Preliminary pharmacokinetic model for adriamycin (NSC-123127).

The systematic chemical control of cancer requires a quantitative knowledge of the pharmacologic disposition of antitumor drugs in both healthy and malignant tissues in the body. Pharmacokinetic models can predict the drug concentration in both tumor sites and healthy organs and hence may provide a predictive capability regarding both antitumor action and concomitant toxicity. Adriamycin is an anthracycline antibiotic that has been demonstrated to possess a broad spectrum of antitticularly solid tumors. Its major toxicity is manifested by the depression of normal cell proliferation in the bone marrow and a delayed dose-dependent cardiac toxicity eventually resulting in congestive heart failure. This study is concerned with the development of a predictve analytic model for the pharmacokinetics of adriamycin. The analytic approach embodies a physiologic multicompartmental model as a framework. This model postulates that specific organs or tissue masses may be simulated by a compartment whose elements consist of physiologic properties such as tissue volume and blood flow and pharmacologic behavior such as tissue binding and metabolic activity. A mass balance is set up across each compartment and all compartments are linked by an independent blood compartment. The mass balance includes terms representing inflow and outflow of the drug as well as its metabolism, protein-binding, and other pharmacologic behavior. A model has been developed that has ten compartments which represent the plasma, heart, liver, kidney, lung, lean tissue, adipose tissue, gut, bone marrow, and spleen. Solutions of the system of equations yield the time course of the drug in each organ. Predictions of adriamycin concentration-time curves in the ten tissues after intravenous (iv) administration were generated using this model. With few exceptions, agreement between predicted and actual tissue data in rabbits was excellent. Human plasma levels of adriamycin were predicted and comparison with patient data demonstrated a reasonable first approximation.