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This paper provides an empirical ethics analysis of the goods and bads enacted in EU ageing and innovation policy discourses. It revolves around a case study of the persona Maria, developed as part of the EU’s Active and Healthy Ageing Policies. Drawing on Pols’ empirical ethics as a theoretical and methodological approach, we describe the variety of goods (practices/situations to be strived for) and bads (practices/situations to be avoided) that are articulated in Maria’s persona. We analyse how certain ideas about good and bad ageing—those associated with the use of sophisticated technologies—come to matter more in the solutions proposed for Maria and the framing of her unmet needs, while others which were initially seen as relevant and that describe her dreams, fears and interactions, are marginalised. The paper adds to existing studies of ageing and technology by analysing specific practices that render visible how the idea of technology and data sharing as evidently the right path towards futures of (good) ageing, comes to prevail.

The new radiotherapy high field, 1.5 Tesla MRI-guided linear accelerator (MR-Linac) is being clinically introduced. Sensing and evaluating opportunities and barriers at an early stage will facilitate its eventual scale-up. This study investigates the opportunities and barriers to the implementation of MR-Linac into prostate cancer care based on 43 semi-structured interviews with Dutch oncology care professionals, hospital and division directors, patients, payers and industry. The analysis was guided by the Non-adoption, Abandonment, Scale-up, Spread, and Sustainability framework of new medical technologies and services. Opportunities included: the acquirement of (1) advanced MRI-guided radiotherapy technology with (2) the potential for improved patient outcomes and (3) economic benefits, as well as (4) professional development and (5) a higher hospital quality profile. Barriers included: (1) technical complexities, (2) substantial staffing and structural investments, (3) the current lack of empirical evidence of clinical benefits, (4) professional silos, and (5) the presence of patient referral patterns. While our study confirms the expected technical and clinical prospects from the literature, it also reveals economic, organizational, and socio-political challenges.

Background Literature on self-management innovations has studied their characteristics and position in healthcare systems. However, less attention has been paid to factors that contribute to successful implementation. This paper aims to answer the question: which factors play a role in a successful implementation of self-management health innovations? Methods We conducted a narrative review of academic literature to explore factors related to successful implementation of self-management health innovations. We further investigated the factors in a qualitative multiple case study to analyse their role in implementation success. Data were collected from nine self-management health projects in the Netherlands. Results Nine factors were found in the literature that foster the implementation of self-management health innovations: 1) involvement of end-users, 2) involvement of local and business partners, 3) involvement of stakeholders within the larger system, 4) tailoring of the innovation, 5) utilisation of multiple disciplines, 6) feedback on effectiveness, 7) availability of a feasible business model, 8) adaption to organisational changes, and 9) anticipation of changes required in the healthcare system. In the case studies, on average six of these factors could be identified. Three projects achieved a successful implementation of a self-management health innovation, but only in one case were all factors present. Conclusions For successful implementation of self-management health innovation projects, the factors identified in the literature are neither necessary nor sufficient. Therefore, it might be insightful to study how successful implementation works instead of solely focusing on the factors that could be helpful in this process.

Objective The objective was to identify barriers and facilitators to the implementation of artificial intelligence (AI) applications in clinical radiology in The Netherlands. Materials and methods Using an embedded multiple case study, an exploratory, qualitative research design was followed. Data collection consisted of 24 semi-structured interviews from seven Dutch hospitals. The analysis of barriers and facilitators was guided by the recently published Non-adoption, Abandonment, Scale-up, Spread, and Sustainability (NASSS) framework for new medical technologies in healthcare organizations. Results Among the most important facilitating factors for implementation were the following: (i) pressure for cost containment in the Dutch healthcare system, (ii) high expectations of AI’s potential added value, (iii) presence of hospital-wide innovation strategies, and (iv) presence of a “local champion.” Among the most prominent hindering factors were the following: (i) inconsistent technical performance of AI applications, (ii) unstructured implementation processes, (iii) uncertain added value for clinical practice of AI applications, and (iv) large variance in acceptance and trust of direct (the radiologists) and indirect (the referring clinicians) adopters. Conclusion In order for AI applications to contribute to the improvement of the quality and efficiency of clinical radiology, implementation processes need to be carried out in a structured manner, thereby providing evidence on the clinical added value of AI applications. Key Points • Successful implementation of AI in radiology requires collaboration between radiologists and referring clinicians. • Implementation of AI in radiology is facilitated by the presence of a local champion. • Evidence on the clinical added value of AI in radiology is needed for successful implementation.

IntroductionPoor translation of efficacy data derived from animal models is a potential contributor to costly and unnecessary attrition in clinical trials.ObjectivesTo develop a tool to assess, validate and compare the clinical translatability of animal models used for the preliminary assessment of efficacy.Design and ResultsWe conducted an exploratory literature search to identify the key aspects to validate animal models. Eight aspects (Epidemiology, Pathophysiology, Genetic, Biochemistry, Aetiology, Histology, Pharmacology and Endpoints) were identified for which questions were drafted to evaluate the different faces of the human disease simulation. Features of the framework include standardised instructions, a weighting and scoring system to compare models as well as contextualising factors regarding model similarity and evidence uncertainty. We included a quality assessment of the internal validity of drug intervention studies included in the Pharmacological validation section for both effective and ineffective drugs in humans. A web-based survey was conducted with experts from different stakeholders to gather input on the framework. Finally, we present a case study of a preliminary validation and comparison of two animal models for Duchenne Muscular Dystrophy (mdx mouse and GRMD dog) and Diabetes Type 2 (ZDF rat and db/db mouse). We show that there are significant differences between the mdx mouse and the GRMD dog, the latter mimicking the human condition to a greater extent than the mouse despite the considerable lack of published data. In DT2, both the ZDF rat and the db/db mouse are comparable with minor differences in pathophysiology.ConclusionsFIMD facilitates drug development by serving as the basis to select the most relevant model that can provide meaningful and translatable results to progress drug candidates to the clinic.

