Blockchain-facilitated sharing to advance outbreak R&D

The pharmaceutical productivity gap - Incremental decline in R&D efficiency despite transient improvements

Rising research and development costs, currently exceeding $3.5 billion per novel drug, reflect a five-decade decline in pharmaceutical R&D efficiency. While recent reports suggest a potential turnaround, this review offers a systems-level analysis to explore whether this marks a structural shift or transient reversal. We analyzed financial data from the 200 largest pharmaceutical firms, novel drug approvals, and more than 80 000 clinical trials between 2012 and 2023. Our analysis revealed that despite recent stabilization, the pharmaceutical industry continues to face challenges, particularly due to elevated late-stage clinical attrition, suggesting that a sustained turnaround in R&D efficiency remains elusive.

The potential of federated learning for public health purposes: a qualitative analysis of GDPR compliance, Europe, 2021

BackgroundThe wide application of machine learning (ML) holds great potential to improve public health by supporting data analysis informing policy and practice. Its application, however, is often hampered by data fragmentation across organisations and strict regulation by the General Data Protection Regulation (GDPR). Federated learning (FL), as a decentralised approach to ML, has received considerable interest as a means to overcome the fragmentation of data, but it is yet unclear to which extent this approach complies with the GDPR.AimOur aim was to understand the potential data protection implications of the use of federated learning for public health purposes.MethodsBuilding upon semi-structured interviews (n = 14) and a panel discussion (n = 5) with key opinion leaders in Europe, including both FL and GDPR experts, we explored how GDPR principles would apply to the implementation of FL within public health.ResultsWhereas this study found that FL offers substantial benefits such as data minimisation, storage limitation and effective mitigation of many of the privacy risks of sharing personal data, it also identified various challenges. These challenges mostly relate to the increased difficulty of checking data at the source and the limited understanding of potential adverse outcomes of the technology.ConclusionSince FL is still in its early phase and under rapid development, it is expected that knowledge on its impracticalities will increase rapidly, potentially addressing remaining challenges. In the meantime, this study reflects on the potential of FL to align with data protection objectives and offers guidance on GDPR compliance.

An elephant in the glasshouse? Trade-offs between acceleration and transformation in COVID-19 vaccine innovation policies

Against the backdrop of a failing vaccine innovation system, innovation policy aimed at creating a COVID-19 vaccine was surprisingly fast and effective. This paper analyzes the influence of the COVID-19 landscape shock and corresponding innovation policy responses on the existing vaccine innovation system. We use document analysis and expert interviews, performed during vaccine development. We find that the sharing of responsibility between public and private actors on various geographical levels, and the focus on accelerating changes in the innovation system were instrumental in achieving fast results. Simultaneously, the acceleration exacerbated existing societal innovation barriers, such as vaccine hesitancy, health inequity, and contested privatization of earnings. Going forward, these innovation barriers may limit the legitimacy of the vaccine innovation system and reduce pandemic preparedness. Next to a focus on acceleration, transformative innovation policies for achieving sustainable pandemic preparedness are still urgently needed. Implications for mission-oriented innovation policy are discussed.

Epidemic management and control through risk-dependent individual contact interventions

Testing, contact tracing, and isolation (TTI) is an epidemic management and control approach that is difficult to implement at scale because it relies on manual tracing of contacts. Exposure notification apps have been developed to digitally scale up TTI by harnessing contact data obtained from mobile devices; however, exposure notification apps provide users only with limited binary information when they have been directly exposed to a known infection source. Here we demonstrate a scalable improvement to TTI and exposure notification apps that uses data assimilation (DA) on a contact network. Network DA exploits diverse sources of health data together with the proximity data from mobile devices that exposure notification apps rely upon. It provides users with continuously assessed individual risks of exposure and infection, which can form the basis for targeting individual contact interventions. Simulations of the early COVID-19 epidemic in New York City are used to establish proof-of-concept. In the simulations, network DA identifies up to a factor 2 more infections than contact tracing when both harness the same contact data and diagnostic test data. This remains true even when only a relatively small fraction of the population uses network DA. When a sufficiently large fraction of the population (≳ 75%) uses network DA and complies with individual contact interventions, targeting contact interventions with network DA reduces deaths by up to a factor 4 relative to TTI. Network DA can be implemented by expanding the computational backend of existing exposure notification apps, thus greatly enhancing their capabilities. Implemented at scale, it has the potential to precisely and effectively control future epidemics while minimizing economic disruption.

