Refined efficacy estimates of the Sanofi Pasteur dengue vaccine CYD-TDV using machine learning

Potential of several triazene derivatives against DENGUE viruses

Dengue fever is an infectious disease caused by the dengue virus (DENV), an RNA Flavivirus transmitted by the mosquitoes Aedes aegypti and Aedes albopictus widespread in tropical, subtropical and also temperate regions. Symptoms range from a simple cold to a severe, life-threatening haemorrhagic fever. According to the WHO, it affects around 390 million people per year. No antiviral treatment for DENV is available, and the Dengvaxia vaccine is only intended for people over 9 years of age who have contracted dengue one time in the past, and shows serotype-specific effectiveness. There is therefore a crying need to discover new molecules with antiviral power against flaviviruses. The present study was carried out to evaluate the anti-DENV activities and cytotoxicity of triazenes obtained by diazocopulation. Some triazenes were highly cytotoxic (16, and 25) to hepatocarcinoma Huh7 cells, whereas others displayed strong anti-DENV potential. The antiviral activity ranged from EC50 = 7.82 µM to 48.12 µM in cellulo, with a selectivity index (CC50/EC50) greater than 9 for two of the compounds (10, and 20). In conclusion, these new triazenes could serve as a lead to develop and optimize drugs against DENV.

Facing the escalating burden of dengue: Challenges and perspectives

Dengue is the most rapidly emerging mosquito-borne infection and, due to climate change and unplanned urbanization, it is predicted that the global burden of dengue will rise further as the infection spreads to new geographical locations. Dengue-endemic countries are often unable to cope with such increases, with health care facilities becoming overwhelmed during each dengue season. Furthermore, although dengue has been predominantly a childhood illness in the past, it currently mostly affects adults in many countries, with higher incidence of severe disease and mortality rates in pregnant women and in those with comorbidities. As there is currently no specific treatment for dengue and no early biomarker to identify those who will progress to develop vascular leakage, all individuals with dengue are closely monitored in case they need fluid management. Furthermore, diagnosing patients with acute dengue is challenging due to the similarity of clinical symptoms during early illness and poor sensitivity and specificity of point-of-care diagnostic tests. Novel vector control methods, such as the release of Wolbachia-infected mosquitoes, have shown promising results by reducing vector density and dengue incidence in clinical trial settings. A new dengue vaccine, TAK-003, had an efficacy of 61.2% against virologically confirmed dengue, 84.1% efficacy against hospitalizations and a 70% efficacy against development of dengue haemorrhagic fever (DHF) at 54 months. While vaccines and mosquito control methods are welcome, they alone are unlikely to fully reduce the burden of dengue, and a treatment for dengue is therefore essential. Several novel antiviral drugs are currently being evaluated along with drugs that inhibit host mediators, such as mast cell products. Although viral proteins such as NS1 contribute to the vascular leak observed in severe dengue, the host immune response to the viral infection also plays a significant role in progression to severe disease. There is an urgent need to discover safe and effective treatments for dengue to prevent disease progression.

Hit-to-Lead Short Peptides against Dengue Type 2 Envelope Protein: Computational and Experimental Investigations

Data from the World Health Organisation show that the global incidence of dengue infection has risen drastically, with an estimated 400 million cases of dengue infection occurring annually. Despite this worrying trend, there is still no therapeutic treatment available. Herein, we investigated short peptide fragments with a varying total number of amino acid residues (peptide fragments) from previously reported dengue virus type 2 (DENV2) peptide-based inhibitors, DN58wt (GDSYIIIGVEPGQLKENWFKKGSSIGQMF), DN58opt (TWWCFYFCRRHHPFWFFYRHN), DS36wt (LITVNPIVTEKDSPVNIEAE), and DS36opt (RHWEQFYFRRRERKFWLFFW), aided by in silico approaches: peptide–protein molecular docking and 100 ns of molecular dynamics (MD) simulation via molecular mechanics using Poisson–Boltzmann surface area (MMPBSA) and molecular mechanics generalised Born surface area (MMGBSA) methods. A library of 11,699 peptide fragments was generated, subjected to in silico calculation, and the candidates with the excellent binding affinity and shown to be stable in the DI-DIII binding pocket of DENV2 envelope (E) protein were determined. Selected peptides were synthesised using conventional Fmoc solid-phase peptide chemistry, purified by RP-HPLC, and characterised using LCMS. In vitro studies followed, to test for the peptides’ toxicity and efficacy in inhibiting the DENV2 growth cycle. Our studies identified the electrostatic interaction (from free energy calculation) to be the driving stabilising force for the E protein–peptide interactions. Five key E protein residues were also identified that had the most interactions with the peptides: (polar) LYS36, ASN37, and ARG350, and (nonpolar) LEU351 and VAL354; these residues might play crucial roles in the effective binding interactions. One of the peptide fragments, DN58opt_8-13 (PFWFFYRH), showed the best inhibitory activity, at about 63% DENV2 plague reduction, compared with no treatment. This correlates well with the in silico studies in which the peptide possessed the lowest binding energy (−9.0 kcal/mol) and was maintained steadily within the binding pocket of DENV2 E protein during the MD simulations. This study demonstrates the use of computational studies to expand research on lead optimisation of antiviral peptides, thus explaining the inhibitory potential of the designed peptides.

Dengue: current state one year before WHO 2010-2020 goals

BACKGROUND

Dengue is a possibly life-threatening human mosquito-borne viral infection widely spread in peridomestic (sub)tropical climates. The global incidence has expanded rapidly in the last decades, with 40% of the world's population currently at risk. To date, no anti-viral treatment other than supportive care exists. In 2015, the first and only dengue-vaccine, CYD-TDV, received marketing authorization.

OBJECTIVES

To present the current understanding of dengue in terms of epidemiology, transmission, pathogenesis, disease management and prevention. To illustrate the knowledge gaps that remain to be filled in order to control dengue and achieve the WHO 2010-2020 goals.

METHODS

An updated systematic review (2009-2019) was carried out. The databases Pubmed, Embase and The Cochrane Library were searched along with WHO and CDC guidelines.

RESULTS

In total, 39 articles were included. Contemporary climatic and economic factors significantly contributed to the emergence of epidemic dengue. Unfortunately, CYD-TDV failed to meet safety and efficacy demands. New vaccination approaches are in the pipeline along with innovative vector-control strategies. Current anti-viral drug research focuses on repurposing drugs in addition to specific anti-dengue strategies that interfere with viral replication.

CONCLUSION

The lack of understanding dengue pathogenesis and immunology has hampered the development of an effective vaccine. Recent research has provided new insights into the therapeutic and prophylactic approach. Implementation of complementary methods to control disease burden are required considering the socio-economic impact of this rapidly emerging global disease.

The Importance of Nanocarrier Design and Composition for an Efficient Nanoparticle-Mediated Transdermal Vaccination

The World Health Organization estimates that the pandemic caused by the SARS-CoV-2 virus claimed more than 3 million lives in 2020 alone. This situation has highlighted the importance of vaccination programs and the urgency of working on new technologies that allow an efficient, safe, and effective immunization. From this perspective, nanomedicine has provided novel tools for the design of the new generation of vaccines. Among the challenges of the new vaccine generations is the search for alternative routes of antigen delivery due to costs, risks, need for trained personnel, and low acceptance in the population associated with the parenteral route. Along these lines, transdermal immunization has been raised as a promising alternative for antigen delivery and vaccination based on a large absorption surface and an abundance of immune system cells. These features contribute to a high barrier capacity and high immunological efficiency for transdermal immunization. However, the stratum corneum barrier constitutes a significant challenge for generating new pharmaceutical forms for transdermal antigen delivery. This review addresses the biological bases for transdermal immunomodulation and the technological advances in the field of nanomedicine, from the passage of antigens facilitated by devices to cross the stratum corneum, to the design of nanosystems, with an emphasis on the importance of design and composition towards the new generation of needle-free nanometric transdermal systems.

Comparison of machine learning methods for estimating case fatality ratios: An Ebola outbreak simulation study

BACKGROUND

Machine learning (ML) algorithms are now increasingly used in infectious disease epidemiology. Epidemiologists should understand how ML algorithms behave within the context of outbreak data where missingness of data is almost ubiquitous.

METHODS

Using simulated data, we use a ML algorithmic framework to evaluate data imputation performance and the resulting case fatality ratio (CFR) estimates, focusing on the scale and type of data missingness (i.e., missing completely at random-MCAR, missing at random-MAR, or missing not at random-MNAR).

RESULTS

Across ML methods, dataset sizes and proportions of training data used, the area under the receiver operating characteristic curve decreased by 7% (median, range: 1%-16%) when missingness was increased from 10% to 40%. Overall reduction in CFR bias for MAR across methods, proportion of missingness, outbreak size and proportion of training data was 0.5% (median, range: 0%-11%).

CONCLUSION

ML methods could reduce bias and increase the precision in CFR estimates at low levels of missingness. However, no method is robust to high percentages of missingness. Thus, a datacentric approach is recommended in outbreak settings-patient survival outcome data should be prioritised for collection and random-sample follow-ups should be implemented to ascertain missing outcomes.

Efficacy profile of the CYD-TDV dengue vaccine revealed by Bayesian survival analysis of individual-level phase III data

Background:

Sanofi-Pasteur’s CYD-TDV is the only licensed dengue vaccine. Two phase three trials showed higher efficacy in seropositive than seronegative recipients. Hospital follow-up revealed increased hospitalisation in 2–5- year-old vaccinees, where serostatus and age effects were unresolved.

Methods:

We fit a survival model to individual-level data from both trials, including year 1 of hospital follow-up. We determine efficacy by age, serostatus, serotype and severity, and examine efficacy duration and vaccine action mechanism.

Results:

Our modelling indicates that vaccine-induced immunity is long-lived in seropositive recipients, and therefore that vaccinating seropositives gives higher protection than two natural infections. Long-term increased hospitalisation risk outweighs short-lived immunity in seronegatives. Independently of serostatus, transient immunity increases with age, and is highest against serotype 4. Benefit is higher in seropositives, and risk enhancement is greater in seronegatives, against hospitalised disease than against febrile disease.

Conclusions:

Our results support vaccinating seropositives only. Rapid diagnostic tests would enable viable ‘screen-then-vaccinate’ programs. Since CYD-TDV acts as a silent infection, long-term safety of other vaccine candidates must be closely monitored.

Funding:

Bill & Melinda Gates Foundation, National Institute for Health Research, UK Medical Research Council, Wellcome Trust, Royal Society.

Clinical trial number:

NCT01373281 and NCT01374516.

Machine Learning in Discovery of New Antivirals and Optimization of Viral Infections Therapy

Nowadays, computational approaches play an important role in the design of new drug-like compounds and optimization of pharmacotherapeutic treatment of diseases. The emerging growth of viral infections, including those caused by the Human Immunodeficiency Virus (HIV), Ebola virus, recently detected coronavirus, and some others, leads to many newly infected people with a high risk of death or severe complications. A huge amount of chemical, biological, clinical data is at the disposal of the researchers. Therefore, there are many opportunities to find the relationships between the particular features of chemical data and the antiviral activity of biologically active compounds based on machine learning approaches. Biological and clinical data can also be used for building models to predict relationships between viral genotype and drug resistance, which might help determine the clinical outcome of treatment. In the current study, we consider machine-learning approaches in the antiviral research carried out during the past decade. We overview in detail the application of machine-learning methods for the design of new potential antiviral agents and vaccines, drug resistance prediction, and analysis of virus-host interactions. Our review also covers the perspectives of using the machine-learning approaches for antiviral research, including Dengue, Ebola viruses, Influenza A, Human Immunodeficiency Virus, coronaviruses, and some others.

Functional Profiling of Antibody Immune Repertoires in Convalescent Zika Virus Disease Patients

The re-emergence of Zika virus (ZIKV) caused widespread infections that were linked to Guillain-Barré syndrome in adults and congenital malformation in fetuses, and epidemiological data suggest that ZIKV infection can induce protective antibody responses. A more detailed understanding of anti-ZIKV antibody responses may lead to enhanced antibody discovery and improved vaccine designs against ZIKV and related flaviviruses. Here, we applied recently-invented library-scale antibody screening technologies to determine comprehensive functional molecular and genetic profiles of naturally elicited human anti-ZIKV antibodies in three convalescent individuals. We leveraged natively paired antibody yeast display and NGS to predict antibody cross-reactivities and coarse-grain antibody affinities, to perform in-depth immune profiling of IgM, IgG, and IgA antibody repertoires in peripheral blood, and to reveal virus maturation state-dependent antibody interactions. Repertoire-scale comparison of ZIKV VLP-specific and non-specific antibodies in the same individuals also showed that mean antibody somatic hypermutation levels were substantially influenced by donor-intrinsic characteristics. These data provide insights into antiviral antibody responses to ZIKV disease and outline systems-level strategies to track human antibody immune responses to emergent viral infections.

Case Fatality Ratio Estimates for the 2013-2016 West African Ebola Epidemic: Application of Boosted Regression Trees for Imputation

BACKGROUND

The 2013-2016 West African Ebola epidemic has been the largest to date with >11 000 deaths in the affected countries. The data collected have provided more insight into the case fatality ratio (CFR) and how it varies with age and other characteristics. However, the accuracy and precision of the naive CFR remain limited because 44% of survival outcomes were unreported.

