Vaccine Discovery Research Articles

Evolving vaccine discovery and development pathways for emerging pathogens

Evolving vaccine discovery and development pathways for emerging pathogens

Recent Advances in Designing Adeno-Associated Virus-Based Vaccines Against Viral Infections

Over 80% of the world’s deadliest pandemics are caused by viral infections, and vaccination remains the most effective way to prevent these infections from spreading. Since the discovery of the first vaccine over two centuries ago, several vaccine design technologies have been developed. Next-generation vaccines, based on mRNA and viral vector technologies, have recently emerged as alternatives to traditional vaccines. Adenoviral vector-based vaccines against coronavirus disease 2019 have demonstrated a more sustained antibody response as compared to mRNA vaccines. However, this has not been without complications, with a few cases of severe adverse events identified in vaccinated individuals, and the underlying mechanism is the subject of intense investigation. Adeno-associated viral vectors induce a weaker cellular immune response compared to adenoviral vectors, and it is mainly for this reason that there has been a diminished interest in exploring them as a vaccine platform until recently. This review will discuss recent developments and the potential of adeno-associated viral vectors as anti-viral vaccines.

Jonas Salk (1914-1995): Pioneering the Fight Against Polio and Beyond.

Jonas Salk (October 28, 1914 - June 23, 1995) was an American medical researcher celebrated for his pioneering work in virology, particularly the development of the first successful polio vaccine. This review highlights Salk's multifaceted talent and contributions. His research on the poliovirus led to the creation of the inactivated polio vaccine, proving that it could prevent the disease. In 1955, the discovery of the polio vaccine was a pivotal moment in the fight against poliomyelitis. Salk's contributions are celebrated in the record of medical history, highlighting his impact on modern medicine and public health. As a professor of bacteriology, preventive medicine, and experimental medicine, Salk's scientific journey, from his innovative methods to the creation and widespread use of the inactivated polio vaccine, helped eradicate polio from various parts of the world. His contributions beyond polio, such as his work on the influenza vaccine and the founding of the Salk Institute for Biological Studies, are also well known. By exploring Salk's legacy, this review examines how his work and dedication continue to influence modern medicine, public health, and science, impacting humanity.

Abstract A085: Engineered novel bioactive Nectin-4 dimer protein significantly enhances the binding of TIGIT

Abstract The goals of this study are to: (a) create a novel soluble bioactive Nectin-4 dimer molecule mimicking its native dimer quaternary structure on the cell surface and (b) evaluate the recombinant Nectin-4 dimer binding potency to its receptor vs the monomer form, so that the dimer can be used as an immunogen and an antigen to increase the potential of cancer therapeutic antibody and vaccine discovery when targeting quaternary structural epitopes. Nectin-4 is a cell adhesion molecule belonging to the nectin family. It's involved in cell-cell junctions and plays a role in cancer progression by influencing cell adhesion, migration, and proliferation. Nectin-4 is overexpressed on various types of tumor cells including breast, lung, ovarian and pancreatic cancers. Nectin-4 has been recently identified as a novel ligand of T cell immunoglobulin and ITIM domain (TIGIT) and interaction between Nectin-4 and TIGIT inhibits NK cell cytotoxicity thus suppressing immune responses, allowing cancer cells to evade immune surveillance. Nectin-4 and Nectin-1 interact in a heterophilic manner, however in the context of cancer, altered expression of Nectin-4 and Nectin-1 can contribute to tumor progression. Nectin-4 is a type 1 single transmembrane protein, and can form homodimers on the cell surface. While therapeutics such as Enfortumab Vedotin, an antibody-drug conjugate, binds Nectin-4 on the surface of cancer cells, mimicking the Nectin-4 dimer quaternary structure can present many more critical opportunities for therapeutic antibody and vaccine discovery. To better mimic the native dimer conformation and quaternary structure, we designed a novel soluble Nectin-4 dimer with 3 extracellular domains fused to a dimer motif at the C-terminus. The Nectin-4 dimer protein was expressed and purified from HEK293 cells and confirmed by SDS-PAGE and Western blot analyses. We characterized the Nectin-4 dimer protein binding potency to TIGIT and Nectin-1 by ELISA. Very interestingly, the results demonstrated that the Nectin-4 dimer binding potency to TIGIT increased dramatically as measured by ELISA, compared to the Nectin-4 monomer protein. Conversely, the Nectin-4 dimer binding to Nectin-1 monomer was significantly decreased, compared to Nectin-4 monomer, as expected. We also studied Nectin-4 dimer immunogenicity using a Nectin-4 dimer DNA immunization to induce potent antibody responses in mice. In summary, these findings imply that the Nectin-4 dimer protein is bioactive and in the desirable conformation, resulting in an increased binding potency to its receptor TIGIT, reduced binding to Nectin-1 (as expected), and eliciting strong antibody responses in mice. This novel Nectin-4 dimer protein can be a very useful molecule for cancer research, therapeutic antibody and vaccine discovery. The Nectin-4 dimer protein can be used to: (i) elicit antibody responses against the dimer quaternary structure, (ii) screen antibodies targeting more conformational epitopes, and (iii) study the Nectin-4 and TIGIT ligand/receptor interactions. Citation Format: Christofer Walsh, Timothy Smith, Mikhail Rashkovskii, Richard Parent, Jean Qiu, Shixia Wang. Engineered novel bioactive Nectin-4 dimer protein significantly enhances the binding of TIGIT [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr A085.

