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

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

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

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

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.

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-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.

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.

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.

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.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-05038-6.

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.

A prospective registry to get insights into profile, management and outcome of cardiogenic shock patients

Background

Cardiogenic shock (CS) is the most severe form of acute heart failure, characterized by life-threatening end-organ hypoperfusion resulting from a low cardiac output state. Data on epidemiology of CS has been mostly drawn from registries focusing on acute myocardial infarction (AMI). However, recent evidence in a contemporary cohort in North America has shown that more than two thirds of all CS cases were related to causes other than AMI and that these patients had outcomes at least as poor as patients with AMICS.

Purpose

To provide data on profile, management, outcome, and evolution over time of CS patients admitted to ICCU/ICU and to compare them between patients with AMICS and acute decompensated heart failure (ADHF-CS).

Methods

The Altshock-2 Registry is a multicenter national prospective data collection, part of the Italian Altshock-2 program. Recruitment started on 2 March 2020 with 11 Italian Centers contributing to patients' enrolment. A total of 238 patients were hospitalized with confirmed diagnosis of CS between March 2020 and February 2022 in a multicenter national initiative. The mean age of this patient population was 64 years (interquartile range [IQR] 54–74) and 76% were male. Ninety-seven patients (41%) were admitted for AMICS, whereas 84 patients (35.3%) had ADHF-CS; 57 patients (24%) had other causes. As compared to AMICS patients, those admitted for ADHF-CS were younger, but with a higher burden of comorbidities (renal, liver, thyroid disease, atrial fibrillation, anemia), pre-existing decreased ejection fraction and a higher number of chronic drugs. Patients with ADHF-CS had a prevalent cardio-metabolic phenotype upon admission with prevalent congestion. Mechanical ventilation was more commonly used in patients with AMICS, compared to ADHF-CS, along with an increased inotropic score. Conversely, sodium nitroprusside was used in about sixty percent of patients with ADHF-CS. Sixty percent of the included population received a temporary mechanical circulatory support (MCS) device, which was intra-aortic balloon pump (IABP) in the eighty percent of the supported patients. Pulmonary artery catheter was used for monitoring only in the 18% of the included patients whereas an extensive echocardiographic approach was applied. Twenty-one patients (25%) underwent heart replacement therapy in the ADHF-CS patients versus 2 (2%) in the AMICS. Thirty-day mortality occurred in 32 patients (33%) in the AMICS group versus 23 (27%) in the ADHF-CS group (p=0.41).

Conclusions

Different diagnostic approaches and uses of mechanical circulatory support devises and inotropes are implemented in transatlantic settings. Uniform definitions and more homogenous protocols tailored on CS etiologies and clinical and biochemical phenotypes are needed in prospective initiatives in order to effectively compared results and outcome.

Funding Acknowledgement

Type of funding sources: None.

24h SCAI stage reclassification to predict outcome. Insights from the prospective Altshock-2 registry

Background

Cardiogenic shock (CS) includes several phenotypes of congestion or hypoperfusion with heterogenous hemodynamic features. Timely prognostication with scoring tools is warranted to identify patients requiring escalation to mechanical circulatory support (MCS) and to avoid futility.

Purpose

Accordingly, we explored the role of the updated Society for Cardiovascular Angiography and Interventions (SCAI) stages classification on in-hospital mortality using a prospective national registry.

Methods

The Altshock-2 Registry includes 237 patients with CS of all etiologies enrolled between March 2020 and February 2022 in 11 Italian Centers. Patients were classified according to the admission SCAI stages (assigned prospectively and independently updated according to the most recently released version); 24-hour re-assessment was prospectively performed in 201 patients. In-hospital mortality was evaluated for association with admission and 24 hours SCAI stages adjusted for the most relevant clinical covariates.

Results

Of the 237 patients included, 20 (8.4%) had SCAI shock stage B, 132 (55.8%) SCAI stage C, 60 (25.3) SCAI stage D and 25 (10.5%) SCAI stage E. Patients in stage B had the worst reclassification at 24-hours, with 42% of them showing worsened status and only 8% improving. In-hospital mortality was 38%. The revised SCAI stages at baseline were not independently associated with in-hospital mortality, whereas the SCAI classification at 24-h correctly and independently predicted mortality (the rate of in-hospital death was 18% for patients in SCAI shock stage B, 27% for SCAI shock stage C, 64% for SCAI shock stage D, 100% for SCAI shock stage E). At the multivariate analysis (adjusted for age, gender, eGFR, inotropic score and MCS) only SCAI classification at 24-hour evaluation was an independent predictor of in-hospital mortality (OR and 95% CI were, respectively, 3.32, 0.36–30.63, p=0.290 for SCAI stage C and 13.07, 1.69–146.3 for SCAI stage D, with E perfectly predicting because all patients died).

