Quality by design modelling to support rapid RNA vaccine production against emerging infectious diseases.
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ABSTRACT:
SUBMITTER: van de Berg D
PROVIDER: S-EPMC8085199 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Infectious diseases are emerging globally at an unprecedented rate while global food demand is projected to increase sharply by 2100. Here, we synthesize the pathways by which projected agricultural expansion and intensification will influence human infectious diseases and how human infectious diseases might likewise affect food production and distribution. Feeding 11 billion people will require substantial increases in crop and animal production that will expand agricultural use of antibiotics, water, pesticides and fertilizer, and contact rates between humans and both wild and domestic animals, all with consequences for the emergence and spread of infectious agents. Indeed, our synthesis of the literature suggests that, since 1940, agricultural drivers were associated with >25% of all - and >50% of zoonotic - infectious diseases that emerged in humans, proportions that will likely increase as agriculture expands and intensifies. We identify agricultural and disease management and policy actions, and additional research, needed to address the public health challenge posed by feeding 11 billion people.
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Project description:Growing interest in camelids presents a unique challenge to scientists and veterinarians engaged in diagnosing infectious diseases of this species. It is estimated that 65 % of fatalities in Old World camels (OWC, i.e., Camelus dromedarius and C. bactrianus) and 50 % in New World camelids/South American camelids (NWC/SAC, i.e., the domestic alpaca (Vicugna pacos) and llama (Lama glama)) are caused by infectious diseases. Factors that contribute to disease emergence in camelids involve climate change and increased demand for camel products resulting in the intensification of production and expanding camel contacts with other animal species and humans. In this chapter, the most important emerging diseases of camelids are described and discussed. The most notable emerging viral infections in OWC include camelpox, Rift Valley fever (RVF), peste des petits ruminants (PPR), and Middle East respiratory syndrome coronavirus (MERS-CoV) infection. Brucellosis, Johne’s disease (JD), and dermatophilosis are the emerging bacterial diseases in OWC. Emerging diseases of NWC include infections with bovine viral diarrhea virus (BVDV), bluetongue (BT), and coronavirus. Parasitic emerging infections in NWCs include the small liver fluke (Dicrocoelium dendriticum) and meningeal worm (Parelaphostrongylus tenuis).
| S-EPMC7121465 | biostudies-literature
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| S-EPMC2600301 | biostudies-literature
Project description:DNA vaccines are considered as a third-generation vaccination approach in which antigenic materials are encoded as DNA plasmids for direct in vivo production to elicit adaptive immunity. As compared to other platforms, DNA vaccination is considered to have a strong safety profile, as DNA plasmids neither replicate nor elicit vector-directed immune responses in hosts. While earlier work found the immune responses induced by DNA vaccines to be sub-optimal in larger mammals and humans, recent developments in key synthetic DNA and electroporation delivery technologies have now allowed DNA vaccines to elicit significantly more potent and consistent responses in several clinical studies. This paper will review findings from the recent clinical and preclinical studies on DNA vaccines targeting emerging infectious diseases (EID) including COVID-19 caused by the SARS-CoV-2 virus, and the technological advancements pivotal to the improved responses-including the use of the advanced delivery technology, DNA-encoded cytokine/mucosal adjuvants, and innovative concepts in immunogen design. With continuous advancement over the past three decades, the DNA approach is now poised to develop vaccines against COVID-19, as well as other EIDs.