Edward Jenner's Discovery of Vaccination: Impact and Legacy.

Edward Jenner's pioneering work in the late 18th century, particularly his development of the smallpox vaccine, marked a transformative moment in medical history. This review meticulously examines Jenner's groundbreaking discovery and its profound impact on modern immunology and public health. By analyzing a comprehensive range of peer-reviewed studies and historical accounts, this review provides an in-depth exploration of Jenner's experimental methodology, the scientific and ethical controversies that surrounded his work, and the subsequent adoption of vaccination practices. Jenner's rigorous approach to scientific inquiry, the broader implications of his work on public health strategies, and its enduring relevance in addressing contemporary health challenges are also highlighted. This comprehensive analysis underscores Jenner's legacy as a cornerstone of modern medicine and public health.

Virus-like Particles for Disease Diagnosis and Drug Delivery Applications

Abstract: Virus-like particles (VLPs) are nanoscale, self-assembling cage structures made out of proteins with practical uses in biomedicine. They might be used to create better vaccinations, imaging equipment, gene and drug therapy delivery systems, and in vitro diagnostic equipment. VLPs are nanostructures that might be used in medicine, immunization, and diagnostics, among other areas. Many VLPs-based vaccines are now in use for the treatment of infectious diseases, and many more are on their way to clinical testing thanks to recent advancements in biomedical engineering. Although VLPs exhibit promising qualities in terms of efficacy, safety, and diversity, they may become more widely used in the future. Vaccines based on virus-like particles (VLPs) might serve as an effective addition to current immunization strategies for the prevention and treatment of emerging infectious diseases. The growing field of healthcare prevention has become increasingly interested in VLPs, leading to the discovery of various VLP-based candidate vaccines for vaccination towards a wide range of infectious pathogens, one of the most recent that has been developed is the vaccine against SARS-CoV-2, the effectiveness of that is now being tested. VLPs can elicit both antibody and cell-mediated immune responses, unlike standard inactivated viral vaccines. However, several problems persist with this surface display method and will need fixing in the future. VLPs-based medicinal delivery, nanoreactors for treatment, and imaging systems are being developed with promising results. The latest developments in the generation and fabrication of VLPs involve explorations of several expression systems for their creation and their application as vaccines for the avoidance of infectious diseases and malignancies. This manuscript offers the most advanced perspective on biomedical applications based on VLPs, as well as details innovative methods for manufacturing, functionalization, and delivery of VLPs.

RLpMIEC: High-Affinity Peptide Generation Targeting Major Histocompatibility Complex-I Guided and Interpreted by Interaction Spectrum-Navigated Reinforcement Learning.

Major histocompatibility complex (MHC) plays a vital role in presenting epitopes (short peptides from pathogenic proteins) to T-cell receptors (TCRs) to trigger the subsequent immune responses. Vaccine design targeting MHC generally aims to find epitopes with a high binding affinity for MHC presentation. Nevertheless, to find novel epitopes usually requires high-throughput screening of bulk peptide database, which is time-consuming, labor-intensive, more unaffordable, and very expensive. Excitingly, the past several years have witnessed the great success of artificial intelligence (AI) in various fields, such as natural language processing (NLP, e.g., GPT-4), protein structure prediction and engineering (e.g., AlphaFold2), and so on. Therefore, herein, we propose a deep reinforcement-learning (RL)-based generative algorithm, RLpMIEC, to quantitatively design peptide targeting MHC-I systems. Specifically, RLpMIEC combines the energetic spectrum (namely, the molecular interaction energy component, MIEC) based on the peptide-MHC interaction and the sequence information to generate peptides with strong binding affinity and precise MIEC spectra to accelerate the discovery of candidate peptide vaccines. RLpMIEC performs well in all the generative capability evaluations and can generate peptides with strong binding affinities and precise MIECs and, moreover, with high interpretability, demonstrating its powerful capability in participation for accelerating peptide-based vaccine development.

