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The Potential of Chronic Myeloid Leukemia-on-a-Chip: From Research Applications to Personalized Medicine

Submitted:

07 October 2026

Posted:

09 October 2026

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Abstract
Resistance to tyrosine kinase inhibitor therapy in patients with chronic myeloid leukemia remains a clinical challenge of which the underlying mechanisms are not always fully understood. The leukemic bone marrow microenvironment is increasingly recognized as an important factor protecting leukemic cells from therapeutic pressure. However, studying this leukemic bone marrow is complicated by limited accessibility of the bone marrow in vivo and the limited physiological relevance of conventional in vitro disease models. Microfluidic bone-marrow-on-a-chip models provide a platform for reconstructing the cellular, biochemical, and biophysical components of the bone marrow niche under controlled conditions. These models may more accurately recapitulate the in vivo microenvironment while avoiding some of the limitations inherent in animal models. Their ability to mimic the bone marrow niche provides novel opportunities to investigate the complex interactions between leukemic cells and their microenvironment. Moreover, the incorpora-tion of patient-derived cells highlights the potential of personalized chronic myeloid leu-kemia-on-a-chip models to study interpatient heterogeneity in treatment response. This review explores the potential of chronic myeloid leukemia-on-a-chip models as platforms for basic research and clinical translation.
Keywords: 
chronic myeloid leukemia
;  
organ-on-a-chip
;  
bone marrow microenvironment
Subject: 
Medicine and Pharmacology  -   Hematology

1. Introduction

The bone marrow is the primary site of hematopoiesis throughout adult life. Studying the bone marrow in health and disease is challenging because of its limited accessibility and the complexity of the microenvironment. The bone marrow niche comprises a dynamic network of interactions between hematopoietic and nonhematopoietic stromal cells, extracellular matrix components, soluble factors, and biophysical cues that collectively regulate hematopoiesis. Chronic myeloid leukemia (CML) should therefore be studied in the context of this niche, as the bone marrow microenvironment plays a critical role in disease development and is actively remodeled during leukemic progression [1,2].
Although most CML patients achieve favorable and durable responses to tyrosine kinase inhibitor (TKI) therapy, a subset of patients has an inadequate response or develops treatment resistance, with a risk of progression to blast phase [3]. TKI resistance may be mediated by mutations in the ABL1 kinase domain of BCR::ABL1 that impair drug binding [4,5]. However, such mutations are not detected in all patients with treatment resistance, indicating that additional BCR::ABL1-independent mechanisms are involved [4]. Interactions between leukemic stem cells (LSCs) and the bone marrow microenvironment may promote LSC quiescence, survival, and persistence during TKI therapy, thereby contributing to treatment resistance [6,7]. Interpatient variation in both leukemic cell-intrinsic characteristics and the bone marrow niche may therefore influence treatment response.
Elucidating these interactions requires experimental platforms that enable LSCs to be studied within a physiologically relevant environment. Conventional two-dimensional (2D) in vitro systems are simple and convenient but fail to recapitulate the key architectures and characteristics of the in vivo bone marrow niche [8,9]. Microfluidic ‘organ-on-a-chip’ technology has advanced rapidly and enables selected components of tissue environments to be investigated under controlled conditions [10]. In this context, bone-marrow-on-a-chip models represent a promising approach for studying the interactions between CML cells and their surrounding niche. Incorporating patient-derived cells into these models could facilitate the investigation of interpatient heterogeneity in treatment response and, ultimately, support the development of patient-specific approaches to therapeutic evaluation.
This review critically evaluates the current literature on interpatient variability in TKI response in CML treatment and on bone marrow-on-a-chip technology. We assess the potential of patient-derived CML-on-a-chip models as platforms for studying niche-mediated treatment resistance and discuss the opportunities and challenges associated with their future application in personalized medicine.

