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Pierluigi Lollini

dblp:80/2206 · DBLP profile ↗
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6ranked-venue papers
1as first author
0since 2021 · last 2010
0000-0003-1702-4108ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Applied, interdisciplinary, general and emerging computing · 6 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Bioinformatics and computational biology · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Bioinformatics and computational biology › immunoinformatics
immune system modeling
0.112005
Modeling and simulation of cancer immunoprevention vaccine · Bioinform. 2005
Bioinformatics and computational biology
immunoinformatics
0.112005
Modeling and simulation of cancer immunoprevention vaccine · Bioinform. 2005

Methods — techniques the papers use, named apart from their topics

agent-based modeling · 0.1
YearPublicationVenuePosition
2010 Cancer Immunoprevention: What Can We Learn from in Silico Models?
Francesco Pappalardo 0001, Marzio Pennisi, Alessandro Cincotti, Ferdinando Chiacchio, Santo Motta, Pierluigi Lollini
ICIC (3)6
2010 Modeling the competition between lung metastases and the immune system using agents
abstract
BACKGROUND: The Triplex cell vaccine is a cancer cellular vaccine that can prevent almost completely the mammary tumor onset in HER-2/neu transgenic mice. In a translational perspective, the activity of the Triplex vaccine was also investigated against lung metastases showing that the vaccine is an effective treatment also for the cure of metastases. A future human application of the Triplex vaccine should take into account several aspects of biological behavior of the involved entities to improve the efficacy of therapeutic treatment and to try to predict, for example, the outcomes of longer experiments in order to move faster towards clinical phase I trials. To help to address this problem, MetastaSim, a hybrid Agent Based - ODE model for the simulation of the vaccine-elicited immune system response against lung metastases in mice is presented. The model is used as in silico wet-lab. As a first application MetastaSim is used to find protocols capable of maximizing the total number of prevented metastases, minimizing the number of vaccine administrations. RESULTS: The model shows that it is possible to obtain "in silico" a 45% reduction in the number of vaccinations. The analysis of the results further suggests that any optimal protocol for preventing lung metastases formation should be composed by an initial massive vaccine dosage followed by few vaccine recalls. CONCLUSIONS: Such a reduction may represent an important result from the point of view of translational medicine to humans, since a downsizing of the number of vaccinations is usually advisable in order to minimize undesirable effects. The suggested vaccination strategy also represents a notable outcome. Even if this strategy is commonly used for many infectious diseases such as tetanus and hepatitis-B, it can be in fact considered as a relevant result in the field of cancer-vaccines immunotherapy. These results can be then used and verified in future "in vivo" experiments, and their outcome can be used to further improve and refine the model.
Marzio Pennisi, Francesco Pappalardo 0001, Arianna Palladini, Giordano Nicoletti, Patrizia Nanni, Pierluigi Lollini, Santo Motta
BMC Bioinform.6
2009 ImmunoGrid, an integrative environment for large-scale simulation of the immune system for vaccine discovery, design and optimization
abstract
Vaccine research is a combinatorial science requiring computational analysis of vaccine components, formulations and optimization. We have developed a framework that combines computational tools for the study of immune function and vaccine development. This framework, named ImmunoGrid combines conceptual models of the immune system, models of antigen processing and presentation, system-level models of the immune system, Grid computing, and database technology to facilitate discovery, formulation and optimization of vaccines. ImmunoGrid modules share common conceptual models and ontologies. The ImmunoGrid portal offers access to educational simulators where previously defined cases can be displayed, and to research simulators that allow the development of new, or tuning of existing, computational models. The portal is accessible at .
Francesco Pappalardo 0001, Mark D. Halling-Brown, Nicolas Rapin, Ping Zhang 0008, Davide Alemani, Andrew P. J. Emerson, Paola Paci, Patrice Duroux, Marzio Pennisi, Arianna Palladini, Olivo Miotto, Daniel Churchill, Elda Rossi, Adrian J. Shepherd, David S. Moss, Filippo Castiglione, Massimo Bernaschi, Marie-Paule Lefranc, Søren Brunak, Santo Motta, Pierluigi Lollini, Kaye E. Basford, Vladimir Brusic
Briefings Bioinform.21
2006 Discovery of cancer vaccination protocols with a genetic algorithm driving an agent based simulator
abstract