Conclusions

The revised SCAI stage classification may improve prognostication only at 24-hour evaluation. Aggressive treatment (either pharmacological or with MCS escalation) should be tailored in order to achieve prompt clinical improvement within the first 24-hours; refractory SCAI stage E at 24 hours portends dismal prognosis.

Funding Acknowledgement

Type of funding sources: None.

Elevation of transaminases associated with teriparatide treatment: a case report

This report describes the case of a 64-year-old Caucasian woman presenting with hypertransaminasemia during treatment with teriparatide for postmenopausal osteoporosis. The patient was also receiving food supplements containing red yeast rice (RYR) to lower her cholesterol levels. RYR has been reported to cause hepatoxicity because it contains monacolin K. According to the results of a causality assessment, carried out via several probability scales, teriparatide was defined as a 'possible' cause of the adverse drug reaction (ADR). Following progressive normalisation of the levels of transaminases after the teriparatide was discontinued, we therefore postulated that teriparatide was the main cause of the ADR. In addition to this, based on a literature review, we considered RYR as a concomitant agent in the aetiology of the ADR. Further post-marketing surveillance studies on teriparatide seem to be necessary.

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.

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.

Supplementary Information

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

Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination

We propose a Bayesian hierarchical method for combining in silico and in vivo data onto an augmented clinical trial with binary end points. The joint posterior distribution from the in silico experiment is treated as a prior, weighted by a measure of compatibility of the shared characteristics with the in vivo data. We also formalise the contribution and impact of in silico information in the augmented trial. We illustrate our approach to inference with in silico data from the UISS-TB simulator, a bespoke simulator of virtual patients with tuberculosis infection, and synthetic physical patients from a clinical trial.

Lysyl oxidase-like 2 is increased in asthma and contributes to asthmatic airway remodelling

Background

Airway smooth muscle (ASM) cells are fundamental to asthma pathogenesis, influencing bronchoconstriction, airway hyperresponsiveness and airway remodelling. The extracellular matrix (ECM) can influence tissue remodelling pathways; however, to date no study has investigated the effect of ASM ECM stiffness and cross-linking on the development of asthmatic airway remodelling. We hypothesised that transforming growth factor-β (TGF-β) activation by ASM cells is influenced by ECM in asthma and sought to investigate the mechanisms involved.

Methods

This study combines in vitro and in vivo approaches: human ASM cells were used in vitro to investigate basal TGF-β activation and expression of ECM cross-linking enzymes. Human bronchial biopsies from asthmatic and nonasthmatic donors were used to confirm lysyl oxidase like 2 (LOXL2) expression in ASM. A chronic ovalbumin (OVA) model of asthma was used to study the effect of LOXL2 inhibition on airway remodelling.

Results

We found that asthmatic ASM cells activated more TGF-β basally than nonasthmatic controls and that diseased cell-derived ECM influences levels of TGF-β activated. Our data demonstrate that the ECM cross-linking enzyme LOXL2 is increased in asthmatic ASM cells and in bronchial biopsies. Crucially, we show that LOXL2 inhibition reduces ECM stiffness and TGF-β activation in vitro, and can reduce subepithelial collagen deposition and ASM thickness, two features of airway remodelling, in an OVA mouse model of asthma.

Conclusion

These data are the first to highlight a role for LOXL2 in the development of asthmatic airway remodelling and suggest that LOXL2 inhibition warrants further investigation as a potential therapy to reduce remodelling of the airways in severe asthma.

