Reduced and optimized trial designs for drugs described by a target mediated drug disposition model.

Monoclonal antibodies against soluble targets are often rich and include the sampling of multiple analytes over a lengthy period of time. Predictive models built on data obtained in such studies can be useful in all drug development phases. If adequate model predictions can be maintained with a reduced design (e.g. fewer samples or shorter duration) the use of such designs may be advocated. The effect of reducing and optimizing a rich design based on a published study for Omalizumab (OMA) was evaluated as an example. OMA pharmacokinetics were characterized using a target-mediated drug disposition model considering the binding of OMA to free IgE and the subsequent formation of an OMA-IgE complex. The performance of the reduced and optimized designs was evaluated with respect to: efficiency, parameter uncertainty and predictions of free target. It was possible to reduce the number of samples in the study by 30% while still maintaining an efficiency of almost 90%. A reduction in sampling duration by two-thirds resulted in an efficiency of 75%. Omission of any analyte measurement or a reduction of the number of dose levels was detrimental to the efficiency of the designs (efficiency ≤ 51%). However, other metrics were, in some cases, relatively unaffected, showing that multiple metrics may be needed to obtain balanced assessments of design performance.

Advanced Methods for Dose and Regimen Finding During Drug Development: Summary of the EMA/EFPIA Workshop on Dose Finding (London 4-5 December 2014).

Inadequate dose selection for confirmatory trials is currently still one of the most challenging issues in drug development, as illustrated by high rates of late-stage attritions in clinical development and postmarketing commitments required by regulatory institutions. In an effort to shift the current paradigm in dose and regimen selection and highlight the availability and usefulness of well-established and regulatory-acceptable methods, the European Medicines Agency (EMA) in collaboration with the European Federation of Pharmaceutical Industries Association (EFPIA) hosted a multistakeholder workshop on dose finding (London 4-5 December 2014). Some methodologies that could constitute a toolkit for drug developers and regulators were presented. These methods are described in the present report: they include five advanced methods for data analysis (empirical regression models, pharmacometrics models, quantitative systems pharmacology models, MCP-Mod, and model averaging) and three methods for study design optimization (Fisher information matrix (FIM)-based methods, clinical trial simulations, and adaptive studies). Pairwise comparisons were also discussed during the workshop; however, mostly for historical reasons. This paper discusses the added value and limitations of these methods as well as challenges for their implementation. Some applications in different therapeutic areas are also summarized, in line with the discussions at the workshop. There was agreement at the workshop on the fact that selection of dose for phase III is an estimation problem and should not be addressed via hypothesis testing. Dose selection for phase III trials should be informed by well-designed dose-finding studies; however, the specific choice of method(s) will depend on several aspects and it is not possible to recommend a generalized decision tree. There are many valuable methods available, the methods are not mutually exclusive, and they should be used in conjunction to ensure a scientifically rigorous understanding of the dosing rationale.

Model Evaluation of Continuous Data Pharmacometric Models: Metrics and Graphics.

This article represents the first in a series of tutorials on model evaluation in nonlinear mixed effect models (NLMEMs), from the International Society of Pharmacometrics (ISoP) Model Evaluation Group. Numerous tools are available for evaluation of NLMEM, with a particular emphasis on visual assessment. This first basic tutorial focuses on presenting graphical evaluation tools of NLMEM for continuous data. It illustrates graphs for correct or misspecified models, discusses their pros and cons, and recalls the definition of metrics used.

Pharmacometrics Markup Language (PharmML): Opening New Perspectives for Model Exchange in Drug Development.

The lack of a common exchange format for mathematical models in pharmacometrics has been a long-standing problem. Such a format has the potential to increase productivity and analysis quality, simplify the handling of complex workflows, ensure reproducibility of research, and facilitate the reuse of existing model resources. Pharmacometrics Markup Language (PharmML), currently under development by the Drug Disease Model Resources (DDMoRe) consortium, is intended to become an exchange standard in pharmacometrics by providing means to encode models, trial designs, and modeling steps.

