Commentary on the EMA Reflection Paper on the use of extrapolation in the development of medicines for paediatrics.

Adopted guidelines reflect a harmonised European approach to a specific scientific issue and should reflect the most recent scientific knowledge. However, whilst EU regulations are mandatory for all member states and EU directives must be followed by national laws in line with the directive, EMA guidelines do not have legal force and alternative approaches may be taken, but these obviously require more justification. This new series of the BJCP, developed in collaboration with the EMA, aims to address this issue by providing an annotated version of some relevant EMA guidelines and regulatory documents by experts. Hopefully, this will help in promoting their diffusion and in opening a forum for discussion with our readers.

The European Medicines Agency Experience With Pediatric Dose Selection.

Getting the right dose regimen for children and adolescents is important but poses great scientific, practical, and ethical challenges. At the same time, the availability of data in adults is a huge advantage and needs to be used optimally when designing studies in children and analyzing pediatric data. Furthermore, the processes of maturation and growth are always key when selecting doses for children. All the above make study adaptations and model-informed approaches imperative for dose exposure-response characterization and dose selection in children. This article summarizes the experience gained in the European Medicines Agency on this topic and proposes some general guiding principles for defining objectives, study designs, and methodology tools for pediatric dose selection.

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility.

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk-informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk-based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick-start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts.

Commentary on the MID3 Good Practices Paper.

During the last 10 years the European Medicines Agency (EMA) organized a number of workshops on modeling and simulation, working towards greater integration of modeling and simulation (M&S) in the development and regulatory assessment of medicines. In the 2011 EMA - European Federation of Pharmaceutical Industries and Associations (EFPIA) Workshop on Modelling and Simulation, European regulators agreed to the necessity to build expertise to be able to review M&S data provided by companies in their dossier. This led to the establishment of the EMA Modelling and Simulation Working Group (MSWG). Also, there was agreement reached on the need for harmonization on good M&S practices and for continuing dialog across all parties. The MSWG acknowledges the initiative of the EFPIA Model-Informed Drug Discovery and Development (MID3) group in promoting greater consistency in practice, application, and documentation of M&S and considers the paper is an important contribution towards achieving this objective.

Population Pharmacokinetics of Edoxaban in Patients with Non-Valvular Atrial Fibrillation in the ENGAGE AF-TIMI 48 Study, a Phase III Clinical Trial.

INTRODUCTION: Edoxaban is a novel factor Xa inhibitor. This study characterizes the population pharmacokinetics of edoxaban in patients with non-valvular atrial fibrillation (NVAF) included in the phase III ENGAGE AF-TIMI 48 study, evaluates covariates for the dose-exposure relationship in this population, and assesses the impact of protocol-specified dose reductions on exposure using simulations. METHODS: Model development was performed using NONMEM(®) and based on sparse data from the ENGAGE AF-TIMI 48 study augmented with dense data from 13 phase I studies to inform and stabilize the model. The influence of body weight (WT), creatinine clearance (CLCR), concomitant P-glycoprotein (P-gp) inhibitors, age, sex, race, and NVAF on pharmacokinetic parameters was evaluated based on statistical significance and clinical relevance. RESULTS: A two-compartment model with first-order elimination and first-order absorption after an absorption lag-time best described the data. Apparent volume and clearance terms increased with increasing WT. Apparent renal clearance increased with increasing CLCR. Apparent non-renal, renal, and inter-compartmental clearance terms differed between phase I volunteers and NVAF patients. Asian patients were found to have increased apparent central volume of distribution, bioavailability, and total apparent clearance. Concomitant P-gp inhibitors increased the bioavailability statistically significantly, but this did not reach clinical relevance. CONCLUSION: Edoxaban disposition and the variability in this disposition, including influence of covariates, after oral administration were adequately characterized in patients with NVAF. The 50 % dose reduction in patients with low WT (≤60 kg), moderate renal impairment (CLCR ≤50 mL/min), or concomitant P-gp inhibitors led to 30 % lower exposure than in the other patients.

Performance of three estimation methods in repeated time-to-event modeling.

It is not uncommon that the outcome measurements, symptoms or side effects, of a clinical trial belong to the family of event type data, e.g., bleeding episodes or emesis events. Event data is often low in information content and the mixed-effects modeling software NONMEM has previously been shown to perform poorly with low information ordered categorical data. The aim of this investigation was to assess the performance of the Laplace method, the stochastic approximation expectation-maximization (SAEM) method, and the importance sampling method when modeling repeated time-to-event data. The Laplace method already existed, whereas the two latter methods have recently become available in NONMEM 7. A stochastic simulation and estimation study was performed to assess the performance of the three estimation methods when applied to a repeated time-to-event model with a constant hazard associated with an exponential interindividual variability. Various conditions were investigated, ranging from rare to frequent events and from low to high interindividual variability. The method performance was assessed by parameter bias and precision. Due to the lack of information content under conditions where very few events were observed, all three methods exhibit parameter bias and imprecision, however most pronounced by the Laplace method. The performance of the SAEM and importance sampling were generally higher than Laplace when the frequency of individuals with events was less than 43%, while at frequencies above that all methods were equal in performance.

Modeling disease progression in acute stroke using clinical assessment scales.

This article demonstrates techniques for describing and predicting disease progression in acute stroke by modeling scores measured using clinical assessment scales, accommodating dropout as an additional source of information. Scores assessed using the National Institutes of Health Stroke Scale and the Barthel Index in acute stroke patients were used to model the time course of disease progression. Simultaneous continuous and probabilistic models for describing the nature and magnitude of score changes were developed, and used to model the trajectory of disease progression using scale scores. The models described the observed data well, and exhibited good simulation properties. Applications include longitudinal analysis of stroke scale data, clinical trial simulation, and prognostic forecasting. Based upon experience in other areas, it is likely that application of this modeling methodology will enable reductions in the number of patients needed to carry out clinical studies of treatments for acute stroke.

Randomized exposure-controlled trials; impact of randomization and analysis strategies.

AIMS: In the literature, five potential benefits of randomizing clinical trials on concentration levels, rather than dose, have been proposed: (i) statistical study power will increase; (ii) study power will be less sensitive to high variability in the pharmacokinetics (PK); (iii) the power of establishing an exposure-response relationship will be robust to correlations between PK and pharmacodynamics (PD); (iv) estimates of the exposure-response relationship are likely to be less biased; and (v) studies will provide a better control of exposure in situations with toxicity issues. The main aim of this study was to investigate if these five statements are valid when the trial results are evaluated using a model-based analysis. METHODS: Quantitative relationships between drug dose, concentration, biomarker and clinical end-point were defined using pharmacometric models. Three randomization schemes for exposure-controlled trials, dose-controlled (RDCT), concentration-controlled (RCCT) and biomarker-controlled (RBCT), were simulated and analysed according to the models. RESULTS: (i) The RCCT and RBCT had lower statistical power than RDCT in a model-based analysis; (ii) with a model-based analysis the power for an RDCT increased with increasing PK variability; (iii) the statistical power in a model-based analysis was robust to correlations between CL and EC(50) or E(max); (iv) under all conditions the bias was negligible (<3%); and (v) for studies with equal power RCCT could produce either more or fewer adverse events compared with an RDCT. CONCLUSION: Alternative randomization schemes may not have the proposed advantages if a model-based analysis is employed.