Many countries have secured larger quantities of COVID-19 vaccines than their populace is willing to take. This abundance and variety of vaccines created a historical moment to understand vaccine hesitancy better. Never before were more types of vaccines available for an illness and the intensity of vaccine-related public discourse is unprecedented. Yet, the heterogeneity of hesitancy by vaccine types has been neglected so far, even though factual or believed vaccine characteristics and patient attributes are known to influence acceptance. We address this problem by analysing acceptance and assessment of five vaccine types using information collected with a nationally representative survey at the end of the third wave of the COVID-19 pandemic in Hungary, where a unique portfolio of vaccines were available to the public in large quantities. Our special case enables us to quantify revealed preferences across vaccine types since one could evaluate a vaccine unacceptable and even could reject an assigned vaccine to wait for another type. We find that the source of information that respondents trust characterizes their attitudes towards vaccine types differently and leads to divergent vaccine hesitancy. Believers of conspiracy theories were significantly more likely to evaluate the mRNA vaccines (Pfizer and Moderna) unacceptable while those who follow the advice of politicians evaluate vector-based (AstraZeneca and Sputnik) or whole-virus vaccines (Sinopharm) acceptable with higher likelihood. We illustrate that the rejection of non-desired and re-selection of preferred vaccines fragments the population by the mRNA versus other type of vaccines while it generally improves the assessment of the received vaccine. These results highlight that greater variance of available vaccine types and individual free choice are desirable conditions that can widen the acceptance of vaccines in societies.

Messenger RNA (mRNA)-based vaccines are accelerating the discovery of new drugs and revolutionizing the pharmaceutical industry. However, selecting particular mRNA sequences for vaccines and therapeutics from extensive mRNA libraries is costly. Effective mRNA therapeutics require carefully designed sequences with optimized expression levels and stability. This paper proposes a novel contextual language model (LM)-based embedding method: mRNA2vec. In contrast to existing mRNA embedding approaches, our method is based on the self-supervised teacher-student learning framework of data2vec. We jointly use the 5' untranslated region (UTR) and coding sequence (CDS) region as the input sequences. We adapt our LM-based approach specifically to mRNA by 1) considering the importance of location on the mRNA sequence with probabilistic masking, 2) using Minimum Free Energy (MFE) prediction and Secondary Structure (SS) classification as additional pretext tasks. mRNA2vec demonstrates significant improvements in translation efficiency (TE) and expression level (EL) prediction tasks in UTR compared to SOTA methods such as UTR-LM. It also gives a competitive performance in mRNA stability and protein production level tasks in CDS such as CodonBERT.

mRNA-based vaccines have become a major focus in the pharmaceutical industry. The coding sequence as well as the Untranslated Regions (UTRs) of an mRNA can strongly influence translation efficiency, stability, degradation, and other factors that collectively determine a vaccine's effectiveness. However, optimizing mRNA sequences for those properties remains a complex challenge. Existing deep learning models often focus solely on coding region optimization, overlooking the UTRs. We present Helix-mRNA, a structured state-space-based and attention hybrid model to address these challenges. In addition to a first pre-training, a second pre-training stage allows us to specialise the model with high-quality data. We employ single nucleotide tokenization of mRNA sequences with codon separation, ensuring prior biological and structural information from the original mRNA sequence is not lost. Our model, Helix-mRNA, outperforms existing methods in analysing both UTRs and coding region properties. It can process sequences 6x longer than current approaches while using only 10% of the parameters of existing foundation models. Its predictive capabilities extend to all mRNA regions. We open-source the model (https://github.com/helicalAI/helical) and model weights (https://huggingface.co/helical-ai/helix-mRNA).