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Project description:BACKGROUND:With the increase in unprecedented and unpredictable disease outbreaks due to human-driven environmental changes in recent years, we need new analytical tools to map and predict the spatial distribution of emerging infectious diseases and identify the biogeographic drivers underpinning their emergence. The aim of the study was to identify and compare the local and global biogeographic predictors such as landscape and climate that determine the spatial structure of leptospirosis and Buruli Ulcer (BU). METHODS:We obtained 232 hospital-confirmed leptospirosis (2007-2017) cases and 236 BU cases (1969-2017) in French Guiana. We performed non-spatial and spatial Bayesian regression modeling with landscape and climate predictor variables to characterize the spatial structure and the environmental drivers influencing the distribution of the two diseases. RESULTS:Our results show that the distribution of both diseases is spatially dependent on environmental predictors such as elevation, topological wetness index, proximity to cropland and increasing minimum temperature at the month of potential infection. However, the spatial structure of the two diseases caused by bacterial pathogens occupying similar aquatic niche was different. Leptospirosis was widely distributed across the territory while BU was restricted to the coastal riverbeds. CONCLUSIONS:Our study shows that a biogeographic approach is an effective tool to identify, compare and predict the geographic distribution of emerging diseases at an ecological scale which are spatially dependent to environmental factors such as topography, land cover and climate.
| S-EPMC6833193 | biostudies-literature
Project description:Whether naturally occurring or man-made, biological threats pose a severe risk in an increasingly globalized world. The dual-use nature of biological research, with its most recent advances in biotechnology (‘synthetic biology’, gene editing, nanotechnologies etc.) and the rapid diffusion of knowledge, raise proliferation concerns of biological weapons by non-state actors. Thus, there is an urgent need to develop measures intended to enhance diagnostic, prophylactic and therapeutic capabilities and capacities to improve the ability of society to combat infectious diseases outbreaks, as well as to alleviate the effects of bioterrorism attacks. We present here two examples of biotechnology usage for biodefence purposes: (i) plants as biofactories for the rapid production of improved biopharmaceuticals (‘Plant Molecular Farming’), and (ii) plant sequences as immune-modulating agents to enhance the efficacy of genetic vaccines. These platforms represent two promising (and complementary) approaches for the rapid and low-cost production of countermeasures (diagnostics and vaccine candidates) against emerging, re-emerging and bioterrorism-related infections.
| S-EPMC7123727 | biostudies-literature
Project description:Emerging infectious diseases (EIDs) are a significant burden on global economies and public health. Their emergence is thought to be driven largely by socio-economic, environmental and ecological factors, but no comparative study has explicitly analysed these linkages to understand global temporal and spatial patterns of EIDs. Here we analyse a database of 335 EID 'events' (origins of EIDs) between 1940 and 2004, and demonstrate non-random global patterns. EID events have risen significantly over time after controlling for reporting bias, with their peak incidence (in the 1980s) concomitant with the HIV pandemic. EID events are dominated by zoonoses (60.3% of EIDs): the majority of these (71.8%) originate in wildlife (for example, severe acute respiratory virus, Ebola virus), and are increasing significantly over time. We find that 54.3% of EID events are caused by bacteria or rickettsia, reflecting a large number of drug-resistant microbes in our database. Our results confirm that EID origins are significantly correlated with socio-economic, environmental and ecological factors, and provide a basis for identifying regions where new EIDs are most likely to originate (emerging disease 'hotspots'). They also reveal a substantial risk of wildlife zoonotic and vector-borne EIDs originating at lower latitudes where reporting effort is low. We conclude that global resources to counter disease emergence are poorly allocated, with the majority of the scientific and surveillance effort focused on countries from where the next important EID is least likely to originate.
| S-EPMC5960580 | biostudies-literature
Project description:Emerging infectious diseases, such as COVID-19, continue to pose significant threats to human beings and their surroundings. In addition, biological warfare, bioterrorism, biological accidents, and harmful consequences arising from dual-use biotechnology also pose a challenge for global biosecurity. Improving the early surveillance capabilities is necessary for building a common biosecurity shield for the global community of health for all. Furthermore, surveillance could provide early warning and situational awareness of biosecurity risks. However, current surveillance systems face enormous challenges, including technical shortages, fragmented management, and limited international cooperation. Detecting emerging biological risks caused by unknown or novel pathogens is of particular concern. Surveillance systems must be enhanced to effectively mitigate biosecurity risks. Thus, a global strategy of meaningful cooperation based on efficient integration of surveillance at all levels, including interdisciplinary integration of techniques and interdepartmental integration for effective management, is urgently needed. In this paper, we review the biosecurity risks by analyzing potential factors at all levels globally. In addition to describing biosecurity risks and their impact on global security, we also focus on analyzing the challenges to traditional surveillance and propose suggestions on how to integrate current technologies and resources to conduct effective global surveillance.