During the ongoing COVID-19 pandemic, exposure notification apps have been developed to scale up manual contact tracing. The apps use proximity data from mobile devices to automate notifying direct contacts of an infection source. The information they provide is limited because users receive only rare and binary alerts. Here we present network data assimilation (DA) as a new digital approach to epidemic management and control. Network DA uses the same data as exposure notification apps but uses it more effectively to provide frequently updated individual risk assessments to users.

Network DA is based on automated learning about individuals’ risk of exposure and infection from crowd-sourced health data and proximity data. The data are aggregated with models of disease transmission to produce statistical assessments of users’ risks. In an extensive simulation study of the COVID-19 epidemic in New York City (NYC), we show that network DA with diagnostic testing achieves epidemic control with fewer than half the deaths that occurred during NYC’s lockdown, while isolating a far smaller fraction of the population (typically only 5–10% of the population at any given time).

Implemented at scale, then, network DA has the potential to effectively control epidemics while minimizing economic and social disruption.

The Gut Microbiota: Master of Puppets Connecting the Epidemiology of Infectious, Autoimmune, and Metabolic Disease

Infectious, autoimmune, and metabolic diseases put an enormous pressure on both quality of life and the economy. For all three disease types, it is known that the quality of the gut microbiota composition is correlated to both onset and progression of disease. Hence, maintaining eubiosis and preventing gradual irreversible loss of beneficial microbes within the gut microbial ecosystem is of utmost importance. As such, the epidemiological trends of these disease types may serve as proxies for the integrity of the human gut microbiota. Here, we present incidence data covering the last decades for prototypical infectious diseases (tuberculosis and measles), autoimmune disorders (type-1 diabetes and multiple sclerosis), and the prevalence of metabolic syndrome. Our findings reveal that vaccination efforts correlate with relatively low levels of archetypal infectious disease incidence. However, autoimmune and metabolic disorders are, together with the usage of antibiotics, steeply on the rise. These findings suggest that the status of the gut microbiota is persistently deteriorating, as reflected by the proxies. As such, the epidemiological trends shown here may serve as a starting point for a mechanistic understanding of the interplay between these different disease types that can be used for future prevention and mitigation strategies like targeted stimulation and suppletion of microorganisms by means of, e.g., fermented foods, prebiotics and probiotics.

On the Verge of a Catastrophic Collapse? The Need for a Multi-Ecosystem Approach to Microbiome Studies

While the COVID-19 pandemic has led to increased focus on pathogenic microbes that cross the animal-human species barrier, calls to include non-pathogenic interactions in our perspective on public health are gaining traction in the academic community. Over generations, the diversity of the human gut microbiota is being challenged by external perturbations and reduced acquisition of symbiotic species throughout life. When such reduced diversity concerns not only the microbial species, but also the higher taxonomic levels and even the guild level, adequate compensation for possible losses may be lacking. Shifts from a high-abundance to a low-abundance state, known as a tipping point, may result in simultaneous shifts in covarying taxa and ultimately to a catastrophic collapse in which the ecosystem abruptly and possibly irreversibly shifts to an alternative state. Here, we propose that co-occurrence patterns within and between microbial communities across human, animal, soil, water, and other environmental domains should be studied in light of such critical transitions. Improved mechanistic understanding of factors that shape structure and function is needed to understand whether interventions can sustainably remodel disease-prone microbiota compositions to robust and resilient healthy microbiota. Prerequisites for a rational approach are a better understanding of the microbial interaction network, both within and inter-domain, as well as the identification of early warning signs for a catastrophic collapse, warranting a timely response for intervention. We should not forget that mutualism and pathogenicity are two sides of the same coin. Building upon the planetary health concept, we argue that microbiome research should include system level approaches to conserve ecosystem resilience. HIGHLIGHTS 1. Non-pathogenic interactions between ecosystems play a key role in maintaining health. 2. The human gut microbiome may be on the verge of a catastrophic collapse. 3. Research should identify keystone taxa and guilds that interconnect different domains. 4. We should not forget that mutualism and pathogenicity are two sides of the same coin.

