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Computational Modeling to Predict Effect of Treatment Schedule on Drug Delivery to Prostate in Humans

Authors' Affiliations: ¹ College of Pharmacy and ² James Cancer Hospital and Solove Research Institute, The Ohio State University, Columbus, Ohio

Requests for reprints: Jessie L-S. Au, College of Pharmacy, 500 West 12th Avenue, Columbus, OH 43210. Phone: 614-292-4244; Fax: 614-688-3597; E-mail: au.1{at}osu.edu.

Purpose: To evaluate a computational approach that incorporates experimental data in preclinical models to depict doxorubicin human tissue pharmacokinetics.

Experimental Design: Beagle dogs were given 2 mg/kg doxorubicin as i.v. bolus, 4-h infusion, or 96-h infusion. Concentrations in plasma, prostate (target tissue), heart (toxicity), and major tissues for disposition were determined and modeled. Model parameters were obtained after the bolus injection with model validation based on the 4-h and 96-h infusion data. Clinical pharmacokinetic data and scale-up gave doxorubicin profiles in human prostate and heart.

Results: In agreement with in vitro results, tissues were best modeled with two compartments, one rapidly and one slowly equilibrating. The developed tissue distribution model predicted concentrations for all three administration regimens well, with an average deviation of 34% (median, 29%). Interspecies scale-up to humans showed that the change from a bolus injection to a slow, 96-h infusion (a) had different effects on the drug partition and accumulation in heart and prostate, and (b) lowered the peak concentration in the plasma by 100-fold but had relatively little effect on maximal heart concentration (33% lower). The simulated drug exposure in a human prostate was above the exposure required to inhibit tumor proliferation but was 30 to 50 times below that needed for cell death.

Conclusion: The present study shows a computation-based paradigm for translating in vitro and in vivo preclinical data and to estimate and compare the drug delivery and pharmacokinetics in target tissues after different treatment schedules.

Commentary

New Practices in Computational Modeling

William R. Greco
Clin. Cancer Res. 2007 13: 1074-1075. [Full Text] [PDF]

This article has been cited by other articles:

				W. R. Greco New Practices in Computational Modeling Clin. Cancer Res., February 15, 2007; 13(4): 1074 - 1075. [Full Text] [PDF]