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Weatherley G, Araujo RP, Dando SJ, Jenner AL. Could Mathematics be the Key to Unlocking the Mysteries of Multiple Sclerosis? Bull Math Biol 2023; 85:75. [PMID: 37382681 PMCID: PMC10310626 DOI: 10.1007/s11538-023-01181-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2023] [Accepted: 06/19/2023] [Indexed: 06/30/2023]
Abstract
Multiple sclerosis (MS) is an autoimmune, neurodegenerative disease that is driven by immune system-mediated demyelination of nerve axons. While diseases such as cancer, HIV, malaria and even COVID have realised notable benefits from the attention of the mathematical community, MS has received significantly less attention despite the increasing disease incidence rates, lack of curative treatment, and long-term impact on patient well-being. In this review, we highlight existing, MS-specific mathematical research and discuss the outstanding challenges and open problems that remain for mathematicians. We focus on how both non-spatial and spatial deterministic models have been used to successfully further our understanding of T cell responses and treatment in MS. We also review how agent-based models and other stochastic modelling techniques have begun to shed light on the highly stochastic and oscillatory nature of this disease. Reviewing the current mathematical work in MS, alongside the biology specific to MS immunology, it is clear that mathematical research dedicated to understanding immunotherapies in cancer or the immune responses to viral infections could be readily translatable to MS and might hold the key to unlocking some of its mysteries.
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Affiliation(s)
- Georgia Weatherley
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Robyn P Araujo
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia
| | - Samantha J Dando
- School of Biomedical Sciences, Centre for Immunology and Infection Control, Faculty of Health, Queensland University of Technology, Brisbane, Australia
| | - Adrianne L Jenner
- School of Mathematical Sciences, Queensland University of Technology, Brisbane, Australia.
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Russo G, Parasiliti Palumbo GA, Pennisi M, Pappalardo F. Model verification tools: a computational framework for verification assessment of mechanistic agent-based models. BMC Bioinformatics 2022; 22:626. [PMID: 35590242 PMCID: PMC9117838 DOI: 10.1186/s12859-022-04684-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Accepted: 04/11/2022] [Indexed: 11/10/2022] Open
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.
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Affiliation(s)
- Giulia Russo
- Department of Drug and Health Sciences, University of Catania, 95125, Catania, Italy
| | | | - Marzio Pennisi
- Computer Science Institute, DiSIT, University of Eastern Piedmont, 15121, Alessandria, Italy
| | - Francesco Pappalardo
- Department of Drug and Health Sciences, University of Catania, 95125, Catania, Italy.
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3
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Kiagias D, Russo G, Sgroi G, Pappalardo F, Juárez MA. Bayesian Augmented Clinical Trials in TB Therapeutic Vaccination. FRONTIERS IN MEDICAL TECHNOLOGY 2022; 3:719380. [PMID: 35047949 PMCID: PMC8757686 DOI: 10.3389/fmedt.2021.719380] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 09/15/2021] [Indexed: 12/17/2022] Open
Abstract
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.
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Affiliation(s)
- Dimitrios Kiagias
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Giulia Russo
- Department of Drug Sciences, University of Catania, Catania, Italy
| | - Giuseppe Sgroi
- Department of Mathematics and Computer Science, University of Catania, Catania, Italy
| | | | - Miguel A Juárez
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
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Pernice S, Follia L, Maglione A, Pennisi M, Pappalardo F, Novelli F, Clerico M, Beccuti M, Cordero F, Rolla S. Computational modeling of the immune response in multiple sclerosis using epimod framework. BMC Bioinformatics 2020; 21:550. [PMID: 33308135 PMCID: PMC7734848 DOI: 10.1186/s12859-020-03823-9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Accepted: 10/19/2020] [Indexed: 12/17/2022] Open
Abstract
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.
