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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Innovations in information and communication technology infuse all branches of science, including life sciences. Nevertheless, healthcare is historically slow in adopting technological innovation, compared with other industrial sectors. In recent years, new approaches in modelling and simulation have started to provide important insights in biomedicine, opening the way for their potential use in the reduction, refinement and partial substitution of both animal and human experimentation. In light of this evidence, the European Parliament and the United States Congress made similar recommendations to their respective regulators to allow wider use of modelling and simulation within the regulatory process. In the context of in silico medicine, the term ' in silico clinical trials' refers to the development of patient-specific models to form virtual cohorts for testing the safety and/or efficacy of new drugs and of new medical devices. Moreover, it could be envisaged that a virtual set of patients could complement a clinical trial (reducing the number of enrolled patients and improving statistical significance), and/or advise clinical decisions. This article will review the current state of in silico clinical trials and outline directions for a full-scale adoption of patient-specific modelling and simulation in the regulatory evaluation of biomedical products. In particular, we will focus on the development of vaccine therapies, which represents, in our opinion, an ideal target for this innovative approach. [ABSTRACT FROM AUTHOR]

The 2nd Computational Methods for the Immune System function Workshop has been held in Madrid in conjunction with the IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2018) in Madrid, Spain, from December 3 to 6, 2018. The workshop has been obtained 100% more submissions in respect to the first edition, highlighting a growing interest for the treated topics. The best papers (9) have been selected for extension in this special issue, with themes about immune system and disease simulation, computer-aided design of novel candidate vaccines, methods for the analysis of immune system involved diseases based on statistical methods, meta-heuristics and game theory, and modelling strategies for improving the simulation of the immune system dynamics. [ABSTRACT FROM AUTHOR]

Abstract In this paper our main results are the multipolar weighted Hardy inequality c ∑ i = 1 n ∫ R N φ 2 | x − a i | 2 d μ ≤ ∫ R N | ∇ φ | 2 d μ + K ∫ R N φ 2 d μ , c ≤ c o , where the functions φ belong to a weighted Sobolev space H μ 1 , and the proof of the optimality of the constant c o = c o (N) : = (N − 2 2) 2. The Gaussian probability measure dμ is the unique invariant measure for Ornstein–Uhlenbeck type operators. This estimate allows us to get necessary and sufficient conditions for the existence of positive solutions to a parabolic problem corresponding to the Kolmogorov operators defined on smooth functions and perturbed by a multipolar inverse square potential L u + V u = (Δ u + ∇ μ μ ⋅ ∇ u) + ∑ i = 1 n c | x − a i | 2 u , x ∈ R N , c > 0 , a 1 , … , a n ∈ R N. [ABSTRACT FROM AUTHOR]

The central nervous system is themost complex network of the human body. The existence and functionality of a large number of molecular species in human brain are still ambiguous and mostly unknown, thus posing a challenge to Science and Medicine. Neurological diseases inherit the same level of complexity,making effective treatments difficult to be found. Multiple sclerosis (MS) is a major neurological disease that causes severe inabilities and also a significant social burden on health care system: between 2 and 2.5million people are affected by it, and the cost associated with it is significantly higher as compared with other neurological diseases because of the chronic nature of the disease and to the partial efficacy of current therapies. Despite difficulties in understanding and treating MS,many computationalmodels have been developed to help neurologists. In the present work, we briefly review the main characteristics of MS and present a selection criteria ofmodeling approaches. [ABSTRACT FROM AUTHOR]

Motivation: Vaccines represent the most effective and cost-efficient weapons against a wide range of diseases. Nowadays new generation vaccines based on subunit antigens reduce adverse effects in high risk individuals. However, vaccine antigens are often poor immunogens when administered alone. Adjuvants represent a good strategy to overcome such hurdles, indeed they are able to: enhance the immune response; allow antigens sparing; accelerate the specific immune response; and increase vaccine efficacy in vulnerable groups such as newborns, elderly or immune-compromised people. However, due to safety concerns and adverse reactions, there are only a few adjuvants approved for use in humans. Moreover, in practice current adjuvants sometimes fail to confer adequate stimulation. Hence, there is an imperative need to develop novel adjuvants that overcome the limitations of the currently available licensed adjuvants. Results: We developed a computational framework that provides a complete pipeline capable of predicting the best citrus-derived adjuvants for enhancing the immune system response using, as a target disease model, influenza A infection. In silico simulations suggested a good immune effi- cacy of specific citrus-derived adjuvant (Beta Sitosterol) that was then confirmed in vivo. [ABSTRACT FROM AUTHOR]

