Dose‐dependent mathematical modeling of interferon‐α‐treatment for personalized treatment of myeloproliferative neoplasms. Issue 4 (7th December 2021)

• Record Type: Journal Article
• Authors: Pedersen, Rasmus K.; Andersen, Morten; Knudsen, Trine A.; Skov, Vibe; Kjær, Lasse; Hasselbalch, Hans C.; Ottesen, Johnny T.
• Is Part Of: Computational and systems oncology. Volume 1:Issue 4(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-07
• Subjects: interferon -- mathematical modeling -- myeloproliferative neoplasms -- personalized treatment; Oncology -- Data processing -- Periodicals; Oncology -- Periodicals; Systems biology -- Periodicals; Medical Oncology; Neoplasms; Electronic journals; Periodicals; 616.994
• DOI: 10.1002/cso2.1030
• Languages: English
• ISSNs: 2689-9655

Abstract

Abstract: Long‐term treatment with interferon‐alfa (IFN) can reduce the disease burden of patients diagnosed with myeloproliferative neoplasms (MPNs). Determining individual patient responses to IFN therapy may allow for efficient personalized treatment, reducing both drop‐out and disease burden. A mathematical model describing hematopoietic stem cells and the immune system is suggested. Considering the bone marrow and the blood allows for modeling disease dynamics both in the absence and presence of IFN treatment. Through comprehensive modeling of the effects of IFN, the model was related to individualized patient‐data consisting of longitudinal hematologic and molecular measurements. Treatment responses were modeled on a population level, allowing for personalized predictions from a single pretreatment data point. Personalized fits were found to agree well with data for individual patients. This allowed for a quantitative description of the treatment response, yielding a mechanistic interpretation of differences from patient to patient. The treatment responses of individual patients were combined and a formulation of treatment responses on the population level was described and simulated. Based on pretreatment 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. Mechanism‐based modeling of treatment effects demonstrates that hematologic and molecular observable quantities 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 modeling, despite variation in hematologic and molecular responses between patients. Mathematical modeling suggests that in general both hematologic and molecular markers must be considered to avoid early relapse. Furthermore, personalized model‐fits provide quantitative measures of the hematologic and molecular responses, determining when treatment‐cessation is appropriate. Proof‐of‐concept population‐level modeling of treatment responses from pretreatment data successfully predicted clinical measures for a 5‐year period. We believe that this approach could have direct clinical relevance, offering expert guidance for clinical decisions about IFN treatment of MPN patients.