A Comprehensive Survey of Prospective Structure-Based Virtual Screening for Early Drug Discovery in the Past Fifteen Years.

Structure-based virtual screening (SBVS), also known as molecular docking, has been increasingly applied to discover small-molecule ligands based on the protein structures in the early stage of drug discovery. In this review, we comprehensively surveyed the prospective applications of molecular docking judged by solid experimental validations in the literature over the past fifteen years. Herein, we systematically analyzed the novelty of the targets and the docking hits, practical protocols of docking screening, and the following experimental validations. Among the 419 case studies we reviewed, most virtual screenings were carried out on widely studied targets, and only 22% were on less-explored new targets. Regarding docking software, GLIDE is the most popular one used in molecular docking, while the DOCK 3 series showed a strong capacity for large-scale virtual screening. Besides, the majority of identified hits are promising in structural novelty and one-quarter of the hits showed better potency than 1 µM, indicating that the primary advantage of SBVS is to discover new chemotypes rather than highly potent compounds. Furthermore, in most studies, only in vitro bioassays were carried out to validate the docking hits, which might limit the further characterization and development of the identified active compounds. Finally, several successful stories of SBVS with extensive experimental validations have been highlighted, which provide unique insights into future SBVS drug discovery campaigns.

Discovery of Peptidic Ligands against the SARS-CoV-2 Spike Protein and Their Use in the Development of a Highly Sensitive Personal Use Colorimetric COVID-19 Biosensor.

In addition to efficacious vaccines and antiviral therapeutics, reliable and flexible in-home personal use diagnostics for the detection of viral antigens are needed for effective control of the COVID-19 pandemic. Despite the approval of several PCR-based and affinity-based in-home COVID-19 testing kits, many of them suffer from problems such as a high false-negative rate, long waiting time, and short storage period. Using the enabling one-bead-one-compound (OBOC) combinatorial technology, several peptidic ligands with a nanomolar binding affinity toward the SARS-CoV-2 spike protein (S-protein) were successfully discovered. Taking advantage of the high surface area of porous nanofibers, immobilization of these ligands on nanofibrous membranes allows the development of personal use sensors that can achieve low nanomolar sensitivity in the detection of the S-protein in saliva. This simple biosensor employing naked-eye reading exhibits detection sensitivity comparable to some of the current FDA-approved home detection kits. Furthermore, the ligand used in the biosensor was found to detect the S-protein derived from both the original strain and the Delta variant. The workflow reported here may enable us to rapidly respond to the development of home-based biosensors against future viral outbreaks.

Modeling of ACE2 and antibodies bound to SARS-CoV-2 provides insights into infectivity and immune evasion.

Given the COVID-19 pandemic, there is interest in understanding ligand-receptor features and targeted antibody-binding attributes against emerging SARS-CoV-2 variants. Here, we developed a large-scale structure-based pipeline for analysis of protein-protein interactions regulating SARS-CoV-2 immune evasion. First, we generated computed structural models of the Spike protein of 3 SARS-CoV-2 variants (B.1.1.529, BA.2.12.1, and BA.5) bound either to a native receptor (ACE2) or to a large panel of targeted ligands (n = 282), which included neutralizing or therapeutic monoclonal antibodies. Moreover, by using the Barnes classification, we noted an overall loss of interfacial interactions (with gain of new interactions in certain cases) at the receptor-binding domain (RBD) mediated by substituted residues for neutralizing complexes in classes 1 and 2, whereas less destabilization was observed for classes 3 and 4. Finally, an experimental validation of predicted weakened therapeutic antibody binding was performed in a cell-based assay. Compared with the original Omicron variant (B.1.1.529), derivative variants featured progressive destabilization of antibody-RBD interfaces mediated by a larger set of substituted residues, thereby providing a molecular basis for immune evasion. This approach and findings provide a framework for rapidly and efficiently generating structural models for SARS-CoV-2 variants bound to ligands of mechanistic and therapeutic value.

In silico Antivirus Repurposing and its Modification to Organoselenium Compounds as SARS-CoV-2 Spike Inhibitors.