Background: Malignant melanoma is an aggressive tumor of the skin and seems to be resistant to current therapeutic approaches. Melanocytic transformation is thought to occur by sequential accumulation of genetic and molecular alterations able to activate the Ras/Raf/MEK/ERK (MAPK) and/or the PI3K/AKT (AKT) signalling pathways. Specifically, mutations of B-RAF activate MAPK pathway resulting in cell cycle progression and apoptosis prevention. According to these findings, MAPK and AKT pathways may represent promising therapeutic targets for an otherwise devastating disease. Result: Here we show a computational model able to simulate the main biochemical and metabolic interactions in the PI3K/AKT and MAPK pathways potentially involved in melanoma development. Overall, this computational approach may accelerate the drug discovery process and encourages the identification of novel pathway activators with consequent development of novel antioncogenic compounds to overcome tumor cell resistance to conventional therapeutic agents. The source code of the various versions of the model are available as . [ABSTRACT FROM AUTHOR]

Motivation: Although results from phase III clinical trials substantially support the use of prophylactic and therapeutic vaccines against cancer, what has yet to be defined is how many and how frequent boosts are needed to sustain a long-lasting and protecting memory T-cell response against tumor antigens. Common experience is that such preclinical tests require the sacrifice of a relatively large number of animals, and are particularly time- and money-consuming.Results: As a first step to overcome these hurdles, we have developed an ordinary differential equation model that includes all relevant entities (such as activated cytotoxic T lymphocytes and memory T cells), and investigated the induction of immunological memory in the context of wild-type mice injected with a dendritic cell-based vaccine. We have simulated the biological behavior both in the presence and in the absence of memory T cells. Comparing results of ex vivo and in silico experiments, we show that the model is able to envisage the expansion and persistence of antigen-specific memory T cells. The model might be applicable to more complex vaccination schedules and substantially in any biological condition of prime-boosting.Availability and implementation: The model is fully described in the article.Contact: fp@francescopappalardo.netSupplementary information: Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Mathematical and computational models are increasingly used to help interpret biomedical data produced by high-throughput genomics and proteomics projects. The application of advanced computer models enabling the simulation of complex biological processes generates hypotheses and suggests experiments. Appropriately interfaced with biomedical databases, models are necessary for rapid access to, and sharing of knowledge through data mining and knowledge discovery approaches. [ABSTRACT FROM PUBLISHER]

Cancer vaccines are a real application of the extensive knowledge of immunology to the field of oncology. Tumors are dynamic complex systems in which several entities, events, and conditions interact amongthem resulting in growth, invasion, and metastases. The immune system includes many cells and molecules that cooperatively act to protect the host organism from foreign agents. Interactions between the immune system and the tumor mass include a huge number of biological factors. Testing of some cancer vaccine features, such as the best conditions for vaccine administration or the identification of candidate antigenic stimuli, can be very difficult or even impossible only through experiments with biological models simply because a high number of variables need to be considered at the same time. This is where computational models, and, to this extent, immunoinformatics, can prove handy as they have shown to be able to reproduce enough biological complexity to be of use in suggesting new experiments. Indeed, computational models can be used in addition to biological models. We now experience that biologists and medical doctors are progressively convinced that modeling can be of great help in understanding experimental results and planning new experiments. This will boost this research in the future. [ABSTRACT FROM AUTHOR]

-Immunological therapy of progressive tumors requires not only activation and expansion of tumor specific cytotoxic T lymphocytes (CTLs), but also an efficient effector phase including migration of CTLs in the tumor tissue followed by conjugation and killing of target cells. We report the application of an agent-based model to recapitulate both the effect of a specific immunotherapy strategy against B16-melanoma in mice and the tumor progression in a generic tissue section. A comparison of the in silico results with the in vivo experiments shows excellent agreement. We therefore use the model to predict a critical role for CD137 expression on tumor vessel endothelium for successful therapy and other mechanistic aspects. Experimental results are fully compatible with the model predictions. The biologically oriented in silico model derived in this work will be used to predict treatment failure or success in other pre-clinical conditions eventually leading new promising in vivo experiments. [ABSTRACT FROM AUTHOR]

Background: The Triplex cell vaccine is a cancer cellular vaccine that can prevent almost completely the mammary tumor onset in HER-2/neu transgenic mice. In a translational perspective, the activity of the Triplex vaccine was also investigated against lung metastases showing that the vaccine is an effective treatment also for the cure of metastases. A future human application of the Triplex vaccine should take into account several aspects of biological behavior of the involved entities to improve the efficacy of therapeutic treatment and to try to predict, for example, the outcomes of longer experiments in order to move faster towards clinical phase I trials. To help to address this problem, MetastaSim, a hybrid Agent Based -- ODE model for the simulation of the vaccine-elicited immune system response against lung metastases in mice is presented. The model is used as in silico wet-lab. As a first application MetastaSim is used to find protocols capable of maximizing the total number of prevented metastases, minimizing the number of vaccine administrations. Results: The model shows that it is possible to obtain "in silico" a 45% reduction in the number of vaccinations. The analysis of the results further suggests that any optimal protocol for preventing lung metastases formation should be composed by an initial massive vaccine dosage followed by few vaccine recalls. Conclusions: Such a reduction may represent an important result from the point of view of translational medicine to humans, since a downsizing of the number of vaccinations is usually advisable in order to minimize undesirable effects. The suggested vaccination strategy also represents a notable outcome. Even if this strategy is commonly used for many infectious diseases such as tetanus and hepatitis-B, it can be in fact considered as a relevant result in the field of cancer-vaccines immunotherapy. These results can be then used and verified in future "in vivo" experiments, and their outcome can be used to further improve and refine the model. [ABSTRACT FROM AUTHOR]

