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Karanasiou G, Edelman E, Boissel FH, Byrne R, Emili L, Fawdry M, Filipovic N, Flynn D, Geris L, Hoekstra A, Jori MC, Kiapour A, Krsmanovic D, Marchal T, Musuamba F, Pappalardo F, Petrini L, Reiterer M, Viceconti M, Zeier K, Michalis LK, Fotiadis DI. Advancing in Silico Clinical Trials for Regulatory Adoption and Innovation. IEEE J Biomed Health Inform 2025; 29:2654-2668. [PMID: 39514353 DOI: 10.1109/jbhi.2024.3486538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2024]
Abstract
The evolution of information and communication technologies has affected all fields of science, including health sciences. However, the rate of technological innovation adoption by the healthcare sector has been historically slow, compared to other industrial sectors. Innovation in computer modeling and simulation approaches has changed the landscape in biomedical applications and biomedicine, paving the way for their potential contribution in reducing, refining, and partially replacing animal and human clinical trials. In Silico Clinical Trials (ISCT) allow the development of virtual populations used in the safety and efficacy testing of new drugs and medical devices. This White Paper presents the current framework for ISCT, the role of in silico medicine research communities, the different perspectives (research, scientific, clinical, regulatory, standardization, data quality, legal and ethical), the barriers, challenges, and opportunities for ISCT adoption. In addition, an overview of successful ISCT projects, market-available platforms, and FDA- approved paradigms, along with their vision, mission and outcomes are presented.
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Arsène S, Parès Y, Tixier E, Granjeon-Noriot S, Martin B, Bruezière L, Couty C, Courcelles E, Kahoul R, Pitrat J, Go N, Monteiro C, Kleine-Schultjann J, Jemai S, Pham E, Boissel JP, Kulesza A. In Silico Clinical Trials: Is It Possible? Methods Mol Biol 2024; 2716:51-99. [PMID: 37702936 DOI: 10.1007/978-1-0716-3449-3_4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/14/2023]
Abstract
Modeling and simulation (M&S), including in silico (clinical) trials, helps accelerate drug research and development and reduce costs and have coined the term "model-informed drug development (MIDD)." Data-driven, inferential approaches are now becoming increasingly complemented by emerging complex physiologically and knowledge-based disease (and drug) models, but differ in setup, bottlenecks, data requirements, and applications (also reminiscent of the different scientific communities they arose from). At the same time, and within the MIDD landscape, regulators and drug developers start to embrace in silico trials as a potential tool to refine, reduce, and ultimately replace clinical trials. Effectively, silos between the historically distinct modeling approaches start to break down. Widespread adoption of in silico trials still needs more collaboration between different stakeholders and established precedence use cases in key applications, which is currently impeded by a shattered collection of tools and practices. In order to address these key challenges, efforts to establish best practice workflows need to be undertaken and new collaborative M&S tools devised, and an attempt to provide a coherent set of solutions is provided in this chapter. First, a dedicated workflow for in silico clinical trial (development) life cycle is provided, which takes up general ideas from the systems biology and quantitative systems pharmacology space and which implements specific steps toward regulatory qualification. Then, key characteristics of an in silico trial software platform implementation are given on the example of jinkō.ai (nova's end-to-end in silico clinical trial platform). Considering these enabling scientific and technological advances, future applications of in silico trials to refine, reduce, and replace clinical research are indicated, ranging from synthetic control strategies and digital twins, which overall shows promise to begin a new era of more efficient drug development.
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Russo G, Crispino E, Maleki A, Di Salvatore V, Stanco F, Pappalardo F. Beyond the state of the art of reverse vaccinology: predicting vaccine efficacy with the universal immune system simulator for influenza. BMC Bioinformatics 2023; 24:231. [PMID: 37271819 PMCID: PMC10239721 DOI: 10.1186/s12859-023-05374-1] [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: 02/10/2023] [Accepted: 06/01/2023] [Indexed: 06/06/2023] Open
Abstract
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.
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Affiliation(s)
- Giulia Russo
- Department of Health and Drug Sciences, Università degli Studi di Catania, Catania, Italy
| | - Elena Crispino
- Department of Biomedical and Biotechnological Sciences, Università degli Studi di Catania, Catania, Italy
| | - Avisa Maleki
- Department of Mathematics and Computer Science, Università degli Studi di Catania, Catania, Italy
| | - Valentina Di Salvatore
- Department of Health and Drug Sciences, Università degli Studi di Catania, Catania, Italy
| | - Filippo Stanco
- Department of Mathematics and Computer Science, Università degli Studi di Catania, Catania, Italy
| | - Francesco Pappalardo
- Department of Health and Drug Sciences, Università degli Studi di Catania, Catania, Italy.