Messenger RNA (mRNA) vaccines are being used for COVID-19, but still suffer from the critical issue of mRNA instability and degradation, which is a major obstacle in the storage, distribution, and efficacy of the vaccine. Previous work showed that optimizing secondary structure stability lengthens mRNA half-life, which, together with optimal codons, increases protein expression. Therefore, a principled mRNA design algorithm must optimize both structural stability and codon usage to improve mRNA efficiency. However, due to synonymous codons, the mRNA design space is prohibitively large, e.g., there are $\sim\!10^{632}$ mRNAs for the SARS-CoV-2 Spike protein, which poses insurmountable challenges to previous methods. Here we provide a surprisingly simple solution to this hard problem by reducing it to a classical problem in computational linguistics, where finding the optimal mRNA is akin to finding the most likely sentence among similar sounding alternatives. Our algorithm, named LinearDesign, takes only 11 minutes for the Spike protein, and can jointly optimize stability and codon usage. Experimentally, without chemical modification, our designs substantially improve mRNA half-life and protein expression in vitro, and dramatically increase antibody response by up to 23$\times$ in vivo, compared to the codon-optimized benchmark. Our work enables the exploration of highly stable and efficient designs that are previously unreachable and is a timely tool not only for vaccines but also for mRNA medicine encoding all therapeutic proteins (e.g., monoclonal antibodies and anti-cancer drugs).

Over the past 150 years, vaccines have revolutionized the relationship between people and disease. During the COVID-19 pandemic, technologies such as mRNA vaccines have received attention due to their novelty and successes. However, more traditional vaccine development platforms have also yielded important tools in the worldwide fight against the SARS-CoV-2 virus. A variety of approaches have been used to develop COVID-19 vaccines that are now authorized for use in countries around the world. In this review, we highlight strategies that focus on the viral capsid and outwards, rather than on the nucleic acids inside. These approaches fall into two broad categories: whole-virus vaccines and subunit vaccines. Whole-virus vaccines use the virus itself, either in an inactivated or attenuated state. Subunit vaccines contain instead an isolated, immunogenic component of the virus. Here, we highlight vaccine candidates that apply these approaches against SARS-CoV-2 in different ways. In a companion manuscript, we review the more recent and novel development of nucleic-acid based vaccine technologies. We further consider the role that these COVID-19 vaccine development programs have played in prophylaxis at the global scale. Well-established vaccine technologies have proved especially important to making vaccines accessible in low- and middle-income countries. Vaccine development programs that use established platforms have been undertaken in a much wider range of countries than those using nucleic-acid-based technologies, which have been led by wealthy Western countries. Therefore, these vaccine platforms, though less novel from a biotechnological standpoint, have proven to be extremely important to the management of SARS-CoV-2.

mRNA therapy is gaining worldwide attention as an emerging therapeutic approach. The widespread use of mRNA vaccines during the COVID-19 outbreak has demonstrated the potential of mRNA therapy. As mRNA-based drugs have expanded and their indications have broadened, more patents for mRNA innovations have emerged. The global patent landscape for mRNA therapy has not yet been analyzed, indicating a research gap in need of filling, from new technology to productization. This study uses social network analysis with the patent quality assessment to investigate the temporal trends, citation relationship, and significant litigation for 16,101 mRNA therapy patents and summarizes the hot topics and potential future directions for this industry. The information obtained in this study not only may be utilized as a tool of knowledge for researchers in a comprehensive and integrated way but can also provide inspiration for efficient production methods for mRNA drugs. This study shows that infectious diseases and cancer are currently the primary applications for mRNA drugs. Emerging patent activity and lawsuits in this field are demonstrating that delivery technology remains one of the key challenges in the field and that drug-targeting research in combination with vector technology will be one of the major directions for the industry going forward. With significant funding, new organizations have developed novel delivery technologies in an attempt to break into the patent thicket established by companies such as Arbutus. The global mRNA therapeutic landscape is undergoing a multifaceted development pattern, and the monopoly of giant companies is being challenged.