Population pharmacometrics in support of analgesics studies.

Population pharmacometric modeling is used to explain both population trends as well as the sources and magnitude of variability in pharmacokinetic and pharmacodynamics data; the later, in part, by taking into account patient characteristics such as weight, age, renal function and genetics. The approach is best known for its ability to analyze sparse data, i.e. when only a few measurements have been collected from each subject, but other benefits include its flexibility and the potential to construct more detailed models than those used in the traditional individual curve fitting approach. This review presents the basic concepts of population pharmacokinetic and pharmacodynamic modeling and includes several analgesic drug examples. In addition, the use of these models to design and optimize future studies is discussed. In this context, finding the best design factors, such as the sampling times or the dose, for future studies within pre-defined criteria using a previously constructed population pharmacokinetic model can help researchers acquire clinically meaningful data without wasting resources and unnecessarily exposing vulnerable patient groups to study drugs and additional blood sampling.

Joint modeling of efficacy, dropout, and tolerability in flexible-dose trials: a case study in depression.

Many difficulties may arise during the modeling of the time course of Hamilton Rating Scale for Depression (HAM D)scores in clinical trials for the evaluation of antidepressant drugs: (i) flexible designs, used to increase the chance of selecting more efficacious doses, (ii) dropout events, and (iii) adverse effects related to the experimental compound.It is crucial to take into account all these factors when designing an appropriate model of the HAM D time course and to obtain a realistic description of the dropout process. In this work, we propose an integrated approach to the modeling of a double-blind, flexible-dose, placebo-controlled, phase II depression trial that comprises response,tolerability, and dropout. We investigate three different dropout mechanisms in terms of informativeness. Goodness of fit is quantitatively assessed with respect to response (HAM D score) and dropout data. We show that dropout is a complex phenomenon that may be influenced by HAM D evolution, dose changes, and occurrence of drug-related adverse effects.

Adaptive-optimal design in PET occupancy studies.

Positron emission tomography (PET) is an imaging technique that is used to investigate ligand-receptor binding in the living brain and to determine the time course of plasma concentration/receptor occupancy (RO). The purpose of this work was to demonstrate the added value of an adaptive-optimal design for PET scan timings and dose selection over traditional study designs involving fixed or educated selections of timings and doses. A k(on)-k(off) model relating plasma concentration to PET data was applied to generate the simulated data. Optimization was performed on scanning timings and doses using the D-optimality criterion. Optimal designs as applied to scanning timings provided unbiased estimates and improved the accuracy of results relative to those of fixed and educated designs. Optimization of both timings and dose provided improvements in accuracy and precision when the initial dose selection was noninformative regarding the time course of RO. These results indicate that adaptive-optimal designs can provide an efficient experimental design for RO studies using PET, by minimizing the number of subjects required and maximizing information related to the plasma concentration-RO relationship.

Population pharmacokinetic model for docetaxel in patients with varying degrees of liver function: incorporating cytochrome P4503A activity measurements.

The relationship between cytochrome P4503A4 (CYP3A4) activity and docetaxel clearance in patients with varying degrees of liver function (LF) was evaluated. Docetaxel 40, 50, or 75 mg/m(2) was administered to 85 patients with advanced cancer; 23 of 77 evaluable patients had abnormalities in LF tests. Baseline CYP3A activity was assessed using the erythromycin breath test (ERMBT). Pharmacokinetic studies and toxicity assessments were performed during cycle 1 of therapy and population modeling was performed using NONMEM. Docetaxel unbound clearance was lower (317 vs. 470 l/h) and more variable in patients with LF abnormalities compared to patients with normal LF. Covariates evaluated accounted for 83% of variability on clearance in patients with liver dysfunction, with CYP3A4 activity accounting for 47% of variation; covariates accounted for only 23% of variability in patients with normal LF. The clinical utility of the ERMBT may lie in identifying safe docetaxel doses for patients with LF abnormalities.