Patients presenting to the ED with nonfatal drug overdose: Self-reported history of overdose and naloxone use.

BACKGROUND: In the context of polysubstance use and fentanyl detection in non-opioid drugs supplies (e.g., cocaine, methamphetamine), it is important to re-evaluate and expand our understanding of which populations are at high risk for fatal drug overdoses. The primary objective of this pilot study was to gather data from the ED to characterize the population presenting with drug overdose, including demographics, drug use patterns and comorbidities, to inform upstream overdose prevention efforts. METHODS: A consecutive sample of ED patients undergoing treatment for non-fatal overdose were prospectively recruited for study participation at the time of ED visit. Participants reported history of substance use over the past six months, recent and lifetime overdose, and naloxone receipt and administration history. RESULTS: A total of 76 eligible participants were enrolled over the course of seven months. Participants reported high rates of opioid (56%), stimulant (56%), and cannabis use (59%). Self-reported polysubstance use, defined as self-reported use of more than one substance, was 83%. Of enrolled participants, 64% reported at least one overdose and 39% reported three or more lifetime overdoses prior to their index overdose ED visit. Participants with no self-reported intentional opioid use (n = 32) in the past six months had fentanyl positive urine drug screen 84% of the time versus 89% in the overall study population (n = 74). Participants who did not report opioid use in the past six months were less likely to possess (34% vs. 55%) or to know how to acquire (50% vs. 74%) naloxone compared to participants with self-reported history of opioid use. CONCLUSION: This study demonstrated high rates of fentanyl exposure on toxicology testing at time of overdose across all participants including study participants without self-reported intentional opioid use. Data gathered in the ED at time of overdose can be used to inform upstream naloxone distribution and public health initiatives.

Natural Language Processing to Extract Meaningful Information from a Corpus of Written Knowledge in Breast Cancer: Transforming Books into Data.

Introduction: Books and papers are the most relevant source of theoretical knowledge for medical education. New technologies of artificial intelligence can be designed to assist in selected educational tasks, such as reading a corpus made up of multiple documents and extracting relevant information in a quantitative way. Methods: Thirty experts were selected transparently using an online public call on the website of the sponsor organization and on its social media. Six books edited or co-edited by members of this panel containing a general knowledge of breast cancer or specific surgical knowledge have been acquired. This collection was used by a team of computer scientists to train an artificial neural network based on a technique called Word2Vec. Results: The corpus of six books contained about 2.2 billion words for 300d vectors. A few tests were performed. We evaluated cosine similarity between different words. Discussion: This work represents an initial attempt to derive formal information from textual corpus. It can be used to perform an augmented reading of the corpus of knowledge available in books and papers as part of a discipline. This can generate new hypothesis and provide an actual estimate of their association within the expert opinions. Word embedding can also be a good tool when used in accruing narrative information from clinical notes, reports, etc., and produce prediction about outcomes. More work is expected in this promising field to generate "real-world evidence."

Moving forward through the in silico modeling of multiple sclerosis: Treatment layer implementation and validation.

Multiple sclerosis is an autoimmune inflammatory disease that affects the central nervous system through chronic demyelination and loss of oligodendrocytes. Since the relapsing-remitting form is the most prevalent, relapse-reducing therapies are a primary choice for specialists. Universal Immune System Simulator is an agent-based model that simulates the human immune system dynamics under physiological conditions and during several diseases, including multiple sclerosis. In this work, we extended the UISS-MS disease layer by adding two new treatments, i.e., cladribine and ocrelizumab, to show that UISS-MS can be potentially used to predict the effects of any existing or newly designed treatment against multiple sclerosis. To retrospectively validate UISS-MS with ocrelizumab and cladribine, we extracted the clinical and MRI data from patients included in two clinical trials, thus creating specific cohorts of digital patients for predicting and validating the effects of the considered drugs. The obtained results mirror those of the clinical trials, demonstrating that UISS-MS can correctly simulate the mechanisms of action and outcomes of the treatments. The successful retrospective validation concurred to confirm that UISS-MS can be considered a digital twin solution to be used as a support system to inform clinical decisions and predict disease course and therapeutic response at a single patient level.

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.

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.

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.

Boosting multiple sclerosis lesion segmentation through attention mechanism.

Magnetic resonance imaging is a fundamental tool to reach a diagnosis of multiple sclerosis and monitoring its progression. Although several attempts have been made to segment multiple sclerosis lesions using artificial intelligence, fully automated analysis is not yet available. State-of-the-art methods rely on slight variations in segmentation architectures (e.g. U-Net, etc.). However, recent research has demonstrated how exploiting temporal-aware features and attention mechanisms can provide a significant boost to traditional architectures. This paper proposes a framework that exploits an augmented U-Net architecture with a convolutional long short-term memory layer and attention mechanism which is able to segment and quantify multiple sclerosis lesions detected in magnetic resonance images. Quantitative and qualitative evaluation on challenging examples demonstrated how the method outperforms previous state-of-the-art approaches, reporting an overall Dice score of 89% and also demonstrating robustness and generalization ability on never seen new test samples of a new dedicated under construction dataset.

Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.

In 2013, the Global Coalition for Regulatory Science Research (GCRSR) was established with members from over ten countries (www.gcrsr.net). One of the main objectives of GCRSR is to facilitate communication among global regulators on the rise of new technologies with regulatory applications through the annual conference Global Summit on Regulatory Science (GSRS). The 11th annual GSRS conference (GSRS21) focused on "Regulatory Sciences for Food/Drug Safety with Real-World Data (RWD) and Artificial Intelligence (AI)." The conference discussed current advancements in both AI and RWD approaches with a specific emphasis on how they impact regulatory sciences and how regulatory agencies across the globe are pursuing the adaptation and oversight of these technologies. There were presentations from Brazil, Canada, India, Italy, Japan, Germany, Switzerland, Singapore, the United Kingdom, and the United States. These presentations highlighted how various agencies are moving forward with these technologies by either improving the agencies' operation and/or preparing regulatory mechanisms to approve the products containing these innovations. To increase the content and discussion, the GSRS21 hosted two debate sessions on the question of "Is Regulatory Science Ready for AI?" and a workshop to showcase the analytical data tools that global regulatory agencies have been using and/or plan to apply to regulatory science. Several key topics were highlighted and discussed during the conference, such as the capabilities of AI and RWD to assist regulatory science policies for drug and food safety, the readiness of AI and data science to provide solutions for regulatory science. Discussions highlighted the need for a constant effort to evaluate emerging technologies for fit-for-purpose regulatory applications. The annual GSRS conferences offer a unique platform to facilitate discussion and collaboration across regulatory agencies, modernizing regulatory approaches, and harmonizing efforts.

Wild Artichoke (Cynara cardunculus subsp. sylvestris, Asteraceae) Leaf Extract: Phenolic Profile and Oxidative Stress Inhibitory Effects on HepG2 Cells.

Cynara cardunculus subsp. sylvestris (wild artichoke) is widespread in Sicily, where it has been used for food and medicinal purposes since ancient times; decoctions of the aerial parts of this plant have been traditionally employed as a remedy for different hepatic diseases. In this study, the phenolic profile and cell-free antioxidant properties of the leaf aqueous extract of wild artichokes grown in Sicily (Italy) were investigated. The crude extract was also tested in cells for its antioxidant characteristics and potential oxidative stress inhibitory effects. To resemble the features of the early stage of mild steatosis in humans, human HepG2 cells treated with free fatty acids at the concentration of 1.5 mM were used. HPLC-DAD analysis revealed the presence of several phenolic acids (caffeoylquinic acids) and flavonoids (luteolin and apigenin derivatives). At the same time, DPPH assay showed a promising antioxidant power (IC50 = 20.04 ± 2.52 µg/mL). Biological investigations showed the safety of the crude extract and its capacity to counteract the injury induced by FFA exposure by restoring cell viability and counteracting oxidative stress through inhibiting reactive oxygen species and lipid peroxidation and increasing thiol-group levels. In addition, the extract increased mRNA expression of some proteins implicated in the antioxidant defense (Nrf2, Gpx, and SOD1) and decreased mRNA levels of inflammatory cytokines (IL-6, TNF-α, and IL-1ß), which were modified by FFA treatment. Results suggest that the total phytocomplex contained in wild artichoke leaves effectively modulates FFA-induced hepatic oxidative stress.

Chemical, Antioxidant and Biological Studies of Brassica incana subsp. raimondoi (Brassicaceae) Leaf Extract.

Brassica incana subsp. raimondoi is an endemic taxon present in a restricted area located on steep limestone cliffs at an altitude of about 500 m a.s.l. in eastern Sicily. In this research, for the first time, studies on the phytochemical profile, the antioxidant properties in cell-free and cell-based systems, the cytotoxicity on normal and cancer cells by 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) assay, and on Artemia salina Leach, were performed. The total phenolic, flavonoid, and condensed tannin contents of the leaf hydroalcoholic extract were spectrophotometrically determined. Ultra-performance liquid chromatography-tandem mass spectrometer (UPLC-MS/MS) analysis highlighted the presence of several phenolic acids, flavonoids, and carotenoids, while High-Performance Liquid Chromatography with Diode-Array Detection (HPLC-DAD) identified various kaempferol and isorhamnetin derivatives. The extract exhibited different antioxidant properties according to the five in vitro methods used. Cytotoxicity by MTT assay evidenced no impact on normal human fibroblasts (HFF-1) and prostate cancer cells (DU145), and cytotoxicity accompanied by necrotic cell death for colon cancer cells (CaCo-2) and hepatoma cells (HepG2), starting from 100 µg/mL and 500 µg/mL, respectively. No cytotoxic effects were detected by the A. salina lethality bioassay. In the H2O2-induced oxidative stress cell model, the extract counteracted cellular reactive oxygen species (ROS) production and preserved non-protein thiol groups (RSH) affected by H2O2 exposure in HepG2 cells. Results suggest the potential of B. incana subsp. raimondoi as a source of bioactive molecules.