Abstract: Vaccines represent a special class of drugs, capable of stimulating immune system responses against pathogens and tumors. Vaccine development is a lengthy process that includes expensive laboratory experiments in order to assess safety and effectiveness. As the efficacy of a vaccine was demonstrated by biological/chemical investigations and pre-clinical studies, then a major problem is represented by the search for an optimal vaccination dosage. Optimality here assumes the meaning of assuring a high degree of efficacy and safety (lack of toxic or side effects). In lack of quantitative methods, this is usually achieved by a consensus technique, a public statement on a particular aspect of medical knowledge available at the time it was written, and that is generally agreed upon as the evidence-based, state-of-the-art (or state-of-science) knowledge by a representative group of experts in that area. In this article, we focus on the difficult problem of the search for an optimal vaccination dosage in the field of tumor immunology, that is a major issue in biomedical research. This, indeed, represents a first step toward a personalized medicine approach. [Copyright &y& Elsevier]

Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common con- ceptual models and ontologies. The lmmunoGrid portal offers access to educational simulators where previously defined cases can be displayed, and to research simulators that allow the development of new, or tuning of existing, computational models. The portal is accessible at . [ABSTRACT FROM AUTHOR]

Abstract: Many physical or biological phenomena deal with the dynamics of interacting entities. These class of phenomena are well described in physics, using a kinetic approach based on Boltzmann equation. A Generalized Kinetic theory has been proposed to extend this approach to biological scenarios. An analytical solution of Boltzmann equation can be found only in very simple cases, so numerical methods become extremely relevant. The particle method is a class of numerical methods used to find a numerical solution of Boltzmann equations. The MWF-method for kinetic equations was firstly proposed by S. Motta and J. Wick in 1992. Here, we show that the MWF-method can be extended to system of Boltzamm equations. [Copyright &y& Elsevier]

As of today, 20 disease-modifying drugs (DMDs) have been approved for the treatment of relapsing multiple sclerosis (MS) and, based on their efficacy, they can be grouped into moderate-efficacy DMDs and high-efficacy DMDs. The choice of the drug mostly relies on the judgment and experience of neurologists and the evaluation of the therapeutic response can only be obtained by monitoring the clinical and magnetic resonance imaging (MRI) status during follow up. In an era where therapies are focused on personalization, this study aims to develop a modeling infrastructure to predict the evolution of relapsing MS and the response to treatments. We built a computational modeling infrastructure named Universal Immune System Simulator (UISS), which can simulate the main features and dynamics of the immune system activities. We extended UISS to simulate all the underlying MS pathogenesis and its interaction with the host immune system. This simulator is a multi-scale, multi-organ, agent-based simulator with an attached module capable of simulating the dynamics of specific biological pathways at the molecular level. We simulated six MS patients with different relapsing–remitting courses. These patients were characterized based on their age, sex, presence of oligoclonal bands, therapy, and MRI lesion load at the onset. The simulator framework is made freely available and can be used following the links provided in the availability section. Even though the model can be further personalized employing immunological parameters and genetic information, we generated a few simulation scenarios for each patient based on the available data. Among these simulations, it was possible to find the scenarios that realistically matched the real clinical and MRI history. Moreover, for two patients, the simulator anticipated the timing of subsequent relapses, which occurred, suggesting that UISS may have the potential to assist MS specialists in predicting the course of the disease and the response to treatment. [ABSTRACT FROM AUTHOR]

Summary Although several bioinformatics tools have been developed to examine signaling pathways, little attention has been given to ever long-distance crosstalk mechanisms. Here, we developed PETAL, a Python tool that automatically explores and detects the most relevant nodes within a KEGG pathway, scanning and performing an in-depth search. PETAL can contribute to discovering novel therapeutic targets or biomarkers that are potentially hidden and not considered in the network under study. Availabilityand implementation PETAL is a freely available open-source software. It runs on all platforms that support Python3. The user manual and source code are accessible from https://github.com/Pex2892/PETAL. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