The Mitogenome of the Haecon-5 Strain of Haemonchus contortus and a Comparative Analysis of Its Nucleotide Variation with Other Laboratory Strains.

Haemonchus contortus (the barber's pole worm)-a highly pathogenic gastric nematode of ruminants-causes significant economic losses in the livestock industry worldwide. H. contortus has become a valuable model organism for both fundamental and applied research (e.g., drug and vaccine discovery) because of the availability of well-defined laboratory strains (e.g., MHco3(ISE).N1 in the UK and Haecon-5 in Australia) and genomic, transcriptomic and proteomic data sets. Many recent investigations have relied heavily on the use of the chromosome-contiguous genome of MHco3(ISE).N1 in the absence of a genome for Haecon-5. However, there has been no genetic comparison of these and other strains to date. Here, we assembled and characterised the mitochondrial genome (14.1 kb) of Haecon-5 and compared it with that of MHco3(ISE).N1 and two other strains (i.e., McMaster and NZ_Hco_NP) from Australasia. We detected 276 synonymous and 25 non-synonymous single nucleotide polymorphisms (SNPs) within Haecon-5. Between the Haecon-5 and MHco3(ISE).N1 strains, we recorded 345 SNPs, 31 of which were non-synonymous and linked to fixed amino acid differences in seven protein-coding genes (nad5, nad6, nad1, atp6, nad2, cytb and nad4) between these strains. Pronounced variation (344 and 435 SNPs) was seen between Haecon-5 and each of the other two strains from Australasia. The question remains as to what impact these mitogenomic mutations might have on the biology and physiology of H. contortus, which warrants exploration. The high degree of mitogenomic variability recorded here among these strains suggests that further work should be undertaken to assess the nature and extent of the nuclear genomic variation within H. contortus.

SARS-CoV-2 Mutations and Their Impacts on Virulence and Transmissibility of The Virus

The emergence of SARS-CoV-2 in 2019 and its rapid global spread led to infrequent problems for human society. This vast spreading and high mortality rate were related mainly to the potential of this virus to develop mutated variants. Due to the emergence of hyper mutated strains escaping from vaccine-induced immune responses, the pandemic is still ongoing despite the discovery of various vaccines, and there is little chance that it will be eradicated in the near future. Therefore, it is crucial to monitor covid-19 mutations globally in order to develop new control measures. The SARS-CoV-2 mutation’s emergence and molecular characteristics have been the major talking points of this article, with a focus on significant variants classified as variants of concern (VOC).

A novel vaccine construct against Zika virus fever: insights from epitope-based vaccine discovery through molecular modeling and immunoinformatics approaches.

The Zika virus (ZIKV) is an emerging virus associated with the Flaviviridae family that mainly causes infection in pregnant women and leads to several abnormalities during pregnancy. This virus has unique properties that may lead to pathological diseases. As the virus has the ability to evade immune response, a crucial effort is required to deal with ZIKV. Vaccines are a safe means to control different pathogenic infectious diseases. In the current research, a multi-epitope-based vaccination against ZIKV is being designed using in silico methods. For the epitope prediction and prioritization phase, ZIKV polyprotein (YP_002790881.1) and flavivirus polyprotein (>YP_009428568.1) were targeted. The predicted B-cell epitopes were used for MHC-I and MHC-II epitope prediction. Afterward, several immunoinformatics filters were applied and nine (REDLWCGSL, MQDLWLLRR, YKKSGITEV, TYTDRRWCF, RDAFPDSNS, KPSLGLINR, ELIGRARVS, AITQGKREE, and EARRSRRAV) epitopes were found to be probably antigenic in nature, non-allergenic, non-toxic, and water soluble without any toxins. Selected epitopes were joined using a particular GPGPG linker to create the base vaccination for epitopes, and an extra EAAAK linker was used to link the adjuvant. A total of 312 amino acids with a molecular weight (MW) of 31.62762 and an instability value of 34.06 were computed in the physicochemical characteristic analysis, indicating that the vaccine design is stable. The molecular docking analysis predicted a binding energy of -329.46 (kcal/mol) for TLR-3 and -358.54 (kcal/mol) for TLR-2. Moreover, the molecular dynamics simulation analysis predicted that the vaccine and receptor molecules have stable binding interactions in a dynamic environment. The C-immune simulation analysis predicted that the vaccine has the ability to generate both humoral and cellular immune responses. Based on the design, the vaccine construct has the best efficacy to evoke immune response in theory, but experimental analysis is required to validate the in silico base approach and ensure its safety.

