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.

Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning.

In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the National Genomics Data Center repository, separating the genome of different virus strains from the Coronavirus family with 98.73% accuracy. The network's behavior is then analyzed, to discover sequences used by the model to identify SARS-CoV-2, ultimately uncovering sequences exclusive to it. The discovered sequences are validated on samples from the National Center for Biotechnology Information and Global Initiative on Sharing All Influenza Data repositories, and are proven to be able to separate SARS-CoV-2 from different virus strains with near-perfect accuracy. Next, one of the sequences is selected to generate a primer set, and tested against other state-of-the-art primer sets, obtaining competitive results. Finally, the primer is synthesized and tested on patient samples (n = 6 previously tested positive), delivering a sensitivity similar to routine diagnostic methods, and 100% specificity. The proposed methodology has a substantial added value over existing methods, as it is able to both automatically identify promising primer sets for a virus from a limited amount of data, and deliver effective results in a minimal amount of time. Considering the possibility of future pandemics, these characteristics are invaluable to promptly create specific detection methods for diagnostics.