<b>Background and Objective:</b> The COVID-19, which has been circulating since late 2019, is caused by SARS-CoV-2. Because of its high infectivity, this virus has spread widely throughout the world. Spike glycoprotein is one of the proteins found in SARS-CoV-2. Spike glycoproteins directly affect infection by forming ACE-2 receptors on host cells. Inhibiting glycoprotein spikes could be one method of treating COVID-19. In this study, the antivirus marketed as a database will be repurposed into an antiviral SARS-CoV-2 and the selected compounds will be modified to become organoselenium compounds. <b>Materials and Methods:</b> The research was carried out using <i>in silico</i> methods, such as rigid docking and flexible docking. To obtain information about the interaction between spike glycoprotein and ligands, MOE 2014.09 was used to perform the molecular docking simulation. <b>Results:</b> The analysis of binding energy values was used to select the ten best ligands from the first stage of the molecular docking simulation, which was then modified according to the previous QSAR study to produce 96 new molecules. The second stage of molecular docking simulation was performed with modified molecules. The best-modified ligand was chosen by analyzing the ADME-Tox property, RMSD value and binding energy value. <b>Conclusion:</b> The best three unmodified ligands, Ombitasvir, Elbasvir and Ledipasvir, have a binding energy value of -15.8065, -15.3842 and -15.1255 kcal mol¹, respectively and the best three modified ligands ModL1, ModL2 and ModL3 has a binding value of -15.6716, -13.9489 and -13.2951 kcal mol¹, respectively with an RMSD value of 1.7109 Å, 2.3179 Å and 1.7836 Å.

Protein-ligand interactions from a quantum fragmentation perspective: The case of the SARS-CoV-2 main protease interacting with α-ketoamide inhibitors.

We present a hybrid, multi-method, computational scheme for protein/ligand systems well suited to be used on modern and forthcoming massively parallel computing systems. The scheme relies on a multi-scale polarizable molecular modeling, approach to perform molecular dynamics simulations, and on an efficient Density Functional Theory (DFT) linear scaling method to post-process simulation snapshots. We use this scheme to investigate recent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2 virus. We assessed the reliability and the coherence of the hybrid scheme, in particular, by checking the ability of MM and DFT to reproduce results from high-end ab initio computations regarding such inhibitors. The DFT approach enables an a posteriori fragmentation of the system and an investigation into the strength of interaction among identified fragment pairs. We show the necessity of accounting for a large set of plausible protease/inhibitor conformations to generate reliable interaction data. Finally, we point out ways to further improve α-ketoamide inhibitors to more strongly interact with particular protease domains neighboring the active site.

Long Time Scale Ensemble Methods in Molecular Dynamics: Ligand-Protein Interactions and Allostery in SARS-CoV-2 Targets.

We subject a series of five protein-ligand systems which contain important SARS-CoV-2 targets, 3-chymotrypsin-like protease (3CLPro), papain-like protease, and adenosine ribose phosphatase, to long time scale and adaptive sampling molecular dynamics simulations. By performing ensembles of ten or twelve 10 µs simulations for each system, we accurately and reproducibly determine ligand binding sites, both crystallographically resolved and otherwise, thereby discovering binding sites that can be exploited for drug discovery. We also report robust, ensemble-based observation of conformational changes that occur at the main binding site of 3CLPro due to the presence of another ligand at an allosteric binding site explaining the underlying cascade of events responsible for its inhibitory effect. Using our simulations, we have discovered a novel allosteric mechanism of inhibition for a ligand known to bind only at the substrate binding site. Due to the chaotic nature of molecular dynamics trajectories, regardless of their temporal duration individual trajectories do not allow for accurate or reproducible elucidation of macroscopic expectation values. Unprecedentedly at this time scale, we compare the statistical distribution of protein-ligand contact frequencies for these ten/twelve 10 µs trajectories and find that over 90% of trajectories have significantly different contact frequency distributions. Furthermore, using a direct binding free energy calculation protocol, we determine the ligand binding free energies for each of the identified sites using long time scale simulations. The free energies differ by 0.77 to 7.26 kcal/mol across individual trajectories depending on the binding site and the system. We show that, although this is the standard way such quantities are currently reported at long time scale, individual simulations do not yield reliable free energies. Ensembles of independent trajectories are necessary to overcome the aleatoric uncertainty in order to obtain statistically meaningful and reproducible results. Finally, we compare the application of different free energy methods to these systems and discuss their advantages and disadvantages. Our findings here are generally applicable to all molecular dynamics based applications and not confined to the free energy methods used in this study.