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Affiliation(s)
- Simone Pernice
- Department of Computer Science, University of Turin, Turin, Italy
| | - Laura Follia
- Department of Computer Science, University of Turin, Turin, Italy.,Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Alessandro Maglione
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy
| | - Marzio Pennisi
- Computer Science Inst., DiSIT, University of Eastern Piedmont, Alessandria, Italy
| | | | - Francesco Novelli
- Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy
| | - Marinella Clerico
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy
| | - Marco Beccuti
- Department of Computer Science, University of Turin, Turin, Italy.
| | | | - Simona Rolla
- Department of Clinical and Biological Sciences, University of Turin, Orbassano, Italy
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Juárez MA, Pennisi M, Russo G, Kiagias D, Curreli C, Viceconti M, Pappalardo F. Generation of digital patients for the simulation of tuberculosis with UISS-TB. BMC Bioinformatics 2020; 21:449. [PMID: 33308156 PMCID: PMC7733699 DOI: 10.1186/s12859-020-03776-z] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Accepted: 09/22/2020] [Indexed: 01/11/2023] Open
Abstract
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.
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Affiliation(s)
- Miguel A. Juárez
- School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH UK
| | - Marzio Pennisi
- Computer Science Institute, DiSIT, University of Eastern Piedmont, Alessandria, Italy
| | - Giulia Russo
- Department of Drug Sciences, University of Catania, Catania, Italy
| | - Dimitrios Kiagias
- School of Mathematics and Statistics, University of Sheffield, Sheffield, S3 7RH UK
| | - Cristina Curreli
- Department of Industrial Engineering, University of Bologna, Bologna, Italy
| | - Marco Viceconti
- Department of Industrial Engineering, University of Bologna, Bologna, Italy
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Russo G, Reche P, Pennisi M, Pappalardo F. The combination of artificial intelligence and systems biology for intelligent vaccine design. Expert Opin Drug Discov 2020; 15:1267-1281. [PMID: 32662677 DOI: 10.1080/17460441.2020.1791076] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
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.
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Affiliation(s)
- Giulia Russo
- Department of Drug Sciences, University of Catania , Catania, Italy
| | - Pedro Reche
- Department of Immunology, Universidad Complutense De Madrid, Ciudad Universitaria , Madrid, Spain
| | - Marzio Pennisi
- Computer Science Institute, DiSIT, University of Eastern Piedmont , Italy
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Pappalardo F, Russo G, Pennisi M, Parasiliti Palumbo GA, Sgroi G, Motta S, Maimone D. The Potential of Computational Modeling to Predict Disease Course and Treatment Response in Patients with Relapsing Multiple Sclerosis. Cells 2020; 9:E586. [PMID: 32121606 PMCID: PMC7140535 DOI: 10.3390/cells9030586] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Revised: 02/26/2020] [Accepted: 02/27/2020] [Indexed: 01/10/2023] Open
Abstract
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.
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Affiliation(s)
| | - Giulia Russo
- Department of Drug Sciences, University of Catania, 95125 Catania, Italy;
| | - Marzio Pennisi
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy; (M.P.); (G.A.P.P.); (G.S.)
| | | | - Giuseppe Sgroi
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy; (M.P.); (G.A.P.P.); (G.S.)
| | - Santo Motta
- National Research Council of Italy, 00185 Rome, Italy;
| | - Davide Maimone
- Multiple Sclerosis Center, Neurology Unit, Garibaldi Hospital, 95124 Catania, Italy;
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Viceconti M, Pappalardo F, Rodriguez B, Horner M, Bischoff J, Musuamba Tshinanu F. In silico trials: Verification, validation and uncertainty quantification of predictive models used in the regulatory evaluation of biomedical products. Methods 2020; 185:120-127. [PMID: 31991193 PMCID: PMC7883933 DOI: 10.1016/j.ymeth.2020.01.011] [Citation(s) in RCA: 92] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2019] [Revised: 11/10/2019] [Accepted: 01/14/2020] [Indexed: 02/03/2023] Open
Abstract
Regulators now consider also evidences produced in silico. We need accepted methods to evaluate the credibility of models. In this paper we describe the use of the ASME V&V-40 technical standard. 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.
