Giulia Russo
@unict.it
Department of Drug Sciences
University of Catania
My mission is to apply computational approaches in a multidisciplinary environment in order to speed-up the drug discovery process, optimise in vitro and in vivo assays, ehance the therapeutic response in cancer and immunotherapies field, and to solve, in general, biomedical issues.
EDUCATION
PhD in Basic and Applied Biomedical Sciences
RESEARCH INTERESTS
Computational modeling in biomedicine and systems biology
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Giulia Russo, Elena Crispino, Avisa Maleki, Valentina Di Salvatore, Filippo Stanco, and Francesco Pappalardo
Springer Science and Business Media LLC
AbstractWhen 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.
Alessia Rondinella, Elena Crispino, Francesco Guarnera, Oliver Giudice, Alessandro Ortis, Giulia Russo, Clara Di Lorenzo, Davide Maimone, Francesco Pappalardo, and Sebastiano Battiato
Elsevier BV
Valentina Di Salvatore, Elena Crispino, Avisa Maleki, Giulia Nicotra, Giulia Russo, and Francesco Pappalardo
Elsevier BV
Valentina Di Salvatore, Giulia Russo, and Francesco Pappalardo
Springer US
Avisa Maleki, Elena Crispino, Serena Anna Italia, Valentina Di Salvatore, Maria Assunta Chiacchio, Fianne Sips, Roberta Bursi, Giulia Russo, Davide Maimone, and Francesco Pappalardo
Elsevier BV
Cristina Curreli, Valentina Di Salvatore, Giulia Russo, Francesco Pappalardo, and Marco Viceconti
Springer Science and Business Media LLC
AbstractTuberculosis 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.
Fianne L. P. Sips, Francesco Pappalardo, Giulia Russo, and Roberta Bursi
Springer Science and Business Media LLC
Abstract 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.
F. Pappalardo, J. Wilkinson, F. Busquet, A. Bril, Mark Palmer, B. Walker, Cristina Curreli, G. Russo, Thierry Marchal, E. Toschi,et al.
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.
G. Catanuto, N. Rocco, A. Maglia, P. Barry, A. Karakatsanis, G. Sgroi, G. Russo, F. Pappalardo, M.B. Nava, Joerg Heil,et al.
Elsevier BV
Avisa Maleki, Alvaro Ras-Carmona, Valentina Di Salvatore, Giulia Russo, Elena Crispino, and Francesco Pappalardo
IEEE
The COVID-19 pandemic motivated an intense debate over high transmissibility and unavailability of effective vaccine to cover all existent variants, and also has raised critical questions, such as concerns about new mutations and genetic recombination that could lead to novel variants of concerns. The density of mutation observed in the different residue indices of spike protein sequence, may correlate to the speed of virus distribution. Therefore, predicting an accurate determination of mutation rates is essential to comprehend this virus evolution and assess the risk of emergent infectious disease. The current study predicts the mutations that may be cause of new variants of concerns using a genetic algorithm approach. In this regard, we mutated randomly the wild-type sequence of SARS-CoV-2 spike protein to generate first 100 different sequences (initial population) that were modelled individually and used to evaluate their discrete optimized protein energy score. After applying cross-over and breeding 200 new generations, one of the sequences with the lowest discrete optimized protein energy score was identified and chosen for a further analysis to realize whether this sequence is potential for being the next variant of concern.
Paul Richmond, Matthew Leach, Peter Heywood, Francesco Pappalardo, Giulia Russo, and Marzio Pennisi
IEEE
The Universal Immune System Simulator (UISS) is a computational framework based on agent-based modelling (ABM) paradigm that has been specifically developed for simulating the immune system behaviour in presence of diseases and treatments. It has a long history of development, ranging from its initial applications into the field of tumor immunology and then moving towards wide disease modelling scenarios such as influenza, Multiple Sclerosis and atherosclerosis. Recently, inside the STriTuVaD H2020 EU project, it has been specialized to simulate tuberculosis dynamics and its interaction with the immune system, including the efficacy of the combined action of various treatments such as isoniazid and novel vaccines. TB simulation entitles large scale (e.g., tissue to organ scale) simulations over a wide digital population cohort. The computational costs of running large scale simulations are prohibitive using traditional forms of CPU simulation. This paper considers the use of parallel to gpu-based computing approaches via an agent-based domain independent complex systems simulator, FLAME GPU. Integration of FLAME GPU with UISS enables the simulation of larger, more complex problem domains. The combined UISS-FLAMEGPU simulator provides vastly increased performance characteristics for large problems, with a speedup of 4.22x for a typical tuberculosis model simulating 128 microlitres. FLAME GPU abstracts away a significant portion of the normal programming that would be required to effectively parallelise a model of this complexity. Adaptations were made to increase performance, such as message mutation and parallelisation of certain algorithms.
Nandu Chandran Nair, Elena Crispino, Avisa Maleki, Valentina Di Salvatore, Giulia Russo, Maria Adelaida Gomez, and Francesco Pappalardo
IEEE
Cutaneous leishmaniasis (CL) is the most common form of leishmaniasis, an infectious disease caused by the Leishmania parasite and transmitted by phlebotomine sandflies. CL is manifested as skin lesions, which typically evolve to ulcerative lesions and may last for months or even years, if not treated. Currently available treatments against CL involve the use of particularly toxic and overpriced drugs, which, among other things, require long times of administration and do not always lead to the complete recovery of the patient. The use of in silico technologies, as the Universal Immune System Simulator (UISS), may be helpful in finding new and potentially more effective drugs against CL.
Giulia Russo, Elena Crispino, Emanuela Corsini, Martina Iulini, Alicia Paini, Andrew Worth, and Francesco Pappalardo
Elsevier BV
Francesco Pappalardo, Giulia Russo, Emanuela Corsini, Alicia Paini, and Andrew Worth
Elsevier BV
Giulia Russo, Valentina Di Salvatore, Giuseppe Sgroi, Giuseppe Alessandro Parasiliti Palumbo, Pedro A Reche, and Francesco Pappalardo
Oxford University Press (OUP)
Abstract 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.
Angela Bonaccorso, Giulia Russo, Francesco Pappalardo, Claudia Carbone, Giovanni Puglisi, Rosario Pignatello, and Teresa Musumeci
Elsevier BV
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.
Giuseppe Sgroi, Giulia Russo, Anna Maglia, Giuseppe Catanuto, Peter Barry, Andreas Karakatsanis, Nicola Rocco, Francesco Pappalardo, and
Springer Science and Business Media LLC
Abstract 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.
Giulia Russo, Giuseppe Alessandro Parasiliti Palumbo, Marzio Pennisi, and Francesco Pappalardo
Springer Science and Business Media LLC
Abstract 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.