Design and Identification of Inhibitors for the Spike-ACE2 Target of SARS-CoV-2.

When an epidemic started in the Chinese city of Wuhan in December 2019, coronavirus was identified as the cause. Infection by the virus occurs through the interaction of viral S protein with the hosts' angiotensin-converting enzyme 2. By leveraging resources such as the DrugBank database and bioinformatics techniques, ligands with potential activity against the SARS-CoV-2 spike protein were designed and identified in this investigation. The FTMap server and the Molegro software were used to determine the active site of the Spike-ACE2 protein's crystal structure. Virtual screening was performed using a pharmacophore model obtained from antiparasitic drugs, obtaining 2000 molecules from molport®. The ADME/Tox profiles were used to identify the most promising compounds with desirable drug characteristics. The binding affinity investigation was then conducted with selected candidates. A molecular docking study showed five structures with better binding affinity than hydroxychloroquine. Ligand_003 showed a binding affinity of -8.645 kcal·mol-1, which was considered an optimal value for the study. The values presented by ligand_033, ligand_013, ligand_044, and ligand_080 meet the profile of novel drugs. To choose compounds with favorable potential for synthesis, synthetic accessibility studies and similarity analyses were carried out. Molecular dynamics and theoretical IC50 values (ranging from 0.459 to 2.371 µM) demonstrate that these candidates are promising for further tests. Chemical descriptors showed that the candidates had strong molecule stability. Theoretical analyses here show that these molecules have potential as SARS-CoV-2 antivirals and therefore warrant further investigation.

Targeting SARS-CoV-2 Main Protease: A Successful Story Guided by an In Silico Drug Repurposing Approach.

The SARS-CoV-2 main protease (Mpro) is a crucial enzyme for viral replication and has been considered an attractive drug target for the treatment of COVID-19. In this study, virtual screening techniques and in vitro assays were combined to identify novel Mpro inhibitors starting from around 8000 FDA-approved drugs. The docking analysis highlighted 17 promising best hits, biologically characterized in terms of their Mpro inhibitory activity. Among them, 7 cephalosporins and the oral anticoagulant betrixaban were able to block the enzyme activity in the micromolar range with no cytotoxic effect at the highest concentration tested. After the evaluation of the degree of conservation of Mpro residues involved in the binding with the studied ligands, the ligands' activity on SARS-CoV-2 replication was assessed. The ability of betrixaban to affect SARS-CoV-2 replication associated to its antithrombotic effect could pave the way for its possible use in the treatment of hospitalized COVID-19 patients.

CD24-Siglec interactions in inflammatory diseases.

CD24 is a small glycosylphosphatidylinositol (GPI)-anchored glycoprotein with broad expression in multiple cell types. Due to differential glycosylation, cell surface CD24 have been shown to interact with various receptors to mediate multiple physiological functions. Nearly 15 years ago, CD24 was shown to interact with Siglec G/10 to selectively inhibit inflammatory response to tissue injuries. Subsequent studies demonstrate that sialylated CD24 (SialoCD24) is a major endogenous ligand for CD33-family of Siglecs to protect the host against inflammatory and autoimmune diseases, metabolic disorders and most notably respiratory distress in COVID-19. The discoveries on CD24-Siglec interactions propelled active translational research to treat graft-vs-host diseases, cancer, COVID-19 and metabolic disorders. This mini-review provides a succinct summary on biological significance of CD24-Siglec pathway in regulation of inflammatory diseases with emphasis on clinical translation.

Elucidating Atomistic Insight into the Dynamical Responses of the SARS-CoV-2 Main Protease for the Binding of Remdesivir Analogues: Leveraging Molecular Mechanics To Decode the Inhibition Mechanism.

