Project description:The timelines for developing vaccines against infectious diseases are lengthy, and often vaccines that reach the stage of large phase 3 field trials fail to provide the desired level of protective efficacy. The application of controlled human challenge models of infection and disease at the appropriate stages of development could accelerate development of candidate vaccines and, in fact, has done so successfully in some limited cases. Human challenge models could potentially be used to gather critical information on pathogenesis, inform strain selection for vaccines, explore cross-protective immunity, identify immune correlates of protection and mechanisms of protection induced by infection or evoked by candidate vaccines, guide decisions on appropriate trial endpoints, and evaluate vaccine efficacy. We prepared this report to motivate fellow scientists to exploit the potential capacity of controlled human challenge experiments to advance vaccine development. In this review, we considered available challenge models for 17 infectious diseases in the context of the public health importance of each disease, the diversity and pathogenesis of the causative organisms, the vaccine candidates under development, and each model's capacity to evaluate them and identify correlates of protective immunity. Our broad assessment indicated that human challenge models have not yet reached their full potential to support the development of vaccines against infectious diseases. On the basis of our review, however, we believe that describing an ideal challenge model is possible, as is further developing existing and future challenge models.
Project description:Sampling across tree space is one of the major challenges in Bayesian phylogenetic inference using Markov chain Monte Carlo (MCMC) algorithms. Standard MCMC tree moves consider small random perturbations of the topology, and select from candidate trees at random or based on the distance between the old and new topologies. MCMC algorithms using such moves tend to get trapped in tree space, making them slow in finding the globally most probable trees (known as "convergence") and in estimating the correct proportions of the different types of them (known as "mixing"). Here, we introduce a new class of moves, which propose trees based on their parsimony scores. The proposal distribution derived from the parsimony scores is a quickly computable albeit rough approximation of the conditional posterior distribution over candidate trees. We demonstrate with simulations that parsimony-guided moves correctly sample the uniform distribution of topologies from the prior. We then evaluate their performance against standard moves using six challenging empirical data sets, for which we were able to obtain accurate reference estimates of the posterior using long MCMC runs, a mix of topology proposals, and Metropolis coupling. On these data sets, ranging in size from 357 to 934 taxa and from 1740 to 5681 sites, we find that single chains using parsimony-guided moves usually converge an order of magnitude faster than chains using standard moves. They also exhibit better mixing, that is, they cover the most probable trees more quickly. Our results show that tree moves based on quick and dirty estimates of the posterior probability can significantly outperform standard moves. Future research will have to show to what extent the performance of such moves can be improved further by finding better ways of approximating the posterior probability, taking the trade-off between accuracy and speed into account. [Bayesian phylogenetic inference; MCMC; parsimony; tree proposal.].
Project description:Rapid vaccine development in response to an outbreak of a new emerging infectious disease (EID) is a goal targeted by public health agencies worldwide. This goal becomes more complicated when there are no standardized sets of viral and immunological assays, no accepted and well-characterized samples, standards or reagents, and no approved diagnostic tests for the EID pathogen. The diagnosis of infections is of critical importance to public health, but also in vaccine development in order to track incident infections during clinical trials, to differentiate natural infection responses from those that are vaccine-related and, if called for by study design, to exclude subjects with prior exposure from vaccine efficacy trials. Here we review emerging infectious disease biological standards development, vaccine clinical assay development and trial execution with the recent experiences of MERS-CoV and Zika virus as examples. There is great need to establish, in advance, the standardized reagents, sample panels, controls, and assays to support the rapid advancement of vaccine development efforts in response to EID outbreaks.
Project description:Vaccines are critical tools for maintaining global health. Traditional vaccine technologies have been used across a wide range of bacterial and viral pathogens, yet there are a number of examples where they have not been successful, such as for persistent infections, rapidly evolving pathogens with high sequence variability, complex viral antigens, and emerging pathogens. Novel technologies such as nucleic acid and viral vector vaccines offer the potential to revolutionize vaccine development as they are well-suited to address existing technology limitations. In this review, we discuss the current state of RNA vaccines, recombinant adenovirus vector-based vaccines, and advances from biomaterials and engineering that address these important public health challenges.
