Model Assessment of Doxorubicin Pharmacokinetics in Multiple Myeloma

Multiple myeloma (MM) is a plasma cell neoplasm that is usually treated with chemotherapeutic drugs such as doxorubicin (DOXO). During treatment, drug administration must be optimized according to the patient’s needs to reduce the side effects of therapy and increase its efficacy. To support the study of DOXO and treatment optimization, we have recently developed a pharmacokinetics (PK) model of DOXO in MM cells. Here we aim to evaluate the robustness of the PK model by assessing its performance in the context of a population-based approach. A series of in vitro experiments in MM1R cells treated with DOXO at 200 nM and 450 nM was performed. The measured intracellular DOXO concentrations have been organized to generate three different population datasets: a naive average dataset (NAD), obtained by averaging all cell readings and two datasets obtained by averaging the cell readings within the same experiment well or the same image field, respectively. The PK model has been identified on each population dataset, and the resulting model predictions and estimated parameters were compared across populations. Model fit was satisfactory in all combinations, and the estimated model parameters were precise and comparable across population datasets. Although slight differences were found between results obtained with NAD and field-population dataset, they were mainly attributable to the expected different intrinsic dataset variability. On the other hand, the population approach based on the within-well cellular data provided almost superimposable results to those obtained with NAD, proving the model’s ability to reliably describe DOXO PK in MM cells