Artificial intelligence and biological misuse: Differentiating risks of language models and biological design tools

As advancements in artificial intelligence propel progress in the life sciences, they may also enable the weaponisation and misuse of biological agents. This article differentiates two classes of AI tools that pose such biosecurity risks: large language models (LLMs) and biological design tools (BDTs). LLMs, such as GPT-4, are already able to provide dual-use information that could have enabled historical biological weapons efforts to succeed. As LLMs are turned into lab assistants and autonomous science tools, this will further increase their ability to support research. Thus, LLMs will in particular lower barriers to biological misuse. In contrast, BDTs will expand the capabilities of sophisticated actors. Concretely, BDTs may enable the creation of pandemic pathogens substantially worse than anything seen to date and could enable forms of more predictable and targeted biological weapons. In combination, LLMs and BDTs could raise the ceiling of harm from biological agents and could make them broadly accessible. The differing risk profiles of LLMs and BDTs have important implications for risk mitigation. LLM risks require urgent action and might be effectively mitigated by controlling access to dangerous capabilities. Mandatory pre-release evaluations could be critical to ensure that developers eliminate dangerous capabilities. Science-specific AI tools demand differentiated strategies to allow access to legitimate users while preventing misuse. Meanwhile, risks from BDTs are less defined and require monitoring by developers and policymakers. Key to reducing these risks will be enhanced screening of gene synthesis, interventions to deter biological misuse by sophisticated actors, and exploration of specific controls of BDTs.Comment: 15 pages, 1 figur

Promoting versatile vaccine development for emerging pandemics

The ongoing COVID-19 pandemic has demonstrated the importance of rapid and versatile development of emergency medical countermeasures such as vaccines. We discuss the role of platform vaccines and prototype pathogen research in modern vaccine development, and outline how previous pathogen-specific funding approaches can be improved to adequately promote vaccine R&D for emerging pandemics. We present a more comprehensive approach to financing vaccine R&D, which maximises biomedical pandemic preparedness by promoting flexible vaccine platforms and translatable research into prototype pathogens. As the numerous platform-based SARS-CoV-2 vaccines show, funders can accelerate pandemic vaccine development by proactively investing in versatile platform technologies. For certain emerging infectious diseases, where vaccine research can translate to other related pathogens with pandemic potential, investment decisions should reflect the full social value of increasing overall preparedness, rather than just the value of bringing a vaccine to market for individual pathogens

Rapid proliferation of pandemic research: implications for dual-use risks

The COVID-19 pandemic has demonstrated the world’s vulnerability to biological catastrophe and elicited unprecedented scientific efforts. Some of this work and its derivatives, however, present dual-use risks (i.e., potential harm from misapplication of beneficial research) that have largely gone unaddressed. For instance, gain-of-function studies and reverse genetics protocols may facilitate the engineering of concerning SARS-CoV-2 variants and other pathogens. The risk of accidental or deliberate release of dangerous pathogens may be increased by large-scale collection and characterization of zoonotic viruses undertaken in an effort to understand what enables animal-to-human transmission. These concerns are exacerbated by the rise of preprint publishing that circumvents a late-stage opportunity for dual-use oversight. To prevent the next global health emergency, we must avoid inadvertently increasing the threat of future biological events. This requires a nuanced and proactive approach to dual-use evaluation throughout the research life cycle, including the conception, funding, conduct, and dissemination of research

Mask wearing in community settings reduces SARS-CoV-2 transmission

The effectiveness of mask wearing at controlling severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission has been unclear. While masks are known to substantially reduce disease transmission in healthcare settings [D. K. Chu et al., Lancet 395, 1973–1987 (2020); J. Howard et al., Proc. Natl. Acad. Sci. U.S.A. 118, e2014564118 (2021); Y. Cheng et al., Science eabg6296 (2021)], studies in community settings report inconsistent results [H. M. Ollila et al., medRxiv (2020); J. Brainard et al., Eurosurveillance 25, 2000725 (2020); T. Jefferson et al., Cochrane Database Syst. Rev. 11, CD006207 (2020)]. Most such studies focus on how masks impact transmission, by analyzing how effective government mask mandates are. However, we find that widespread voluntary mask wearing, and other data limitations, make mandate effectiveness a poor proxy for mask-wearing effectiveness. We directly analyze the effect of mask wearing on SARS-CoV-2 transmission, drawing on several datasets covering 92 regions on six continents, including the largest survey of wearing behavior ([Formula: see text] 20 million) [F. Kreuter et al., https://gisumd.github.io/COVID-19-API-Documentation (2020)]. Using a Bayesian hierarchical model, we estimate the effect of mask wearing on transmission, by linking reported wearing levels to reported cases in each region, while adjusting for mobility and nonpharmaceutical interventions (NPIs), such as bans on large gatherings. Our estimates imply that the mean observed level of mask wearing corresponds to a 19% decrease in the reproduction number R. We also assess the robustness of our results in 60 tests spanning 20 sensitivity analyses. In light of these results, policy makers can effectively reduce transmission by intervening to increase mask wearing

Improved understanding of biorisk for research involving microbial modification using annotated sequences of concern

Regulation of research on microbes that cause disease in humans has historically been focused on taxonomic lists of ‘bad bugs’. However, given our increased knowledge of these pathogens through inexpensive genome sequencing, 5 decades of research in microbial pathogenesis, and the burgeoning capacity of synthetic biologists, the limitations of this approach are apparent. With heightened scientific and public attention focused on biosafety and biosecurity, and an ongoing review by US authorities of dual-use research oversight, this article proposes the incorporation of sequences of concern (SoCs) into the biorisk management regime governing genetic engineering of pathogens. SoCs enable pathogenesis in all microbes infecting hosts that are ‘of concern’ to human civilization. Here we review the functions of SoCs (FunSoCs) and discuss how they might bring clarity to potentially problematic research outcomes involving infectious agents. We believe that annotation of SoCs with FunSoCs has the potential to improve the likelihood that dual use research of concern is recognized by both scientists and regulators before it occurs

Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe.