Project description:Children with sickle cell disease (SCD) suffer life-threatening transient aplastic crisis (TAC) when infected with parvovirus B19. In utero, infection of healthy fetuses may result in anemia, hydrops, and death. Unfortunately, although promising vaccine candidates exist, no product has yet been licensed. One barrier to vaccine development has been the lack of a cost-effective, standardized parvovirus B19 neutralization assay. To fill this void, we evaluated the unique region of VP1 (VP1u), which contains prominent targets of neutralizing antibodies. We discovered an antigenic cross-reactivity between VP1 and VP2 that, at first, thwarted the development of a surrogate neutralization assay. We overcame the cross-reactivity by designing a mutated VP1u (VP1uAT) fragment. A new VP1uAT ELISA yielded results well correlated with neutralization (Spearman's correlation coefficient = 0.581; p = 0.001), superior to results from a standard clinical diagnostic ELISA or an ELISA with virus-like particles. Virus-specific antibodies from children with TAC, measured by the VP1uAT and neutralization assays, but not other assays, gradually increased from days 0 to 120 post-hospitalization. We propose that this novel and technically simple VP1uAT ELISA might now serve as a surrogate for the neutralization assay to support rapid development of a parvovirus B19 vaccine.
Project description:The global COVID-19 (coronavirus disease 2019) pandemic, which was caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has resulted in a significant loss of human life around the world. The SARS-CoV-2 has caused significant problems to medical systems and healthcare facilities due to its unexpected global expansion. Despite all of the efforts, developing effective treatments, diagnostic techniques, and vaccinations for this unique virus is a top priority and takes a long time. However, the foremost step in vaccine development is to identify possible antigens for a vaccine. The traditional method was time taking, but after the breakthrough technology of reverse vaccinology (RV) was introduced in 2000, it drastically lowers the time needed to detect antigens ranging from 5-15 years to 1-2 years. The different RV tools work based on machine learning (ML) and artificial intelligence (AI). Models based on AI and ML have shown promising solutions in accelerating the discovery and optimization of new antivirals or effective vaccine candidates. In the present scenario, AI has been extensively used for drug and vaccine research against SARS-COV-2 therapy discovery. This is more useful for the identification of potential existing drugs with inhibitory human coronavirus by using different datasets. The AI tools and computational approaches have led to speedy research and the development of a vaccine to fight against the coronavirus. Therefore, this paper suggests the role of artificial intelligence in the field of clinical trials of vaccines and clinical practices using different tools.
Project description:Glycosylation is a key quality attribute that must be closely monitored for protein therapeutics. Established assays such as HILIC-Fld of released glycans and LC-MS of glycopeptides work well for glycoproteins with a few glycosylation sites but are less amenable for those with multiple glycosylation sites, resulting in complex datasets that are time consuming to generate and difficult to analyze. As part of efforts to improve preparedness for future pandemics, researchers are currently assessing where time can be saved in the vaccine development and production process. In this context, we evaluated if neutral and acidic monosaccharides analysis via HPAEC-PAD could be used as a rapid and robust alternative to LC-MS and HILIC-Fld for monitoring glycosylation between protein production batches. Using glycoengineered spike proteins we show that the HPAEC-PAD monosaccharide assays could quickly and reproducibly detect both major and minor glycosylation differences between batches. Moreover, the monosaccharide results aligned well with those obtained by HILIC-Fld and LC-MS.
Project description:Adenoviral vector has been employed as one of the most efficient means against infectious diseases and cancer. It can be genetically modified and armed with foreign antigens to elicit specific antibody responses and T cell responses in hosts as well as engineered to induce apoptosis in cancer cells. The chimpanzee adenovirus-based vector is one kind of novel vaccine carriers whose unique features and non-reactivity to pre-existing human adenovirus neutralizing antibodies makes it an outstanding candidate for vaccine research and development. Here, we review the different strategies for constructing chimpanzee adenoviral vectors and their applications in recent clinical trials and also discuss the oncolytic virotherapy and immunotherapy based on chimpanzee adenoviral vectors.
Project description:Insect guts house a complex community of microbes that affect host physiology, performance and behavior. Gut microbiome research has largely focused on bacteria-host symbioses and paid less attention to other taxa, such as yeasts. We found that axenic Drosophila melanogaster (reared free of microbes) develops from egg to adult more slowly (ca. 13 days) than those with a natural microbiota (ca. 11.5 days). Here, we showed that live yeasts are present and reproducing in the guts of flies and that the fast development time can be restored by inoculating larvae with a single yeast species (either Saccharomyces cerevisiae or Lachancea kluyveri). Nutritional supplements (either heat-killed yeasts, or a mix of essential vitamins and amino acids) slightly sped the development of axenic flies (to ca. 12.5 days), but not to the same extent as live yeasts. During the first two instars, this acceleration appears to result from additional macronutrient availability, but during the third instar, when most growth occurs, live yeasts increased feeding rate, implying an effect mediated by the gut-brain axis. Thus, the fly-yeast interaction extends beyond yeasts-as-food to yeasts as beneficial interactive symbionts.