BACKGROUND: Immunological prevention of cancer has been obtained in HER-2/neu transgenic mice using a vaccine that combines 3 different immune stimuli (Triplex vaccine) that is repeatedly administered for the entire lifespan of the host (Chronic protocol). Biological experiments leave open the question of whether the Chronic protocol is indeed the minimal vaccination schedule affording 100% protection, or whether shorter protocols could be applied that would result in the same efficacy. A biological solution would require an enormous number of experiments, each lasting at least one year. Therefore we approached this problem by developing a simulator (SimTriplex) which describes the immune response activated by Triplex vaccine. This simulator, tested against in vivo experiments on HER-2/neu mice, reproduces all the vaccination protocols used in the in vivo experiments. The simulator should describe any vaccination protocol within the tested range. A possible solution to the former open question using a minimal search strategy based on a genetic algorithm is presented. This is the first step toward a more general approach of biological or clinical constraints for the search of an effective vaccination schedule. RESULTS: The results suggest that the Chronic protocol included a good number of redundant vaccine administrations, and that maximal protection could still be obtained with a number of vaccinations approximately 40% less than with the Chronic protocol. CONCLUSION: This approach may have important connotations with regard to translation of cancer immunopreventive approaches to human situations, in which it is desirable to minimize the number of vaccinations. We are currently setting up experiments in mice to test whether the actual effectiveness of the vaccination protocol agrees with the genetic algorithm.
Pierluigi Lollini, Santo Motta, Francesco Pappalardo 0001
BMC Bioinform.1
2005 Modeling and simulation of cancer immunoprevention vaccine
abstract
UNLABELLED: We present an in silico model that simulates the immune system responses to tumor cells in naive and vaccinated mice. We have demonstrated the ability of this model to accurately reproduce the experimental results. MOTIVATION: In vivo experiments on HER-2/neu mice have shown the effectiveness of Triplex vaccine in the protection of mice from mammary carcinoma. Full protection was conferred using chronic (prophylactic) vaccination protocol while therapeutic vaccination was less efficient. Our in silico model was able to closely reproduce the effects of various vaccination protocols. This model is the first step towards the development of in silico experiments searching for optimal vaccination protocols. RESULTS: In silico experiments carried out on two large statistical samples of virtual mice showed very good agreements with in vivo experiments for all experimental vaccination protocols. They also show, as supported by in vivo experiments, that the humoral response is fundamental in controlling the tumor growth and therefore suggest the selection and timing of experiments for measuring the activity of T cells. CONTACT: [email protected] SUPPLEMENTARY INFORMATION: http://www.dmi.unict.it/CIG/suppdata_bioinf.html.
Francesco Pappalardo 0001, Pierluigi Lollini, Filippo Castiglione, Santo Motta
Bioinform.2
2005 An integrated approach of immunogenomics and bioinformatics to identify new Tumor Associated Antigens (TAA) for mammary cancer immunological prevention
abstract
BACKGROUND: Neoplastic transformation is a multistep process in which distinct gene products of specific cell regulatory pathways are involved at each stage. Identification of overexpressed genes provides an unprecedented opportunity to address the immune system against antigens typical of defined stages of neoplastic transformation. HER-2/neu/ERBB2 (Her2) oncogene is a prototype of deregulated oncogenic protein kinase membrane receptors. Mice transgenic for rat Her2 (BALB-neuT mice) were studied to evaluate the stage in which vaccines can prevent the onset of Her2 driven mammary carcinomas. As Her2 is not overexpressed in all mammary carcinomas, definition of an additional set of tumor associated antigens (TAAs) expressed at defined stages by most breast carcinomas would allow a broader coverage of vaccination. To address this question, a meta-analysis was performed on two transcription profile studies to identify a set of new TAA targets to be used instead of or in conjunction with Her2. RESULTS: The five TAAs identified (Tes, Rcn2, Rnf4, Cradd, Galnt3) are those whose expression is linearly related to the tumor mass increase in BALB-neuT mammary glands. Moreover, they have a low expression in normal tissues and are generally expressed in human breast tumors, though at a lower level than Her2. CONCLUSION: Although the number of putative TAAs identified is limited, this pilot study suggests that meta-analysis of expression profiles produces results that could assist in the designing of pre-clinical immunopreventive vaccines.
Federica Cavallo, Annalisa Astolfi, Manuela Iezzi, Francesca Cordero, Pierluigi Lollini, Guido Forni, Raffaele A. Calogero
BMC Bioinform.5