It is coming nowadays more clear that in order to obtain a unified description of the different mechanisms governing the behavior and causality relations among the various parts of a living system, the development of comprehensive computational and mathematical models at different space and time scales is required. This is one of the most formidable challenges of modern biology characterized by the availability of huge amount of high throughput measurements. In this paper we draw attention to the importance of multiscale modeling in the framework of studies of biological systems in general and of the immune system in particular. [ABSTRACT FROM AUTHOR]

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-1β), which were modified by FFA treatment. Results suggest that the total phytocomplex contained in wild artichoke leaves effectively modulates FFA-induced hepatic oxidative stress. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The definition of artificial immunity, realized through vaccinations, is nowadays a practice widely developed in order to eliminate cancer disease. The present paper deals with an improved version of a mathematical model recently analyzed and related to the competition between immune system cells and mammary carcinoma cells under the action of a vaccine (Triplex). The model describes in detail both the humoral and cellular response of the immune system to the tumor associate antigen and the recognition process between B cells, T cells and antigen presenting cells. The control of the tumor cells growth occurs through the definition of different vaccine protocols. The performed numerical simulations of the model are in agreement with in vivo experiments on transgenic mice. [ABSTRACT FROM AUTHOR]

Background: Immunological strategies that achieve the prevention of tumor growth are based on the presumption that the immune system, if triggered before tumor onset, could be able to defend from specific cancers. In supporting this assertion, in the last decade active immunization approaches prevented some virus-related cancers in humans. An immunopreventive cell vaccine for the non-virus-related human breast cancer has been recently developed. This vaccine, called Triplex, targets the HER-2-neu oncogene in HER-2/neu transgenic mice and has shown to almost completely prevent HER-2/neu-driven mammary carcinogenesis when administered with an intensive and life-long schedule. Methods: To better understand the preventive efficacy of the Triplex vaccine in reduced schedules we employed a computational approach. The computer model developed allowed us to test in silico specific vaccination schedules in the quest for optimality. Specifically here we present a parallel genetic algorithm able to suggest optimal vaccination schedule. Results & Conclusions: The enormous complexity of combinatorial space to be explored makes this approach the only possible one. The suggested schedule was then tested in vivo, giving good results. Finally, biologically relevant outcomes of optimization are presented. [ABSTRACT FROM AUTHOR]

Many human tumors cannot easily be avoided. In most cases a prophylactic vaccination prevents the tumor growth. In particular the Triplex vaccine prevented mammary carcinoma formation using a Chronic schedule, but it is not known if this schedule is minimal. A computational model named SimTriplex was able to reproduce in silico the in vivo experiments. The combination of SimTriplex and Genetic Algorithms (GA), produced a minimal vaccination schedule capable to guarantee in silico survival. This process required the use of a High Performance Computing (HPC) infrastructure for several days. In this paper we show another optimization procedure based on simulated annealing approach used to get a faster algorithm response. [ABSTRACT FROM AUTHOR]

The value of in silico methods in drug development and evaluation has been demonstrated repeatedly and convincingly. While their benefits are now unanimously recognized, international standards for their evaluation, accepted by all stakeholders involved, are still to be established. In this white paper, we propose a risk‐informed evaluation framework for mechanistic model credibility evaluation. To properly frame the proposed verification and validation activities, concepts such as context of use, regulatory impact and risk‐based analysis are discussed. To ensure common understanding between all stakeholders, an overview is provided of relevant in silico terminology used throughout this paper. To illustrate the feasibility of the proposed approach, we have applied it to three real case examples in the context of drug development, using a credibility matrix currently being tested as a quick‐start tool by regulators. Altogether, this white paper provides a practical approach to model evaluation, applicable in both scientific and regulatory evaluation contexts. [ABSTRACT FROM AUTHOR]

Background: SARS-CoV-2 is a severe respiratory infection that infects humans. Its outburst entitled it as a pandemic emergence. To get a grip on this outbreak, specific preventive and therapeutic interventions are urgently needed. It must be said that, until now, there are no existing vaccines for coronaviruses. To promptly and rapidly respond to pandemic events, the application of in silico trials can be used for designing and testing medicines against SARS-CoV-2 and speed-up the vaccine discovery pipeline, predicting any therapeutic failure and minimizing undesired effects. Results: We present an in silico platform that showed to be in very good agreement with the latest literature in predicting SARS-CoV-2 dynamics and related immune system host response. Moreover, it has been used to predict the outcome of one of the latest suggested approach to design an effective vaccine, based on monoclonal antibody. Universal Immune System Simulator (UISS) in silico platform is potentially ready to be used as an in silico trial platform to predict the outcome of vaccination strategy against SARS-CoV-2. Conclusions: In silico trials are showing to be powerful weapons in predicting immune responses of potential candidate vaccines. Here, UISS has been extended to be used as an in silico trial platform to speed-up and drive the discovery pipeline of vaccine against SARS-CoV-2. [ABSTRACT FROM AUTHOR]

