Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy

Kheifetz, Yuri; Scholz, Markus

doi:10.1371/journal.pcbi.1006775

Cited by 22 publications

(28 citation statements)

References 52 publications

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“…In the present paper, we evaluate the predictive potential of two dynamical models of thrombocytopoiesis under chemotherapy proposed in the literature, a semi-mechanistic model developed for general haematopoiesis [18] and a more comprehensive model of thrombocytopoiesis developed by us which also considers mechanisms of bone marrow niche interaction [19]. A major aim was to assess the gain in prediction accuracy by the mechanistic model which is a major prerequisite for model-guided individual therapy management.…”

Section: Discussionmentioning

confidence: 99%

“…We estimate the predictive potential of our comprehensive mechanistic thrombopoiesis model [19] and compare it with that of the semi-mechanistic model of general haematopoiesis and bone marrow exhaustion proposed by Henrich et al [18], serving as a reference model. Figure 1 provides an overview of both modelling concepts.…”

Section: Modelling Concepts To Be Evaluatedmentioning

confidence: 99%

“…Our mechanistic model is an updated and individualized version of our previous work [24]. Model structure as well as all biological assumptions, mathematical equations and parameter estimation can be found elsewhere [19]. In brief, the model describes the dynamics of active and dormant stem cells, colony-forming units of megakaryocytes, megakaryocytes, of different ploidies and platelets in both spleen and circulation.…”

Section: Comprehensive Mechanistic Thrombocytopoiesis Modelmentioning

confidence: 99%

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Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances

Kheifetz

Scholz

2021

Brit J Clinical Pharma

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Thrombocytopenia is a common major side-effect of cytotoxic cancer therapies. A clinically relevant problem is to predict an individual's thrombotoxicity in the next planned chemotherapy cycle in order to decide on treatment adaptation. To support this task, 2 dynamic mathematical models of thrombopoiesis under chemotherapy were proposed, a simple semimechanistic model and a comprehensive mechanistic model. In this study, we assess the performance of these models with respect to existing thrombocytopenia grading schemes. Methods: We consider close-meshed individual time series data of 135 non-Hodgkin's lymphoma patients treated with 6 cycles of CHOP/CHOEP chemotherapies. Individual parameter estimates were derived on the basis of these data considering a varying number of cycles per patient. Parsimony assumptions were applied to optimize parameter identifiability. Models' predictability are assessed by determining deviations of predicted and observed degrees of thrombocytopenia in the next cycles.Results: The mechanistic model results in better agreement of model prediction and individual time series data. Prediction accuracy of future cycle toxicities by the mechanistic model is higher even if the semimechanistic model is provided with data of more cycles for calibration. Conclusion:We successfully established a quantitative and clinically relevant method for assessing prediction performances of biomathematical models of thrombopoiesis under chemotherapy. We showed that the more comprehensive mechanistic model outperforms the semimechanistic model. We aim at implementing the mechanistic model into clinical practice to assess its utility in real life clinical decision-making. K E Y W O R D Sadverse drug reactions, biomathematical model, medical, oncology (oncology) pharmacodynamics, pharmacometrics, platelets (haematology)The notion of principal investigator is not applicable in our case. We performed no studies with human subjects/patients. All data are derived from published clinical studies.

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Section: Discussionmentioning

confidence: 99%

Section: Modelling Concepts To Be Evaluatedmentioning

confidence: 99%

Section: Comprehensive Mechanistic Thrombocytopoiesis Modelmentioning

confidence: 99%

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Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances

Kheifetz

Scholz

2021

Brit J Clinical Pharma

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“…The second example is a model that quantitatively describes thrombopoiesis ( [17,18]). It is part of a more general class of ordinary differential equation-based compartment models of human haematopoiesis ( [2,19]).…”

Section: Implemented Mathematical Disease Modelsmentioning

confidence: 99%

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

Hoffmann

Cazemier

Baldow

et al. 2020

BMC Med Inform Decis Mak

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Background: Individualization and patient-specific optimization of treatment is a major goal of modern health care. One way to achieve this goal is the application of high-resolution diagnostics together with the application of targeted therapies. However, the rising number of different treatment modalities also induces new challenges: Whereas randomized clinical trials focus on proving average treatment effects in specific groups of patients, direct conclusions at the individual patient level are problematic. Thus, the identification of the best patient-specific treatment options remains an open question. Systems medicine, specifically mechanistic mathematical models, can substantially support individual treatment optimization. In addition to providing a better general understanding of disease mechanisms and treatment effects, these models allow for an identification of patient-specific parameterizations and, therefore, provide individualized predictions for the effect of different treatment modalities. Results: In the following we describe a software framework that facilitates the integration of mathematical models and computer simulations into routine clinical processes to support decision-making. This is achieved by combining standard data management and data exploration tools, with the generation and visualization of mathematical model predictions for treatment options at an individual patient level. Conclusions: By integrating model results in an audit trail compatible manner into established clinical workflows, our framework has the potential to foster the use of systems-medical approaches in clinical practice. We illustrate the framework application by two use cases from the field of haematological oncology.

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“…Several mathematical models have been developed to understand and quantify megakaryopoiesis and platelet production. Most of these models represent multi-compartment models focusing on the modulating effects of chemotherapy [12][13][14] and radiation 15,16 . Taking a different approach, Apostu and Mackey 17 developed a rather simplistic model using a delay differential equation to understand oscillations observed in patients with cyclic thrombocytopenia.…”

Section: Introductionmentioning

confidence: 99%

Mathematical modelling of megakaryopoiesis in Mpl-deficient and continuously thrombopoietin-stimulated mice points to an unknown control mechanism

Diebner

Gottschalk

Baldow

et al. 2020

Preprint

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Thrombopoietin (TPO) is the ligand of the Mpl receptor and the key regulator of megakaryopoiesis and platelet production. A loss or gain of the TPO-receptor function affects haematopoiesis and results in severe diseases in humans. Appropriate mouse strains are available to mimic both myeloproliferative neoplasm (MPN) and congenital amegakaryocytic thrombocytopenia (CAMT) resulting from TPO overexpression or knockout of TPO receptor Mpl on megakaryocytes and platelets, respectively. However, at a quantitative level it is not understood, how the known regulations can establish the impaired but stable disease phenotypes. Starting out from an established mathematical model for megakaryopoiesis, we aim to adapt it to both the healthy situation and to distinct diseased phenotypes mimicking MPN. We thereby identify, that some of the model parameters are invariant with respect to the mouse strain while others have to be estimated in a strain-dependent manner. A systematic process of parameter identification provides strong evidence that the well-known excess production of megakaryocytes and early progenitors in MPN is either directly contingent on Mpl expression of platelets and megakaryocytes or, alternatively, that the knockout of Mpl is not as precisely restricted to megakaryocytes and platelets but may also effect their progenitors. In conclusion, our analysis hints towards an opaque control mechanism rendering megakaryopoiesis at a yet unknown level, and awaiting further experimental evaluation.

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Modeling individual time courses of thrombopoiesis during multi-cyclic chemotherapy

Cited by 22 publications

References 52 publications

Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances

Individual prediction of thrombocytopenia at next chemotherapy cycle: Evaluation of dynamic model performances

Integration of mathematical model predictions into routine workflows to support clinical decision making in haematology

Mathematical modelling of megakaryopoiesis in Mpl-deficient and continuously thrombopoietin-stimulated mice points to an unknown control mechanism

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