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We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue., (© 2023. Springer Nature Limited.)

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug-omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug-drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities., (© 2023. The Author(s).)

Biomodifying technologies-such as gene editing, induced pluripotent stem cells, and bioprinting-are being developed for a wide range of applications, from pest control to lab-grown meat. In medicine, regulators have responded to the challenge of evaluating modified 'natural' material as a therapeutic 'product' by introducing more flexible assessment schemes. Attempts have also been made to engage stakeholders across the globe on the acceptable parameters for these technologies, particularly in the case of gene editing. Regulatory flexibility and stakeholder engagement are important, but a broader perspective is also needed to respond to the potential disruption of biomodification. Our case-study technologies problematize basic ideas-such as 'nature', 'product', and 'donation'-that underpin the legal categories used to regulate biotechnology. Where such foundational concepts are rendered uncertain, a socially responsive and sustainable solution would involve exploring evolutions in these concepts across different societies. We suggest that the global observatory model is a good starting point for this 'Adaptive Societal Governance' approach, in which a self-organizing network of scholars and interested parties could carry out the multi-modal (meta)analyses needed to understand societal constructions of ideas inherent to our understanding of 'life'., (© The Author(s) 2022. Published by Oxford University Press on behalf of Duke University School of Law, Harvard Law School, Oxford University Press, and Stanford Law School.)

Integrative drug safety research in translational health informatics has rapidly evolved and included data that are drawn in from many resources, combining diverse data that are either reused from (curated) repositories, or newly generated at source. Each resource is mandated by different sets of metadata rules that are imposed on the incoming data. Combination of the data cannot be readily achieved without interference of data stewardship and the top-down policy guidelines that supervise and inform the process for data combination to aid meaningful interpretation and analysis of such data. The eTRANSAFE Consortium's effort to drive integrative drug safety research at a large scale hereby present the lessons learnt and the proposal of solution at the guidelines in practice at this Innovative Medicines Initiative (IMI) project. Recommendations in these guidelines were compiled from feedback received from key stakeholders in regulatory agencies, EFPIA companies, and academic partners. The research reproducibility guidelines presented in this study lay the foundation for a comprehensive data sharing and knowledge management plans accounting for research data management in the drug safety space - FAIR data sharing guidelines, and the model verification guidelines as generic deliverables that best practices that can be reused by other scientific community members at large. FAIR data sharing is a dynamic landscape that rapidly evolves with fast-paced technology advancements. The research reproducibility in drug safety guidelines introduced in this study provides a reusable framework that can be adopted by other research communities that aim to integrate public and private data in biomedical research space., Competing Interests: No competing interests were disclosed., (Copyright: © 2022 Sarntivijai S et al.)

The UK government announced in March 2020 that it would create an NHS Covid-19 'Data Store' from information routinely collected as part of the health service. This 'Store' would use a number of sources of population data to provide a 'single source of truth' about the spread of the coronavirus in England. The initiative illustrates the difficulty of relying on automated processing when making healthcare decisions under the General Data Protection Regulation (GDPR). The end-product of the store, a number of 'dashboards' for decision-makers, was intended to include models and simulations developed through artificial intelligence. Decisions made on the basis of these dashboards would be significant, even (it was suggested) to the point of diverting patients and critical resources between hospitals based on their predictions. How these models will be developed, and externally validated, remains unclear. This is an issue if they are intended to be used for decisions which will affect patients so directly and acutely. We have (by default) a right under the GDPR not to be subject to significant decisions based solely on automated decision-making. It is not obvious, at present, whether resource allocation within the NHS could take place in reliance on this automated modelling. The recent A Level debacle illustrates, in the context of education, the risks of basing life-changing decisions on the national application of a single equation. It is worth considering the potential consequences for the health service if the NHS Data Store is used for resource planning as part of the Covid-19 response., Competing Interests: Conflicts of interest: I am not aware of any conflicts of interest relating to the content of this piece.