Background: Multiple Sclerosis (MS) represents nowadays in Europe the leading cause of non-traumatic disabilities in young adults, with more than 700,000 EU cases. Although huge strides have been made over the years, MS etiology remains partially unknown. Furthermore, the presence of various endogenous and exogenous factors can greatly influence the immune response of different individuals, making it difficult to study and understand the disease. This becomes more evident in a personalized-fashion when medical doctors have to choose the best therapy for patient well-being. In this optics, the use of stochastic models, capable of taking into consideration all the fluctuations due to unknown factors and individual variability, is highly advisable. Results: We propose a new model to study the immune response in relapsing remitting MS (RRMS), the most common form of MS that is characterized by alternate episodes of symptom exacerbation (relapses) with periods of disease stability (remission). In this new model, both the peripheral lymph node/blood vessel and the central nervous system are explicitly represented. The model was created and analysed using Epimod, our recently developed general framework for modeling complex biological systems. Then the effectiveness of our model was shown by modeling the complex immunological mechanisms characterizing RRMS during its course and under the DAC administration. Conclusions: Simulation results have proven the ability of the model to reproduce in silico the immune T cell balance characterizing RRMS course and the DAC effects. Furthermore, they confirmed the importance of a timely intervention on the disease course. [ABSTRACT FROM AUTHOR]

Background: The STriTuVaD project, funded by Horizon 2020, aims to test through a Phase IIb clinical trial one of the most advanced therapeutic vaccines against tuberculosis. As part of this initiative, we have developed a strategy for generating in silico patients consistent with target population characteristics, which can then be used in combination with in vivo data on an augmented clinical trial. Results: One of the most challenging tasks for using virtual patients is developing a methodology to reproduce biological diversity of the target population, ie, providing an appropriate strategy for generating libraries of digital patients. This has been achieved through the creation of the initial immune system repertoire in a stochastic way, and through the identification of a vector of features that combines both biological and pathophysiological parameters that personalise the digital patient to reproduce the physiology and the pathophysiology of the subject. Conclusions: We propose a sequential approach to sampling from the joint features population distribution in order to create a cohort of virtual patients with some specific characteristics, resembling the recruitment process for the target clinical trial, which then can be used for augmenting the information from the physical the trial to help reduce its size and duration. [ABSTRACT FROM AUTHOR]

Background: In 2018, about 10 million people were found infected by tuberculosis, with approximately 1.2 million deaths worldwide. Despite these numbers have been relatively stable in recent years, tuberculosis is still considered one of the top 10 deadliest diseases worldwide. Over the years, Mycobacterium tuberculosis has developed a form of resistance to first-line tuberculosis treatments, specifically to isoniazid, leading to multi-drug-resistant tuberculosis. In this context, the EU and Indian DBT funded project STriTuVaD—In Silico Trial for Tuberculosis Vaccine Development—is supporting the identification of new interventional strategies against tuberculosis thanks to the use of Universal Immune System Simulator (UISS), a computational framework capable of predicting the immunity induced by specific drugs such as therapeutic vaccines and antibiotics. Results: Here, we present how UISS accurately simulates tuberculosis dynamics and its interaction within the immune system, and how it predicts the efficacy of the combined action of isoniazid and RUTI vaccine in a specific digital population cohort. Specifically, we simulated two groups of 100 digital patients. The first group was treated with isoniazid only, while the second one was treated with the combination of RUTI vaccine and isoniazid, according to the dosage strategy described in the clinical trial design. UISS-TB shows to be in good agreement with clinical trial results suggesting that RUTI vaccine may favor a partial recover of infected lung tissue. Conclusions: In silico trials innovations represent a powerful pipeline for the prediction of the effects of specific therapeutic strategies and related clinical outcomes. Here, we present a further step in UISS framework implementation. Specifically, we found that the simulated mechanism of action of RUTI and INH are in good alignment with the results coming from past clinical phase IIa trials. [ABSTRACT FROM AUTHOR]

Background: In recent years, the study of immune response behaviour using bottom up approach, Agent Based Modeling (ABM), has attracted considerable efforts. The ABM approach is a very common technique in the biological domain due to high demand for a large scale analysis tools for the collection and interpretation of information to solve biological problems. Simulating massive multi-agent systems (i.e. simulations containing a large number of agents/entities) requires major computational effort which is only achievable through the use of parallel computing approaches. Results: This paper explores different approaches to parallelising the key component of biological and immune system models within an ABM model: pairwise interactions. The focus of this paper is on the performance and algorithmic design choices of cell interactions in continuous and discrete space where agents/entities are competing to interact with one another within a parallel environment. Conclusions: Our performance results demonstrate the applicability of these methods to a broader class of biological systems exhibiting typical cell to cell interactions. The advantage and disadvantage of each implementation is discussed showing each can be used as the basis for developing complete immune system models on parallel hardware. [ABSTRACT FROM AUTHOR]

