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Dose-dependent Mathematical modelling of Interferon-α-treatment for personalized treatment of Myeloproliferative Neoplasms
  • Rasmus Pedersen,
  • Morten Andersen,
  • Johnny Ottesen
Rasmus Pedersen
Roskilde University

Corresponding Author:[email protected]

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Morten Andersen
Roskilde University
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Johnny Ottesen
Roskilde University
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Intro: Long-term treatment with interferon-alfa (IFN) can reduce the disease burden of patients diagnosed with myeloproliferative neoplasms (MPN). Determining individual patient-responses to IFN-therapy may allow for efficient personalized treatment, reducing both drop out and disease burden. Methods: A mathematical model describing hematopoietic stem cells and the immune system is suggested. Considering the bone marrow and the blood allows for modelling disease dynamics both in the absence and presence of treatment. Through comprehensive modelling of the effects of IFN, the model was related to individualized patient-data consisting of longitudinal hematologic and molecular measurements. Treatment responses are modelled on a population-level, allowing for personalized predictions from a single pre-treatment data point. Results: Personalized fits were found to agree well with data. This allowed for a quantitative description of the treatment-response, yielding a mechanistic interpretation of differences between individual patients. Population-level treatment-responses were simulated. Based on pre-treatment data and the actual treatment scheduling, the population-level response was found to predict the treatment-response of particular patients accurately over a five-year period. Conclusion: Mechanism-based modelling of treatment effects demonstrates that hematologic and molecular observables can be predicted on the level of individual patients. Personalized patient-fits suggest that the effect of IFN-treatment can be quantified and interpreted through mathematical modelling, despite variation in hematologic and molecular response for different patients. Modelling suggests that both hematologic and molecular markers must be considered to avoid immediate relapse. Furthermore, personalized model-fits provides quantitative measures of the hematologic and molecular response, determining when treatment-cessation is appropriate. Proof-of-concept population-level modelling of treatment-responses from pre-treatment data successfully predicted clinical measures for a five-year period. This approach could have direct clinical relevance, offering expert guidance for clinical decisions about IFN-treatment of MPN-patients.
08 Jan 2021Submitted to Computational and Systems Oncology
12 Jan 2021Submission Checks Completed
12 Jan 2021Assigned to Editor
26 Jan 2021Reviewer(s) Assigned
03 Jun 2021Review(s) Completed, Editorial Evaluation Pending
03 Jun 2021Editorial Decision: Revise Major
11 Aug 20211st Revision Received
12 Aug 2021Submission Checks Completed
12 Aug 2021Assigned to Editor
12 Aug 2021Review(s) Completed, Editorial Evaluation Pending
16 Aug 2021Reviewer(s) Assigned
23 Sep 2021Editorial Decision: Accept
Dec 2021Published in Computational and Systems Oncology volume 1 issue 4.