| S-EPMC8918423 | biostudies-literature
Project description:Vaccine allocation strategies become crucial during vaccine shortages, especially in the face of potential outbreaks of new infectious diseases, as witnessed during the COVID-19 pandemic. To address this, a specialized compartmental model is created, which simulates an emerging infectious disease similar to COVID-19. This model divides the population into different age groups and is used to compare various vaccine prioritisation approaches, aiming to minimize the total number of fatalities. The model is an improvement upon previous ones as it incorporates essential behavioural factors and is adapted to account for the protective effects of vaccination against both disease infection and transmission. It takes into account human behaviors such as mask-wearing and social distancing by utilizing specific parameters related to self-protection, awareness levels, and the frequency of daily person-to-person interactions within each age group. Furthermore, a novel method for dynamic vaccine prioritisation was introduced in this study. This approach is model-independent and relies on the dynamic R number. It is the first time such a method has been developed, offering a decision-making approach that is not tied to any specific model. This innovation provides a flexible and adaptable strategy for determining vaccine priorities based on real-time data and the current state of the outbreak. Our findings reveal crucial insights into vaccine allocation strategies. When the daily rollout rates are fast (0.75% or higher) and children are eligible for vaccination, prioritising groups with high daily person-to-person interactions can lead to substantial reductions in total fatalities (up to approximately 40% lower). On the other hand, if rollout rates are slower and overall vaccination coverage is high, focusing on vaccinating elders emerges as the most effective strategy, resulting in up to approximately 10% fewer fatalities. However, the scenario changes significantly when children are not eligible for vaccination, as they constitute a highly interactive population group. In this case, the differences between priority strategies become smaller. With fast daily rollout rates, prioritisation based on interactions achieves only a 7% reduction in total fatalities, while a slower rollout with vaccination of elders first leads to an approximately 11% reduction in fatalities compared to the scenario where children are eligible for vaccination. The impact of behavioural parameters is equally critical. When the self-protection levels exercised by the population are low, it significantly affects the optimal vaccine prioritisation strategy to be followed, making it essential to consider behavioural factors in decision-making.
| S-EPMC10477745 | biostudies-literature
Project description:BackgroundInfectious diseases such as SARS and H1N1 can significantly impact people's lives and cause severe social and economic damages. Recent outbreaks have stressed the urgency of effective research on the dynamics of infectious disease spread. However, it is difficult to predict when and where outbreaks may emerge and how infectious diseases spread because many factors affect their transmission, and some of them may be unknown.MethodsOne feasible means to promptly detect an outbreak and track the progress of disease spread is to implement surveillance systems in regional or national health and medical centres. The accumulated surveillance data, including temporal, spatial, clinical, and demographic information can provide valuable information that can be exploited to better understand and model the dynamics of infectious disease spread. The aim of this work is to develop and empirically evaluate a stochastic model that allows the investigation of transmission patterns of infectious diseases in heterogeneous populations.ResultsWe test the proposed model on simulation data and apply it to the surveillance data from the 2009 H1N1 pandemic in Hong Kong. In the simulation experiment, our model achieves high accuracy in parameter estimation (less than 10.0 % mean absolute percentage error). In terms of the forward prediction of case incidence, the mean absolute percentage errors are 17.3 % for the simulation experiment and 20.0 % for the experiment on the real surveillance data.ConclusionWe propose a stochastic model to study the dynamics of infectious disease spread in heterogeneous populations from temporal-spatial surveillance data. The proposed model is evaluated using both simulated data and the real data from the 2009 H1N1 epidemic in Hong Kong and achieves acceptable prediction accuracy. We believe that our model can provide valuable insights for public health authorities to predict the effect of disease spread and analyse its underlying factors and to guide new control efforts.
| S-EPMC5178099 | biostudies-literature