Moving Toward a Question-Centric Approach for Regulatory Decision Making in the Context of Drug Assessment.

The most intuitive question for market access for medicinal products is the benefit/risk (B/R) balance. The B/R assessment can conceptually be divided into subquestions related to establishing efficacy and safety. There are additional layers to the B/R ratio for medical products, including questions related to dose selection, clinical and nonclinical pharmacology, and drug quality. Explicitly stating the actual questions and how they contribute to the overall B/R provides a structure that fosters better informed cross-domain discussions. There is currently no systematic approach in the regulatory setting to assess and establish the acceptability of alternative methods and data sources. In most cases, the medicinal product sponsors tend to prioritize traditional data types and methods, which are well accepted by regulators for inclusion in regulatory submissions. This, in addition to the absence of rigor in the use and validation of new data types and methods, and the limited training of assessors in related fields can lead to increased regulatory skepticism toward new data types and methods. A data-knowledge backbone is needed to mitigate the uncertainty in efficacy and safety characterization. This white paper discusses the value of explicitly redefining and restructuring the regulatory scientific decision making around the scientific question to be addressed. The ecosystem proposed is based on three pillars: (i) a repository connecting questions, data, and methods; (ii) the development and validation of high-quality standards for data and methods; and (iii) credibility assessment. The ecosystem is applied to four use cases for illustration. The need for training and regulatory guidance is also discussed.

A Credibility Assessment Plan for an In Silico Model that Predicts the Dose-Response Relationship of New Tuberculosis Treatments.

Tuberculosis is one of the leading causes of death in several developing countries and a public health emergency of international concern. In Silico Trials can be used to support innovation in the context of drug development reducing the duration and the cost of the clinical experimentations, a particularly desirable goal for diseases such as tuberculosis. The agent-based Universal Immune System Simulator was used to develop an In Silico Trials environment that can predict the dose-response of new therapeutic vaccines against pulmonary tuberculosis, supporting the optimal design of clinical trials. But before such in silico methodology can be used in the evaluation of new treatments, it is mandatory to assess the credibility of this predictive model. This study presents a risk-informed credibility assessment plan inspired by the ASME V&V 40-2018 technical standard. Based on the selected context of use and regulatory impact of the technology, a detailed risk analysis is described together with the definition of all the verification and validation activities and related acceptability criteria. The work provides an example of the first steps required for the regulatory evaluation of an agent-based model used in the context of drug development.

Microparticles Decorated with Cell-Instructive Surface Chemistries Actively Promote Wound Healing.

Wound healing is a complex biological process involving close crosstalk between various cell types. Dysregulation in any of these processes, such as in diabetic wounds, results in chronic nonhealing wounds. Fibroblasts are a critical cell type involved in the formation of granulation tissue, essential for effective wound healing. 315 different polymer surfaces are screened to identify candidates which actively drive fibroblasts toward either pro- or antiproliferative functional phenotypes. Fibroblast-instructive chemistries are identified, which are synthesized into surfactants to fabricate easy to administer microparticles for direct application to diabetic wounds. The pro-proliferative microfluidic derived particles are able to successfully promote neovascularization, granulation tissue formation, and wound closure after a single application to the wound bed. These active novel bio-instructive microparticles show great potential as a route to reducing the burden of chronic wounds.

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.

Evaluation of word embedding models to extract and predict surgical data in breast cancer.

BACKGROUND: Decisions in healthcare usually rely on the goodness and completeness of data that could be coupled with heuristics to improve the decision process itself. However, this is often an incomplete process. Structured interviews denominated Delphi surveys investigate experts' opinions and solve by consensus complex matters like those underlying surgical decision-making. Natural Language Processing (NLP) is a field of study that combines computer science, artificial intelligence, and linguistics. NLP can then be used as a valuable help in building a correct context in surgical data, contributing to the amelioration of surgical decision-making. RESULTS: We applied NLP coupled with machine learning approaches to predict the context (words) owning high accuracy from the words nearest to Delphi surveys, used as input. CONCLUSIONS: The proposed methodology has increased the usefulness of Delphi surveys favoring the extraction of keywords that can represent a specific clinical context. It permits the characterization of the clinical context suggesting words for the evaluation process of the data.

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.

Toward A Regulatory Pathway for the Use of in Silico Trials in the CE Marking of Medical Devices.

In Silico Trials methodologies will play a growing and fundamental role in the development and de-risking of new medical devices in the future. While the regulatory pathway for Digital Patient and Personal Health Forecasting solutions is clear, it is more complex for In Silico Trials solutions, and therefore deserves a deeper analysis. In this position paper, we investigate the current state of the art towards the regulatory system for in silico trials applied to medical devices while exploring the European regulatory system toward this topic. We suggest that the European regulatory system should start a process of innovation: in principle to limit distorted quality by different internal processes within notified bodies, hence avoiding that the more innovative and competitive companies focus their attention on the needs of other large markets, like the USA, where the use of such radical innovations is already rapidly developing.

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 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 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.