NDLI: A Minimal Model of Tumor Growth Inhibition

Content Provider	IEEE Xplore Digital Library
Author	Magni, P. Germani, M. De Nicolao, G. Bianchini, G. Simeoni, M. Poggesi, I. Rocchetti, M.
Copyright Year	1964
Abstract	The preclinical development of antitumor drugs greatly benefits from the availability of models capable of predicting tumor growth as a function of the drug administration schedule. For being of practical use, such models should be simple enough to be identifiable from standard experiments conducted on animals. In the present paper, a stochastic model is derived from a set of minimal assumptions formulated at cellular level. Tumor cells are divided in two groups: proliferating and nonproliferating. The probability that a proliferating cell generates a new cell is a function of the tumor weight. The probability that a proliferating cell becomes nonproliferating is a function of the plasma drug concentration. The time-to-death of a nonproliferating cell is a random variable whose distribution reflects the nondeterministic delay between drug action and cell death. The evolution of the expected value of tumor weight obeys two differential equations (an ordinary and a partial differential one), whereas the variance is negligible. Therefore, the tumor growth dynamics can be well approximated by the deterministic evolution of its expected value. The tumor growth inhibition model, which is a lumped parameter model that in the last few years has been successfully applied to several antitumor drugs, is shown to be a special case of the minimal model presented here.
Sponsorship	IEEE Engineering in Medicine and Biology Society
Page Count	8
File Size	372057
Starting Page	2683
Ending Page	2690
File Format	PDF
ISSN	00189294
Volume Number	55
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-12-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Neoplasms Drugs Predictive models Animals Stochastic processes Tumors Plasmas Random variables Delay Differential equations tumor growth model Anticancer drug discovery pharmacodynamics models Poisson events stochastic model tumor growth dynamics
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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