To combat mischievous coronavirus disease followed by continuous upgrading of therapeutic strategy against the antibody-resistant variants, the molecular mechanistic understanding of protein-drug interactions is a prerequisite in the context of target-specific rational drug development. Herein, we attempt to decipher the structural basis for the inhibition of SARS-CoV-2 main protease (Mpro) through the elemental analysis of potential energy landscape and the associated thermodynamic and kinetic properties of the enzyme-inhibitor complexes using automated molecular docking calculations in conjunction with classical force field-based molecular dynamics (MD) simulations. The crux of the scalable all-atom MD simulations consummated in explicit solvent media is to capture the structural plasticity of the viral enzyme due to the binding of remdesivir analogues and ascertain the subtle interplay of noncovalent interactions in stabilizing specific conformational states of the receptor that controls the biomolecular processes related to the ligand binding and dissociation kinetics. To unravel the critical role of modulation of the ligand scaffold, we place further emphasis on the estimation of binding free energy as well as the energy decomposition analysis by employing the generalized Born and Poisson-Boltzmann models. The estimated binding affinities are found to vary between -25.5 and -61.2 kcal/mol. Furthermore, the augmentation of inhibitory efficacy of the remdesivir analogue crucially stems from the van der Waals interactions with the active site residues of the protease. The polar solvation energy contributes unfavorably to the binding free energy and annihilates the contribution of electrostatic interactions as derived from the molecular mechanical energies.

nCoVDock2: a docking server to predict the binding modes between COVID-19 targets and its potential ligands.

The rapid emergence of SARS-CoV-2 variants with multi-sites mutations is considered as a major obstacle for the development of drugs and vaccines. Although most of the functional proteins essential for SARS-CoV-2 have been determined, the understanding of the COVID-19 target-ligand interactions remains a key challenge. The old version of this COVID-19 docking server was built in 2020, and free and open to all users. Here, we present nCoVDock2, a new docking server to predict the binding modes for targets from SARS-CoV-2. First, the new server supports more targets. We replaced the modeled structures with newly resolved structures and added more potential targets of COVID-19, especially for the variants. Second, for small molecule docking, Autodock Vina was upgraded to the latest version 1.2.0, and a new scoring function was added for peptide or antibody docking. Third, the input interface and molecular visualization were updated for a better user experience. The web server, together with an extensive help and tutorial, are freely available at: https://ncovdock2.schanglab.org.cn.

tinyIFD: A High-Throughput Binding Pose Refinement Workflow Through Induced-Fit Ligand Docking.

A critical step in structure-based drug discovery is predicting whether and how a candidate molecule binds to a model of a therapeutic target. However, substantial protein side chain movements prevent current screening methods, such as docking, from accurately predicting the ligand conformations and require expensive refinements to produce viable candidates. We present the development of a high-throughput and flexible ligand pose refinement workflow, called "tinyIFD". The main features of the workflow include the use of specialized high-throughput, small-system MD simulation code mdgx.cuda and an actively learning model zoo approach. We show the application of this workflow on a large test set of diverse protein targets, achieving 66% and 76% success rates for finding a crystal-like pose within the top-2 and top-5 poses, respectively. We also applied this workflow to the SARS-CoV-2 main protease (Mpro) inhibitors, where we demonstrate the benefit of the active learning aspect in this workflow.

Adaptive immunity to SARS-CoV-2 infection: A systematic review.