Funder: European and Developing Countries Clinical Trials Partnership (EDCTP); doi: https://doi.org/10.13039/501100001713Funder: MRC Centre for Global Infectious Disease Analysis (MR/R015600/1), jointly funded by the U.K. Medical Research Council (MRC) and the U.K. Foreign, Commonwealth and Development Office (FCDO), under the MRC/FCDO Concordat agreement. Community Jameel. The UK Research and Innovation (MR/V038109/1), the Academy of Medical Sciences Springboard Award (SBF004/1080), The MRC (MR/R015600/1), The BMGF (OPP1197730), Imperial College Healthcare NHS Trust- BRC Funding (RDA02), The Novo Nordisk Young Investigator Award (NNF20OC0059309) and The NIHR Health Protection Research Unit in Modelling Methodology. S. Bhatt thanks Microsoft AI for Health and Amazon AWS for computational credits.Funder: EA FundsFunder: University of Oxford (Oxford University); doi: https://doi.org/10.13039/501100000769Funder: DeepMindFunder: OpenPhilanthropyFunder: UKRI Centre for Doctoral Training in Interactive Artificial Intelligence (EP/S022937/1)Funder: Augustinus Fonden (Augustinus Foundation); doi: https://doi.org/10.13039/501100004954Funder: Knud Højgaards Fond (Knud Højgaard Fund); doi: https://doi.org/10.13039/501100009938Funder: Kai Lange og Gunhild Kai Langes Fond (Kai Lange and Gunhild Kai Lange Foundation); doi: https://doi.org/10.13039/501100008206Funder: Aage og Johanne Louis-Hansens Fond (Aage and Johanne Louis-Hansen Foundation); doi: https://doi.org/10.13039/501100010344Funder: William Demant FoundationFunder: Boehringer Ingelheim Fonds (Stiftung für medizinische Grundlagenforschung); doi: https://doi.org/10.13039/501100001645Funder: Imperial College COVID-19 Research FundFunder: Cancer Research UK (CRUK); doi: https://doi.org/10.13039/501100000289European governments use non-pharmaceutical interventions (NPIs) to control resurging waves of COVID-19. However, they only have outdated estimates for how effective individual NPIs were in the first wave. We estimate the effectiveness of 17 NPIs in Europe's second wave from subnational case and death data by introducing a flexible hierarchical Bayesian transmission model and collecting the largest dataset of NPI implementation dates across Europe. Business closures, educational institution closures, and gathering bans reduced transmission, but reduced it less than they did in the first wave. This difference is likely due to organisational safety measures and individual protective behaviours-such as distancing-which made various areas of public life safer and thereby reduced the effect of closing them. Specifically, we find smaller effects for closing educational institutions, suggesting that stringent safety measures made schools safer compared to the first wave. Second-wave estimates outperform previous estimates at predicting transmission in Europe's third wave

Biosecurity in an age of open science.

The risk of accidental or deliberate misuse of biological research is increasing as biotechnology advances. As open science becomes widespread, we must consider its impact on those risks and develop solutions that ensure security while facilitating scientific progress. Here, we examine the interaction between open science practices and biosecurity and biosafety to identify risks and opportunities for risk mitigation. Increasing the availability of computational tools, datasets, and protocols could increase risks from research with misuse potential. For instance, in the context of viral engineering, open code, data, and materials may increase the risk of release of enhanced pathogens. For this dangerous subset of research, both open science and biosecurity goals may be achieved by using access-controlled repositories or application programming interfaces. While preprints accelerate dissemination of findings, their increased use could challenge strategies for risk mitigation at the publication stage. This highlights the importance of oversight earlier in the research lifecycle. Preregistration of research, a practice promoted by the open science community, provides an opportunity for achieving biosecurity risk assessment at the conception of research. Open science and biosecurity experts have an important role to play in enabling responsible research with maximal societal benefit

Insidious Insights: Implications of viral vector engineering for pathogen enhancement

AbstractOptimizing viral vectors and their properties will be important for improving the effectiveness and safety of clinical gene therapy. However, such research may generate dual-use insights relevant to the enhancement of pandemic pathogens. In particular, reliable and generalizable methods of immune evasion could increase viral fitness sufficient to cause a new pandemic. High potential for misuse is associated with (1) the development of universal genetic elements for immune modulation, (2) specific insights on capsid engineering for antibody evasion applicable to viruses with pandemic potential, and (3) the development of computational methods to inform capsid engineering. These risks may be mitigated by prioritizing non-viral delivery systems, pharmacological immune modulation methods, non-genetic vector surface modifications, and engineering methods specific to AAV and other viruses incapable of unassisted human-to-human transmission. We recommend that computational vector engineering and the publication of associated code and data be limited to AAV until a technical solution for preventing malicious access to viral engineering tools has been established.</jats:p