Background: Multiple Sclerosis (MS) is an immune-mediated inflammatory disease of the Central Nervous System (CNS) which damages the myelin sheath enveloping nerve cells thus causing severe physical disability in patients. Relapsing Remitting Multiple Sclerosis (RRMS) is one of the most common form of MS in adults and is characterized by a series of neurologic symptoms, followed by periods of remission. Recently, many treatments were proposed and studied to contrast the RRMS progression. Among these drugs, daclizumab (commercial name Zinbryta), an antibody tailored against the Interleukin-2 receptor of T cells, exhibited promising results, but its efficacy was accompanied by an increased frequency of serious adverse events. Manifested side effects consisted of infections, encephalitis, and liver damages. Therefore daclizumab has been withdrawn from the market worldwide. Another interesting case of RRMS regards its progression in pregnant women where a smaller incidence of relapses until the delivery has been observed. Results: In this paper we propose a new methodology for studying RRMS, which we implemented in GreatSPN, a state-of-the-art open-source suite for modelling and analyzing complex systems through the Petri Net (PN) formalism. This methodology exploits: (a) an extended Colored PN formalism to provide a compact graphical description of the system and to automatically derive a set of ODEs encoding the system dynamics and (b) the Latin Hypercube Sampling with PRCC index to calibrate ODE parameters for reproducing the real behaviours in healthy and MS subjects.To show the effectiveness of such methodology a model of RRMS has been constructed and studied. Two different scenarios of RRMS were thus considered. In the former scenario the effect of the daclizumab administration is investigated, while in the latter one RRMS was studied in pregnant women. Conclusions: We propose a new computational methodology to study RRMS disease. Moreover, we show that model generated and calibrated according to this methodology is able to reproduce the expected behaviours. [ABSTRACT FROM AUTHOR]

Background: Tuberculosis (TB) represents a worldwide cause of mortality (it infects one third of the world’s population) affecting mostly developing countries, including India, and recently also developed ones due to the increased mobility of the world population and the evolution of different new bacterial strains capable to provoke multidrug resistance phenomena. Currently, antitubercular drugs are unable to eradicate subpopulations of Mycobacterium tuberculosis (MTB) bacilli and therapeutic vaccinations have been postulated to overcome some of the critical issues related to the increase of drug-resistant forms and the difficult clinical and public health management of tuberculosis patients. The Horizon 2020 EC funded project “In Silico Trial for Tuberculosis Vaccine Development” (STriTuVaD) to support the identification of new therapeutic interventions against tuberculosis through novel in silico modelling of human immune responses to disease and vaccines, thereby drastically reduce the cost of clinical trials in this critical sector of public healthcare. Results: We present the application of the Universal Immune System Simulator (UISS) computational modeling infrastructure as a disease model for TB. The model is capable to simulate the main features and dynamics of the immune system activities i.e., the artificial immunity induced by RUTI® vaccine, a polyantigenic liposomal therapeutic vaccine made of fragments of Mycobacterium tuberculosis cells (FCMtb). Based on the available data coming from phase II Clinical Trial in subjects with latent tuberculosis infection treated with RUTI® and isoniazid, we generated simulation scenarios through validated data in order to tune UISS accordingly to STriTuVaD objectives. The first case simulates the establishment of MTB latent chronic infection with some typical granuloma formation; the second scenario deals with a reactivation phase during latent chronic infection; the third represents the latent chronic disease infection scenario during RUT® vaccine administration. Conclusions: The application of this computational modeling strategy helpfully contributes to simulate those mechanisms involved in the early stages and in the progression of tuberculosis infection and to predict how specific therapeutical strategies will act in this scenario. In view of these results, UISS owns the capacity to open the door for a prompt integration of in silico methods within the pipeline of clinical trials, supporting and guiding the testing of treatments in patients affected by tuberculosis. [ABSTRACT FROM AUTHOR]

Motivation Val600Glu (V600E) mutation is the most common BRAF mutation detected in thyroid cancer. Hence, recent research efforts have been performed trying to explore several inhibitors of the V600E mutation-containing BRAF kinase as potential therapeutic options in thyroid cancer refractory to standard interventions. Among them, vemurafenib is a selective BRAF inhibitor approved by Food and Drug Administration for clinical practice. Unfortunately, vemurafenib often displays limited efficacy in poorly differentiated and anaplastic thyroid carcinomas probably because of intrinsic and/or acquired resistance mechanisms. In this view, cancer stem cells (CSCs) may represent a possible mechanism of resistance to vemurafenib, due to their self-renewal and chemo resistance properties. Results We present a computational framework to suggest new potential targets to overcome drug resistance. It has been validated with an in vitro model based upon a spheroid-forming method able to isolate thyroid CSCs that may mimic resistance to vemurafenib. Indeed, vemurafenib did not inhibit cell proliferation of BRAF V600E thyroid CSCs, but rather stimulated cell proliferation along with a paradoxical over-activation of ERK and AKT pathways. The computational model identified a fundamental role of mitogen-activated protein kinase 8 (MAP3K8), a serine/threonine kinase expressed in thyroid CSCs, in mediating this drug resistance. To confirm model prediction, we set a suitable in vitro experiment revealing that the treatment with MAP3K8 inhibitor restored the effect of vemurafenib in terms of both DNA fragmentation and poly (ADP-ribose) polymerase cleavage (apoptosis) in thyroid CSCs. Moreover, MAP3K8 expression levels may be a useful marker to predict the response to vemurafenib. Availability and implementation The model is available in GitHub repository visiting the following URL: https://github.com/francescopappalardo/MAP3K8-Thyroid-Spheres-V-3.0. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

