From concept to delivery: a yeast-expressed recombinant protein-based COVID-19 vaccine technology suitable for global access.

INTRODUCTION: The development of a yeast-expressed recombinant protein-based vaccine technology co-developed with LMIC vaccine producers and suitable as a COVID-19 vaccine for global access is described. The proof-of-concept for developing a SARS-CoV-2 spike protein receptor-binding domain (RBD) antigen as a yeast-derived recombinant protein vaccine technology is described. AREAS COVERED: Genetic Engineering: The strategy is presented for the design and genetic modification used during cloning and expression in the yeast system. Process and Assay Development: A summary is presented of how a scalable, reproducible, and robust production process for the recombinant protein COVID-19 vaccine antigen was developed. Formulation and Pre-clinical Strategy: We report on the pre-clinical and formulation strategy used for the proof-of-concept evaluation of the SARS-CoV-2 RBD vaccine antigen. Technology Transfer and Partnerships: The process used for the technology transfer and co-development with LMIC vaccine producers is described. Clinical Development and Delivery: The approach used by LMIC developers to establish the industrial process, clinical development, and deployment is described. EXPERT OPINION: Highlighted is an alternative model for developing new vaccines for emerging infectious diseases of pandemic importance starting with an academic institution directly transferring their technology to LMIC vaccine producers without the involvement of multinational pharma companies.

Factors Associated with the Underestimation of Manual CPAP Titration Pressure.

During the SARS-CoV-2 pandemic, the use of in-laboratory positive airway pressure (PAP) titration studies was not routinely suggested. PAP pressure prediction calculations are emerging as alternative methods for the treatment of these patients. The underestimation of PAP titration pressure usually leads to unsatisfactory results for PAP therapy. This study aimed to evaluate the factors associated with the underestimation of PAP titration pressure when using PAP pressure prediction equations. Estimated PAP pressure formulas based on body mass index (BMI) and apnea-hypopnea index (AHI) were chosen to validate the accuracy of equations in the successful prediction of titration pressure. Among 341 adult patients diagnosed with obstructive sleep apnea (OSA) by overnight polysomnography (PSG) and who received overnight PAP titration in order to select a successful pressure, the mean age of the total subjects was 55.4 years old and 78.9% of patients were male. The average BMI and AHI scores were 27.1 ± 4.8 and 37 ± 21.7, respectively. After multivariate stepwise regression analysis, the odds ratio of participants with a pretitration AHI was 1.017 (95% CI: 1.005-1.030). Only the severity of OSA was significantly different between the underestimated group and the adequately assessed group. In conclusion, a high AHI, but not BMI, is associated with an underestimated CPAP titration pressure in adult patients with OSA.

The safety of digestive tract cancer surgery during COVID-19: A living systematic review and meta-analysis.

Surgery is the primary curative treatment of solid cancers. However, its safety has been compromised by the outbreak of COVID-19. Therefore, it is necessary to evaluate the safety of digestive tract cancer surgery in the context of COVID-19. We used the Review Manager software (v.5.4) and Stata software (version 16.0) for meta-analysis and statistical analysis. Sixteen retrospective studies involving 17,077 patients met the inclusion criteria. The data indicates that performing digestive tract cancer surgery during the COVID-19 pandemic led to increased blood loss(MD = -11.31, 95%CI:-21.43 to -1.20, P = 0.03), but did not increase postoperative complications(OR = 1.03, 95%CI:0.78 to1.35, P = 0 0.86), anastomotic leakage (OR = 0.96, 95%CI:0.52 to1.77, P = 0 0.89), postoperative mortality (OR = 0.65, 95%CI:0.40 to1.07, P = 0 0.09), number of transfusions (OR = 0.74, 95%CI:0.30 to 1.80, P = 0.51), number of patients requiring ICU care(OR = 1.37, 95%CI:0.90 to 2.07, P = 0.14), postoperative 30-d readmission (OR = 0.94, 95%CI:0.82 to 1.07, P = 0 0.33), total hospital stay (MD = 0.11, 95%CI:-2.37 to 2.59, P = 0.93), preoperative waiting time(MD = - 0.78, 95%CI:-2.34 to 0.79, P = 0.33), postoperative hospital stay(MD = - 0.44, 95%CI:-1.61 to 0.74, P = 0.47), total operation time(MD = -12.99, 95%CI:-28.00 to 2.02, P = 0.09) and postoperative ICU stay (MD = - 0.02, 95%CI:-0.62 to 0.57, P = 0.94). Digestive tract cancer surgery can be safely performed during the COVID-19.