Background: There is evidence that the adaptive or acquired immune system is one of the crucial variables in differentiating the course of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This work aimed to analyze the immunopathological aspects of adaptive immunity that are involved in the progression of this disease. Methods: This is a systematic review based on articles that included experimental evidence from in vitro assays, cohort studies, reviews, cross-sectional and case-control studies from PubMed, SciELO, MEDLINE, and Lilacs databases in English, Portuguese, or Spanish between January 2020 and July 2022. Results: Fifty-six articles were finalized for this review. CD4+ T cells were the most resolutive in the health-disease process compared with B cells and CD8+ T lymphocytes. The predominant subpopulations of T helper lymphocytes (Th) in critically ill patients are Th1, Th2, Th17 (without their main characteristics) and regulatory T cells (Treg), while in mild cases there is an influx of Th1, Th2, Th17 and follicular T helper cells (Tfh). These cells are responsible for the secretion of cytokines, including interleukin (IL) - 6, IL-4, IL-10, IL-7, IL-22, IL-21, IL-15, IL-1α, IL-23, IL-5, IL-13, IL-2, IL-17, tumor necrosis factor alpha (TNF-α), CXC motivating ligand (CXCL) 8, CXCL9 and tumor growth factor beta (TGF-ß), with the abovementioned first 8 inflammatory mediators related to clinical benefits, while the others to a poor prognosis. Some CD8+ T lymphocyte markers are associated with the severity of the disease, such as human leukocyte antigen (HLA-DR) and programmed cell death protein 1 (PD-1). Among the antibodies produced by SARS-CoV-2, Immunoglobulin (Ig) A stood out due to its potent release associated with a more severe clinical form. Conclusions: It is concluded that through this study it is possible to have a brief overview of the main immunological biomarkers and their function during SARS-CoV-2 infection in particular cell types. In critically ill individuals, adaptive immunity is varied, aberrantly compromised, and late. In particular, the T-cell response is also an essential and necessary component in immunological memory and therefore should be addressed in vaccine formulation strategies.

Rapid Quantitation of Various Therapeutic Monoclonal Antibodies Using Membranes with Fc-Specific Ligands.

Therapeutic monoclonal antibodies (mAbs) provide effective treatments for many diseases, including cancer, autoimmune disorders, and, lately, COVID-19. Monitoring the concentrations of mAbs is important during their production and subsequent processing. This work demonstrates a 5 min quantitation of most human immunoglobulin G (IgG) antibodies through capture of mAbs in membranes modified with ligands that bind to the fragment crystallizable (Fc) region. This enables binding and quantitation of most IgG mAbs. Layer-by-layer (LBL) adsorption of carboxylic acid-rich polyelectrolytes in glass-fiber membranes in 96-well plates allows functionalization of the membranes with Protein A or a peptide, oxidized Fc20 (oFc20), with high affinity for the Fc region of human IgG. mAb capture occurs in <1 min during the flow of solutions through modified membranes, and subsequent binding of a fluorophore-labeled secondary antibody enables quantitation of the captured mAbs using fluorescence. The intra- and inter-plate coefficients of variations (CV) are <10 and 15%, respectively, satisfying the acceptance criteria for many assays. The limit of detection (LOD) of 15 ng/mL is on the high end of commercial enzyme-linked immunosorbent assays (ELISAs) but certainly low enough for monitoring of manufacturing solutions. Importantly, the membrane-based method requires <5 minutes, whereas ELISAs typically take at least 90 min. Membranes functionalized with oFc20 show greater mAb binding and lower LODs than membranes with Protein A. Thus, the membrane-based 96-well-plate assay, which is effective in diluted fermentation broths and in mixtures with cell lysates, is suitable for near-real-time monitoring of the general class of human IgG mAbs during their production.

In Silico Design of New Dual Inhibitors of SARS-CoV-2 MPRO through Ligand- and Structure-Based Methods.

The viral main protease is one of the most attractive targets among all key enzymes involved in the life cycle of SARS-CoV-2. Considering its mechanism of action, both the catalytic and dimerization regions could represent crucial sites for modulating its activity. Dual-binding the SARS-CoV-2 main protease inhibitors could arrest the replication process of the virus by simultaneously preventing dimerization and proteolytic activity. To this aim, in the present work, we identified two series' of small molecules with a significant affinity for SARS-CoV-2 MPRO, by a hybrid virtual screening protocol, combining ligand- and structure-based approaches with multivariate statistical analysis. The Biotarget Predictor Tool was used to filter a large in-house structural database and select a set of benzo[b]thiophene and benzo[b]furan derivatives. ADME properties were investigated, and induced fit docking studies were performed to confirm the DRUDIT prediction. Principal component analysis and docking protocol at the SARS-CoV-2 MPRO dimerization site enable the identification of compounds 1b,c,i,l and 2i,l as promising drug molecules, showing favorable dual binding site affinity on SARS-CoV-2 MPRO.

