Design of optimal patient-specific chemotherapy protocols for the treatment of acute myeloid leukemia (AML)

Pefani, Eleni; Panoskaltsis, Nicki; Mantalaris, Athanasios; Georgiadis, Michael C.; Pistikopoulos, Efstratios N.

doi:10.1016/j.compchemeng.2013.02.003

Cited by 27 publications

(27 citation statements)

References 27 publications

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“…An initial approach is to model cell cycle phases with ODEs, with the parameters needed being the transition rates and the initial cell populations for each phase (refer to Table S2 in Supporting Information for variable and parameter definitions). Pefani et al 19 give an example with three compartments (G0/G1, S and G2/M):…”

Section: Model Definitionsmentioning

confidence: 99%

“…22 In this paper, we present three cell cycle phase models whose parameters can be measured experimentally. Two of them have been previously proposed: the cell cycle phase PBM (CCP-PBM) developed by Fuentes-Garí et al 23 and García Münzer et al; 24,25 and the Pefani et al 19 CCP-ODE model. A new cell cycle phase DDE (CCP-DDE) model featuring phase-specific time delays is presented here as an example of a DDE model with measurable parameters.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, modeling patient response in silico could provide valuable insights as a tool for clinicians. 17,18 To this end, Pefani et al 19,20 developed a complete phar-macokinetic/pharmacodynamic (PK/PD) model of patient response; the model considered infusion, transport and degradation of chemotherapeutic drugs inside the body, as well as drug effects at the point of action (bone marrow). The leukemic cell cycle was represented as a set of 3 ODEs, one per compartment (cell number totals per phase: G1, S and G2/M).…”

Section: Introductionmentioning

confidence: 99%

“…All three models (CCP-ODE, CCP-PBM, CCP-DDE) are compared in: (i) their ability to fit chemotherapy-free experimental data; 23 (ii) chemother-apy outcomes when embedded in the pharmacokinetic/pharmacodynamic (PK/PD) model of Pefani et al. 19 …”

Section: Introductionmentioning

confidence: 99%

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Selecting a Differential Equation Cell Cycle Model for Simulating Leukemia Treatment

Fuentes-Garí¹,

Misener²,

Georgiadis³

et al. 2015

Ind. Eng. Chem. Res.

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This manuscript studies three differential equation models of the leukemia cell cycle: a population balance model (PBM) using intracellular protein expression levels as state variables representing phase progress; a delay differential equation model (DDE) with temporal phase durations as delays; and an ordinary differential equation model (ODE) of inter-phase progression. In each type of model, global sensitivity analysis determines the most significant parameters while parameter estimation fits experimental data. In order to compare models based on the output of their structural properties, an expected behavior was defined, and each model was coupled to a pharmacokinetic/pharmacodynamic model of chemotherapy delivery. Results suggest that the particular cell cycle model chosen highly affects the simulated treatment outcome, given the same steady state kinetic parameters and drug dosage/scheduling. The manuscript shows how cell cycle models should be selected according to the complexity, sensitivity and parameter availability of the application envisioned.2

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Section: Model Definitionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Selecting a Differential Equation Cell Cycle Model for Simulating Leukemia Treatment

Fuentes-Garí¹,

Misener²,

Georgiadis³

et al. 2015

Ind. Eng. Chem. Res.

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“…This work belongs to an ongoing effort developing building blocks for modeling and optimizing biomedical systems [12] such as that for Acute Myeloid Leukemia [13,14], Type 1 Diabetes Mellitus [15] and for the delivery of anesthesia [16,17]. We previously performed a more limited global sensi- [8]- [10,19,20] Fig.…”

Section: Introductionmentioning

confidence: 99%

A Personalized Framework for Dynamic Modeling of Disease Trajectories in Chronic Lymphocytic Leukemia

Savvopoulos

Misener

Panoskaltsis

et al. 2016

IEEE Trans. Biomed. Eng.

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Abstract-Chronic Lymphocytic Leukemia (CLL) is the most common peripheral blood and bone marrow cancer in the developed world. This manuscript proposes mathematical model equations representing the disease dynamics of B-cell CLL. We interconnect delay differential cell cycle models in each of the tumor-involved disease centers using physiologically-relevant cell migration. We further introduce 5 hypothetical case studies representing CLL heterogeneity commonly seen in clinical practice and demonstrate how the proposed CLL model framework may capture disease pathophysiology across patient types. We conclude by exploring the capacity of the proposed temporally-and spatially-distributed model to capture the heterogeneity of CLL disease progression. By using Global Sensitivity Analysis, the critical parameters influencing disease trajectory over space and time are: (i) the initial number of CLL cells in peripheral blood, the number of involved lymph nodes, the presence and degree of splenomegaly; (ii) the migratory fraction of nonproliferating as well as proliferating CLL cells from bone marrow into blood and of proliferating CLL cells from blood into lymph nodes; (iii) the parameters inducing nonproliferative cells to proliferate. The proposed model offers a practical platform which may be explored in future personalized patient protocols once validated.

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An Integrated Platform for the Study of Leukaemia

Velliou¹,

Fuentes-Garí²,

Misener³

et al. 2017

Modelling Optimization and Control of Biomedical Systems

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Leukemia is a severe cancer of the blood and the most common treatment for acute myeloid leukemia (AML), a severe and aggressive type of leukemia, is intensive chemotherapy. The latter involves exposure of the patient to cytotoxic drugs which interact with highly proliferative cells. More specifically, only cells that are in specific phases of the cell cycle will be eliminated. Since healthy cells also proliferate in order to renew the cellular material. Therefore, “closing the loop” from in vivo to in vitro and in silico is a first step towards optimization and personalization of chemotherapy treatment. Clinical treatment protocols ignore the mechanisms behind drug action on the normal and abnormal populations. The main models are pharmacokinetics (PK), which describes the elimination of the drug by the organs, and pharmacodynamics (PD), which accounts for the effects of the drug on the cells in the bone marrow, which is the location of the tumor generation

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Design of optimal patient-specific chemotherapy protocols for the treatment of acute myeloid leukemia (AML)

Cited by 27 publications

References 27 publications

Selecting a Differential Equation Cell Cycle Model for Simulating Leukemia Treatment

Selecting a Differential Equation Cell Cycle Model for Simulating Leukemia Treatment

A Personalized Framework for Dynamic Modeling of Disease Trajectories in Chronic Lymphocytic Leukemia

An Integrated Platform for the Study of Leukaemia

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