Novel role for matrix cross-linking enzyme LOXL2 in asthmatic airway remodelling: LOXL2 is increased in #asthma but LOXL2 inhibition reduces matrix stiffness in airway smooth muscle cells and reduces remodelling in vivohttps://bit.ly/3FnzGb3

Quality by Design tools reducing the gap from bench to bedside for nanomedicine

Pharmaceutical nanotechnology research is focused on smart nano-vehicles, which can deliver active pharmaceutical ingredients to enhance their efficacy through any route of administration and in the most varied therapeutical application. The design and development of new nanopharmaceuticals can be very laborious. In recent years, the application of mathematics, statistics and computational tools is emerging as a convenient strategy for this purpose. The application of Quality by Design (QbD) tools has been introduced to guarantee quality for pharmaceutical products and improve translational research from the laboratory bench into applicable therapeutics. In this review, a collection of basic-concept, historical overview and application of QbD in nanomedicine are discussed. A specific focus has been put on Response Surface Methodology and Artificial Neural Network approaches in general terms and their application in the development of nanomedicine to monitor the process parameters obtaining optimized system ensuring its quality profile.

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

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

Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility

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.

Verification of an agent-based disease model of human Mycobacterium tuberculosis infection

Agent-based models (ABMs) are a powerful class of computational models widely used to simulate complex phenomena in many different application areas. However, one of the most critical aspects, poorly investigated in the literature, regards an important step of the model credibility assessment: solution verification. This study overcomes this limitation by proposing a general verification framework for ABMs that aims at evaluating the numerical errors associated with the model. A step-by-step procedure, which consists of two main verification studies (deterministic and stochastic model verification), is described in detail and applied to a specific mission critical scenario: the quantification of the numerical approximation error for UISS-TB, an ABM of the human immune system developed to predict the progression of pulmonary tuberculosis. Results provide indications on the possibility to use the proposed model verification workflow to systematically identify and quantify numerical approximation errors associated with UISS-TB and, in general, with any other ABMs.

Possible Contexts of Use for In Silico trials methodologies: a consensus- based review

The term In Silico Trial indicates the use of computer modelling and simulation to evaluate the safety and efficacy of a medical product, whether a drug, a medical device, a diagnostic product or an advanced therapy medicinal product. Predictive models are positioned as new methodologies for the development and the regulatory evaluation of medical products. New methodologies are qualified by regulators such as FDA and EMA through formal processes, where a first step is the definition of the Context of Use (CoU), which is a concise description of how the new methodology is intended to be used in the development and regulatory assessment process. As In Silico Trials are a disruptively innovative class of new methodologies, it is important to have a list of possible CoUs highlighting potential applications for the development of the relative regulatory science. This review paper presents the result of a consensus process that took place in the InSilicoWorld Community of Practice, an online forum for experts in in silico medicine. The experts involved identified 46 descriptions of possible CoUs which were organised into a candidate taxonomy of nine CoU categories. Examples of 31 CoUs were identified in the available literature; the remaining 15 should, for now, be considered speculative.

ECPella: Concept, Physiology and Clinical Applications

Addition of Impella on top of venoarterial extracorporeal membrane oxygenation (VA-ECMO) has gained wide interest as it might portend improved outcomes in patients with cardiogenic shock. This has been consistently reported in retrospective propensity-matched studies, case series, and meta-analyses. The pathophysiologic background is based on the mitigation of ECMO-related side effects and the additive benefit of myocardial unloading. In this perspective, thorough knowledge of these mechanisms is required to optimize the management of mechanical circulatory support with this approach and introduce best practices, as the interplay between the two devices and the implantation-explantation strategies are key for success.

An Early Stage Researcher's Primer on Systems Medicine Terminology

Background: Systems Medicine is a novel approach to medicine, that is, an interdisciplinary field that considers the human body as a system, composed of multiple parts and of complex relationships at multiple levels, and further integrated into an environment. Exploring Systems Medicine implies understanding and combining concepts coming from diametral different fields, including medicine, biology, statistics, modeling and simulation, and data science. Such heterogeneity leads to semantic issues, which may slow down implementation and fruitful interaction between these highly diverse fields. Methods: In this review, we collect and explain more than100 terms related to Systems Medicine. These include both modeling and data science terms and basic systems medicine terms, along with some synthetic definitions, examples of applications, and lists of relevant references. Results: This glossary aims at being a first aid kit for the Systems Medicine researcher facing an unfamiliar term, where he/she can get a first understanding of them, and, more importantly, examples and references for digging into the topic.