A machine learning method for the identification and characterization of novel COVID-19 drug targets.

In addition to vaccines, the World Health Organization sees novel medications as an urgent matter to fight the ongoing COVID-19 pandemic. One possible strategy is to identify target proteins, for which a perturbation by an existing compound is likely to benefit COVID-19 patients. In order to contribute to this effort, we present GuiltyTargets-COVID-19 ( https://guiltytargets-covid.eu/ ), a machine learning supported web tool to identify novel candidate drug targets. Using six bulk and three single cell RNA-Seq datasets, together with a lung tissue specific protein-protein interaction network, we demonstrate that GuiltyTargets-COVID-19 is capable of (i) prioritizing meaningful target candidates and assessing their druggability, (ii) unraveling their linkage to known disease mechanisms, (iii) mapping ligands from the ChEMBL database to the identified targets, and (iv) pointing out potential side effects in the case that the mapped ligands correspond to approved drugs. Our example analyses identified 4 potential drug targets from the datasets: AKT3 from both the bulk and single cell RNA-Seq data as well as AKT2, MLKL, and MAPK11 in the single cell experiments. Altogether, we believe that our web tool will facilitate future target identification and drug development for COVID-19, notably in a cell type and tissue specific manner.

Aptamers Targeting Membrane Proteins for Sensor and Diagnostic Applications.

Many biological processes (physiological or pathological) are relevant to membrane proteins (MPs), which account for almost 30% of the total of human proteins. As such, MPs can serve as predictive molecular biomarkers for disease diagnosis and prognosis. Indeed, cell surface MPs are an important class of attractive targets of the currently prescribed therapeutic drugs and diagnostic molecules used in disease detection. The oligonucleotides known as aptamers can be selected against a particular target with high affinity and selectivity by iterative rounds of in vitro library evolution, known as Systematic Evolution of Ligands by EXponential Enrichment (SELEX). As an alternative to antibodies, aptamers offer unique features like thermal stability, low-cost, reuse, ease of chemical modification, and compatibility with various detection techniques. Particularly, immobilized-aptamer sensing platforms have been under investigation for diagnostics and have demonstrated significant value compared to other analytical techniques. These "aptasensors" can be classified into several types based on their working principle, which are commonly electrochemical, optical, or mass-sensitive. In this review, we review the studies on aptamer-based MP-sensing technologies for diagnostic applications and have included new methodological variations undertaken in recent years.

In Silico Identification and Analysis of Potentially Bioactive Antiviral Phytochemicals against SARS-CoV-2: A Molecular Docking and Dynamics Simulation Approach.

SARS-CoV-2, a deadly coronavirus sparked COVID-19 pandemic around the globe. With an increased mutation rate, this infectious agent is highly transmissible inducing an escalated rate of infections and death everywhere. Hence, the discovery of a viable antiviral therapy option is urgent. Computational approaches have offered a revolutionary framework to identify novel antimicrobial treatment regimens and allow a quicker, cost-effective, and productive conversion into the health center by evaluating preliminary and safety investigations. The primary purpose of this research was to find plausible plant-derived antiviral small molecules to halt the viral entrance into individuals by clogging the adherence of Spike protein with human ACE2 receptor and to suppress their genome replication by obstructing the activity of Nsp3 (Nonstructural protein 3) and 3CLpro (main protease). An in-house library of 1163 phytochemicals were selected from the NPASS and PubChem databases for downstream analysis. Preliminary analysis with SwissADME and pkCSM revealed 149 finest small molecules from the large dataset. Virtual screening using the molecular docking scoring and the MM-GBSA data analysis revealed that three candidate ligands CHEMBL503 (Lovastatin), CHEMBL490355 (Sulfuretin), and CHEMBL4216332 (Grayanoside A) successfully formed docked complex within the active site of human ACE2 receptor, Nsp3, and 3CLpro, respectively. Dual method molecular dynamics (MD) simulation and post-MD MM-GBSA further confirmed efficient binding and stable interaction between the ligands and target proteins. Furthermore, biological activity spectra and molecular target analysis revealed that all three preselected phytochemicals were biologically active and safe for human use. Throughout the adopted methodology, all three therapeutic candidates significantly outperformed the control drugs (Molnupiravir and Paxlovid). Finally, our research implies that these SARS-CoV-2 protein antagonists might be viable therapeutic options. At the same time, enough wet lab evaluations would be needed to ensure the therapeutic potency of the recommended drug candidates for SARS-CoV-2.