Development of Disaster Response Strategies for Multi-Disasters (preprint)

The purpose of this research is to develop a disaster response strategy that can be used when more than one disaster happens at the same time. When different types of disasters occur at the same time, disaster response operations will become extremely complex. It is difficult for responders to directly perform response operations based on a single standard operation procedure. This research used flooding events that occurred in Taiwan during the COVID pandemic as an example case to develop the response strategies. The standard operating procedures before and after the pandemic were first reviewed. The authors also joined the response operations to have close observations on how responders execute response operations under restrictions of the pandemic. User interviews were then conducted to collect feedback from four responders. Finally, the multi-disaster response strategies, coined as OPERATE, was developed based on results of procedure review, field observations, and user interviews. OPERATE was developed for disaster response teams to smoothly execute response operations when multiple disasters occur simultaneously. It includes seven perspectives: simplified operation, flexible procedures, adjustable environment, personalized reminder, positive attitude, well-developed decision support tool, and preparatory education. OPERATE is developed to reduce the impact of multi-disasters on response teams, including the impact of the disaster itself and orders issued by other government departments in response to other disasters. Following the developed strategies, disaster response teams are expected to conduct stable and efficient operations in the context of multiple disasters.

Liver injury in COVID-19: Clinical features, potential mechanisms, risk factors and clinical treatments.

The coronavirus disease 2019 (COVID-19) pandemic has been a serious threat to global health for nearly 3 years. In addition to pulmonary complications, liver injury is not uncommon in patients with novel COVID-19. Although the prevalence of liver injury varies widely among COVID-19 patients, its incidence is significantly increased in severe cases. Hence, there is an urgent need to understand liver injury caused by COVID-19. Clinical features of liver injury include detectable liver function abnormalities and liver imaging changes. Liver function tests, computed tomography scans, and ultrasound can help evaluate liver injury. Risk factors for liver injury in patients with COVID-19 include male sex, preexisting liver disease including liver transplantation and chronic liver disease, diabetes, obesity, and hypertension. To date, the mechanism of COVID-19-related liver injury is not fully understood. Its pathophysiological basis can generally be explained by systemic inflammatory response, hypoxic damage, ischemia-reperfusion injury, and drug side effects. In this review, we systematically summarize the existing literature on liver injury caused by COVID-19, including clinical features, underlying mechanisms, and potential risk factors. Finally, we discuss clinical management and provide recommendations for the care of patients with liver injury.

Effective approaches to disaster evacuation during a COVID-like pandemic (preprint)

Since COVID-19 vaccines became available, no studies have quantified how different disaster evacuation strategies can mitigate pandemic risks in shelters. Therefore, we applied an age-structured epidemiological model, known as the Susceptible-Exposed-Infectious-Recovered (SEIR) model, to investigate to what extent different vaccine uptake levels and the Diversion protocol implemented in Taiwan decrease infections and delay pandemic peak occurrences. Taiwan's Diversion protocol involves diverting those in self-quarantine due to exposure, thus preventing them from mingling with the general public at a congregate shelter. The Diversion protocol, combined with sufficient vaccine uptake, can decrease the maximum number of infections and delay outbreaks relative to scenarios without such strategies. When the diversion of all exposed people is not possible, or vaccine uptake is insufficient, the Diversion protocol is still valuable. Furthermore, a group of evacuees that consists primarily of a young adult population tends to experience pandemic peak occurrences sooner and have up to 180% more infections than does a majority elderly group when the Diversion protocol is implemented. However, when the Diversion protocol is not enforced, the majority elderly group suffers from up to 20% more severe cases than the majority young adult group.

SARS-CoV-2 nucleocapsid protein triggers hyperinflammation via protein-protein interaction-mediated intracellular Cl- accumulation in respiratory epithelium.