Background: DNA methylation is an epigenetic mechanism of genomic regulation involved in the maintenance of homeostatic balance. Dysregulation of DNA methylation status is one of the driver alterations occurring in neoplastic transformation and cancer progression. The identification of methylation hotspots associated to gene dysregulation may contribute to discover new prognostic and diagnostic biomarkers, as well as, new therapeutic targets. Results: We present EpiMethEx (Epigenetic Methylation and Expression), a R package to perform a large-scale integrated analysis by cyclic correlation analyses between methylation and gene expression data. For each gene, samples are segmented according to the expression levels to select genes that are differentially expressed. This stratification allows to identify CG methylation probesets modulated among gene-stratified samples. Subsequently, the methylation probesets are grouped by their relative position in gene sequence to identify wide genomic methylation events statically related to genetic modulation. Conclusions: The beta-test study showed that the global methylation analysis was in agreement with scientific literature. In particular, this analysis revealed a negative association between promoter hypomethylation and overexpression in a wide number of genes. Less frequently, this overexpression was sustained by intragenic hypermethylation events. [ABSTRACT FROM AUTHOR]

Background: Human papillomavirus infection is a global social burden that, every year, leads to thousands new diagnosis of cancer. The introduction of a protocol of immunization, with Gardasil and Cervarix vaccines, has radically changed the way this infection easily spreads among people. Even though vaccination is only preventive and not therapeutic, it is a strong tool capable to avoid the consequences that this pathogen could cause. Gardasil vaccine is not free from side effects and the duration of immunity is not always well determined. This work aim to enhance the effects of the vaccination by using a new class of adjuvants and a different administration protocol. Due to their minimum side effects, their easy extraction, their low production costs and their proven immune stimulating activity, citrus-derived molecules are valid candidates to be administered as adjuvants in a vaccine formulation against Hpv. Results: With the aim to get a stronger immune response against Hpv infection we built an in silico model that delivers a way to predict the best adjuvants and the optimal means of administration to obtain such a goal. Simulations envisaged that the use of Neohesperidin elicited a strong immune response that was then validated in vivo. Conclusions: We built up a computational infrastructure made by a virtual screening approach able to preselect promising citrus derived compounds, and by an agent based model that reproduces HPV dynamics subject to vaccine stimulation. This integrated methodology was able to predict the best protocol that confers a very good immune response against HPV infection. We finally tested the in silico results through in vivo experiments on mice, finding good agreement. [ABSTRACT FROM AUTHOR]

Background: Mathematical and computational models showed to be a very important support tool for the comprehension of the immune system response against pathogens. Models and simulations allowed to study the immune system behavior, to test biological hypotheses about diseases and infection dynamics and to improve and optimize novel and existing drugs and vaccines. Continuous models, mainly based on differential equations, usually allow to qualitatively study the system but lack in description; conversely discrete models, such as agent based models and cellular automata, permit to describe in detail entities properties at the cost of losing most qualitative analyses. Petri Nets (PN) are a graphical modeling tool developed to model concurrency and synchronization in distributed systems. Their use has become increasingly marked also thanks to the introduction in the years of many features and extensions which lead to the born of "high level" PN. Results: We propose a novel methodological approach that is based on high level PN and in particular on Colored Petri Nets (CPN), that can be used to model the immune system response at the cellular scale. To demonstrate the potentiality of the approach we provide a simple model of the humoral immune system response that is able of reproducing some of the most complex well-known features of the adaptive response like memory and specificity features. Conclusions: The methodology we present has advantages of both the two classical approaches based on continuous and discrete models, since it allows to gain good level of granularity in the description of cells behavior without losing the possibility of having a qualitative analysis. Furthermore, the presented methodology based on CPN allows the adoption of the same graphical modeling technique well known to life scientists that use PN for the modeling of signaling pathways. Finally, such an approach may open the floodgates to the realization of multi scale models that integrate both signaling pathways (intra cellular) models and cellular (population) models built upon the same technique and software. [ABSTRACT FROM AUTHOR]