ARTIFICIAL INTELLIGENCE IN CLINICAL APPLICATIONS FOR INFECTIOUS DISEASES: DIAGNOSIS, TREATMENT AND IMMUNIZATION

Despite scientific and technological advances in recent years, infectious diseases continue to pose a significant threat to public health. These diseases can cause serious health problems as they have the potential to spread rapidly. In addition, they occur in the form of epidemics and affect populations. The difficulty of rapid and accurate diagnosis and increasing antimicrobial resistance create difficulties in the treatment of infectious diseases. Artificial intelligence technology has developed useful applications in many areas such as the development of diagnosis and treatment methods, anti-infective drug and vaccine discovery, and prevention of increasing anti-infective drug resistance. In particular, AI-assisted clinical decision support systems can help predict disease outbreaks, support diagnosis of diseases, optimise treatment options and monitor epidemiological trends by analysing large datasets. It can also provide more accurate and faster results in analysing diagnostic images and identifying diseases. Advances in this field need to be supported by multidisciplinary studies and a strong ethical framework. In this review, we outline approaches to the application and use of artificial intelligence in infectious diseases, highlight the progress supported by artificial intelligence, and discuss how it can be used. We outline the applications and benefits of AI in infectious diseases. In this way, more effective intervention strategies can be developed to control infectious diseases and protect public health.

Why did I participate in an HIV vaccine study? Experiences of participation in the first phase II HIV vaccine trial in Mozambique: An ancillary study using a mixed-method approach

IntroductionThis study recognized the lack of information regarding recruitment and retention factors associated with implementing HIV vaccine trials from the perspective of de facto participants. It aimed to describe the motives and experiences of 31 young adults who participated in a phase II HIV vaccine clinical trial conducted in Maputo, Mozambique. MethodsThis was an ancillary study with a mixed-method approach that employed a convergent design, combining both quantitative and qualitative methodologies. Data collection involved questionnaire surveys, in-depth interviews, and focus group discussions. Participants were assessed before and after learning whether they received the experimental vaccine or placebo. Thematic analysis was used for qualitative data, while descriptive analysis and statistical tests such as Fischer’s test and McNemar’s exact test were applied to quantitative data. The study also utilized the Health Belief Model to understand the decision-making process of participating in an HIV vaccine study. ResultsMost of our participants were young females, single, with limited financial resources. Participants joined the trial with the belief that they had a unique opportunity to help the fight against HIV and contribute to the research for the discovery of an HIV vaccine. Positive experiences related to trial participation include gaining knowledge about HIV and personal health and receiving risk reduction counseling. Participants reported blood collection as a negative experience and that they suffered social harm because of trial participation. Participants felt abandoned after the trial ended. ConclusionPreventive HIV vaccine trials should integrate a social-behavioral component to assess reasons for participation and refusal in real-time. Providing ongoing personal attention is crucial for young individuals who have committed 1–2 years to trial participation, extending beyond the trial period. Implementing tailored strategies for HIV risk assessment and reduction during and after the trial is essential. Addressing these factors can enhance preventive HIV vaccine trial implementation.

MEASUREMENT OF CLASSIFICATION PERFORMANCE WITH THE LEARNING VECTOR QUANTIZATION METHOD ON COVID-19 VACCINATION DATA AT THE PARUMPANAI HEALTH CENTER

In the midst of the COVID-19 pandemic, various countries are always trying their best to restore global stability. One effective way is the discovery of several vaccines to prevent transmission of the virus. Indonesia is one of the countries that is aggressively implementing the COVID-19 vaccination. The vaccination process which has been carried out from February 2021 until the end of 2021 has covered approximately 160 million people or 76.83% of the target set by the government. Vaccine recipients have criteria to be able to get vaccinated to avoid side effects or complications. So it is necessary to classify groups that can receive vaccines and also delay vaccination. This research aims to determine the performance of the learning vector quantization classification method. Learning vector quantization method classification produces 95% accuracy, 97% precision, and 96% sensitivity. From these performance measurements, it can be concluded that the learning vector quantization method is very good and can be used in the classification of COVID-19 vaccination recipients at the Parumpanai Public Health Center, East Luwu Regency.