SARS-CoV-2, the culprit pathogen of COVID-19, elicits prominent immune responses and cytokine storms. Intracellular Cl- is a crucial regulator of host defense, whereas the role of Cl- signaling pathway in modulating pulmonary inflammation associated with SARS-CoV-2 infection remains unclear. By using human respiratory epithelial cell lines, primary cultured human airway epithelial cells, and murine models of viral structural protein stimulation and SARS-CoV-2 direct challenge, we demonstrated that SARS-CoV-2 nucleocapsid (N) protein could interact with Smad3, which downregulated cystic fibrosis transmembrane conductance regulator (CFTR) expression via microRNA-145. The intracellular Cl- concentration ([Cl-]i) was raised, resulting in phosphorylation of serum glucocorticoid regulated kinase 1 (SGK1) and robust inflammatory responses. Inhibition or knockout of SGK1 abrogated the N protein-elicited airway inflammation. Moreover, N protein promoted a sustained elevation of [Cl-]i by depleting intracellular cAMP via upregulation of phosphodiesterase 4 (PDE4). Rolipram, a selective PDE4 inhibitor, countered airway inflammation by reducing [Cl-]i. Our findings suggested that Cl- acted as the crucial pathological second messenger mediating the inflammatory responses after SARS-CoV-2 infection. Targeting the Cl- signaling pathway might be a novel therapeutic strategy for COVID-19.

Yeast-expressed recombinant SARS-CoV-2 receptor binding domain RBD203-N1 as a COVID-19 protein vaccine candidate.

SARS-CoV-2 protein subunit vaccines are currently being evaluated by multiple manufacturers to address the global vaccine equity gap, and need for low-cost, easy to scale, safe, and effective COVID-19 vaccines. In this paper, we report on the generation of the receptor-binding domain RBD203-N1 yeast expression construct, which produces a recombinant protein capable of eliciting a robust immune response and protection in mice against SARS-CoV-2 challenge infections. The RBD203-N1 antigen was expressed in the yeast Pichia pastoris X33. After fermentation at the 5 L scale, the protein was purified by hydrophobic interaction chromatography followed by anion exchange chromatography. The purified protein was characterized biophysically and biochemically, and after its formulation, the immunogenicity was evaluated in mice. Sera were evaluated for their efficacy using a SARS-CoV-2 pseudovirus assay. The RBD203-N1 protein was expressed with a yield of 492.9 ± 3.0 mg/L of fermentation supernatant. A two-step purification process produced a >96% pure protein with a recovery rate of 55 ± 3% (total yield of purified protein: 270.5 ± 13.2 mg/L fermentation supernatant). The protein was characterized to be a homogeneous monomer that showed a well-defined secondary structure, was thermally stable, antigenic, and when adjuvanted on Alhydrogel in the presence of CpG it was immunogenic and induced high levels of neutralizing antibodies against SARS-CoV-2 pseudovirus. The characteristics of the RBD203-N1 protein-based vaccine show that this candidate is another well suited RBD-based construct for technology transfer to manufacturing entities and feasibility of transition into the clinic to evaluate its immunogenicity and safety in humans.

Routing algorithms as tools for integrating social distancing with emergency evacuation.

One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe this evacuation process as a Capacitated Vehicle Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep Algorithm). A central question is whether Deep Reinforcement Learning provides sufficient extra routing efficiency to accommodate increased social distancing in a time-constrained evacuation operation. We found that, in comparison to the Sweep Algorithm, Deep Reinforcement Learning can provide decision-makers with more efficient routing. However, the evacuation time saved by Deep Reinforcement Learning does not come close to compensating for the extra time required for social distancing, and its advantage disappears as the emergency vehicle capacity approaches the number of people per household.

A mobile swabbing booth to address Singapore GPs' concerns about swabber protection: human-centred design during the COVID-19 pandemic.

BACKGROUND: During the COVID-19 pandemic, the Ministry of Health asked Singapore's private general practitioners (GPs) to perform swab testing in their clinics, but some GPs had concerns about swabber protection. Our aim was to develop a swabbing booth to address these concerns. METHODS: We developed a prototype with potential GP users using a human-centred design approach and piloted it with 10 GP clinics. The pilot was then extended to 170 GP clinics around Singapore. These GPs were then surveyed on user satisfaction. RESULTS: Ninety-three GPs (54%) responded. The majority (75%) practiced in public residential estates in small practices (mean 1.95 doctors). 86% requested the booth to enhance swabber protection. 74% "would recommend" or "would strongly recommend" the booth to colleagues. 79% continue to use the booth to conduct swab tests. 92% liked that it offered swabber protection. 71% liked that the booth created a separate space for swabbing and 64% liked its ease of disinfection. 47% started swabbing only after receiving the booth and 58% said the booth was "important" or "very important" to their decision to participate in swab testing. However, 34% disliked that it took up too much space and the most frequently critiqued area was the gloves. CONCLUSION: The human-centred design approach generated a product that had high user satisfaction, addressed GPs' concerns of swabber protection and increased GPs' participation in swab testing. The booth may be useful where GPs are concerned about swabber protection and space is limited.