The COVID-19 outbreak rapidly became a pandemic in the first quarter of 2020, posing an unprecedented threat and challenge to healthcare systems and the public. Governments in nearly every country focused on immunization programs for the general population using mRNA vaccines against this disease, marking the first large-scale use of this technology. Previously overlooked research papers on mRNA vaccine preparation or administration gained prominence. The impact was documented bibliographically through a surge in citations these papers received. These reports exemplify the Sleeping Beauty bibliometric phenomenon, while the articles that triggered this awakening act as the Sweet Prince, leading to the resurgence of the previous papers' bibliometric impact. Here, a backward reference search was performed in the Scopus bibliographic database to identify Sleeping Beauties by applying the Beauty Coefficient metric. A total of 915 original research articles were published in 2020, citing 21,979 referenced papers, including 1,181 focused on mRNA vaccines, with 671 of these being original research reports. By setting a threshold of at least 30 citations received before 2020, 272 papers published between 2005 and 2022 were examined. The finding that nearly half of the papers included were published in scientific journals between 2020 and 2022 is explained by the fact that these works received a significant number of citations as preprints or prepublications. We found that 28 papers from this bibliographic portfolio exhibited a Beauty Coefficient following the Sleeping Beauty bibliometric phenomenon. Our findings reveal that disruptive technological innovations may be built upon previously neglected reports that experienced sharp citation increases, driven by their crucial applicability to worldwide distresses.

Messenger RNA (mRNA) vaccines and therapeutics are emerging as powerful tools against a variety of diseases, including infectious diseases and cancer. The design of mRNA molecules, particularly the untranslated region (UTR) and coding sequence (CDS) is crucial for optimizing translation efficiency and stability. Current design approaches generally focus solely on either the 5' UTR or the CDS, which limits their ability to comprehensively enhance translation efficiency and stability. To address this, we introduce LinearDesign2, an algorithm that enables the co-design of the 5' UTR and CDS. This integrated approach optimizes translation initiation efficiency (TIE), codon adaptation index (CAI), and minimum free energy (MFE) simultaneously. Comparative analyses reveal that sequences designed by LinearDesign2 exhibit significantly higher TIE than those designed by LinearDesign, with only a slight increase in MFE. Further, we validate the accuracy of the computational TIE metric using large-scale parallel translation experimental data. This study highlights the importance of a joint design strategy for the 5' UTR and CDS in optimizing mRNA performance, paving the way for more efficient mRNA vaccines and therapeutics.

Vaccination against COVID-19 with the recently approved mRNA vaccines BNT162b2 (BioNTech/Pfizer) and mRNA-1273 (Moderna) is currently underway in a large number of countries. However, high incidence rates and rapidly spreading SARS-CoV-2 variants are concerning. In combination with acute supply deficits in Europe in early 2021, the question arises of whether stretching the vaccine, for instance by delaying the second dose, can make a significant contribution to preventing deaths, despite associated risks such as lower vaccine efficacy, the potential emergence of escape mutants, enhancement, waning immunity, reduced social acceptance of off-label vaccination, and liability shifts. A quantitative epidemiological assessment of risks and benefits of non-standard vaccination protocols remains elusive. To clarify the situation and to provide a quantitative epidemiological foundation we develop a stochastic epidemiological model that integrates specific vaccine rollout protocols into a risk-group structured infectious disease dynamical model. Using the situation and conditions in Germany as a reference system, we show that delaying the second vaccine dose is expected to prevent deaths in the four to five digit range, should the incidence resurge. We show that this considerable public health benefit relies on the fact that both mRNA vaccines provide substantial protection against severe COVID-19 and death beginning 12 to 14 days after the first dose. The benefits of protocol change are attenuated should vaccine compliance decrease substantially. To quantify the impact of protocol change on vaccination adherence we performed a large-scale online survey. We find that, in Germany, changing vaccination protocols may lead to small reductions in vaccination intention. In sum, we therefore expect the benefits of a strategy change to remain substantial and stable.

This study examines the roles of public and private sector actors in the development of mRNA vaccines, a breakthrough innovation in modern medicine. Using a dataset of 151 core patent families and 2,416 antecedent (cited) patents, we analyze the structure and dynamics of the mRNA vaccine knowledge network through network theory. Our findings highlight the central role of biotechnology firms, such as Moderna and BioNTech, alongside the crucial contributions of universities and public research organizations (PROs) in providing foundational knowledge.We develop a novel credit allocation framework, showing that universities, PROs, government and research centers account for at least 27% of the external technological knowledge base behind mRNA vaccine breakthroughs - representing a minimum threshold of their overall contribution. Our study offers new insights into pharmaceutical and biotechnology innovation dynamics, emphasizing how Moderna and BioNTech's mRNA technologies have benefited from academic institutions, with notable differences in their institutional knowledge sources.