Unchaining Collective Intelligence for Science, Research, and Technology Development by Blockchain-Boosted Community Participation

Since its launch just over a decade ago by the cryptocurrency Bitcoin, the distributed ledger technology (DLT) blockchain has followed a breathtaking trajectory into manifold application spaces. This study aper analyses how key factors underpinning the success of this ground-breaking “Internet of value” technology, such as staking of collateral (“skin in the game”), competitive crowdsourcing, crowdfunding, and prediction markets, can be applied to substantially innovate the legacy organization of science, research, and technology development (RTD). Here, we elaborate a highly integrative, community-based strategy where a token-based crypto-economy supports finding best possible consensus, trust, and truth by adding unconventional elements known from reputation systems, betting, secondary markets, and social networking. These tokens support the holder’s formalized reputation and are used in liquid-democracy style governance and arbitration within projects or community-driven initiatives. This participatory research model serves as a solid basis for comprehensively leveraging collective intelligence by effectively incentivizing contributions from the crowd, such as intellectual property work, validation, assessment, infrastructure, education, assessment, governance, publication, and promotion of projects. On the analogy of its current blockbusters like peer-to-peer structured decentralized finance (“DeFi”), blockchain technology can seminally enhance the efficiency of science and RTD initiatives, even permitting to fully stage operations as a chiefless decentralized autonomous organization (DAOs).

Barriers Influencing Vaccine Development Timelines, Identification, Causal Analysis, and Prioritization of Key Barriers by KOLs in General and Covid-19 Vaccine R&D

A frequently mentioned factor holding back the introduction of new vaccines on the market are their prohibitively long development timelines. These hamper their potential societal benefit and impairs the ability to quickly respond to emerging new pathogens. This is especially worrisome since new pathogens are emerging at all-time high rates of over one per year, and many age-old pathogens are still not vaccine preventable.Through interviews with 20 key-opinion-leaders (KOLs), this study identified innovation barriers that increase vaccine development timelines. These innovation barriers were visualized, and their underlying causes revealed by means of qualitative root cause analysis. Based on a survey the innovation barriers were quantitatively ranked based on their relative impact on both regular, and Covid-19 vaccine development timelines. KOLs identified 20 key innovation barriers, and mapping these barriers onto the Vaccine Innovation Cycle model revealed that all phases of vaccine development were affected. Affected by most barriers is the area between the preclinical studies and the market entry. Difficult hand-off between academia and industry, lack of funding, and lack of knowledge of pathogen targets were often mentioned as causes. Quantitative survey responses from 93 KOLs showed that general vaccine development and Covid-19 vaccine development are impacted by distinct sets of innovation barriers. For the general vaccine development three barriers were perceived of the highest impact; limited ROI for vaccines addressing disease with limited market size, limited ROI for vaccines compared to non-vaccine projects, and academia not being able to progress beyond proof of principle. Of highest impact on Covid-19 vaccine development, are lack of knowledge concerning pathogen target, high risk of upscaling unlicensed vaccines, and proof of principle not meeting late-stage requirements. In conclusion, the current study demonstrates that barriers hampering timelines in vaccine development are present across the Vaccine Innovation Cycle. Prioritizing the impact of barriers in general, and in Covid-19 vaccine development, shows clear differences that can be used to inform policies to speed up development in both war and peace time.

Policy assessments for the carbon emission flows and sustainability of Bitcoin blockchain operation in China

The growing energy consumption and associated carbon emission of Bitcoin mining could potentially undermine global sustainable efforts. By investigating carbon emission flows of Bitcoin blockchain operation in China with a simulation-based Bitcoin blockchain carbon emission model, we find that without any policy interventions, the annual energy consumption of the Bitcoin blockchain in China is expected to peak in 2024 at 296.59 Twh and generate 130.50 million metric tons of carbon emission correspondingly. Internationally, this emission output would exceed the total annualized greenhouse gas emission output of the Czech Republic and Qatar. Domestically, it ranks in the top 10 among 182 cities and 42 industrial sectors in China. In this work, we show that moving away from the current punitive carbon tax policy to a site regulation policy which induces changes in the energy consumption structure of the mining activities is more effective in limiting carbon emission of Bitcoin blockchain operation.