkage. The objectives of this research were to investigate the type I error for LRT and EBE approaches, to confirm the similarity of power between the LRT and EBE approaches from a previous report and to explore the influence of shrinkage on LRT and EBE inferences. Using an oral one-compartment PK model with a single covariate impacting on clearance, we conducted a wide range of simulations according to a two-way factorial design. The results revealed that the EBE-based regression not only provided almost identical power for detecting a covariate effect, but also controlled the false positive rate better than the LRT approach. Shrinkage of EBEs is likely not the root cause for decrease in power or inflated false positive rate although the size of the covariate effect tends to be underestimated at high shrinkage. In summary, contrary to the current recommendations, EBEs may be a better choice for statistical tests in PPK covariate analysis compared to LRT. We proposed a three-step covariate modeling approach for population PK analysis to utilize the advantages of EBEs while overcoming their shortcomings, which allows not only markedly reducing the run time for population PK analysis, but also providing more accurate covariate tests.

Dynamic population PK/PD modeling and simulation supports similar efficacy in HbA1c response with once or twice‐daily dosing of canagliflozin

Aim Canagliflozin is an SGLT2 inhibitor approved for the treatment of type‐2 diabetes. A dynamic population pharmacokinetic–pharmacodynamic (PK/PD) model relating 24‐h canagliflozin exposure profiles to effects on glycosylated haemoglobin was developed to compare the efficacy of once‐daily and twice‐daily dosing. Methods Data from two clinical studies, one with once‐daily, and the other with twice‐daily dosing of canagliflozin as add‐on to metformin were used (n = 1347). An established population PK model was used to predict full 24‐h profiles from measured trough concentrations and/or baseline covariates. The dynamic PK/PD model incorporated an Emax relationship between 24‐h canagliflozin exposure and HbA1c‐lowering with baseline HbA1c affecting the efficacy. Results Internal and external model validation demonstrated that the model adequately predicted HbA1c‐lowering for canagliflozin once‐daily and twice‐daily dosing regimens. The differences in HbA1c reduction between the twice‐daily and daily mean profiles were minimal (at most 0.023% for 100 mg total daily dose [TDD] and 0.011% for 300 mg TDD, up to week 26, increasing with time and decreasing with TDD) and not considered clinically meaningful. Conclusions Simulations using this model demonstrated the absence of clinically meaningful between‐regimen differences in efficacy, supported the regulatory approval of a canagliflozin‐metformin immediate release fixed‐dose combination tablet and alleviated the need for an additional clinical study.

Dose-Response Determination in Multistage Endpoint Clinical Trials

Improper dose selection remains one of the key drivers of the large attrition rates observed in confirmatory studies in clinical drug development. Many factors contribute to this problem, such as insufficient resources allocated to dose-ranging studies and the use of statistical methods better suited for phase 3 studies than for dose selection. This paper describes a model-based dose-finding method that leverages all longitudinal data collected in the trial to estimate the dose-response relationship at any desired visit, using it to estimate target doses of interest, such as the minimum dose producing a desired clinical benefit. The approach uses a Markov chain model to account for correlation in the repeated measures obtained on the same patient. An actual phase 2 study and simulations are used to illustrate the methodology.