Yeast-expressed Recombinant SARS-CoV-2 Receptor Binding Domain, RBD203-N1 as a COVID-19 Protein Vaccine Candidate (preprint)

BackgroundSARS-CoV-2 protein subunit vaccines are being evaluated by multiple manufacturers to fill the need for low-cost, easy to scale, safe, and effective COVID-19 vaccines for global access. Vaccine candidates relying on the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein have been the focus of our development program. In this paper, we report on the generation of the RBD203-N1 yeast expression construct, which produces a recombinant protein that when formulated with alum and the TLR-9 agonist, CpG1826 elicits a robust immune response and protection in mice. MethodThe RBD203-N1 antigen was expressed in the yeast Pichia pastoris X33. After fermentation at the 5 L scale, the protein was purified by hydrophobic interaction chromatography followed by anion exchange chromatography. The purified protein was characterized biophysically and biochemically, and after its formulation, the immunogenicity and efficacy were evaluated in mice. Results, Conclusions, and SignificanceThe RBD203-N1 production process yielded 492.9 {+/-} 3.0 mg/L of protein in the fermentation supernatant. A two-step purification process produced a >96% pure protein with a recovery rate of 55 {+/-} 3% (total yield of purified protein: 270.5 {+/-} 13.2 mg/L fermentation supernatant). The protein was characterized as a homogeneous monomer with well-defined secondary structure, thermally stable, antigenic, and when adjuvanted on alum and CpG, it was immunogenic and induced robust levels of neutralizing antibodies against SARS-CoV-2 pseudovirus. These characteristics show that this vaccine candidate is well suited for technology transfer with feasibility of its transition into the clinic to evaluate its immunogenicity and safety in humans.

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1.

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm for breath phase detection and adventitious sound detection at the recording level has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchus labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests using long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.

Routing Algorithms as Tools for Integrating Social Distancing with Emergency Evacuation (preprint)

We explore the implications of integrating social distancing with emergency evacuation, as would be expected when a hurricane approaches a city during the COVID-19 pandemic. Specifically, we compare DNN (Deep Neural Network)-based and non-DNN methods for generating evacuation strategies that minimize evacuation time while allowing for social distancing in emergency vehicles. A central question is whether a DNN-based method provides sufficient extra routing efficiency to accommodate increased social distancing in a time-constrained evacuation operation. We describe the problem as a Capacitated Vehicle Routing Problem and solve it using a non-DNN solution (Sweep Algorithm) and a DNN-based solution (Deep Reinforcement Learning). The DNN-based solution can provide decision-makers with more efficient routing than the typical non-DNN routing solution. However, it does not come close to compensating for the extra time required for social distancing, and its advantage disappears as the emergency vehicle capacity approaches the number of people per household.

The SARS-CoV-2 receptor and other key components of the Renin-Angiotensin-Aldosterone System related to COVID-19 are expressed in enterocytes in larval zebrafish.

People with underlying conditions, including hypertension, obesity, and diabetes, are especially susceptible to negative outcomes after infection with coronavirus SARS-CoV-2, which causes COVID-19. Hypertension and respiratory inflammation are exacerbated by the Renin-Angiotensin-Aldosterone System (RAAS), which normally protects from rapidly dropping blood pressure via Angiotensin II (Ang II) produced by the enzyme Ace. The Ace paralog Ace2 degrades Ang II, counteracting its chronic effects, and serves as the SARS-CoV-2 receptor. Ace, the coronavirus, and COVID-19 comorbidities all regulate Ace2, but we do not yet understand how. To exploit zebrafish (Danio rerio) to help understand the relationship of the RAAS to COVID-19, we must identify zebrafish orthologs and co-orthologs of human RAAS genes and understand their expression patterns. To achieve these goals, we conducted genomic and phylogenetic analyses and investigated single cell transcriptomes. Results showed that most human RAAS genes have one or more zebrafish orthologs or co-orthologs. Results identified a specific type of enterocyte as the specific site of expression of zebrafish orthologs of key RAAS components, including Ace, Ace2, Slc6a19 (SARS-CoV-2 co-receptor), and the Angiotensin-related peptide cleaving enzymes Anpep (receptor for the common cold coronavirus HCoV-229E), and Dpp4 (receptor for the Middle East Respiratory Syndrome virus, MERS-CoV). Results identified specific vascular cell subtypes expressing Ang II receptors, apelin, and apelin receptor genes. These results identify genes and cell types to exploit zebrafish as a disease model for understanding mechanisms of COVID-19.

