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\begin{document}

\title{Dose-dependent Mathematical modelling of Interferon-\selectlanguage{greek}α-\selectlanguage{english}treatment for
personalized treatment of Myeloproliferative Neoplasms\selectlanguage{english}}



\author[1]{Rasmus Pedersen}%
\author[1]{Morten Andersen}%
\author[1]{Johnny Ottesen}%
\affil[1]{Roskilde University}%


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  \date{June 17, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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