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Curreli C, Di Salvatore V, Russo G, Pappalardo F, Viceconti M. A Credibility Assessment Plan for an In Silico Model that Predicts the Dose-Response Relationship of New Tuberculosis Treatments. Ann Biomed Eng 2023; 51:200-210. [PMID: 36115895 PMCID: PMC9483464 DOI: 10.1007/s10439-022-03078-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 09/06/2022] [Indexed: 01/13/2023]
Abstract
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.
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Affiliation(s)
- Cristina Curreli
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy.
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136, Bologna, Italy.
| | | | - Giulia Russo
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
- Mimesis srl, Catania, Italy
| | | | - Marco Viceconti
- Department of Industrial Engineering, Alma Mater Studiorum - University of Bologna, Bologna, Italy
- Medical Technology Lab, IRCCS Istituto Ortopedico Rizzoli, Via di Barbiano 1/10, 40136, Bologna, Italy
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5
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Pappalardo F, Russo G, Corsini E, Paini A, Worth A. Translatability and transferability of in silico models: Context of use switching to predict the effects of environmental chemicals on the immune system. Comput Struct Biotechnol J 2022; 20:1764-1777. [PMID: 35495116 PMCID: PMC9035946 DOI: 10.1016/j.csbj.2022.03.024] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 03/23/2022] [Accepted: 03/24/2022] [Indexed: 02/08/2023] Open
Abstract
Immunotoxicity hazard identification of chemicals aims to evaluate the potential for unintended effects of chemical exposure on the immune system. Perfluorinated alkylate substances (PFAS), such as perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA), are persistent, globally disseminated environmental contaminants known to be immunotoxic. Elevated PFAS exposure is associated with lower antibody responses to vaccinations in children and in adults. In addition, some studies have reported a correlation between PFAS levels in the body and lower resistance to disease, in other words an increased risk of infections or cancers. In this context, modelling and simulation platforms could be used to simulate the human immune system with the aim to evaluate the adverse effects that immunotoxicants may have. Here, we show the conditions under which a mathematical model developed for one purpose and application (e.g., in the pharmaceutical domain) can be successfully translated and transferred to another (e.g., in the chemicals domain) without undergoing significant adaptation. In particular, we demonstrate that the Universal Immune System Simulator was able to simulate the effects of PFAS on the immune system, introducing entities and new interactions that are biologically involved in the phenomenon. This also revealed a potentially exploitable pathway for assessing immunotoxicity through a computational model.
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Affiliation(s)
- Francesco Pappalardo
- Department of Health and Drug Sciences, Università degli Studi di Catania, Italy
| | - Giulia Russo
- Department of Health and Drug Sciences, Università degli Studi di Catania, Italy
| | - Emanuela Corsini
- Department of Pharmacological and Biomolecular Sciences, Università degli Studi di Milano, Italy
| | - Alicia Paini
- European Commission, Joint Research Centre (JRC), Ispra, Italy
| | - Andrew Worth
- European Commission, Joint Research Centre (JRC), Ispra, Italy
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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: 1.0] [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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Russo G, Di Salvatore V, Sgroi G, Parasiliti Palumbo GA, Reche PA, Pappalardo F. A multi-step and multi-scale bioinformatic protocol to investigate potential SARS-CoV-2 vaccine targets. Brief Bioinform 2021; 23:6381250. [PMID: 34607353 PMCID: PMC8500048 DOI: 10.1093/bib/bbab403] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/30/2021] [Accepted: 09/02/2021] [Indexed: 12/21/2022] Open
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.
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Affiliation(s)
- Giulia Russo
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
| | | | - Giuseppe Sgroi
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
| | | | - Pedro A Reche
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
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Henyš P, Vořechovský M, Kuchař M, Heinemann A, Kopal J, Ondruschka B, Hammer N. Bone mineral density modeling via random field: Normality, stationarity, sex and age dependence. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 210:106353. [PMID: 34500142 DOI: 10.1016/j.cmpb.2021.106353] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Accepted: 08/06/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE Capturing the population variability of bone properties is of paramount importance to biomedical engineering. The aim of the present paper is to describe variability and correlations in bone mineral density with a spatial random field inferred from routine computed tomography data. METHODS Random fields were simulated by transforming pairwise uncorrelated Gaussian random variables into correlated variables through the spectral decomposition of an age-detrended correlation matrix. The validity of the random field model was demonstrated in the spatiotemporal analysis of bone mineral density. The similarity between the computed tomography samples and those generated via random fields was analyzed with the energy distance metric. RESULTS The random field of bone mineral density was found to be approximately Gaussian/slightly left-skewed/strongly right-skewed at various locations. However, average bone density could be simulated well with the proposed Gaussian random field for which the energy distance, i.e., a measure that quantifies discrepancies between two distribution functions, is convergent with respect to the number of correlation eigenpairs. CONCLUSIONS The proposed random field model allows the enhancement of computational biomechanical models with variability in bone mineral density, which could increase the usability of the model and provides a step forward in in-silico medicine.