Routing algorithms as tools for integrating social distancing with emergency evacuation (preprint)

One of the lessons from the COVID-19 pandemic is the importance of social distancing, even in challenging circumstances such as pre-hurricane evacuation. To explore the implications of integrating social distancing with evacuation operations, we describe this evacuation process as a Capacitated Vehicle Routing Problem (CVRP) and solve it using a DNN (Deep Neural Network)-based solution (Deep Reinforcement Learning) and a non-DNN solution (Sweep Algorithm). A central question is whether Deep Reinforcement Learning provides sufficient extra routing efficiency to accommodate increased social distancing in a time-constrained evacuation operation. We found that, in comparison to the Sweep Algorithm, Deep Reinforcement Learning can provide decision-makers with more efficient routing. However, the evacuation time saved by Deep Reinforcement Learning does not come close to compensating for the extra time required for social distancing, and its advantage disappears as the emergency vehicle capacity approaches the number of people per household.

Benchmarking of eight recurrent neural network variants for breath phase and adventitious sound detection on a self-developed open-access lung sound database-HF_Lung_V1 (preprint)

A reliable, remote, and continuous real-time respiratory sound monitor with automated respiratory sound analysis ability is urgently required in many clinical scenarios-such as in monitoring disease progression of coronavirus disease 2019-to replace conventional auscultation with a handheld stethoscope. However, a robust computerized respiratory sound analysis algorithm has not yet been validated in practical applications. In this study, we developed a lung sound database (HF_Lung_V1) comprising 9,765 audio files of lung sounds (duration of 15 s each), 34,095 inhalation labels, 18,349 exhalation labels, 13,883 continuous adventitious sound (CAS) labels (comprising 8,457 wheeze labels, 686 stridor labels, and 4,740 rhonchi labels), and 15,606 discontinuous adventitious sound labels (all crackles). We conducted benchmark tests for long short-term memory (LSTM), gated recurrent unit (GRU), bidirectional LSTM (BiLSTM), bidirectional GRU (BiGRU), convolutional neural network (CNN)-LSTM, CNN-GRU, CNN-BiLSTM, and CNN-BiGRU models for breath phase detection and adventitious sound detection. We also conducted a performance comparison between the LSTM-based and GRU-based models, between unidirectional and bidirectional models, and between models with and without a CNN. The results revealed that these models exhibited adequate performance in lung sound analysis. The GRU-based models outperformed, in terms of F1 scores and areas under the receiver operating characteristic curves, the LSTM-based models in most of the defined tasks. Furthermore, all bidirectional models outperformed their unidirectional counterparts. Finally, the addition of a CNN improved the accuracy of lung sound analysis, especially in the CAS detection tasks.

In silico immune infiltration profiling combined with functional enrichment analysis reveals a potential role for naïve B cells as a trigger for severe immune responses in the lungs of COVID-19 patients.