Data platforms represent a new paradigm for carrying out health research. In the platform model, datasets are pooled for remote access and analysis, so novel insights for developing better stratified and/or personalised medicine approaches can be derived from their integration. If the integration of diverse datasets enables development of more accurate risk indicators, prognostic factors, or better treatments and interventions, this obviates the need for the sharing and reuse of data; and a platform-based approach is an appropriate model for facilitating this. Platform-based approaches thus require new thinking about consent. Here we defend an approach to meeting this challenge within the data platform model, grounded in: the notion of 'reasonable expectations' for the reuse of data; Waldron's account of 'integrity' as a heuristic for managing disagreement about the ethical permissibility of the approach; and the element of the social contract that emphasises the importance of public engagement in embedding new norms of research consistent with changing technological realities. While a social contract approach may sound appealing, however, it is incoherent in the context at hand. We defend a way forward guided by that part of the social contract which requires public approval for the proposal and argue that we have moral reasons to endorse a wider presumption of data reuse. However, we show that the relationship in question is not recognisably contractual and that the social contract approach is therefore misleading in this context. We conclude stating four requirements on which the legitimacy of our proposal rests.

Responsible Research and Innovation ('RRI') is a cross-cutting priority for scientific research in the European Union and beyond. This paper considers whether the way such research is organised and delivered lends itself to the aims of RRI. We focus particularly on international consortia, which have emerged as a common model to organise large-scale, multi-disciplinary research in contemporary biomedical science. Typically, these consortia operate through fixed-term contracts, and employ governance frameworks consisting of reasonably standard, modular components such as management committees, advisory boards, and data access committees, to co-ordinate the activities of partner institutions and align them with funding agency priorities. These have advantages for organisation and management of the research, but can actively inhibit researchers seeking to implement RRI activities. Conventional consortia governance structures pose specific problems for meaningful public and participant involvement, data sharing, transparency, and 'legacy' planning to deal with societal commitments that persist beyond the duration of the original project. In particular, the 'upstream' negotiation of contractual terms between funders and the institutions employing researchers can undermine the ability for those researchers to subsequently make decisions about data, or participant remuneration, or indeed what happens to consortia outputs after the project is finished, and can inhibit attempts to make project activities and goals responsive to input from ongoing dialogue with various stakeholders. Having explored these challenges, we make some recommendations for alternative consortia governance structures to better support RRI in future.

Data sharing has long been a cornerstone of healthcare and research and is only due to become more important with the rise of Big Data analytics and advanced therapies. Cell therapies, for example, rely not only on donated cells but also essentially on donated information to make them traceable. Despite the associated importance of concepts such as 'donor anonymity', the concept of anonymisation remains contentious. The Article 29 Working Party's 2014 guidance on 'Anonymisation Techniques' has perhaps helped encourage a perception that anonymity is the result of data modification 'techniques', rather than a broader process involving management of information and context. In light of this enduring ambiguity, this article advocates a 'relative' understanding of anonymity and supports this interpretation with reference not only to the General Data Protection Regulation but also to European Union health-related legislation, which also alludes to the concept. Anonymity, I suggest, should be understood not as a 'technique' which removes the need for information governance but rather as a legal standard of reasonable risk-management, which can only be satisfied by effective data protection. As such, anonymity can be not so much an alternative to data protection as its mirror, requiring similar safeguards to maintain privacy and confidentiality., (© The Author(s) 2020. Published by Oxford University Press; All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Somatic gene therapies may be authorised for marketing in the EU under the advanced therapy medicinal product regulation. These therapeutic compounds are sufficiently novel and complex in their potential effects to require specialist evaluation. However, the current definition of gene therapy medicinal products ('GTMP') risks excluding molecules which are not manufactured through techniques involving recombination. We consider the way, in which the 'recombinant nucleic acid' aspect of the GTMP definition is challenged by developments in gene-editing technology, and why a broader scope of GTMP regulation may be desirable.

Over the last decade dramatic advances have been made in both the technology and data available to better understand the multifactorial influences on child and adolescent health and development. This paper seeks to clarify methods that can be used to link information from health, education, social care and research datasets. Linking these different types of data can facilitate epidemiological research that investigates mental health from the population to the patient; enabling advanced analytics to better identify, conceptualise and address child and adolescent needs. The majority of adolescent mental health research is not able to maximise the full potential of data linkage, primarily due to four key challenges: confidentiality, sampling, matching and scalability. By presenting five existing and proposed models for linking adolescent data in relation to these challenges, this paper aims to facilitate the clinical benefits that will be derived from effective integration of available data in understanding, preventing and treating mental disorders., Competing Interests: Competing interests: MF has an honorary contract with Oxford Health NHS Foundation Trust (OHNHSFT). KM is supported by the Oxford Health BRC, a collaboration between the University of Oxford and OHNHSFT., (© Author(s) (or their employer(s)) 2020. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)