Analysing the Automation of Artificial Knowledge in Virology for Safety and Effectiveness in Healthcare: Equilibrium of Advancement and Trials for Secure and Productive Health Necessities

In virology, artificial intelligence (AI) technologies have demonstrated potentials to revolutionize the detection of diseases, understanding the behaviors of viruses, and developing strategies that are effective for treatments. This article explores the current applications of AI in healthcare and virology universally, focusing on India and highlighting the advancements, challenges, and potentials in these critical domains. The dialogue underscores the transformative power of AI and the strides made globally and in India. While AI unfolds tremendous opportunities, setbacks related to data privacy, ethical considerations, frameworks of regulation, training the workforce, and liaisons that are collaborative require attention to fully realize the potential of AI. While AI technologies stand primed for refraining detection and understanding of viruses and speeding discovery of vaccines, ethical considerations such as privacy of data, biases in algorithms, and integration of judicious AI require circumspection to ensure ethical and equitable utilization of AI technologies in healthcare. By tackling these challenges and harnessing opportunities presented by AI, we can extract transformative vigor for enhancement of outcomes of healthcare and efficiently traverse adversities of health globally.

Vaccine process technology-Adecade of progress.

In the past decade, new approaches to the discovery and development of vaccines have transformed the field. Advances during the COVID-19 pandemic allowed the production of billions of vaccine doses per year using novel platforms such as messenger RNA and viral vectors. Improvements in the analytical toolbox, equipment, and bioprocess technology have made it possible to achieve both unprecedented speed in vaccine development and scale of vaccine manufacturing. Macromolecular structure-function characterization technologies, combined with improved modeling and data analysis, enable quantitative evaluation of vaccine formulations at single-particle resolution and guided design of vaccine drug substances and drug products. These advances play a major role in precise assessment of critical quality attributes of vaccines delivered by newer platforms. Innovations in label-free and immunoassay technologies aid in the characterization of antigenic sites and the development of robust in vitro potency assays. These methods, along with molecular techniques such as next-generation sequencing, will accelerate characterization and release of vaccines delivered by all platforms. Process analytical technologies for real-time monitoring and optimization of process steps enable the implementation of quality-by-design principles and faster release of vaccine products. In the next decade, the field of vaccine discovery and development will continue to advance, bringing together new technologies, methods, and platforms to improve human health.

A Second-Generation Oral SARS-CoV-2 Main Protease Inhibitor Clinical Candidate for the Treatment of COVID-19.

Despite the record-breaking discovery, development and approval of vaccines and antiviral therapeutics such as Paxlovid, coronavirus disease 2019 (COVID-19) remained the fourth leading cause of death in the world and third highest in the United States in 2022. Here, we report the discovery and characterization of PF-07817883, a second-generation, orally bioavailable, SARS-CoV-2 main protease inhibitor with improved metabolic stability versus nirmatrelvir, the antiviral component of the ritonavir-boosted therapy Paxlovid. We demonstrate the in vitro pan-human coronavirus antiviral activity and off-target selectivity profile of PF-07817883. PF-07817883 also demonstrated oral efficacy in a mouse-adapted SARS-CoV-2 model at plasma concentrations equivalent to nirmatrelvir. The preclinical in vivo pharmacokinetics and metabolism studies in human matrices are suggestive of improved oral pharmacokinetics for PF-07817883 in humans, relative to nirmatrelvir. In vitro inhibition/induction studies against major human drug metabolizing enzymes/transporters suggest a low potential for perpetrator drug-drug interactions upon single-agent use of PF-07817883.