2. Interindividual Variability in TKI Response

Resistance against TKI treatment can broadly be divided into BCR::ABL1-dependent and BCR::ABL1-independent mechanisms. BCR::ABL1-dependent resistance is primarily mediated by mutations in the ABL1 kinase domain that reduce TKI binding, or by increased BCR::ABL1 expression or amplification, which may restore downstream oncogenic signaling despite TKI treatment [11,12,13,14,15]. In this context, the so-called ‘gatekeeper’ T315I mutation in the ATP-binding pocket is one of the most extensively studied and clinically relevant resistance mutations. It confers resistance to imatinib, nilotinib, dasatinib and bosutinib treatment, whereas ponatinib and asciminib retain activity [16,17,18,19]. Other BCR::ABL1 mutations have specific TKI sensitivity profiles making selection of subsequent therapy dependent on the specific mutation detected [11].
In the absence of resistance mutations, inadequate responses may result from a variety of factors. These include suboptimal medication adherence, pharmacokinetic variability, drug influx and efflux alterations, activation of alternative signaling pathways, epigenetic changes, and the protective effect of the bone marrow microenvironment on the leukemic cells [20,21,22,23,24,25,26]. Importantly, poor adherence and pharmacological variability do not constitute true biological resistance, although both may lead to inadequate TKI exposure and an apparently resistant phenotype.
Most TKIs are metabolized by cytochrome P450 enzymes, particularly CYP3A4 and CYP3A5 [27]. Variations in the activity of these enzymes may therefore lead to differences in systemic TKI exposure. Higher activity of CYP3A4 and CYP3A5 was reported in CML patients achieving improved molecular remission rates compared to poor responders, suggesting that active metabolites might contribute to this seemingly counterintuitive association [28]. Altered TKI metabolism due to cytochrome P450 polymorphisms was also related to improved TKI response [29]. Genetic variation and enzyme activity of cytochrome P450 enzymes have been investigated as a potential determinant of TKI pharmacokinetics and treatment response, although its predictive value for clinical response remains inconsistent [11,30,31,32,33,34].
Besides hepatic metabolism, interindividual differences in drug transporting protein expression might contribute to differences in TKI response. The expression of the organic cation transporter 1 has been suggested as a predictor of long-term imatinib response in CML patients in several studies [35,36]. However, other experimental and clinical studies have shown conflicting results [37,38,39,40,41,42]. Efflux transporters such as P-glycoprotein and breast cancer resistance protein are involved in TKI efflux, but, likewise, evidence on their application as predictive biomarkers is lacking [43,44].
Epigenetic mechanisms may provide another source of interpatient variability. Differences in DNA methylation and histone modifications might influence CML progression and response to drug treatment [45,46,47]. MicroRNAs may also modify TKI sensitivity by regulating expression of tumor suppressor genes and oncogenes involved in cell survival. Differences in microRNA expression between responding and non-responding patients have therefore been proposed as potential biomarkers to predict clinical TKI resistance, although further clinical validation is required [48,49].
Thus, although several cellular mechanisms of TKI resistance are well-characterized, resistance cannot always be explained by ABL1 kinase domain mutations or pharmacogenetic factors. Increasing evidence indicates that CML cells may be protected by interactions with nonmalignant cells and extracellular components of the leukemic niche [50]. Interindividual differences in both the leukemic cell population and its microenvironment may consequently influence the depth and durability of response to TKI therapy.

3. The Bone Marrow Niche in CML Pathophysiology

The bone marrow consists of a dynamic microenvironment composed of, amongst others, hematopoietic, mesenchymal, endothelial, adipocytic, osteoblastic, and immune cells that collectively regulate hematopoiesis, immune function, and skeletal homeostasis. Historically, spatially and functionally heterogeneous microenvironments within the bone marrow have been described as distinct endosteal and vascular niches [51]. However, current evidence suggests that these environments are not discrete, but rather interconnected compartments that collectively provide survival and maintenance signals to hematopoietic stem and progenitor cells (HSPCs) [52]. Studying the exact cellular composition of ex vivo-derived bone marrow samples is hampered by the incomplete enzymatic digestion prior to flow cytometric analysis [53].
Hematopoietic stem cells (HSCs) form the most primitive compartment within the hematopoietic system [54,55]. CD34+ HSPCs are used for most clinical gene therapy applications, making CD34+ a widely accepted HSPC marker [56]. The majority of HSPCs lie proximal to sinusoidal surfaces, indicating the importance of the vascular network within the bone marrow [57]. Besides its function in nutrient delivery, waste removal, cellular trafficking, and angiocrine-mediated regulation of hematopoiesis, the vascular system fulfills an important structural role. Moreover, endothelial cells of the vascular system are involved in HSPC maintenance through, amongst others, stem cell factor (SCF) signaling [7,58,59,60,61,62,63,64,65,66]. Mesenchymal stromal cells (MSCs) are multipotent progenitor cells capable of differentiation towards adipocytes, chondrocytes, reticular stromal cells, and osteoblasts. The latter cell type has been studied extensively in the context of HSC maintenance [52]. Collectively, these cells provide structural support and regulatory factors important for hematopoiesis such as interleukin-6, RANKL, Jagged1, angiopoietin-1, interleukin-7, SCF, and CXCL12 [67,68,69,70,71]. Neural cells have been proposed to regulate hematopoiesis through both direct effects on HSPCs and indirect effects mediated by their influence on the stromal components [72]. For example, non-myelinating Schwann cells regulate TGFβ activation in HSCs, inducing phosphorylation of SMAD2/3 proteins that regulates transcriptional programs associated with HSC quiescence and maintenance [73].
The CML-associated microenvironment, known as the LSC niche, is known to provide a protective sanctuary that gives these cells a clonal advantage to outcompete HSCs and contributes to drug resistance [7,74,75,76]. The BCR::ABL1 translocation is the most striking distinction between HSCs and CML LSCs, resulting in constitutive activation of the ABL1 kinase and key downstream signaling pathways including JAK/STAT, Ras/MAPK and PI-3K that increase proliferation of malignant cells [77]. LSCs are characterized by their ability to initiate and serially propagate disease upon transplantation [78]. Besides BCR::ABL1, increased ROS and the dependence on upregulated oxidative metabolism differ between HSCs and LSCs [79,80].
LSCs and HSCs differ in their interactions with the bone marrow niche, including both how they influence and respond to their surrounding microenvironment. LSCs are more dependent on CD44 and selectins to facilitate extracellular anchoring, homing, and engraftment in the bone marrow compared to HSCs [81,82]. Moreover, inhibiting E-selectin-mediated adhesion of CML-initiating cells to the bone marrow endothelium enhanced the efficacy of imatinib treatment and prolonged survival in mice with CML compared with imatinib alone [83]. In addition, in vitro co-culture experiments showed that contact with endothelial cells favors the maintenance of primitive CML cells in culture [84].
CML progenitors were also shown to exhibit reduced adhesion to stroma, in part caused by upregulation of dipeptidyl peptidase-4 (DPP4 or CD26), which results in disruption of the CXCR4-CXCL12 axis by cleaving CXCL12 on stromal cells [85,86,87,88,89,90]. Moreover, MSCs isolated from CML patients exhibit an abnormal gene expression pattern, possibly established during the leukemogenic process, that persists in patients in deep molecular response [91]. CML-MSC-derived cells also play a role in the suppression of autologous T lymphocytes and the overexpression of immunomodulatory factors like TGFß, IL6, and IL10, contributing to CML immune escape [92].
Collectively, these findings highlight the complex interplay between the bone marrow microenvironment and HSPCs or LSCs, emphasizing that oncogenic transformation cannot be fully understood in isolation from its surrounding niche. A comprehensive understanding of the cellular composition of the bone marrow microenvironment and the intricate interactions between its constituent cell populations is therefore essential to elucidate how the niche influences the behavior of both normal and transformed stem cells. Consequently, disease models aiming to faithfully recapitulate the bone marrow microenvironment should incorporate relevant cell types, including LSCs, vascular cells, stromal cells, and neural cells. Moreover, the model should account for cell-matrix interactions, rather than focusing solely on the leukemic cell population.

