A multiscale pharmacokinetic-Boolean modelling framework for ibrutinib resistance in a Chronic Lymphocytic Leukaemia case study
- 1Univ Toulouse, Oncopole Claudius Regaud, IUCT-Oncopole, Inserm, CRCT, Toulouse, France
Abstract
Ibrutinib, a covalent Bruton's tyrosine kinase (BTK) inhibitor, achieves durable remissions in chronic lymphocytic leukaemia (CLL), and yet a substantial proportion of patients relapse. A fundamental question is how the quantitative dynamics of drug exposure relate to the regulatory programmes and cell fate decisions that characterise response and relapse. Addressing it requires a multiscale framework that bridges the quantitative pharmacology of drug exposure with the molecular mechanisms encoded in the gene regulatory network, a coupling that has remained largely unexplored in the context of targeted therapy resistance. Here we present a multi-scale computational framework that addresses this question by coupling continuous patient-specific pharmacokinetic profiles with discrete Boolean dynamics, bridging two modelling paradigms that have so far operated in isolation. Applied to a longitudinal single-cell dataset from one CLL patient treated with ibrutinib across three clinical timepoints (pre-treatment, response, relapse), the framework integrates gene regulatory network inference, cell fate trajectory analysis, and a stochastic Boolean model, whose dynamics is driven by the patient-specific monthly drug exposure. AP-1 and integrated stress response regulons were specific to relapse, and the coupled model reproduced the clinical course. Its perturbation screens identified co-inhibition of BTK with BCL2L1 or NFKB1 as synergistic, in line with the rationale for dual targeting of BTK and the BCL2 family in treatment-naive CLL. These results show how the framework turns single-patient data into mechanistic, testable hypotheses on resistance and combination therapy, which can be evaluated in larger cohorts and in experimental models.
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BibTeX
@misc{marku2026multiscale,
title = {{A multiscale pharmacokinetic-Boolean modelling framework for ibrutinib resistance in a Chronic Lymphocytic Leukaemia case study}},
author = {M. Marku and C. Pineau and M. Hurtado and H. Chenel and J.-B. Woillard and L. Ysebaert and E. Chatelut and V. Pancaldi and M. White-Koning},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.26.759125},
url = {https://www.biorxiv.org/content/10.64898/2026.10.26.759125v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - A multiscale pharmacokinetic-Boolean modelling framework for ibrutinib resistance in a Chronic Lymphocytic Leukaemia case study
AU - M. Marku
AU - C. Pineau
AU - M. Hurtado
AU - H. Chenel
AU - J.-B. Woillard
AU - L. Ysebaert
AU - E. Chatelut
AU - V. Pancaldi
AU - M. White-Koning
PY - 2026
DA - 2026/10/26
PB - bioRxiv
DO - 10.64898/2026.10.26.759125
UR - https://www.biorxiv.org/content/10.64898/2026.10.26.759125v1
AB - Ibrutinib, a covalent Bruton's tyrosine kinase (BTK) inhibitor, achieves durable remissions in chronic lymphocytic leukaemia (CLL), and yet a substantial proportion of patients relapse. A fundamental question is how the quantitative dynamics of drug exposure relate to the regulatory programmes and cell fate decisions that characterise response and relapse. Addressing it requires a multiscale framework that bridges the quantitative pharmacology of drug exposure with the molecular mechanisms encoded in the gene regulatory network, a coupling that has remained largely unexplored in the context of targeted therapy resistance. Here we present a multi-scale computational framework that addresses this question by coupling continuous patient-specific pharmacokinetic profiles with discrete Boolean dynamics, bridging two modelling paradigms that have so far operated in isolation. Applied to a longitudinal single-cell dataset from one CLL patient treated with ibrutinib across three clinical timepoints (pre-treatment, response, relapse), the framework integrates gene regulatory network inference, cell fate trajectory analysis, and a stochastic Boolean model, whose dynamics is driven by the patient-specific monthly drug exposure. AP-1 and integrated stress response regulons were specific to relapse, and the coupled model reproduced the clinical course. Its perturbation screens identified co-inhibition of BTK with BCL2L1 or NFKB1 as synergistic, in line with the rationale for dual targeting of BTK and the BCL2 family in treatment-naive CLL. These results show how the framework turns single-patient data into mechanistic, testable hypotheses on resistance and combination therapy, which can be evaluated in larger cohorts and in experimental models.
ER -