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

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

Single-Cell Tracking on Polymer Microarrays Reveals the Impact of Surface Chemistry on Pseudomonas aeruginosa Twitching Speed and Biofilm Development

Bacterial biofilms exhibit up to 1000 times greater resistance to antibiotic or host immune clearance than planktonic cells. Pseudomonas aeruginosa produces retractable type IV pili (T4P) that facilitate twitching motility on surfaces. The deployment of pili is one of the first responses of bacteria to surface interactions and because of their ability to contribute to cell surface adhesion and biofilm formation, this has relevance to medical device-associated infections. While polymer chemistry is known to influence biofilm development, its impact on twitching motility is not understood. Here, we combine a polymer microarray format with time-lapse automated microscopy to simultaneously assess P. aeruginosa twitching motility on 30 different methacrylate/acrylate polymers over 60 min post inoculation using a high-throughput system. During this critical initial period where the decision to form a biofilm is thought to occur, similar numbers of bacterial cells accumulate on each polymer. Twitching motility is observed on all polymers irrespective of their chemistry and physical surface properties, in contrast to the differential biofilm formation noted after 24 h of incubation. However, on the microarray polymers, P. aeruginosa cells twitch at significantly different speeds, ranging from 5 to ∼13 nm/s, associated with crawling or walking and are distinguishable from the different cell surface tilt angles observed. Chemometric analysis using partial least-squares (PLS) regression identifies correlations between surface chemistry, as measured by time-of-flight secondary ion mass spectrometry (ToF-SIMS), and both biofilm formation and single-cell twitching speed. The relationships between surface chemistry and these two responses are different for each process. There is no correlation between polymer surface stiffness and roughness as determined by atomic force measurement (AFM), or water contact angle (WCA), and twitching speed or biofilm formation. This reinforces the dominant and distinct contributions of material surface chemistry to twitching speed and biofilm formation.

PETAL: a python tool for deep analysis of biological pathways

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.

AVAILABILITY

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.

Toward computational modelling on immune system function

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

Computational modeling of the immune response in multiple sclerosis using epimod framework

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.

Generation of digital patients for the simulation of tuberculosis with UISS-TB

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.

Moving forward through the in silico modeling of tuberculosis: a further step with UISS-TB

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.

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

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 PO-050: Computational modeling of immunologic response to immune checkpoint inhibitors in COVID-19 patients with and without cancer

Cancer patients have an increased risk of severe COVID-19 infection due to the suppression of the immune system and the development of cytokine release syndrome (CRS) that favor respiratory syndromes and interstitial pneumonia. However, substantial differences exist between patients treated with chemotherapy and patients treated with immune checkpoint inhibitors (ICIs), for which the risk of COVID-19 infection and the immunologic and cytokine profile in case of infection have not yet been well characterized. The administration of ICIs for the treatment of severe COVID-19 infection has been recently suggested. However, no conclusive data have been generated on this matter. To recognize the therapeutic potential of ICIs administration in COVID-19 patients with or without cancer, the Universal Immune System Simulator (UISS) prediction model was used to simulate the immunologic response of COVID-19 patients after ICIs administration. Briefly, UISS represents an appropriate computational modeling infrastructure able to simulate the dynamics of every single entity of the immune system after a stimulus or a therapeutic intervention by using an agent-based methodology. Therefore, the UISS platform, already used for the prediction of the efficacy of specific SARS-CoV-2 candidate vaccines, was here adopted to characterize the immunologic behavior in both COVID-19 and cancer patients and to predict the effects of ICIs in these patients. The computational results allowed us to identify key inflammatory and immune-related factors responsible for severe respiratory syndromes in COVID-19 infected patients with and without cancer. UISS results suggest that the administration of ICIs modulates the immune system and the inflammatory status in both groups of patients with COVID-19 infection, reducing the risk of severe symptoms. Although the results of the present study are still under validation in peripheral blood samples obtained from COVID-19 patients and from cancer patients after two cycles of treatment with ICIs, we can speculate that ICIs may be a good therapeutic approach for the treatment of COVID-19 severe respiratory syndrome even with a concomitant cancer diagnosis. If this is the case, the lower expression levels of inflammatory biomarkers can result in the drop-down of the viral load, assessed by droplet digital PCR in COVID-19 patients.

Citation Format: Giulia Russo, Luca Falzone, Bruno Cacopardo, Giuseppe Nunnari, Francesco Torino, Giuseppa Scandurra, Stefania Stefani, Francesco Pappalardo, Massimo Libra. Computational modeling of immunologic response to immune checkpoint inhibitors in COVID-19 patients with and without cancer [abstract]. In: Proceedings of the AACR Virtual Meeting: COVID-19 and Cancer; 2020 Jul 20-22. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(18_Suppl):Abstract nr PO-050.

