M. Rocchetti
Pharmacia
11 Papers
107 Citations
M. Rocchetti is an academic researcher from Pharmacia. The author has contributed to research in topics: Population & PK/PD models. The author has an hindex of 6, co-authored 11 publications.
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Papers
Assessment of normal and tumor tissue uptake of MAG-CPT, a polymer-bound prodrug of camptothecin, in patients undergoing elective surgery for colorectal carcinoma
Nenad Sarapa,Margaret R. Britto,William Speed,MariaGabriella Jannuzzo,Massimo Breda,C. A. James,MariaGrazia Porro,M. Rocchetti,Alkvin Wanders,Haile Mahteme,Peter Nygren +10 more
TL;DR: Delivery of CPT to the target tumor tissue is achievable by means of the MAG-CPT polymer-bound delivery system, with the equilibrium between plasma and tumor tissue concentrations of released CPT being established within 24 h after dosing.
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Nonparametric AUC estimation in population studies with incomplete sampling: a Bayesian approach.
TL;DR: This work proposes and evaluates a nonparametric Bayesian scheme for AUC estimation in population studies with arbitrary sampling protocols and describes the individual plasma concentration curves and the “mean” population curve in the stochastic model representing the whole population.
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Modeling tumor growth inhibition and toxicity outcome after administration of anticancer agents in xenograft mice: A Dynamic Energy Budget (DEB) approach.
TL;DR: A new PK-PD model, based on a set of tumor-host interaction rules taken from Dynamic Energy Budget (DEB) theory and a setof drug tumor inhibition equations taken from the well-known Simeoni TGI model, was developed and was tested in typical preclinical studies, predicting both the dynamics of tumor and host growth could be predicted in xenograft mice untreated or treated with different anticancer agents and following different schedules.
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In vitro cell growth pharmacodynamic studies: a new nonparametric approach to determining the relative importance of drug concentration and treatment time
Massimiliano Germani,Paolo Magni,Giuseppe De Nicolao,Italo Poggesi,Aurelio Marsiglio,Dario Ballinari,M. Rocchetti +6 more
TL;DR: The proposed RBF-NN can be reliably applied for the analysis in cell-based tumor growth inhibition studies and can be used for optimizing the administration regimens to be adopted in vivo.
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