Modelling of immune response in chronic myeloid leukemia patients suggests potential for treatment reduction prior to cessation

Abstract

IntroductionDiscontinuation of tyrosine kinase inhibitor (TKI) treatment is emerging as the main therapy goal for Chronic Myeloid Leukemia (CML) patients. The DESTINY trial showed that TKI dose reduction prior to cessation can lead to an increased number of patients achieving sustained treatment free remission (TFR). However, there has been no systematic investigation to evaluate how dose reduction regimens can further improve the success of TKI stop trials.MethodsHere, we apply an established mathematical model of CML therapy to investigate different TKI dose reduction schemes prior to therapy cessation and evaluate them with respect to the total amount of drug used and the expected TFR success.ResultsOur systematic analysis confirms clinical findings that the overall time of TKI treatment is a major determinant of TFR success, while highlighting that lower dose TKI treatment for the same duration is equally sufficient for many patients. Our results further suggest that a stepwise dose reduction prior to TKI cessation can increase the success rate of TFR, while substantially reducing the amount of administered TKI.DiscussionOur findings illustrate the potential of dose reduction schemes prior to treatment cessation and suggest corresponding and clinically testable strategies that are applicable to many CML patients.

Data integration between clinical research and patient care: A framework for context-depending data sharing and in silico predictions

“…We also developed a mathematical model of CML-immune interaction [ 31 ], which can be fitted to available time course data of TKI-treated CML patients and after therapy stop. We showed that qualitatively different dynamic treatment response patterns can be characterized in the context of a mathematical model by different stable steady states, describing the individual immune response [ 32 ]. The different steady states can be interpreted as “immune classes”.…”

Mathematical Model of Leukemia Treatment with Chimeric Antigen Receptor (CAR) T Cell Therapy

“…(Agarwal & Bhadauria, 2015) studied a treatment model cancer with immunotherapy, (Sharma & Samanta, 2016) studied dynamic analysis of the tumor and immune system with chemotherapy and immunotherapy. (Moore & Li, 2004), (Dimitriu, 2019), (Valle et al, 2021), and (Karg et al, 2022), proposed and analyzed the dynamical model of chronic myelogenous leukemia. In addition, (Guzev et al, 2022), (Rodrigues et al, 2019), and (Fadaei et al, 2021) formulate and analyse a mathematical model of chronic lymphocytic leukemia with a treatment such as chemotherapy or chemoimmunotherapy.…”

Computational modeling reveals key factors driving treatment-free remission in chronic myeloid leukemia patients

“…However, these interactions alone cannot explain the outcome of TFR, and other factors must be considered. For example, Fassoni and Glauche developed a mathematical model for CML that incorporates the immune system’s antileukemic effect and applied the model to 21 CML patients who ceased TKI treatment 52 , 53 . The model provided strong evidence that different immunologic configurations in patients with CML determine their response to therapy cessation.…”