Doxorubicin (DOXO) is commonly employed as chemotherapy drug to treat several kinds of cancer, including multiple myeloma (MM). Hence, a full characterization of DOXO pharmacokinetics/pharmacodynamics (PK/PD) is essential to maximize its efficacy while minimizing possible side effects. We recently proposed a mathematical model of DOXO PK in MM cells. Thus, as a natural succession, here we aim at modeling DOXO PD to describe its effects on MM cells. We monitored in vitro the MM cell proliferation in eight 2-week experiments, one under untreated conditions (control) and seven after a 3-h administration of DOXO at different concentrations (from 15 nM to 900 nM). A logistic growth and treatment response model, accounting for both cell proliferation and death rates, was developed and identified on each experiment to fit the collected cell count time series. Results show that the proposed model is able to describe cellular growth both in untreated conditions and after DOXO treatment. We also propose a simple model to characterize the relationship between the administered DOXO amount and the corresponding drug-induced cell death rate parameter. The proposed model is perfectly scalable to obtain a more precise descriptive implementation but also to develop a predictive framework that could lead to advancements in the current MM treatment paradigms. © 2023 Convegno Nazionale di Bioingegneria. All rights reserved
Modeling Doxorubicin Treatment Effect in Multiple Myeloma
Andrean D.;Pedersen M. G.;Visentin R.
2023
Abstract
Doxorubicin (DOXO) is commonly employed as chemotherapy drug to treat several kinds of cancer, including multiple myeloma (MM). Hence, a full characterization of DOXO pharmacokinetics/pharmacodynamics (PK/PD) is essential to maximize its efficacy while minimizing possible side effects. We recently proposed a mathematical model of DOXO PK in MM cells. Thus, as a natural succession, here we aim at modeling DOXO PD to describe its effects on MM cells. We monitored in vitro the MM cell proliferation in eight 2-week experiments, one under untreated conditions (control) and seven after a 3-h administration of DOXO at different concentrations (from 15 nM to 900 nM). A logistic growth and treatment response model, accounting for both cell proliferation and death rates, was developed and identified on each experiment to fit the collected cell count time series. Results show that the proposed model is able to describe cellular growth both in untreated conditions and after DOXO treatment. We also propose a simple model to characterize the relationship between the administered DOXO amount and the corresponding drug-induced cell death rate parameter. The proposed model is perfectly scalable to obtain a more precise descriptive implementation but also to develop a predictive framework that could lead to advancements in the current MM treatment paradigms. © 2023 Convegno Nazionale di Bioingegneria. All rights reserved
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