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Affiliation(s)
- Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Italy; Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.
| | | | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, UK
| | | | - Jeff Bischoff
- Corporate Research Department, Zimmer Biomet, Warsaw, IN, USA
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Pennisi M, Russo G, Sgroi G, Bonaccorso A, Parasiliti Palumbo GA, Fichera E, Mitra DK, Walker KB, Cardona PJ, Amat M, Viceconti M, Pappalardo F. Predicting the artificial immunity induced by RUTI® vaccine against tuberculosis using universal immune system simulator (UISS). BMC Bioinformatics 2019; 20:504. [PMID: 31822272 PMCID: PMC6904993 DOI: 10.1186/s12859-019-3045-5] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2019] [Accepted: 08/21/2019] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Tuberculosis (TB) represents a worldwide cause of mortality (it infects one third of the world's population) affecting mostly developing countries, including India, and recently also developed ones due to the increased mobility of the world population and the evolution of different new bacterial strains capable to provoke multi-drug resistance phenomena. Currently, antitubercular drugs are unable to eradicate subpopulations of Mycobacterium tuberculosis (MTB) bacilli and therapeutic vaccinations have been postulated to overcome some of the critical issues related to the increase of drug-resistant forms and the difficult clinical and public health management of tuberculosis patients. The Horizon 2020 EC funded project "In Silico Trial for Tuberculosis Vaccine Development" (STriTuVaD) to support the identification of new therapeutic interventions against tuberculosis through novel in silico modelling of human immune responses to disease and vaccines, thereby drastically reduce the cost of clinical trials in this critical sector of public healthcare. RESULTS We present the application of the Universal Immune System Simulator (UISS) computational modeling infrastructure as a disease model for TB. The model is capable to simulate the main features and dynamics of the immune system activities i.e., the artificial immunity induced by RUTI® vaccine, a polyantigenic liposomal therapeutic vaccine made of fragments of Mycobacterium tuberculosis cells (FCMtb). Based on the available data coming from phase II Clinical Trial in subjects with latent tuberculosis infection treated with RUTI® and isoniazid, we generated simulation scenarios through validated data in order to tune UISS accordingly to STriTuVaD objectives. The first case simulates the establishment of MTB latent chronic infection with some typical granuloma formation; the second scenario deals with a reactivation phase during latent chronic infection; the third represents the latent chronic disease infection scenario during RUTI® vaccine administration. CONCLUSIONS The application of this computational modeling strategy helpfully contributes to simulate those mechanisms involved in the early stages and in the progression of tuberculosis infection and to predict how specific therapeutical strategies will act in this scenario. In view of these results, UISS owns the capacity to open the door for a prompt integration of in silico methods within the pipeline of clinical trials, supporting and guiding the testing of treatments in patients affected by tuberculosis.
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Affiliation(s)
- Marzio Pennisi
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
| | - Giulia Russo
- Department of Drug Sciences, University of Catania, Italy, 95125 Catania, Italy
| | - Giuseppe Sgroi
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy
| | - Angela Bonaccorso
- Department of Drug Sciences, University of Catania, Italy, 95125 Catania, Italy
| | | | | | - Dipendra Kumar Mitra
- Department of Transplant Immunology and Immunogenetics, All India Institute of Medical Sciences, New Delhi, 110029 India
| | - Kenneth B. Walker
- TuBerculosis Vaccine Initiative (TBVI), Lelystad, 8219 The Netherlands
| | - Pere-Joan Cardona
- Archivel Farma, S.L, 08916 Badalona, Spain
- Experimental Tuberculosis Unit (UTE), Fundació Institut Germans Trias i Pujol (IGTP), Universitat Autònoma de Barcelona (UAB), Badalona, Spain
- Centro de Investigación Biomédica en Red (CIBER) de Enfermedades Respiratorias, Madrid, Spain
| | - Merce Amat
- Archivel Farma, S.L, 08916 Badalona, Spain
| | - Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum, University of Bologna, 40136 Bologna, Italy
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Pappalardo F, Rajput AM, Motta S. Computational modeling of brain pathologies: the case of multiple sclerosis. Brief Bioinform 2019; 19:318-324. [PMID: 28011755 DOI: 10.1093/bib/bbw123] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2016] [Indexed: 01/09/2023] Open
Abstract
The central nervous system is the most complex network of the human body. The existence and functionality of a large number of molecular species in human brain are still ambiguous and mostly unknown, thus posing a challenge to Science and Medicine. Neurological diseases inherit the same level of complexity, making effective treatments difficult to be found. Multiple sclerosis (MS) is a major neurological disease that causes severe inabilities and also a significant social burden on health care system: between 2 and 2.5 million people are affected by it, and the cost associated with it is significantly higher as compared with other neurological diseases because of the chronic nature of the disease and to the partial efficacy of current therapies. Despite difficulties in understanding and treating MS, many computational models have been developed to help neurologists. In the present work, we briefly review the main characteristics of MS and present a selection criteria of modeling approaches.
