Predicting long-term vaccine efficacy against metastases using agents

Marzio Pennisi; Dario Motta; Alessandro Cincotti; Francesco Pappalardo

doi:10.1007/978-3-642-24553-4_15

Predicting long-term vaccine efficacy against metastases using agents

Marzio Pennisi
, Dario Motta
, Alessandro Cincotti
, Francesco Pappalardo

Risultato della ricerca: Capitolo in libro/report/atti di convegno › Contributo a conferenza › peer review

Abstract

To move faster from preclinical studies (experiments in mice) towards clinical phase I trials (experiments in advanced cancer patients), the chance to predict the outcome of longer experiments represents a key step. We use the MetastaSim model to predict the long-term effects of the Triplex vaccine against metastases. To this end we simulate follow-ups of two and three of three months (equivalent approximately to 5.83 and 8.75 years in humans) to compare the long-term efficacy of the best protocol used "in vivo" against the one found by the MetastaSim model. We also check the efficacy of these two protocols by delaying the time of the first administration, in order to catch up the maximum time delay between the appearing of metastases and the administration of the vaccine needed to guarantee reasonable treatment efficacy.

Lingua originale	Inglese
Titolo della pubblicazione ospite	Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers
Editore	Springer Verlag
Pagine	97-106
Numero di pagine	10
ISBN (stampa)	9783642245527
DOI	https://doi.org/10.1007/978-3-642-24553-4_15
Stato di pubblicazione	Pubblicato - 2012
Pubblicato esternamente	Sì
Evento	7th International Conference on Intelligent Computing, ICIC 2011 - Zhengzhou, China Durata: 11 ago 2011 → 14 ago 2011

Serie di pubblicazioni

Nome	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6840 LNBI
ISSN (stampa)	0302-9743
ISSN (elettronico)	1611-3349

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???event.eventtypes.event.conference???	7th International Conference on Intelligent Computing, ICIC 2011
Paese/Territorio	China
Città	Zhengzhou
Periodo	11/08/11 → 14/08/11

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10.1007/978-3-642-24553-4_15

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Pennisi, M., Motta, D., Cincotti, A., & Pappalardo, F. (2012). Predicting long-term vaccine efficacy against metastases using agents. In Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers (pagg. 97-106). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6840 LNBI). Springer Verlag. https://doi.org/10.1007/978-3-642-24553-4_15

Pennisi, Marzio ; Motta, Dario ; Cincotti, Alessandro et al. / Predicting long-term vaccine efficacy against metastases using agents. Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers. Springer Verlag, 2012. pagg. 97-106 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Predicting long-term vaccine efficacy against metastases using agents",

abstract = "To move faster from preclinical studies (experiments in mice) towards clinical phase I trials (experiments in advanced cancer patients), the chance to predict the outcome of longer experiments represents a key step. We use the MetastaSim model to predict the long-term effects of the Triplex vaccine against metastases. To this end we simulate follow-ups of two and three of three months (equivalent approximately to 5.83 and 8.75 years in humans) to compare the long-term efficacy of the best protocol used {"}in vivo{"} against the one found by the MetastaSim model. We also check the efficacy of these two protocols by delaying the time of the first administration, in order to catch up the maximum time delay between the appearing of metastases and the administration of the vaccine needed to guarantee reasonable treatment efficacy.",

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Pennisi, M, Motta, D, Cincotti, A & Pappalardo, F 2012, Predicting long-term vaccine efficacy against metastases using agents. in Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6840 LNBI, Springer Verlag, pagg. 97-106, 7th International Conference on Intelligent Computing, ICIC 2011, Zhengzhou, China, 11/08/11. https://doi.org/10.1007/978-3-642-24553-4_15

Predicting long-term vaccine efficacy against metastases using agents. / Pennisi, Marzio; Motta, Dario; Cincotti, Alessandro et al.
Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers. Springer Verlag, 2012. pag. 97-106 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6840 LNBI).

Risultato della ricerca: Capitolo in libro/report/atti di convegno › Contributo a conferenza › peer review

TY - GEN

T1 - Predicting long-term vaccine efficacy against metastases using agents

AU - Pennisi, Marzio

AU - Motta, Dario

AU - Cincotti, Alessandro

AU - Pappalardo, Francesco

PY - 2012

Y1 - 2012

N2 - To move faster from preclinical studies (experiments in mice) towards clinical phase I trials (experiments in advanced cancer patients), the chance to predict the outcome of longer experiments represents a key step. We use the MetastaSim model to predict the long-term effects of the Triplex vaccine against metastases. To this end we simulate follow-ups of two and three of three months (equivalent approximately to 5.83 and 8.75 years in humans) to compare the long-term efficacy of the best protocol used "in vivo" against the one found by the MetastaSim model. We also check the efficacy of these two protocols by delaying the time of the first administration, in order to catch up the maximum time delay between the appearing of metastases and the administration of the vaccine needed to guarantee reasonable treatment efficacy.

AB - To move faster from preclinical studies (experiments in mice) towards clinical phase I trials (experiments in advanced cancer patients), the chance to predict the outcome of longer experiments represents a key step. We use the MetastaSim model to predict the long-term effects of the Triplex vaccine against metastases. To this end we simulate follow-ups of two and three of three months (equivalent approximately to 5.83 and 8.75 years in humans) to compare the long-term efficacy of the best protocol used "in vivo" against the one found by the MetastaSim model. We also check the efficacy of these two protocols by delaying the time of the first administration, in order to catch up the maximum time delay between the appearing of metastases and the administration of the vaccine needed to guarantee reasonable treatment efficacy.

UR - https://www.scopus.com/pages/publications/84855643304

U2 - 10.1007/978-3-642-24553-4_15

DO - 10.1007/978-3-642-24553-4_15

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AN - SCOPUS:84855643304

SN - 9783642245527

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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PB - Springer Verlag

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Pennisi M, Motta D, Cincotti A, Pappalardo F. Predicting long-term vaccine efficacy against metastases using agents. In Bio-Inspired Computing and Applications - 7th International Conference on Intelligent Computing, ICIC 2011, Revised Selected Papers. Springer Verlag. 2012. pag. 97-106. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-24553-4_15

Predicting long-term vaccine efficacy against metastases using agents

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