The Antioxidant Activities of Betula etnensis Rafin. Ethanolic Extract Exert Protective and Anti-diabetic Effects on Streptozotocin-Induced Diabetes in Rats

Pathophysiological mechanisms correlating diabetes mellitus with associated complications are still not completely clear, even though oxidative stress seems to play a pivotal role. Literature data suggest that cell damages induced by hyperglycemia, although multifactorial, have a common pathway in oxidative/nitrosative stress. The present study evaluated the effects of Betula etnensis Raf. bark extract, a plant belonging to the Betulaceae family endemic to Sicily, on oxidative stress and in preventing and/or retarding diabetes-associated complications in streptozotocin diabetic rats treated with the extract at dose of 0.5 g/kg body weight per day for 28 consecutive days. The extract administration significant decreased food and water intake, fasting blood glucose, weight loss and polyuria, compared with untreated diabetic animals. Furthermore, oxidative stress markers particularly, lipid hydroperoxides (LOOH) and nitrite/nitrate levels, non-proteic thiol groups (RSH), γ-glutamyl-cysteine-synthetase (γ-GCS) activities and expression, heme oxygenase-1 (HO-1), endothelial and inducible nitric oxide synthases (i-NOS e-NOS) expression, significantly changed by streptozocin treatment, were markedly restored both in plasma and tissues together with nuclear sirtuins activity (Sirt1). Results suggested that B. etnensis bark alcoholic extract is able to counteract oxidative stress and to ameliorate some general parameters related to diabetes.

In silico clinical trials: concepts and early adoptions

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.

The combination of artificial intelligence and systems biology for intelligent vaccine design

INTRODUCTION

A new body of evidence depicts the applications of artificial intelligence and systems biology in vaccine design and development. The combination of both approaches shall revolutionize healthcare, accelerating clinical trial processes and reducing the costs and time involved in drug research and development.

AREAS COVERED

This review explores the basics of artificial intelligence and systems biology approaches in the vaccine development pipeline. The topics include a detailed description of epitope prediction tools for designing epitope-based vaccines and agent-based models for immune system response prediction, along with a focus on their potentiality to facilitate clinical trial phases.

EXPERT OPINION

Artificial intelligence and systems biology offer the opportunity to avoid the inefficiencies and failures that arise in the classical vaccine development pipeline. One promising solution is the combination of both methodologies in a multiscale perspective through an accurate pipeline. We are entering an 'in silico era' in which scientific partnerships, including a more and more increasing creation of an 'ecosystem' of collaboration and multidisciplinary approach, are relevant for addressing the long and risky road of vaccine discovery and development. In this context, regulatory guidance should be developed to qualify the in silico trials as evidence for intelligent vaccine development.

Computational modeling reveals MAP3K8 as mediator of resistance to vemurafenib in thyroid cancer stem cells

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.

The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis

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.

In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products

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Regulators now consider also evidences produced in silico.

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We need accepted methods to evaluate the credibility of models.

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In this paper we describe the use of the ASME V&V-40 technical standard.

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We also discuss its application to various types of modelling methods.

Historically, the evidences of safety and efficacy that companies provide to regulatory agencies as support to the request for marketing authorization of a new medical product have been produced experimentally, either in vitro or in vivo. More recently, regulatory agencies started receiving and accepting evidences obtained in silico, i.e. through modelling and simulation. However, before any method (experimental or computational) can be acceptable for regulatory submission, the method itself must be considered “qualified” by the regulatory agency. This involves the assessment of the overall “credibility” that such a method has in providing specific evidence for a given regulatory procedure. In this paper, we describe a methodological framework for the credibility assessment of computational models built using mechanistic knowledge of physical and chemical phenomena, in addition to available biological and physiological knowledge; these are sometimes referred to as “biophysical” models. Using guiding examples, we explore the definition of the context of use, the risk analysis for the definition of the acceptability thresholds, and the various steps of a comprehensive verification, validation and uncertainty quantification process, to conclude with considerations on the credibility of a prediction for a specific context of use. While this paper does not provide a guideline for the formal qualification process, which only the regulatory agencies can provide, we expect it to help researchers to better appreciate the extent of scrutiny required, which should be considered early on in the development/use of any (new) in silico evidence.