Meeting the Need for a Discussion of Unmet Medical Need.

As Europe and the world continue to battle against COVID, the customary complacency of society over future threats is clearly on display. Just 30 months ago, such a massive disruption to global lives, livelihoods and quality of life seemed unimaginable. Some remedial European Union action is now emerging, and more is proposed, including in relation to tackling "unmet medical need" (UMN). This initiative-directing attention to the future of treating disease and contemplating incentives to stimulate research and development-is welcome in principle. But the current approach being considered by EU officials merits further discussion, because it may prove counter-productive, impeding rather than promoting innovation. This paper aims to feed into these ongoing policy discussions, and rather than presenting research in the classical sense, it discusses the key elements from a multistakeholder perspective. Its central concern is over the risk that the envisaged support will fail to generate valuable new treatments if the legislation is phrased in a rigidly linear manner that does not reflect the serpentine realities of the innovation process, or if the definition placed on unmet medical need is too restrictive. It cautions that such an approach presumes that "unmet need" can be precisely and comprehensively defined in advance on the basis of the past. It cautions that such an approach can reinforce the comfortable delusion that the future is totally predictable-the delusion that left the world as easy prey to COVID. Instead, the paper urges reflection on how the legislation that will shortly enter the pipeline can be phrased so as to allow for the flourishing of a culture capable of rapid adaptation to the unexpected.

Host genetic loci LZTFL1 and CCL2 associated with SARS-CoV-2 infection and severity of COVID-19.

OBJECTIVES: Host genetic factors contribute to the variable severity of COVID-19. We examined genetic variants from genome-wide association studies and candidate gene association studies in a cohort of patients with COVID-19 and investigated the role of early SARS-CoV-2 strains in COVID-19 severity. METHODS: This case-control study included 123 COVID-19 cases (hospitalized or ambulatory) and healthy controls from the state of Baden-Wuerttemberg, Germany. We genotyped 30 single nucleotide polymorphisms, using a custom-designed panel. Cases were also compared with the 1000 genomes project. Polygenic risk scores were constructed. SARS-CoV-2 genomes from 26 patients with COVID-19 were sequenced and compared between ambulatory and hospitalized cases, and phylogeny was reconstructed. RESULTS: Eight variants reached nominal significance and two were significantly associated with at least one of the phenotypes "susceptibility to infection", "hospitalization", or "severity": rs73064425 in LZTFL1 (hospitalization and severity, P <0.001) and rs1024611 near CCL2 (susceptibility, including 1000 genomes project, P = 0.001). The polygenic risk score could predict hospitalization. Most (23/26, 89%) of the SARS-CoV-2 genomes were classified as B.1 lineage. No associations of SARS-CoV-2 mutations or lineages with severity were observed. CONCLUSION: These host genetic markers provide insights into pathogenesis and enable risk classification. Variants which reached nominal significance should be included in larger studies.

SARS-CoV-2 infection paralyzes cytotoxic and metabolic functions of the immune cells.

The SARS-CoV-2 virus is the causative agent of the global COVID-19 infectious disease outbreak, which can lead to acute respiratory distress syndrome (ARDS). However, it is still unclear how the virus interferes with immune cell and metabolic functions in the human body. In this study, we investigated the immune response in acute or convalescent COVID-19 patients. We characterized the peripheral blood mononuclear cells (PBMCs) using flow cytometry and found that CD8+ T cells were significantly subsided in moderate COVID-19 and convalescent patients. Furthermore, characterization of CD8+ T cells suggested that convalescent patients have significantly diminished expression of both perforin and granzyme A. Using 1H-NMR spectroscopy, we characterized the metabolic status of their autologous PBMCs. We found that fructose, lactate and taurine levels were elevated in infected (mild and moderate) patients compared with control and convalescent patients. Glucose, glutamate, formate and acetate levels were attenuated in COVID-19 (mild and moderate) patients. In summary, our report suggests that SARS-CoV-2 infection leads to disrupted CD8+ T cytotoxic functions and changes the overall metabolic functions of immune cells.

Swarm Learning for decentralized and confidential clinical machine learning.

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning-a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine.