In silico trial to test COVID-19 candidate vaccines: a case study with UISS platform

An overview of immunotoxicity in drug discovery and development

The immune system is one of the common targets of drugs' toxicity (Immunotoxicity) and/or efficacy (Immunotherapy). Immunotoxicity leads to adverse effects on human health, which raises serious concerns for the regulatory agencies. Currently, immunotoxicity assessment is conducted using different in vitro and in vivo assays. In silico and in vitro human cell-based immunotoxicity assays should also be explored for screening purposes as these are time and cost effective as well as for ethical reasons. For in vivo studies, tier 1-3 assessments (Tier 1: hematology, serum globulin levels, lymphoid organ's weight and histopathology; Tier 2: immunophenotyping, TDAR and cell mediated immunity; and Tier 3: host resistance) should be used. These non-clinical in vivo assessments are useful to select immunological endpoints for clinical trials as well as for precautionary labeling. As per regulatory guidelines, adverse immunogenicity information of drug should be included in product's labeling to make health care practitioner aware of safety concerns before prescribing medicines and patient management (USFDA, 2022a, 2022b). This review mainly focuses on the importance of immunotoxicity assessment during drug discovery and development.

Computational Drug Design Strategies for Fighting the COVID-19 Pandemic

The advent of COVID-19 has brought the use of computer tools to the fore in health research. In recent years, computational methods have proven to be highly effective in a variety of areas, including genomic surveillance, host range prediction, drug target identification, and vaccine development. They were also instrumental in identifying new antiviral compounds and repurposing existing therapeutics to treat COVID-19. Using computational approaches, researchers have made significant advances in understanding the molecular mechanisms of COVID-19 and have developed several promising drug candidates and vaccines. This chapter highlights the critical importance of computational drug design strategies in elucidating various aspects of COVID-19 and their contribution to advancing global drug design efforts during the pandemic. Ultimately, the use of computing tools will continue to play an essential role in health research, enabling researchers to develop innovative solutions to combat new and emerging diseases.

Chimeric vaccine design against the conserved TonB-dependent receptor-like β-barrel domain from the outer membrane tbpA and hpuB proteins of Kingella kingae ATCC 23330

Kingella kingae is a Gram-negative bacterium that primarily causes pediatric infections such as septicemia, endocarditis, and osteoarticular infections. Its virulence is attributed to the outer membrane proteins having implications in bacterial adhesion, invasion, nutrition, and host tissue damage. TonB-dependent receptors (TBDRs) play an important role in nutrition and were previously implicated as vaccine targets in other bacteria. Therefore, we targeted the conserved β-barrel TBDR domain of these proteins for designing a vaccine construct that could elicit humoral and cellular immune responses. We used bioinformatic tools to mine TBDR-containing proteins from K. kingae ATCC 23330 and then predict B- and T-cell epitopes from their conserved β-barrel TDR domain. A chimeric vaccine construct was designed using three antigenic epitopes, covering >98% of the world population and capable of inciting humoral and adaptive immune responses. The final construct elicited a robust immune response. Docking and dynamics simulation showed good binding affinity of the vaccine construct to various receptors of the immune system. Additionally, the vaccine was predicted to be safe and non-allergenic, making it a promising candidate for further development. In conclusion, our study demonstrates the potential of immunoinformatics approaches in designing chimeric vaccines against K. kingae infections. The chimeric vaccine we designed can serve as a blueprint for future experimental studies to develop an effective vaccine against this pathogen, which can serve as a potential strategy to prevent K. kingae infections.

A systematic review of cardiac in-silico clinical trials

Computational models of the heart are now being used to assess the effectiveness and feasibility of interventions through in-silico clinical trials (ISCTs). As the adoption and acceptance of ISCTs increases, best practices for reporting the methodology and analysing the results will emerge. Focusing in the area of cardiology, we aim to evaluate the types of ISCTs, their analysis methods and their reporting standards. To this end, we conducted a systematic review of cardiac ISCTs over the period of 1 January 2012-1 January 2022, following the preferred reporting items for systematic reviews and meta-analysis (PRISMA). We considered cardiac ISCTs of human patient cohorts, and excluded studies of single individuals and those in which models were used to guide a procedure without comparing against a control group. We identified 36 publications that described cardiac ISCTs, with most of the studies coming from the US and the UK. In 75% of the studies, a validation step was performed, although the specific type of validation varied between the studies. ANSYS FLUENT was the most commonly used software in 19% of ISCTs. The specific software used was not reported in 14% of the studies. Unlike clinical trials, we found a lack of consistent reporting of patient demographics, with 28% of the studies not reporting them. Uncertainty quantification was limited, with sensitivity analysis performed in only 19% of the studies. In 97% of the ISCTs, no link was provided to provide easy access to the data or models used in the study. There was no consistent naming of study types with a wide range of studies that could potentially be considered ISCTs. There is a clear need for community agreement on minimal reporting standards on patient demographics, accepted standards for ISCT cohort quality control, uncertainty quantification, and increased model and data sharing.

Computational identification of differentially-expressed genes as suggested novel COVID-19 biomarkers: A bioinformatics analysis of expression profiles

COVID-19 was declared a pandemic in March 2020, and since then, it has not stopped spreading like wildfire in almost every corner of the world, despite the many efforts made to stem its spread. SARS-CoV-2 has one of the biggest genomes among RNA viruses and presents unique characteristics that differentiate it from other coronaviruses, making it even more challenging to find a cure or vaccine that is efficient enough. This work aims, using RNA sequencing (RNA-Seq) data, to evaluate whether the expression of specific human genes in the host can vary in different grades of disease severity and to determine the molecular origins of the differences in response to SARS-CoV-2 infection in different patients. In addition to quantifying gene expression, data coming from RNA-Seq allow for the discovery of new transcripts, the identification of alternative splicing events, the detection of allele-specific expression, and the detection of post-transcriptional alterations. For this reason, we performed differential expression analysis on different expression profiles of COVID-19 patients, using RNA-Seq data coming from NCBI public repository, and we obtained the lists of all differentially expressed genes (DEGs) emerging from 7 experimental conditions. We performed a Gene Set Enrichment Analysis (GSEA) on these genes to find possible correlations between DEGs and known disease phenotypes. We mainly focused on DEGs coming out from the analysis of the contrasts involving severe conditions to infer any possible relation between a worsening of the clinical picture and an over-representation of specific genes. Based on the obtained results, this study indicates a small group of genes that result up-regulated in the severe form of the disease. EXOSC5, MESD, REXO2, and TRMT2A genes are not differentially expressed or not present in the other conditions, being for that reason, good biomarkers candidates for the severe form of COVID-19 disease. The use of specific over-expressed genes, whether up-regulated or down-regulated, which have an individual role in each different condition of COVID-19 as a biomarker, can assist in early diagnosis.

Beyond the state of the art of reverse vaccinology: predicting vaccine efficacy with the universal immune system simulator for influenza

When it was first introduced in 2000, reverse vaccinology was defined as an in silico approach that begins with the pathogen's genomic sequence. It concludes with a list of potential proteins with a possible, but not necessarily, list of peptide candidates that need to be experimentally confirmed for vaccine production. During the subsequent years, reverse vaccinology has dramatically changed: now it consists of a large number of bioinformatics tools and processes, namely subtractive proteomics, computational vaccinology, immunoinformatics, and in silico related procedures. However, the state of the art of reverse vaccinology still misses the ability to predict the efficacy of the proposed vaccine formulation. Here, we describe how to fill the gap by introducing an advanced immune system simulator that tests the efficacy of a vaccine formulation against the disease for which it has been designed. As a working example, we entirely apply this advanced reverse vaccinology approach to design and predict the efficacy of a potential vaccine formulation against influenza H5N1. Climate change and melting glaciers are critical due to reactivating frozen viruses and emerging new pandemics. H5N1 is one of the potential strains present in icy lakes that can raise a pandemic. Investigating structural antigen protein is the most profitable therapeutic pipeline to generate an effective vaccine against H5N1. In particular, we designed a multi-epitope vaccine based on predicted epitopes of hemagglutinin and neuraminidase proteins that potentially trigger B-cells, CD4, and CD8 T-cell immune responses. Antigenicity and toxicity of all predicted CTL, Helper T-lymphocytes, and B-cells epitopes were evaluated, and both antigenic and non-allergenic epitopes were selected. From the perspective of advanced reverse vaccinology, the Universal Immune System Simulator, an in silico trial computational framework, was applied to estimate vaccine efficacy using a cohort of 100 digital patients.

In Silico Studies to Support Vaccine Development

The progress that has been made in computer science positioned in silico studies as an important and well-recognized methodology in the drug discovery and development process. It has numerous advantages in terms of costs and also plays a huge impact on the way the research is conducted since it can limit the use of animal models leading to more sustainable research. Currently, human trials are already being partly replaced by in silico trials. EMA and FDA are both endorsing these studies and have been providing webinars and guidance to support them. For instance, PBPK modeling studies are being used to gather data on drug interactions with other drugs and are also being used to support clinical and regulatory requirements for the pediatric population, pregnant women, and personalized medicine. This trend evokes the need to understand the role of in silico studies in vaccines, considering the importance that these products achieved during the pandemic and their promising hope in oncology. Vaccines are safer than other current oncology treatments. There is a huge variety of strategies for developing a cancer vaccine, and some of the points that should be considered when designing the vaccine technology are the following: delivery platforms (peptides, lipid-based carriers, polymers, dendritic cells, viral vectors, etc.), adjuvants (to boost and promote inflammation at the delivery site, facilitating immune cell recruitment and activation), choice of the targeted antigen, the timing of vaccination, the manipulation of the tumor environment, and the combination with other treatments that might cause additive or even synergistic anti-tumor effects. These and many other points should be put together to outline the best vaccine design. The aim of this article is to perform a review and comprehensive analysis of the role of in silico studies to support the development of and design of vaccines in the field of oncology and infectious diseases. The authors intend to perform a literature review of all the studies that have been conducted so far in preparing in silico models and methods to support the development of vaccines. From this point, it was possible to conclude that there are few in silico studies on vaccines. Despite this, an overview of how the existing work could support the design of vaccines is described.

Reverse Vaccinology for Influenza A Virus: From Genome Sequencing to Vaccine Design

Reverse vaccinology (RV) consists in the identification of potentially protective antigens expressed by any organism starting from genomic information and derived from in silico analysis, with the aim of promoting the discovery of new candidate vaccines against different types of pathogens. This approach makes use of bioinformatics techniques to screen the whole genomic sequence of a specific pathogen for the identification of the epitopes that could elicit the best immune response. The use of in silico techniques allows to reduce dramatically both the time and cost required for the identification of a potential vaccine, also facilitating the laborious process of selection of those antigens that, with a traditional approach, would be completely impossible to detect or culture. RV methodologies have been successfully applied for the identification of new vaccines against serogroup B meningococcus (MenB), Bacillus anthracis, Streptococcus pneumonia, Staphylococcus aureus, Chlamydia pneumoniae, Porphyromonas gingivalis, Edwardsiella tarda, and Mycobacterium tuberculosis. As a case of study, we will go in depth into the application of RV techniques on Influenza A virus.

In silico thrombectomy trials for acute ischemic stroke

BACKGROUND AND OBJECTIVE

In silico trials aim to speed up the introduction of new devices in clinical practice by testing device design and performance in different patient scenarios and improving patient stratification for optimizing clinical trials. In this paper, we demonstrate an in silico trial framework for thrombectomy treatment of acute ischemic stroke and apply this framework to compare treatment outcomes in different subpopulations and with different thrombectomy stent-retriever devices. We employ a novel surrogate thrombectomy model to evaluate the thrombectomy success in the in silico trial.

METHODS

The surrogate thrombectomy model, built using data from a fine-grained finite-element model, is a device-specific binary classifier (logistic regression), to estimate the probability of successful recanalization, the outcome of interest. We incorporate this surrogate model within our previously developed in silico trial framework and demonstrate its use with three examples of in silico clinical trials. The first trial is a validation trial for the surrogate thrombectomy model. We then present two exploratory trials: one evaluating the performance of a commercially available device based on the fibrin composition in the occluding thrombus and one comparing the performance of two commercially available stent retrievers.

RESULTS

The Validation Trial showed the surrogate thrombectomy model was able to reproduce a similar recanalization rate as the real-life MR CLEAN trial (p=0.6). Results from the first exploratory trial showed that the chance of successful thrombectomy increases with higher blood cell concentrations in the thrombi, which is in line with observations from clinical data. The second exploratory trial showed improved recanalization success with a newer stent retriever device; however, these results require further investigation as the surrogate model for the newer stent retriever device has not yet been validated.

CONCLUSIONS

In this novel study, we have shown that in silico trials have the potential to help inform medical device developers on the performance of a new device and may also be used to select populations of interest for a clinical trial. This would reduce the time and costs involved in device development and traditional clinical trials.

In silico clinical trials for relapsing-remitting multiple sclerosis with MS TreatSim

BACKGROUND

The last few decades have seen the approval of many new treatment options for Relapsing-Remitting Multiple Sclerosis (RRMS), as well as advances in diagnostic methodology and criteria. These developments have greatly improved the available treatment options for today's Relapsing-Remitting Multiple Sclerosis patients. This increased availability of disease modifying treatments, however, has implications for clinical trial design in this therapeutic area. The availability of better diagnostics and more treatment options have not only contributed to progressively decreasing relapse rates in clinical trial populations but have also resulted in the evolution of control arms, as it is often no longer sufficient to show improvement from placebo. As a result, not only have clinical trials become longer and more expensive but comparing the results to those of "historical" trials has also become more difficult.

METHODS

In order to aid design of clinical trials in RRMS, we have developed a simulator called MS TreatSim which can simulate the response of customizable, heterogeneous groups of patients to four common Relapsing-Remitting Multiple Sclerosis treatment options. MS TreatSim combines a mechanistic, agent-based model of the immune-based etiology of RRMS with a simulation framework for the generation and virtual trial simulation of populations of digital patients.

RESULTS

In this study, the product was first applied to generate diverse populations of digital patients. Then we applied it to reproduce a phase III trial of natalizumab as published 15 years ago as a use case. Within the limitations of synthetic data availability, the results showed the potential of applying MS TreatSim to recreate the relapse rates of this historical trial of natalizumab.

CONCLUSIONS

MS TreatSim's synergistic combination of a mechanistic model with a clinical trial simulation framework is a tool that may advance model-based clinical trial design.

Computational modelling and simulation for immunotoxicity prediction induced by skin sensitisers

In many domains regulating chemicals and chemical products, there is a legal requirement to determine skin sensitivity to allergens. While many in vitro assays to detect contact hypersensitivity have been developed as alternatives to animal testing over the past ten years and significant progress has been made in this area, there is still a need for continued investment in the creation of techniques and strategies that will allow accurate identification of potential contact allergens and their potency in vitro. In silico models are promising tools in this regard. However, none of the state-of-the-art systems seems to function well enough to serve as a stand-alone hazard identification tool, especially in evaluating the possible allergenicity effects in humans. The Universal Immune System Simulator, a mechanistic computational platform that simulates the human immune system response to a specific insult, provides a means of predicting the immunotoxicity induced by skin sensitisers, enriching the collection of computational models for the assessment of skin sensitization. Here, we present a specific disease layer implementation of the Universal Immune System Simulator for the prediction of allergic contact dermatitis induced by specific skin sensitizers.

Comprehensive Highlights of the Universal Efforts towards the Development of COVID-19 Vaccine

The world has taken proactive measures to combat the pandemic since the coronavirus disease 2019 (COVID-19) outbreak, which was caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). These measures range from increasing the production of personal protective equipment (PPE) and highlighting the value of social distancing to the emergency use authorization (EUA) of therapeutic drugs or antibodies and their appropriate use; nonetheless, the disease is still spreading quickly and is ruining people’s social lives, the economy, and public health. As a result, effective vaccines are critical for bringing the pandemic to an end and restoring normalcy in society. Several potential COVID-19 vaccines are now being researched, developed, tested, and reviewed. Since the end of June 2022, several vaccines have been provisionally approved, whereas others are about to be approved. In the upcoming years, a large number of new medications that are presently undergoing clinical testing are anticipated to hit the market. To illustrate the advantages and disadvantages of their technique, to emphasize the additives and delivery methods used in their creation, and to project potential future growth, this study explores these vaccines and the related research endeavors, including conventional and prospective approaches.

The Promises of Speeding Up: Changes in Requirements for Animal Studies and Alternatives during COVID-19 Vaccine Approval-A Case Study

On 21 December 2020, the European Commission granted conditional marketing authorisation for the BNT162b2 COVID-19 vaccine 'Comirnaty', produced by Pfizer/BioNTech. This happened only twelve months after scientists first identified SARS-CoV-2. This stands in stark contrast with the usual ten years needed for vaccine development and approval. Many have suggested that the changes in required animal tests have sped up the development of Comirnaty and other vaccine candidates. However, few have provided an overview of the changes made and interviewed stakeholders on the potential of the pandemic's pressure to achieve a lasting impact. Our research question is: how have stakeholders, including regulatory agencies and pharmaceutical companies, dealt with requirements concerning in vivo animal models in the expedited approval of vaccine candidates such as 'Comirnaty'? We interviewed key stakeholders at the Dutch national and European levels (n = 11 individuals representing five stakeholder groups in eight interviews and two written statements) and analysed relevant publications, policy documents and other grey literature (n = 171 documents). Interviewees observed significant changes in regulatory procedures and requirements for the 'Comirnaty' vaccine compared to vaccine approval in non-pandemic circumstances. Specifically, the European Medicines Agency (EMA) actively promoted changes by using an accelerated assessment and rolling review procedure for fast conditional marketing authorisation, requiring a reduced number of animal studies and accepting more alternatives, allowing pre-clinical in vivo animal experiments to run in parallel with clinical trials and allowing re-use of historical data from earlier vaccine research. Pharmaceutical companies, in turn, actively anticipated these changes and contributed data from non-animal alternative sources for the development phase. After approval, they could also use in vitro methods only for all batch releases due to the thorough characterisation of the mRNA vaccine. Pharmaceutical companies were optimistic about future change because of societal concerns surrounding the use of animals, adding that, in their view, non-animal alternatives generally obtain faster, better, and cheaper results. Regulators we interviewed were more hesitant to permanently implement these changes as they feared backlash regarding safety issues and uncertainty surrounding adverse effects. Our analysis shows how the EMA shortened its approval timeline in times of crisis by reducing the number of requested animal studies and promoting alternative methods. It also highlights the readiness of pharmaceutical companies to contribute to these changes. More research is warranted to investigate these promising possibilities toward further replacement in science and regulations, contributing to faster vaccine development.

Model verification tools: a computational framework for verification assessment of mechanistic agent-based models

BACKGROUND

Nowadays, the inception of computer modeling and simulation in life science is a matter of fact. This is one of the reasons why regulatory authorities are open in considering in silico trials evidence for the assessment of safeness and efficacy of medicinal products. In this context, mechanistic Agent-Based Models are increasingly used. Unfortunately, there is still a lack of consensus in the verification assessment of Agent-Based Models for regulatory approval needs. VV&UQ is an ASME standard specifically suited for the verification, validation, and uncertainty quantification of medical devices. However, it can also be adapted for the verification assessment of in silico trials for medicinal products.

RESULTS

Here, we propose a set of automatic tools for the mechanistic Agent-Based Model verification assessment. As a working example, we applied the verification framework to an Agent-Based Model in silico trial used in the COVID-19 context.

CONCLUSIONS

Using the described verification computational workflow allows researchers and practitioners to easily perform verification steps to prove Agent-Based Models robustness and correctness that provide strong evidence for further regulatory requirements.

In-vitro NET-osis induced by COVID-19 sera is associated to severe clinical course in not vaccinated patients and immune-dysregulation in breakthrough infection

Since neutrophil extracellular traps formation (NET-osis) can be assessed indirectly by treating healthy neutrophils with blood-derived fluids from patients and then measuring the NETs response, we designed a pilot study to convey high-dimensional cytometry of peripheral blood immune cells and cytokines, combined with clinical features, to understand if NET-osis assessment could be included in the immune risk profiling to early prediction of clinical patterns, disease severity, and viral clearance at 28 days in COVID-19 patients. Immune cells composition of peripheral blood, cytokines concentration and in-vitro NETosis were detected in peripheral blood of 41 consecutive COVID-19 inpatients, including 21 mild breakthrough infections compared to 20 healthy donors, matched for sex and age. Major immune dysregulation in peripheral blood in not-vaccinated COVID-19 patients compared to healthy subjects included: a significant reduction of percentage of unswitched memory B-cells and transitional B-cells; loss of naïve CD3⁺CD4⁺CD45RA⁺ and CD3⁺CD8⁺CD45RA⁺ cells, increase of IL-1β, IL-17A and IFN-γ. Myeloid compartment was affected as well, due to the increase of classical (CD14⁺⁺CD16⁻) and intermediate (CD14⁺⁺CD16⁺) monocytes, overexpressing the activation marker CD64, negatively associated to the absolute counts of CD8+ CD45R0+ cells, IFN-γ and IL-6, and expansion of monocytic-like myeloid derived suppressor cells. In not-vaccinated patients who achieved viral clearance by 28 days we found at hospital admission lower absolute counts of effector cells, namely CD8⁺T cells, CD4⁺ T-cells and CD4⁺CD45RO⁺ T cells. Percentage of in-vitro NET-osis induced by patients’ sera and NET-osis density were progressively higher in moderate and severe COVID-19 patients than in mild disease and controls. The percentage of in-vitro induced NET-osis was positively associated to circulating cytokines IL-1β, IFN-γ and IL-6. In breakthrough COVID-19 infections, characterized by mild clinical course, we observed increased percentage of in-vitro NET-osis, higher CD4+ CD45RO+ and CD8+ CD45RO+ T cells healthy or mild-COVID-19 not-vaccinated patients, reduced by 24 h of treatment with ACE inhibitor ramipril. Taken together our data highlight the role of NETs in orchestrating the complex immune response to SARS-COV-2, that should be considered in a multi-target approach for COVID-19 treatment.

Translatability and transferability of in silico models: Context of use switching to predict the effects of environmental chemicals on the immune system

Immunotoxicity hazard identification of chemicals aims to evaluate the potential for unintended effects of chemical exposure on the immune system. Perfluorinated alkylate substances (PFAS), such as perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), are persistent, globally disseminated environmental contaminants known to be immunotoxic. Elevated PFAS exposure is associated with lower antibody responses to vaccinations in children and in adults. In addition, some studies have reported a correlation between PFAS levels in the body and lower resistance to disease, in other words an increased risk of infections or cancers. In this context, modelling and simulation platforms could be used to simulate the human immune system with the aim to evaluate the adverse effects that immunotoxicants may have. Here, we show the conditions under which a mathematical model developed for one purpose and application (e.g., in the pharmaceutical domain) can be successfully translated and transferred to another (e.g., in the chemicals domain) without undergoing significant adaptation. In particular, we demonstrate that the Universal Immune System Simulator was able to simulate the effects of PFAS on the immune system, introducing entities and new interactions that are biologically involved in the phenomenon. This also revealed a potentially exploitable pathway for assessing immunotoxicity through a computational model.

Model Integration in Computational Biology: The Role of Reproducibility, Credibility and Utility

During the COVID-19 pandemic, mathematical modeling of disease transmission has become a cornerstone of key state decisions. To advance the state-of-the-art host viral modeling to handle future pandemics, many scientists working on related issues assembled to discuss the topics. These discussions exposed the reproducibility crisis that leads to inability to reuse and integrate models. This document summarizes these discussions, presents difficulties, and mentions existing efforts towards future solutions that will allow future model utility and integration. We argue that without addressing these challenges, scientists will have diminished ability to build, disseminate, and implement high-impact multi-scale modeling that is needed to understand the health crises we face.

New Biomed-Tech Advances Poised to Change the Future

Biomedical and health technology is progressing at breakneck speed. From specialty pharmacies to general discount shops, store shelves are packed with a vast assortment of wearable medical devices that measure glucose levels, heart rate, and other health metrics; and over-the-counter test kits are helping to check for a wide array of infections. At the same time, electronic health records and other data-sharing platforms have smoothed the mass shift from in-person to virtual office visits over the past two years, and new imaging technologies are allowing earlier disease detection so treatments can begin sooner when they are more effective.

In silico design of recombinant multi-epitope vaccine against influenza A virus

Background

Influenza A virus is one of the leading causes of annual mortality. The emerging of novel escape variants of the influenza A virus is still a considerable challenge in the annual process of vaccine production. The evolution of vaccines ranks among the most critical successes in medicine and has eradicated numerous infectious diseases. Recently, multi-epitope vaccines, which are based on the selection of epitopes, have been increasingly investigated.

Results

This study utilized an immunoinformatic approach to design a recombinant multi-epitope vaccine based on a highly conserved epitope of hemagglutinin, neuraminidase, and membrane matrix proteins with fewer changes or mutate over time. The potential B cells, cytotoxic T lymphocytes (CTL), and CD4 T cell epitopes were identified. The recombinant multi-epitope vaccine was designed using specific linkers and a proper adjuvant. Moreover, some bioinformatics online servers and datasets were used to evaluate the immunogenicity and chemical properties of selected epitopes. In addition, Universal Immune System Simulator (UISS) in silico trial computational framework was run after influenza exposure and recombinant multi-epitope vaccine administration, showing a good immune response in terms of immunoglobulins of class G (IgG), T Helper 1 cells (TH1), epithelial cells (EP) and interferon gamma (IFN-g) levels. Furthermore, after a reverse translation (i.e., convertion of amino acid sequence to nucleotide one) and codon optimization phase, the optimized sequence was placed between the two EcoRV/MscI restriction sites in the PET32a⁺ vector.

Conclusions

The proposed “Recombinant multi-epitope vaccine” was predicted with unique and acceptable immunological properties. This recombinant multi-epitope vaccine can be successfully expressed in the prokaryotic system and accepted for immunogenicity studies against the influenza virus at the in silico level. The multi-epitope vaccine was then tested with the Universal Immune System Simulator (UISS) in silico trial platform. It revealed slight immune protection against the influenza virus, shedding the light that a multistep bioinformatics approach including molecular and cellular level is mandatory to avoid inappropriate vaccine efficacy predictions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04581-6.

A contemporary review on the important role of in silico approaches for managing different aspects of COVID-19 crisis

In the last century, the emergence of in silico tools has improved the quality of healthcare studies by providing high quality predictions. In the case of COVID-19, these tools have been advantageous for bioinformatics analysis of SARS-CoV-2 structures, studying potential drugs and introducing drug targets, investigating the efficacy of potential natural product components at suppressing COVID-19 infection, designing peptide-mimetic and optimizing their structure to provide a better clinical outcome, and repurposing of the previously known therapeutics. These methods have also helped medical biotechnologists to design various vaccines; such as multi-epitope vaccines using reverse vaccinology and immunoinformatics methods, among which some of them have showed promising results through in vitro, in vivo and clinical trial studies. Moreover, emergence of artificial intelligence and machine learning algorithms have helped to classify the previously known data and use them to provide precise predictions and make plan for future of the pandemic condition. At this contemporary review, by collecting related information from the collected literature on valuable data sources; such as PubMed, Scopus, and Web of Science, we tried to provide a brief outlook regarding the importance of in silico tools in managing different aspects of COVID-19 pandemic infection and how these methods have been helpful to biomedical researchers.

Towards in silico Models of the Inflammatory Response in Bone Fracture Healing

In silico modeling is a powerful strategy to investigate the biological events occurring at tissue, cellular and subcellular level during bone fracture healing. However, most current models do not consider the impact of the inflammatory response on the later stages of bone repair. Indeed, as initiator of the healing process, this early phase can alter the regenerative outcome: if the inflammatory response is too strongly down- or upregulated, the fracture can result in a non-union. This review covers the fundamental information on fracture healing, in silico modeling and experimental validation. It starts with a description of the biology of fracture healing, paying particular attention to the inflammatory phase and its cellular and subcellular components. We then discuss the current state-of-the-art regarding in silico models of the immune response in different tissues as well as the bone regeneration process at the later stages of fracture healing. Combining the aforementioned biological and computational state-of-the-art, continuous, discrete and hybrid modeling technologies are discussed in light of their suitability to capture adequately the multiscale course of the inflammatory phase and its overall role in the healing outcome. Both in the establishment of models as in their validation step, experimental data is required. Hence, this review provides an overview of the different in vitro and in vivo set-ups that can be used to quantify cell- and tissue-scale properties and provide necessary input for model credibility assessment. In conclusion, this review aims to provide hands-on guidance for scientists interested in building in silico models as an additional tool to investigate the critical role of the inflammatory phase in bone regeneration.

A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets

The COVID-19 pandemic has highlighted the need to come out with quick interventional solutions that can now be obtained through the application of different bioinformatics software to actively improve the success rate. Technological advances in fields such as computer modeling and simulation are enriching the discovery, development, assessment and monitoring for better prevention, diagnosis, treatment and scientific evidence generation of specific therapeutic strategies. The combined use of both molecular prediction tools and computer simulation in the development or regulatory evaluation of a medical intervention, are making the difference to better predict the efficacy and safety of new vaccines. An integrated bioinformatics pipeline that merges the prediction power of different software that act at different scales for evaluating the elicited response of human immune system against every pathogen is proposed. As a working example, we applied this problem solving protocol to predict the cross-reactivity of pre-existing vaccination interventions against SARS-CoV-2.

Forecasting the impact of environmental stresses on the frequent waves of COVID19

A novel approach to link the environmental stresses with the COVID-19 cases is adopted during this research. The time-dependent data are extracted from the online repositories that are freely available for knowledge and research. Since the time series data analysis is desired for the COVID-19 time-dependent frequent waves, here in this manuscript, we have developed a time series model with the aid of "nonlinear autoregressive network with exogenous inputs (NARX)" approach. The distribution of infectious agent-containing droplets from an infected person to an uninfected person is a common form of respiratory disease transmission. SARS-CoV-2 has mainly spread via short-range respiratory droplet transmission. Airborne transmission of SARS-CoV-2 seems to have occurred over long distances or times in unusual conditions; SARS-CoV-2 RNA was found in PM10 collected in Italy. This research shows that SARS-CoV-2 particles adsorbed to outdoor PM remained viable for a long time, given the epidemiology of COVID-19, outdoor air pollution is unlikely to be a significant route of transmission. In this research, ANN time series is used to analyze the data resulting from the COVID-19 first and second waves and the forecasted results show that air pollution affects people in different areas of Italy and make more people sick with covid-19. The model is developed based on the disease transmission data of Italy.

The COVID-19 vaccine development: A pandemic paradigm

COVID-19 pandemic has resulted in millions of deaths and a social-economic crisis. A worldwide effort was made to develop efficient vaccines for this disease. A vaccine should produce immune responses with specific and neutralizing antibodies, and without harmful effects such as the antibody-dependent enhancement that may be associated with severe acute respiratory syndrome. Vaccine design involves the selection of platforms that includes viral, viral-vector, protein, nucleic acid, or trained immunity-based strategies. Its development initiates at a pre-clinical stage, followed by clinical trials when successful. Only if clinical trials show no significant evidence of safety concerns, vaccines can be manufactured, stored, and distributed to immunize the population. So far, regulatory authorities from many countries have approved nine vaccines with phase 3 results. In the current pandemic, a paradigm for the COVID-19 vaccine development has arisen, as many challenges must be overcome. Mass-production and cold-chain storage to immunize large human populations should be feasible and fast, and a combination of different vaccines may boost logistics and immunization. In silico trials is an emerging and innovative field that can be applied to predict and simulate immune, molecular, clinical, and epidemiological outcomes of vaccines to refine, reduce, and partially replace steps in vaccine development. Vaccine-resistant variants of SARS-CoV-2 might emerge, leading to the necessity of updates. A globally fair vaccine distribution system must prevail over vaccine nationalism for the world to return to its pre-pandemic status.

Immune-checkpoint inhibitors from cancer to COVID‑19: A promising avenue for the treatment of patients with COVID‑19 (Review)

The severe acute respiratory syndrome associated coronavirus‑2 (SARS‑CoV‑2) poses a threat to human life worldwide. Since early March, 2020, coronavirus disease 2019 (COVID‑19), characterized by an acute and often severe form of pneumonia, has been declared a pandemic. This has led to a boom in biomedical research studies at all stages of the pipeline, from the in vitro to the clinical phase. In line with this global effort, known drugs, currently used for the treatment of other pathologies, including antivirals, immunomodulating compounds and antibodies, are currently used off‑label for the treatment of COVID‑19, in association with the supportive standard care. Yet, no effective treatments have been identified. A new hope stems from medical oncology and relies on the use of immune‑checkpoint inhibitors (ICIs). In particular, amongst the ICIs, antibodies able to block the programmed death‑1 (PD‑1)/PD ligand-1 (PD‑L1) pathway have revealed a hidden potential. In fact, patients with severe and critical COVID‑19, even prior to the appearance of acute respiratory distress syndrome, exhibit lymphocytopenia and suffer from T‑cell exhaustion, which may lead to viral sepsis and an increased mortality rate. It has been observed that cancer patients, who usually are immunocompromised, may restore their anti‑tumoral immune response when treated with ICIs. Moreover, viral-infected mice and humans, exhibit a T‑cell exhaustion, which is also observed following SARS‑CoV‑2 infection. Importantly, when treated with anti‑PD‑1 and anti‑PD‑L1 antibodies, they restore their T‑cell competence and efficiently counteract the viral infection. Based on these observations, four clinical trials are currently open, to examine the efficacy of anti‑PD‑1 antibody administration to both cancer and non‑cancer individuals affected by COVID‑19. The results may prove the hypothesis that restoring exhausted T‑cells may be a winning strategy to beat SARS‑CoV‑2 infection.

Toward computational modelling on immune system function

The 3rd edition of the computational methods for the immune system function workshop has been held in San Diego, CA, in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2019) from November 18 to 21, 2019. The workshop has continued its growing tendency, with a total of 18 accepted papers that have been presented in a full day workshop. Among these, the best 10 papers have been selected and extended for presentation in this special issue. The covered topics range from computer-aided identification of T cell epitopes to the prediction of heart rate variability to prevent brain injuries, from In Silico modeling of Tuberculosis and generation of digital patients to machine learning applied to predict type-2 diabetes risk.