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The recent benefit–risk framework (BRF) developed by the Food and Drug Administration (FDA) is intended to improve the clarity and consistency in communicating the reasoning behind the FDA's decisions, acting as an important advancement in US drug regulation. In the PDUFA VI implementation plan, the FDA states that it will continue to explore more structured or quantitative decision analysis approaches; however, it restricts their use within the current BRF that is purely qualitative. By contrast, European regulators and researchers have been long exploring the use of quantitative decision analysis approaches for evaluating drug benefit–risk balance. In this paper, we show how quantitative modelling, backed by decision theory, could complement and extend the FDA's BRF to better support the appraisal of evidence and improve decision outcomes. After providing relevant scientific definitions for benefit–risk assessment and describing the FDA and European Medicines Agency (EMA) frameworks, we explain the components of and differences between qualitative and quantitative approaches. We present lessons learned from the EMA experience with the use of quantitative modelling and we provide evidence of its benefits, illustrated by a real case study that helped to resolve differences of judgements among EMA regulators. [ABSTRACT FROM AUTHOR]

Aims: Dose adjustment for drugs eliminated by the kidneys generally assume a linear relationship between renal drug clearance (CLR) and glomerular filtration rate (GFR). This assumption may not hold for drugs that undergo extensive tubular secretion where nonlinearity in drug handling is expected. The aim of this study is to determine if renal drug study designs recommended by the European Medicines Agency (EMA) and Food and Drug Administration (FDA) could distinguish linear from nonlinear renal drug handling. Methods: In this simulation and estimation study, the study designs based on the EMA and FDA guidelines for Phase I renal drug studies were evaluated for their ability to discriminate a linear from a nonlinear relationship between CLR and GFR. The number of subjects for each simulated study ranged from 4 to 960. Power, relative standard error and bias were calculated. Results: Study designs under the EMA and FDA guidelines required ≥8 and ≥48 subjects, respectively, to achieve ≥80% power to discriminate a linear from nonlinear relationship between CLR and GFR. The relative standard error of estimated parameters were 13–37 and 17–44% for the designs with 24 subjects under the EMA and FDA guidelines, respectively. The bias in parameter estimates under the EMA designs were not evident, however, they were biased (13–21%) under the FDA designs. Conclusion: The EMA design was found to require fewer subjects (n = 8) compared to the FDA (n = 48) to discriminate linear from nonlinear drug renal handling at ≥80% study power while both the designs perform poorly for the parameter precision. [ABSTRACT FROM AUTHOR]

Aim In 2006, Omnitrope (by Sandoz) was the first approved biosimilar in Europe. To date, 21 biosimilars for seven different biologics are on the market. The present study compared the clinical trials undertaken to obtain market authorization. Methods We summarized the findings of a comprehensive review of all clinical trials up to market authorization of approved biosimilars, using the European public assessment reports (EPARs) published by the European Medicines Agency (EMA). The features compared were, among others, the number of patients enrolled, the number of trials, the types of trial design, choice of endpoints and equivalence margins for pharmacokinetic (PK)/pharmacodynamic (PD) and phase III trials. Results The variability between the clinical development strategies is high. Some differences are explainable by the characteristics of the product; if, for example, the PD marker can be assumed to predict the clinical outcome, no efficacy trials might be necessary. However, even for products with the same reference product, the sample size, endpoints and statistical models are not always the same. Conclusions There seems to be flexibility for sponsors regarding the decision as to how best to prove biosimilarity. [ABSTRACT FROM AUTHOR]

Background In 2010, the European Medicines Agency (EMA) initiated a pilot project on parallel scientific advice with Health Technology Assessment bodies (HTABs) that allows manufacturers to receive simultaneous feedback from both the European Union (EU) regulators and HTABs on their development plans for medicines. Aims The present retrospective qualitative analysis aimed to explore how the parallel scientific advice system is working and levels of commonality between the EU regulators and HTABs, and among HTABs, when applicants obtain parallel scientific advice from both a regulatory and an HTA perspective. Methods We analysed the minutes of discussion meetings held at the EMA between 2010, when parallel advice was launched, and 1 May 2015, when the cutoff date for data extraction was set. The analysis was based on predefined criteria and conducted at two different levels of comparison: the answers of the HTABs vs. those of the regulators, and between the answers of the participating HTA agencies. Results The analysis was based on 31 procedures of parallel scientific advice. The level of full agreements was highest for questions on patient population (77%), while disagreements reached a peak for questions on the study comparator (30%). With regard to comparisons among HTABs, there was a high level of agreement for all domains. Conclusions There is evident commonality, in terms of evidence requirements between the EU regulators and participating HTABs, as well as among HTABs, on most aspects of clinical development. Indeed, regardless of the question content, the analysis showed that a high level of overall agreement was reached through the process of parallel scientific advice. [ABSTRACT FROM AUTHOR]