triethyltin and Heavy-Metal-Poisoning--Nervous-System

Zhu et al. (Zhu, Y., Wessel, M., Liu, T., Moser, V.C., 2005. Analyses of neurobehavioral screening data: dose-time-response modeling of continuous outcomes. Regul. Toxicol. Pharmacol. 41, 240-255) have recently applied dose-time-response models to longitudinal or time-course neurotoxicity data, and have illustrated the modeling process using continuous data from a functional observational battery (FOB). Following the work of these authors, the purpose of this paper is to show that the benchmark dose (BMD) method for single time point dose-response data can be generalized and applied to longitudinal data such as those generated in neurotoxicity studies. We propose a statistical procedure called bootstrap method for computing the lower confidence limits for the BMD. We demonstrate the method using three previously published FOB datasets of triethyltin (Moser, V.C., Becking, G.C., Cuomo, V., Frantik, E., Kulig, B., MacPhail, R.C., Tilson, H.A., Winneke, G., Brightwell, W.S., DeSalvia, M.A., Gill, M.W., Haggerty, G.C., Hornychova, M., Lammers, J., Larsson, J., McDaniel, K.L., Nelson, B.K., Ostergaard, G., 1997a. The IPCS study on neurobehavioral screening methods: results of chemical testing. Neurotoxicology 18, 969-1056.) and the models of Zhu et al. (Zhu, Y., Wessel, M., Liu, T., Moser, V.C., 2005. Analyses of neurobehavioral screening data: dose-time-response modeling of continuous outcomes. Regul. Toxicol. Pharmacol. 41, 240-255).

Topics: Algorithms; Animals; Behavior, Animal; Benchmarking; Dose-Response Relationship, Drug; Forelimb; Heavy Metal Poisoning, Nervous System; Hindlimb; Models, Biological; Rats; Time Factors; Triethyltin Compounds