METHODS

Using a boosted regression tree model, we imputed survival outcomes (ie, survival or death) when unreported, corrected for model imperfection to estimate the CFR without imputation, with imputation, and adjusted with imputation. The method allowed us to further identify and explore relevant clinical and demographic predictors of the CFR.

RESULTS

The out-of-sample performance (95% confidence interval [CI]) of our model was good: sensitivity, 69.7% (52.5-75.6%); specificity, 69.8% (54.1-75.6%); percentage correctly classified, 69.9% (53.7-75.5%); and area under the receiver operating characteristic curve, 76.0% (56.8-82.1%). The adjusted CFR estimates (95% CI) for the 2013-2016 West African epidemic were 82.8% (45.6-85.6%) overall and 89.1% (40.8-91.6%), 65.6% (61.3-69.6%), and 79.2% (45.4-84.1%) for Sierra Leone, Guinea, and Liberia, respectively. We found that district, hospitalisation status, age, case classification, and quarter (date of case reporting aggregated at three-month intervals) explained 93.6% of the variance in the naive CFR.

CONCLUSIONS

The adjusted CFR estimates improved the naive CFR estimates obtained without imputation and were more representative. Used in conjunction with other resources, adjusted estimates will inform public health contingency planning for future Ebola epidemics, and help better allocate resources and evaluate the effectiveness of future inventions.

Novel anti-flavivirus drugs targeting the nucleolar distribution of core protein

The risk of infectious diseases caused by Flavivirus is increasing globally. Here, we developed a novel high-throughput screening (HTS) system to evaluate the inhibitory effects of compounds targeting the nuclear localization of the flavivirus core protein. We screened 4000 compounds based on their ability to inhibit the nuclear localization of the core protein, and identified over 20 compounds including inhibitors for cyclin dependent kinase and glycogen synthase kinase. The efficacy of the identified compounds to suppress viral growth was validated in a cell-based infection system. Remarkably, the nucleolus morphology was affected by the treatment with the compounds, suggesting that the nucleolus function is critical for viral propagation. The present HTS system provides a useful strategy for the identification of antivirals against flavivirus by targeting the nucleolar localization of the core protein.

Cross-serotype interactions and disease outcome prediction of dengue infections in Vietnam

Dengue pathogenesis is extremely complex. Dengue infections are thought to induce life-long immunity from homologous challenges as well as a multi-factorial heterologous risk enhancement. Here, we use the data collected from a prospective cohort study of dengue infections in schoolchildren in Vietnam to disentangle how serotype interactions modulate clinical disease risk in the year following serum collection. We use multinomial logistic regression to correlate the yearly neutralizing antibody measurements obtained with each infecting serotype in all dengue clinical cases collected over the course of 6 years (2004–2009). This allowed us to extrapolate a fully discretised matrix of serotype interactions, revealing clear signals of increased risk of clinical illness in individuals primed with a previous dengue infection. The sequences of infections which produced a higher risk of dengue fever upon secondary infection are: DEN1 followed by DEN2; DEN1 followed by DEN4; DEN2 followed by DEN3; and DEN4 followed by DEN3. We also used this longitudinal data to train a machine learning algorithm on antibody titre differences between consecutive years to unveil asymptomatic dengue infections and estimate asymptomatic infection to clinical case ratios over time, allowing for a better characterisation of the population’s past exposure to different serotypes.

Expression of soluble, glycosylated and correctly folded dengue virus NS1 protein in Pichia pastoris

The dengue virus (DENV) non structural protein 1 (NS1) is a 46-55 kDa protein that exists as homodimer inside cells and as hexamer in the extracellular milieu. Several lines of evidence have demonstrated that the biochemical and structural properties of recombinant NS1 (rNS1) vary depending on the protein expression system used. Aiming to improve current tools for studying NS1 multiple roles, a recombinant tag-free NS1 protein was expressed and purified from P. pastoris yeast cells. The best expression condition was achieved using GS115 strain and induction for 72 h with 0.7% methanol addition every 24 h. Total secreted rNS1 reached 2.18 mg in 1 L culture and the final yield of the purified protein was 1 mg per liter of culture (recovery yield of approximately 45.9%). Size-exclusion chromatography and treatment with EndoH and PNGase indicate that it exists as an N-glycosylated homodimer. Moreover, an ELISA assay with specific DENV anti-NS1 antibody that recognizes conformational epitopes revealed that rNS1 has most of its conformational epitopes preserved. The expression of rNS1 in its native conformation is an important tool for further studies of its role in DENV pathogenesis and replication.