Intranasal administration of sodium nitroprusside augments antigen-specific mucosal and systemic antibody production in mice.

The coronavirus disease 2019, i.e., the COVID-19 pandemic, caused by a highly virulent and transmissible pathogen, has profoundly impacted global society. One approach to combat infectious diseases caused by pathogenic microbes is using mucosal vaccines, which can induce antigen-specific immune responses at both the mucosal and systemic sites. Despite its potential, the clinical implementation of mucosal vaccination is hampered by the lack of safe and effective mucosal adjuvants. Therefore, developing safe and effective mucosal adjuvants is essential for the fight against infectious diseases and the widespread clinical use of mucosal vaccines. In this study, we demonstrated the potent mucosal adjuvant effects of intranasal administration of sodium nitroprusside (SNP), a known nitric oxide (NO) donor, in mice. The results showed that intranasal administration of ovalbumin (OVA) in combination with SNP induced the production of OVA-specific immunoglobulin A in the mucosa and increased serum immunoglobulin G1 levels, indicating a T helper-2 (Th2)-type immune response. However, an analog of SNP, sodium ferrocyanide, which does not generate NO, failed to show any adjuvant effects, suggesting the critical role of NO generation in activating an immune response. In addition, SNPs facilitated the delivery of antigens to the lamina propria, where antigen-presenting cells are located, when co-administered with antigens, and also transiently elicited the expression of interleukin-6, interleukin-1ß, granulocyte colony-stimulating factor, C-X-C motif chemokine ligand 1, and C-X-C motif chemokine ligand 2 in nasal tissue. These result suggest that SNP is a dual-functional formulation with antigen delivery capabilities to the lamina propria and the capacity to activate innate immunity. In summary, these results demonstrate the ability of SNP to induce immune responses via an antigen-specific Th2-type response, making it a promising candidate for further development as a mucosal vaccine formulation against infectious diseases.

Potential mechanisms of vaccine-induced thrombosis.

Vaccine-induced immune thrombocytopenia and thrombosis (VITT) is a rare syndrome characterized by high-titer anti-platelet factor 4 (PF4) antibodies, thrombocytopenia and arterial and venous thrombosis in unusual sites, as cerebral venous sinuses and splanchnic veins. VITT has been described to occur almost exclusively after administration of ChAdOx1 nCoV-19 and Ad26.COV2.S adenovirus vector- based COVID-19 vaccines. Clinical and laboratory features of VITT resemble those of heparin-induced thrombocytopenia (HIT). It has been hypothesized that negatively charged polyadenylated hexone proteins of the AdV vectors could act as heparin to induce the conformational changes of PF4 molecule that lead to the formation of anti-PF4/polyanion antibodies. The anti-PF4 immune response in VITT is fostered by the presence of a proinflammatory milieu, elicited by some impurities found in ChAdOx1 nCoV-19 vaccine, as well as by soluble spike protein resulting from alternative splice events. Anti-PF4 antibodies bind PF4, forming immune complexes which activate platelets, monocytes and granulocytes, resulting in the VITT's immunothrombosis. The reason why only a tiny minority of patents receiving AdV-based COVID-19 vaccines develop VITT is still unknown. It has been hypothesized that individual intrinsic factors, either acquired (i.e., pre-priming of B cells to produce anti-PF4 antibodies by previous contacts with bacteria or viruses) or inherited (i.e., differences in platelet T-cell ubiquitin ligand-2 [TULA-2] expression) can predispose a few subjects to develop VITT. A better knowledge of the mechanistic basis of VITT is essential to improve the safety and the effectiveness of future vaccines and gene therapies using adenovirus vectors.