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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. [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]

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." [ABSTRACT FROM AUTHOR]

Historical masonry structures often suffer gradual deterioration that in many cases can compromise the safety levels and the operating conditions of the buildings. In this context, Structural Health Monitoring (SHM) is an effective tool for the prediction of the structural behaviour and the state of conservation of buildings. Although many monitoring systems have recently been proposed, there is a lack of practical application of low-cost systems. This paper presents an experimental study based on the use of two innovative stress sensors—capacitive stress sensor and ceramic stress sensor—for the monitoring of existing masonry elements. In order to reproduce the actual conditions of onsite masonry, sensors are post-installed in the mortar joints of two series of pre-stressed specimens made of calcarenite stone masonry and clay brick masonry. The best practice of post-installation of the two sensors is investigated. The reliability of the proposed sensors is evaluated through comparison with data recorded from classical measurement devices. [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]

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]

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]

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. [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]

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. [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]

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. [ABSTRACT FROM AUTHOR]

[Display omitted] 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. [ABSTRACT FROM AUTHOR]

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., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

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., (© 2022 The Authors. Advanced Materials published by Wiley-VCH GmbH.)

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., Competing Interests: Conflict of interest: J. Ramis has a patent “Industrial Synthesis of Modified Crosslinkable Biopolymer” pending. Conflict of interest: R. Middlewick has nothing to disclose. Conflict of interest: F. Pappalardo has nothing to disclose. Conflict of interest: J.T. Cairns has nothing to disclose. Conflict of interest: I.D. Stewart has nothing to disclose. Conflict of interest: A.E. John has nothing to disclose. Conflict of interest: S-U-N. Naveed has nothing to disclose. Conflict of interest: R. Krishnan has nothing to disclose. Conflict of interest: S. Miller has nothing to disclose. Conflict of interest: D.E. Shaw has nothing to disclose. Conflict of interest: C.E. Brightling has nothing to disclose. Conflict of interest: L. Buttery has nothing to disclose. Conflict of interest: F. Rose has nothing to disclose. Conflict of interest: G. Jenkins reports personal fees and other (sponsored research agreement paid to institution) from Biogen, GlaxoSmithKline and MedImmune, personal fees from Galapagos, Heptares, Boehringer Ingelheim, Pliant, Roche/InterMune, PharmAkea, Bristol Myers Squibb, Chiesi and Roche/Promedior, other (sponsored research agreement paid to institution) from Galecto, other (collaborative awards) from RedX and Nordic Biosciences, other (advisory board membership) from NuMedii, outside the submitted work; is supported by a National Institute of Health Research Professorship (RP-2017-08-ST2-014); and is a trustee for Action for Pulmonary Fibrosis. Conflict of interest: S.R. Johnson reports grants from the Medical Research Council (MRC), during the conduct of the study; grants from the National Institute of Health Research, MRC and Pfizer, personal fees from AstraZeneca, outside the submitted work. Conflict of interest: A.L. Tatler reports personal fees for consultancy from Pliant Therapeutics, outside the submitted work., (Copyright ©The authors 2022.)

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., (© 2022. The Author(s).)

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., (© 2022. The Author(s).)

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., (© The Author(s) 2021. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kiagias, Russo, Sgroi, Pappalardo and Juárez.)