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.

Multiple sclerosis is an autoimmune inflammatory disease of the central nervous system with a relapsing or progressive course, potentially leading to severe disability. In this manuscript, a well-known sensitivity analysis technique, Latin Hypercube Sampling along with Partial Rank Correlation Coefficient, was applied to an Agent-Based Model in silico trial used in Multiple Sclerosis context to evaluate the correlation of vitamin D dosage with the number of oligodendrocytes and the thymus efficiency with regulatory T cells.

Technological advances in fields such as computer modelling and simulation are playing a fundamental role, during the COVID-19 pandemic, that has been afflicting us for 2 years now. These methodologies may help in the development, assessment and monitoring for better prevention, diagnosis, treatment and generation of specific therapeutic strategies. In this perspective, in silico platforms are emerging thanks to their ability to predict the efficacy and safety of new therapies and vaccines. Here, our integrated bioinformatics pipeline for evaluating the elicited response of human immune system against every pathogen is applied to predict the cross-reactive immunity induced by pre-existing vaccinations against SARS-CoV-2 UK variants.

Abstract 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.

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.

The application of Agent Based Models (ABMs) in biology and immunology has recently come to the fore, thanks to their ability to accurately describe complex biological behaviors, rules, and interactions, without the need to use complex mathematical formalisms. However, even if there is a growing interest in applying such methodologies to improve and speed up the research of novel pharmaceutical products, verification and validation procedures voted at assessing ABMs credibility are far from being well-established. We present Verify, the first toolbox of instruments selected and designed for the verification of discrete-time models, with a focus on agent-based approaches. The toolbox has a friendly GUI, does not require the installation of any additional software, and can easily find possible numerical errors and incongruences that may affect such models.

An ever-increasing number of tools and databases offers pieces of evidence and knowledge about gene function annotations, protein interactions, and experimentally validated biological pathways. These resources represent an excellent and essential instrument to facilitate pathway analysis. Among them, particular attention should be given to the one capable of finding alternative pathways or cross-talks events and potential drug target candidates. Here, we evaluated PETAL predictive capability, a Python tool that automatically explores and scans the relevant nodes within a KEGG pathway. Starting from three specific cancer scenarios (chronic myelogenous leukemia, non-small cell lung cancer, and head and neck squamous cell carcinoma) and related literature results about potential driver genes of drug resistance to EGFR tyrosine kinases inhibitors (SNCA, BCL-6 and YAP-1), we used PETAL to test its capability to identify in parallel these potential target genes involved in tumor progression and EGFR inhibitors resistance. By searching in-depth for ancestor and descendent nodes of SNCA, BCL-6 and YAP-1, across the EGFR pathway, we found that PETAL was able to detect the same targets investigated in the recent literature. Finally, this retrospective work emphasizes that PETAL could represent a powerful tool that can be used to improve the understanding of complex biological pathways and speed-up the identification of potential biomarkers and therapeutical candidates in cancer potentially in any other disease.

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.

Intracellular biochemical reactions emerge from the interaction among multiple extracellular signaling components. Considering the number, type and connections of the signaling components represents a needed step to characterize, identify and describe potential targets for a clinical purpose. However, it is increasingly documented that the presence of sub-types of signaling proteins, branching and crosstalk may lead to very variable outcomes in the same path, which is not always well defined experimentally. For this reason, we present an improved version of the algorithm based on the MapReduce paradigm to facilitate the discovery of new therapeutic targets. Our algorithm allows you to scan and perform a search in depth of biological pathway in order to analyze less recurrent and therefore non-trivial paths. These routes represent a chain of biochemical interactions among different biological actors that can be represented by quite distant nodes along the pathway. This type of analysis can also be performed manually, but with high execution times due to the large amount of pathways and genes present. Thus, our tool performs exhaustive analysis in an automated way, drastically reducing the time required. Our proposal allows us to discover the genes far from the initial target genes, also showing the number of occurrences of a given path found within the set of biological pathways analyzed during the simulation.

We present here a novel algorithm based on a MapReduce approach to facilitate the discovery of novel therapeutic targets. The proposed algorithm has been enabled to scan a set biological pathways in order to discover non-trivial (less common) routes. Such routes represent a chain of biochemical interactions among different biological actors. These actors can be represented by quite distant nodes along the devised pathway. Our approach detects nodes that are far from the initial target nodes, also showing the number of times that a given route has been found inside the selected set of biological pathways.

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.

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 multi-drug 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 RUTI® 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.