Reliability assessment of industrial processes is traditionally performed with RAMS techniques. Such techniques are static in nature because they are unable to consider the multi-state operational and failure nature of systems and the dynamic variations of the environment in which they operate. Stochastic Hybrid Automaton appears to overcome this weakness coupling a deterministic and a stochastic process and integrating the features of a dynamic system with the concepts of dynamic reliability. At the state of the art, no attempts to enhance a formal RAMS technique with dynamic reliability has been tried, nor a computer-aided tool that plays as expert system has been coded yet. The aim of this paper is to fill this gap with a simulation formalism and a modelling tool able to combine the Dynamic Fault Tree technique and the Stochastic Hybrid Automaton within the Simulink environment. To this aim the MatCarloRE toolbox was adapted to interact with a Simulink dynamic system. The resulting assembly represents an important step ahead for the delivering of a user-friendly computer-aided tool for the dynamic reliability. [ABSTRACT FROM AUTHOR]

Motivation: Many important problems in cell biology require dense non-linear interactions between functional modules to be considered. The importance of computer simulation in understanding cellular processes is now widely accepted, and a variety of simulation algorithms useful for studying certain subsystems have been designed. Expansion of hematopoietic stem and progenitor cells (HSC/HPC) in ex vivo culture with cytokines and small molecules is a method to increase the restricted numbers of stem cells found in umbilical cord blood (CB), while also enhancing the content of early engrafting neutrophil and platelet precursors. The efficacy of the expanded product depends on the composition of the cocktail of cytokines and small molecules used for culture. Testing the influence of a cytokine or small molecule on the expansion of HSC/HPC is a laborious and expensive process. We therefore developed a computational model based on cellular signaling interactions that predict the influence of a cytokine on the survival, duplication and differentiation of the CD133+ HSC/HPC subset from human umbilical CB. Results: We have used results from in vitro expansion cultures with different combinations of one or more cytokines to develop an ordinary differential equation model that includes the effect of cytokines on survival, duplication and differentiation of the CD133+ HSC/HPC. Comparing the results of in vitro and in silico experiments, we show that the model can predict the effect of a cytokine on the fold expansion and differentiation of CB CD133+ HSC/HPC after 8-day culture on a 3D scaffold. [ABSTRACT FROM AUTHOR]

Several components that interact with each other to evolve a complex, and, in some cases, unexpected behavior, represents one of the main and fascinating features of the mammalian immune system. Agent-based modeling and cellular automata belong to a class of discrete mathematical approaches in which entities (agents) sense local information and undertake actions over time according to predefined rules. The strength of this approach is characterized by the appearance of a global behavior that emerges from interactions among agents. This behavior is unpredictable, as it does not follow linear rules. There are a lot of works that investigates the immune system with agent-based modeling and cellular automata. They have shown the ability to see clearly and intuitively into the nature of immunological processes. NetLogo is amultiagent programming language and modeling environment for simulating complex phenomena. It is designed for both research and education and is used across a wide range of disciplines and education levels. In this paper, we summarize NetLogo applications to immunology and, particularly, how this framework can help in the development and formulation of hypotheses that might drive further experimental investigations of disease mechanisms. [ABSTRACT FROM AUTHOR]

The revolutions in biotechnology and information technology have produced clinical data, which complement biological data. These data enable detailed descriptions of various healthy and diseased states and responses to therapies. For the investigation of the physiology and pathology of the immune responses, computer and mathematical models have been used in the last decades, enabling the representation of biological processes. In this modeling effort, a major issue is represented by the communication between models that work at cellular and molecular level, that is, multiscale representation. Here we sketch some attempts to model immune system dynamics at both levels. [ABSTRACT FROM AUTHOR]

Background: Multiple sclerosis (MS) is a disease of central nervous system that causes the removal of fatty myelin sheath from axons of the brain and spinal cord. Autoimmunity plays an important role in this pathology outcome and body’s own immune system attacks on the myelin sheath causing the damage. The etiology of the disease is partially understood and the response to treatment cannot easily be predicted. Results: We presented the results obtained using 8 genetically predisposed randomly chosen individuals reproducing both the absence and presence of malfunctions of the Teff-Treg cross-balancing mechanisms at a local level. For simulating the absence of a local malfunction we supposed that both Teff and Treg populations had similar maximum duplication rates. Results presented here suggest that presence of a genetic predisposition is not always a sufficient condition for developing the disease. Other conditions such as a breakdown of the mechanisms that regulate and allow peripheral tolerance should be involved. Conclusions: The presented model allows to capture the essential dynamics of relapsing-remitting MS despite its simplicity. It gave useful insights that support the hypothesis of a breakdown of Teff-Treg cross balancing mechanisms. [ABSTRACT FROM AUTHOR]