COVID-19, caused by SARS-CoV-2, has rapidly spread to more than 160 countries worldwide since 2020. Despite tremendous efforts and resources spent worldwide trying to explore antiviral drugs, there is still no effective clinical treatment for COVID-19 to date. Approximately 15% of COVID-19 cases progress to pneumonia, and patients with severe pneumonia may die from acute respiratory distress syndrome (ARDS). It is believed that pulmonary fibrosis from SARS-CoV-2 infection further leads to ARDS, often resulting in irreversible impairment of lung function. If the mechanisms by which SARS-CoV-2 infection primarily causes an immune response or immune cell infiltration can be identified, it may be possible to mitigate excessive immune responses by modulating the infiltration and activation of specific targets, thereby reducing or preventing severe lung damage. However, the extent to which immune cell subsets are significantly altered in the lung tissues of COVID-19 patients remains to be elucidated. This study applied the CIBERSORT-X method to comprehensively evaluate the transcriptional estimated immune infiltration landscape in the lung tissues of COVID-19 patients and further compare it with the lung tissues of patients with idiopathic pulmonary fibrosis (IPF). We found a variety of immune cell subtypes in the COVID-19 group, especially naïve B cells were highly infiltrated. Comparison of functional transcriptomic analyses revealed that non-differentiated naïve B cells may be the main cause of the over-active humoral immune response. Using several publicly available single-cell RNA sequencing data to validate the genetic differences in B-cell populations, it was found that the B-cells collected from COVID-19 patients were inclined towards naïve B-cells, whereas those collected from IPF patients were inclined towards memory B-cells. Further differentiation of B cells between COVID-19 mild and severe patients showed that B cells from severe patients tended to be antibody-secreting cells, and gene expression showed that B cells from severe patients were similar to DN2 B cells that trigger extrafollicular response. Moreover, a higher percentage of B-cell infiltration seems associated with poorer clinical outcome. Finally, a comparison of several specific COVID-19 cases treated with targeted B-cell therapy suggests that appropriate suppression of naïve B cells might potentially be a novel strategy to alleviate the severe symptoms of COVID-19.

A Novel SARS-CoV-2 Multitope Protein/Peptide Vaccine Candidate is Highly Immunogenic and Prevents Lung Infection in an Adeno Associated Virus Human Angiotensin-Converting Enzyme 2 (AAV hACE2) Mouse Model (preprint)

In this report, we describe the initial development and proof-of-concept studies for UB-612, the first multitope protein-peptide vaccine against Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the pathogen responsible for the Coronavirus Disease of 2019 (COVID-19). UB-612 consists of eight components rationally designed for induction of high neutralizing antibodies and broad T cell responses against SARS-CoV-2: the S1-RBD-sFc fusion protein, six synthetic peptides (one universal peptide and five SARS-CoV-2-derived peptides), a proprietary CpG TLR-9 agonist, and aluminum phosphate adjuvant. Through immunogenicity studies in guinea pigs and rats, we optimized the design of protein/peptide immunogens and selected an adjuvant system, yielding a vaccine that provided excellent S1-RBD binding and high neutralizing antibody responses, robust cellular responses, and a Th1-oriented response at low doses of the vaccine. Our candidate vaccine was then advanced into challenge studies, in which it reduced viral load and prevented development of disease in a mouse challenge model and in nonhuman primates (NHP, immunogenicity part is completed, challenge is ongoing). A GLP-compliant toxicity study has shown a favorable safety profile for the vaccine. With the Phase 1 trial ongoing in Taiwan and additional trials planned worldwide, UB-612 is a highly promising and differentiated vaccine candidate for prevention of SARS-CoV-2 transmission and COVID-19 disease.

Emetine as an antiviral agent suppresses SARS-CoV-2 replication by inhibiting interaction of viral mRNA with eIF4E: An in vitro study (preprint)

Emetine is a FDA-approved drug for the treatment of amebiasis. In the recent times we had also demonstrated the antiviral efficacy of emetine against some RNA and DNA viruses. Following emergence of the COVID-19, we further evaluated the in vitro antiviral activity of emetine against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The therapeutic index of emetine was determined to be 10910.4, at a cytotoxic concentration 50 (CC50) of 1603.8 nM and effective concentration 50 (EC50) of 0.147 nM. Besides, we also demonstrated the protective efficacy of emetine against lethal challenge with infectious bronchitis virus (IBV; a chicken coronavirus) in the embryonated chicken egg infection model. Emetine treatment was shown to decrease viral RNA and protein synthesis without affecting other steps of viral life cycle such as attachment, entry and budding. In a chromatin immunoprecipitation (CHIP) assay, emetine was shown to disrupt the binding of SARS-CoV-2 RNA with eIF4E (eukaryotic translation initiation factor 4E, a cellular cap-binding protein required for initiation of protein translation). Further, SARS-CoV-2 was shown to exploit ERK/MNK1/eIF4E signalling pathway for its effective replication in the target cells. To conclude, emetine targets SARS-CoV-2 protein synthesis which is mediated via inhibiting the interaction of SARS-CoV-2 RNA with eIF4E. This is a novel mechanistic insight on the antiviral efficacy of emetine. In vitro antiviral efficacy against SARS-CoV-2 and its ability to protect chicken embryos against IBV suggests that emetine could be repurposed to treat COVID-19.