The Past, Present, and Future of Genetic Manipulation in Human Fungal Pathogen Talaromyces marneffei

The fungus Talaromyces marneffei has been discovered and its pathogenicity to humans has been recognized for over sixty years. The advances in organism-wide studies and the development of genetic manipulation tools contribute greatly to our current understanding of host-pathogen interactions. Several classes of genes have been identified to be involved in stress response, morphogenesis, and virulence based on the characterization of the generated mutants. Here, we summarize the main techniques for T. marneffei genetic manipulation, including chemical mutagenesis, insertional mutagenesis, homologous recombination-mediated gene replacement, knockdown methods, and the recent popular CRISPR-Cas9 (Clustered Regularly Interspaced Short Palindromic Repeats; Cas=CRISPR associated protein) technology. The advantages and disadvantages of each technique were determined from a historical perspective. We also describe potential strategies to improve the current genetics studies, such as the generation of new selection markers and genetically modified strains. The genetic approaches will continue to impact the studies of T. marneffei and can lead to the discovery of new diagnostic tools, drugs, and vaccines.

Exploring the Impact of Artificial Intelligence in Healthcare

The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications. AI's impact is observed across various domains, including detecting clinical conditions in medical imaging, early diagnosis of coronavirus disease 2019 (COVID-19), virtual patient care utilizing AI-powered tools, electronic health record management, enhancing patient engagement and treatment compliance, reducing administrative burdens for healthcare professionals (HCPs), drug and vaccine discovery, identification of medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. However, the integration of AI in healthcare encounters several technical, ethical, and social challenges, such as privacy concerns, safety issues, autonomy and consent, cost considerations, information transparency, access disparities, and efficacy uncertainties. Effective governance of AI applications is imperative to ensure patient safety, accountability, and to bolster HCPs' confidence, thus fostering acceptance and yielding significant health benefits. Precise governance is essential to address regulatory, ethical, and trust concerns while advancing the adoption and implementation of AI in healthcare. With the onset of the COVID-19 pandemic, AI has sparked a healthcare revolution, signaling a promising leap forward to meet future healthcare demands.

Exploring the Impact of Artificial Intelligence in Healthcare

The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications. AI's impact is observed across various domains, including detecting clinical conditions in medical imaging, early diagnosis of coronavirus disease 2019 (COVID-19), virtual patient care utilizing AI-powered tools, electronic health record management, enhancing patient engagement and treatment compliance, reducing administrative burdens for healthcare professionals (HCPs), drug and vaccine discovery, identification of medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. However, the integration of AI in healthcare encounters several technical, ethical, and social challenges, such as privacy concerns, safety issues, autonomy and consent, cost considerations, information transparency, access disparities, and efficacy uncertainties. Effective governance of AI applications is imperative to ensure patient safety, accountability, and to bolster HCPs' confidence, thus fostering acceptance and yielding significant health benefits. Precise governance is essential to address regulatory, ethical, and trust concerns while advancing the adoption and implementation of AI in healthcare. With the onset of the COVID-19 pandemic, AI has sparked a healthcare revolution, signaling a promising leap forward to meet future healthcare demands.

A Pseudovirus-Based Neutralization Assay for SARS-CoV-2 Variants: A Rapid, Cost-Effective, BSL-2-Based High-Throughput Assay Useful for Vaccine Immunogenicity Evaluation.

Neutralizing antibody responses from COVID-19 vaccines are pivotal in conferring protection against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Effective COVID-19 vaccines and assays measuring neutralizing antibodies against emerging variants (i.e., XBB.1.5, XBB.1.16, and XBB.2.3) are needed. The use of biosafety level (BSL)-3 laboratories for live virus assays results in higher costs and a longer turnaround time; therefore, a BSL-2-based pseudovirus neutralization assay (PNT) was developed. The pseudoviruses were produced by cotransfecting cells with plasmids encoding a lentiviral backbone-expressing luciferase reporter; non-surface proteins for lentiviral production; and ancestral or Omicron (BA.1 and BA.5) SARS-CoV-2 spike (S) proteins. The PNT was developed and optimized in dose and kinetics experiments. The representative serum samples (COVID-19-convalescent or NVX-CoV2373-vaccinated participants enrolled in the 2019nCoV-101 trial) demonstrated a wide dynamic range. The neutralization data showed robust correlation with validated anti-recombinant spike IgG levels and angiotensin-converting enzyme 2 inhibition titers (ancestral). This assay is suitable for measurement of the neutralization ability in clinical samples from individuals infected with SARS-CoV-2 or immunized with a COVID-19 vaccine. The results suggest that this PNT provides a lower cost, high-throughput, rapid turnaround alternative to BSL-3-based microneutralization assays and enables the discovery and development of effective vaccines against emerging variants.