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Cytotoxic assays for screening anticancer agents
Author(s) -
Baharith Lamya A.,
AlKhouli Abeer,
Raab Gillian M.
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2272
Subject(s) - interpolation (computer graphics) , bayesian probability , linear model , linear interpolation , cytotoxic t cell , cancer cell lines , computer science , set (abstract data type) , mathematics , algorithm , statistics , cancer , cancer cell , artificial intelligence , medicine , chemistry , pattern recognition (psychology) , in vitro , motion (physics) , biochemistry , programming language
Abstract In the process of identifying potential anticancer agents, the ability of a new agent is tested for cytotoxic activity against a panel of standard cancer cell lines. The National Cancer Institute (NCI) present the cytotoxic profile for each agent as a set of estimates of the dose required to inhibit the growth of each cell line. The NCI estimates are obtained from a linear interpolation method applied to the dose–response curves. In this paper non‐linear fits are proposed as an alternative to interpolation. This is illustrated with data from two agents recently submitted to NCI for potential anticancer activity. Fitting of individual non‐linear curves proved difficult, but a non‐linear mixed model applied to the full set of cell lines overcame most of the problems. Two non‐linear functional forms were fitted using random effect models by both maximum likelihood and a full Bayesian approach. Model‐based toxicity estimates have some advantages over those obtained from interpolation. They provide standard errors for toxicity estimates and other derived quantities, allow model comparisons. Examples of each are illustrated. Copyright © 2005 John Wiley & Sons, Ltd.