Description of the modelling, simulating, predictive and treatment managing tool for hematological disorders
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Introduction
The platform can be found at the following address:
https://apps.health-atlas.de/hemato-models/
This platform enables data fitting, results presentation and next cycle prediction/management using hematopoietic models, developed by our modeling group in IMISE:
a) Erythropoiesis model
b) Individualised thrombopoiesis model [1] integrated with a model of bone-remodelling [2] as well as with PK models of few cytotoxic drugs [3,4]

c) Integral hemathopoiesis model (above thrombopoiesis model fused with our earlier granulopoiesis model [5] and our novel lymphopoiesis model)

Our models have several parameters, whose estimation requires clinical (individual or averaged) data as well as additional external data sets. We solved this by combining large numbers of literature data sets of different studies introducing the principle of “virtual participation”. This principle can be understood as a data-driven regularization of models [1]. We implemented “virtual participation” strategy in the current tool as well.
This tool provides options for simulating fits of individual as well as biological data for every modelled biological property under a large variety of treatments: CHOEP-like multi-cyclic treatments, supporting treatments (growth factors, platelets transfusions etc.).
It is possible to simulate and visualize predictions of hematopoiesis dynamics for the next-cycle treatments. Next cycle can be modified (dose intensification/reduction/postponing, addition of supporting treatments). For the chemotherapy treated patients the grade of granulocytopenia and thrombocytopenia are shown as well. This option is currently under construction and will be in nearest weeks available.
The platform has three panels: Individual tool, Population tool and the models description tool.
Individual tool enables visualization of individual patient’s dynamics of different hematopoietic variables (blood cells, precursor cells, growth factors) under real and virtual experimental conditions. MCMC sampling is currently under construction.
Population tool (for parameters fits are currently) is under construction.
The model’s description has links to relevant topics.
Short guideline

  1. Chose the hematopoiesis line fort the modeling: choose “Erythropoiesis” at the select input “Chose the line”.
    2. Choose the project at “choose project” select input (Rutherford 600 mg, CHOP or Souillard)
    3. Press “Select project” button
    4. Choose whether and which biological data are used for virtual simulation (currently implemented only for integral and thrombopoiesis models, not for erythropoiesis model).
    5. Press “Upload the model” button on the second row.
    6. Proceed to the default “Individual tool” panel: press “Chose Patient” button on the right (for the Erythropoiesis model there is only one patient - no IIV (inter-individual variability) for the current project) .
    7. Press “Show simulations” button on the next line and wait for a pop-up panel “starting simulation”.
    a. Press “Simulate the data” button, after that press “Dismiss” button to close pop-up panel and wait.
    b. The relevant figures appear.
    8. For the next figure start from 1.

References
1. Kheifetz Y, Scholz M. Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy. PLoS Comput Biol. 2019; 15: e1006775. doi: 10.1371/journal.pcbi.1006775.
2. Komarova SV, Smith RJ, Dixon SJ, Sims SM, Wahl LM. Mathematical model predicts a critical role for osteoclast autocrine regulation in the control of bone remodeling. Bone. 2003; 33: 206–215. doi: 10.1016/S8756-3282(03)00157-1.
3. Faivre C, El Cheikh R, Barbolosi D, Barlesi F. Mathematical optimisation of the cisplatin plus etoposide combination for managing extensive-stage small-cell lung cancer patients. Br J Cancer. 2017; 116: 344–348. doi: 10.1038/bjc.2016.439.
4. Crombag M-RBS, Joerger M, Thürlimann B, Schellens JHM, Beijnen JH, Huitema ADR. Pharmacokinetics of Selected Anticancer Drugs in Elderly Cancer Patients: Focus on Breast Cancer. Cancers (Basel). 2016; 8. doi: 10.3390/cancers8010006.
5. Scholz M, Schirm S, Wetzler M, Engel C, Loeffler M. Pharmacokinetic and -dynamic modelling of G-CSF derivatives in humans. Theor Biol Med Model. 2012; 9: 32. doi: 10.1186/1742-4682-9-32.

Health Atlas ID: 81RXPQ600Y-4

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Created: 9th Apr 2020 at 13:55

Last updated: 2nd Jul 2020 at 11:20

Last used: 23rd Jan 2021 at 20:57

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