Biosimilars have been available on the European market since 2006 and experience with their use is increasing. The next wave of biopharmaceuticals that are about to lose patent protection consists of more-complicated products, including many monoclonal antibodies. Guidance has been released on the particulars of a biosimilarity exercise involving these products. Considerable challenges exist to establish biosimilarity for anticancer products. An especially challenging product is bevacizumab (Avastin(®)). On the basis of data available for the innovator product (bevacizumab) we will discuss strengths and weaknesses of preclinical and clinical models and explore the application of novel endpoints to the biosimilar comparability exercise.

BACKGROUND: The nature of adverse drug reactions observed post-authorization for biopharmaceuticals differs from that observed for chemically synthesized, small molecules (SMs). However, it remains unclear how much of the observed differences can be attributed to differences in authorized indications of the two product groups. OBJECTIVE: To investigate if the nature of adverse drug reactions identified post-authorization for biopharmaceuticals differs from those of SMs within the same anatomical therapeutic chemical (ATC) group. METHODS: We analyzed safety issues included in post-authorization, changes to the Summary of Product Characteristics of centrally approved products in the European Union classified in the ATC main group of 'antineoplastic and immunomodulating agents'. Generics and biosimilars were excluded. All issues identified during 2004-2011 were analyzed for differences in nature and timing between biopharmaceutical and SM products, at different ATC levels. RESULTS: A total of 747 adverse drug reactions were identified; 361 for biopharmaceuticals and 386 for SMs. Within the sub group of immunosuppressants, neoplasms (20 % vs 2 %, p < 0.01) and infections and infestations (22 % vs 9 %, p < 0.01) occurred significantly more frequent for biopharmaceuticals. Adverse drug reactions of SMs were more often renal and urinary disorders (7 % vs 0 %, p < 0.01), blood and lymphatic system disorders (10 % vs 3 %, p = 0.04), and vascular disorders (7 % vs 1 %, p = 0.02). In the subgroup of antineoplastics, immune system disorders occurred more frequently for biopharmaceuticals, (6 % vs 1 %, p = 0.04). With the exception of immune system disorders and renal disorders, the overall differences between biopharmaceuticals and SMs were mostly caused by products authorized as immunosuppressants. For the subset of products authorized after 2004, the median time to the first safety issue was 18 months (95 % CI 12.4-21.5) for biopharmaceuticals and 17 months (95 % CI 12.5-21.5) for SMs and did not differ significantly within subgroups. CONCLUSION: Even within a group of medicinal products approved in the same indication, differences were observed in the nature of adverse drug reactions between biopharmaceuticals and SMs. The considerable differences in the nature of adverse drug reactions between biopharmaceuticals and SMs were not associated with differences in the timing of regulatory actions.

PURPOSE: To describe and assess the outcomes of Periodic Safety Update Report (PSUR) evaluations of biopharmaceuticals. METHODS: A cross-sectional analysis was performed of follow-up requirements of PSURs submitted for centrally approved biopharmaceuticals in the European Union between 1 July 2008 and 30 June 2010. A follow-up analysis on a subset of products that submitted multiple PSURs within the study period was also performed. RESULTS: The cross-sectional analysis included 70 PSURs. Potential safety concerns occurred in 57 (83 %) of all PSURs, and 26 (37 %) concluded a need to change the Summary of Product Characteristics (SPC). In comparison to newer products, products authorized for more than 10 years contained significantly fewer potential safety concerns (60 vs. 92 %; p

The European Union (EU) was the first region to establish a regulatory framework for biosimilars, in which animal studies are required to confirm similarity to a reference product. However, animal studies described in European public assessment reports (EPARs) or marketing authorization applications (MAAs) did not identify clinically or toxicologically relevant differences despite differences in quality, suggesting that animal studies lack the sensitivity to confirm biosimilarity. Scientific advice provided learning opportunities to evolve existing guidance. Altogether, the data support a step-wise approach to develop biosimilars that focuses on quality and clinical efficacy of biosimilar. This approach might be more effective and does not necessarily require animal studies, which is also reflected in new EU draft guidance.

Background: The characteristics of biopharmaceuticals may require a tailored approach to their safety management. However, information on what tools and methods are employed to assess the safety of biopharmaceuticals post-authorization is lacking. Objective: This study investigates determinants that contribute to the post-authorization management of biopharmaceuticals. Methods: A cohort study was performed including all centrally approved biopharmaceuticals for which a Direct Healthcare Professional Communication (DHPC) was issued during 1997–2009. Safety-related regulatory actions were defined as updates of the summary of product characteristics through type II variations. Determinants of these actions were identified based on publicly available data. Urgent variations, defined as variations accompanied by a DHPC, were compared with other, ‘non-urgent’, safety-related variations. Results: We identified 133 variations relating to 15 products, 24 urgent and 109 other variations. For 55% of urgent variations, spontaneous reports were the sole source of regulatory action, post-approval studies accounted for 33%, and 12% were based on other sources or combinations of sources. For the non-urgent variations, spontaneous reports were the sole source for 36%, post-approval studies for 28%, and 36% were based on other sources or combinations. Overall, most variations included safety issues categorized as ‘infections and infestations’ (33.1%), ‘general disorders and administration site conditions’ (25.6%), and ‘neoplasms’ (14.3%). Conclusion: Determinants of urgent and non-urgent safety-related regulatory actions of biopharmaceuticals are largely similar. Spontaneous reports are an important pillar for both urgent and non-urgent actions and remain an important tool in the post-authorization safety management of biopharmaceuticals.