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Affiliation(s)
| | | | - Santo Motta
- Istitute for Applied Calculus (IAC) "M. Picone", National Research Council of Italy (CNR), Italy
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Pappalardo F, Fichera E, Paparone N, Lombardo A, Pennisi M, Russo G, Leotta M, Pappalardo F, Pedretti A, De Fiore F, Motta S. A computational model to predict the immune system activation by citrus-derived vaccine adjuvants. Bioinformatics 2016; 32:2672-80. [PMID: 27162187 DOI: 10.1093/bioinformatics/btw293] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2016] [Accepted: 04/20/2016] [Indexed: 12/12/2022] Open
Abstract
MOTIVATION Vaccines represent the most effective and cost-efficient weapons against a wide range of diseases. Nowadays new generation vaccines based on subunit antigens reduce adverse effects in high risk individuals. However, vaccine antigens are often poor immunogens when administered alone. Adjuvants represent a good strategy to overcome such hurdles, indeed they are able to: enhance the immune response; allow antigens sparing; accelerate the specific immune response; and increase vaccine efficacy in vulnerable groups such as newborns, elderly or immuno-compromised people. However, due to safety concerns and adverse reactions, there are only a few adjuvants approved for use in humans. Moreover, in practice current adjuvants sometimes fail to confer adequate stimulation. Hence, there is an imperative need to develop novel adjuvants that overcome the limitations of the currently available licensed adjuvants. RESULTS We developed a computational framework that provides a complete pipeline capable of predicting the best citrus-derived adjuvants for enhancing the immune system response using, as a target disease model, influenza A infection. In silico simulations suggested a good immune efficacy of specific citrus-derived adjuvant (Beta Sitosterol) that was then confirmed in vivoAvailability: The model is available visiting the following URL: http://vaima.dmi.unict.it/AdjSim CONTACT francesco.pappalardo@unict.it; fp@francescopappalardo.net.
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Affiliation(s)
| | - Epifanio Fichera
- Etna Biotech S.R.L, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1
| | - Nicoletta Paparone
- Parco Scientifico E Tecnologico Della Sicilia, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1
| | - Alessandro Lombardo
- Parco Scientifico E Tecnologico Della Sicilia, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1
| | - Marzio Pennisi
- Department of Mathematics and Computer Science, University of Catania
| | - Giulia Russo
- Department of Biomedical and Biotechnological Sciences, University of Catania, Catania, Italy
| | - Marco Leotta
- Department of Drug Sciences, University of Catania
| | - Francesco Pappalardo
- Parco Scientifico E Tecnologico Della Sicilia, via Vincenzo Lancia, 57 - Zona Industriale Blocco Palma 1
| | | | | | - Santo Motta
- Department of Mathematics and Computer Science, University of Catania
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Pennisi M, Russo G, Di Salvatore V, Candido S, Libra M, Pappalardo F. Computational modeling in melanoma for novel drug discovery. Expert Opin Drug Discov 2016; 11:609-21. [PMID: 27046143 DOI: 10.1080/17460441.2016.1174688] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
INTRODUCTION There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches. AREAS COVERED This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials. EXPERT OPINION Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.
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Affiliation(s)
- Marzio Pennisi
- a Department of Mathematics and Computer Science , University of Catania , Catania , Italy
| | - Giulia Russo
- b Department of Biomedical and Biotechnological Sciences , University of Catania , Catania , Italy
| | - Valentina Di Salvatore
- c Researcher at National Research Council , Institute of Neurological Sciences , Catania , Italy
| | - Saverio Candido
- b Department of Biomedical and Biotechnological Sciences , University of Catania , Catania , Italy
| | - Massimo Libra
- b Department of Biomedical and Biotechnological Sciences , University of Catania , Catania , Italy
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