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Affiliation(s)
- Petr Henyš
- Institute of New Technologies and Applied Informatics, Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Technical University of Liberec, Studentskí 1402/2, Liberec 461 17, Czech Republic
| | - Miroslav Vořechovský
- Institute of Structural Mechanics, Faculty of Civil Engineering, Brno University of Technology, Veveří 331/95, Brno 602 00, Czech Republic
| | - Michal Kuchař
- Department of Anatomy, Faculty of Medicine in Hradec Králové, Charles University, Šimkova 870, Hradec Králové, 500 03, Czech Republic.
| | - Axel Heinemann
- Institut für Rechtsmedizin, Universitätsklinikum Hamburg-Eppendorf, Butenfeld 34, Hamburg 22529, Germany
| | - Jiří Kopal
- Institute of New Technologies and Applied Informatics, Faculty of Mechatronics, Informatics and Interdisciplinary Studies, Technical University of Liberec, Studentskí 1402/2, Liberec 461 17, Czech Republic
| | - Benjamin Ondruschka
- Institut für Rechtsmedizin, Universitätsklinikum Hamburg-Eppendorf, Butenfeld 34, Hamburg 22529, Germany
| | - Niels Hammer
- Department of Macroscopic and Clinical Anatomy, Medical University of Graz, Auenbruggerpl. 2, Graz 8036, Austria; Department of Orthopedic and Trauma Surgery, University of Leipzig, Leipzig, Germany; Fraunhofer Institute for Machine Tools and Forming Technology IWU, Nöthnitzer Straße 44, 01187, Dresden, Germany
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9
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Musuamba FT, Skottheim Rusten I, Lesage R, Russo G, Bursi R, Emili L, Wangorsch G, Manolis E, Karlsson KE, Kulesza A, Courcelles E, Boissel JP, Rousseau CF, Voisin EM, Alessandrello R, Curado N, Dall'ara E, Rodriguez B, Pappalardo F, Geris L. Scientific and regulatory evaluation of mechanistic in silico drug and disease models in drug development: Building model credibility. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2021; 10:804-825. [PMID: 34102034 PMCID: PMC8376137 DOI: 10.1002/psp4.12669] [Citation(s) in RCA: 63] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/27/2021] [Accepted: 05/27/2021] [Indexed: 01/08/2023]
Abstract
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.
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Affiliation(s)
- Flora T Musuamba
- EMA Modelling and Simulation Working Party, Amsterdam, The Netherlands.,Federal Agency for Medicines and Health Products, Brussels, Belgium.,Faculté des Sciences Pharmaceutiques, Université de Lubumbashi, Lubumbashi, Congo
| | - Ine Skottheim Rusten
- EMA Modelling and Simulation Working Party, Amsterdam, The Netherlands.,Norvegian Medicines Agency, Oslo, Norway
| | - Raphaëlle Lesage
- Biomechanics Section, KU Leuven, Leuven, Belgium.,Virtual Physiological Human Institute, Leuven, Belgium
| | - Giulia Russo
- Department of Drug and Health Sciences, University of Catania, Catania, Italy
| | | | - Luca Emili
- InSilicoTrials Technologies, Milano, Italy
| | - Gaby Wangorsch
- EMA Modelling and Simulation Working Party, Amsterdam, The Netherlands.,Paul-Ehrlich-Institut (Federal Institute for Vaccines and Biomedicines), Langen, Germany
| | - Efthymios Manolis
- EMA Modelling and Simulation Working Party, Amsterdam, The Netherlands.,European Medicines Agency, Amsterdam, The Netherlands
| | - Kristin E Karlsson
- EMA Modelling and Simulation Working Party, Amsterdam, The Netherlands.,Swedish Medical Products Agency, Uppsala, Sweden
| | | | | | | | | | | | | | | | | | - Blanca Rodriguez
- Department of Computer Science, British Heart Foundation Centre of Research Excellence, University of Oxford, Oxford, UK
| | | | - Liesbet Geris
- Biomechanics Section, KU Leuven, Leuven, Belgium.,Virtual Physiological Human Institute, Leuven, Belgium.,GIGA In silico Medicine, Université de Liège, Liège, Belgium
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10
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Abstract
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.
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Affiliation(s)
- Francesco Pappalardo
- Department of Drug and Health Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy
| | - Giulia Russo
- Department of Drug and Health Sciences, University of Catania, V.le A. Doria 6, 95125 Catania, Italy
| | - Pedro A. Reche
- Departamento de Immunología (Microbiología I), Universidad Complutense de Madrid, Facultad de Medicina, Plaza Ramón y Cajal, 28040 Madrid, Spain
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