4. Experimental Models of the Bone Marrow Niche

Various in vitro models have been proposed to study the interaction between HSCs or LSCs and the bone marrow niche. 2D co-cultures of bone marrow-derived cell types have shed light on processes like angiogenesis and osteogenesis in the bone marrow [93]. However, these 2D cultures lack components that might influence leukemic stem cell behavior such as the spatial organization, immune complexity, vascular involvement, matrix composition, and metabolic gradients, which has driven the development of more complex 3D culture systems. Examples of these include organoids, spheroids, and scaffold-based systems, which more closely reproduce the architecture and cell-cell/cell-matrix interactions within a human bone marrow-like milieu [9,94,95,96,97,98,99,100,101]. Nevertheless, these static 3D cultures cannot recapitulate tissue-tissue interfaces, vascular perfusion, interstitial flow, circulating immune cells, or organ-specific mechanical cues that are critical for accurate mimicry of the absorption, distribution, metabolism, and excretion of pharmaceutical compounds and ultimately pharmacokinetics and pharmacodynamics [102]. Moreover, oxygen and nutrient supply may also be a critical aspect.
3D models with a perfused bioreactor setup have therefore been introduced, combining 3D cellular organization with controlled perfusion. Studies suggest that this bioreactor-based 3D setup provides an advantageous system for maintaining immature progenitor cells compared to a 2D system, as an increased proportion of CD34+ and CD34+/CD38- cells was maintained in this system [103]. Meanwhile, these models have been shown to accurately predict patient responses [104].
Other groups have used xenotransplantation of humanized bone marrow microenvironments into immunocompromised mice. The subsequent engraftment of normal and leukemic human hematopoietic cells served as a tool to recapitulate and model human hematopoietic development and malignancies in vivo [105]. The use of this model facilitates the study of hematopoiesis and malignancies in a physiological context, although the mouse-derived vascular and bone marrow sinusoidal structures limit the translatability to the human context. Similar limitations apply to transgenic mouse models used in CML research; they may provide valuable information on disease initiation, progression, and niche remodeling, but differences between murine and human hematopoiesis, immunology, stromal biology, and pharmacology limit translation to humans [75,106,107]. Regulatory authorities have removed the emphasis on animal testing and allowed certain qualified non-animal methods in preclinical safety assessment, making ‘new approach methodologies’ (NAMs) increasingly attractive [108,109]. With the growing understanding of the role of the bone marrow niche in leukemia pathogenesis, this development has created a framework for bone-marrow-on-a-chip and leukemia-on-a-chip models to emerge as promising tools for studying disease biology.

5. Microfluidic Bone Marrow and Leukemia Models

Bone marrow-on-a-chip and leukemia-on-a-chip models are in vitro micro-engineered microfluidic systems designed to reproduce key structural, cellular, biochemical, and biophysical features of the bone marrow microenvironment. By combining human cells, three-dimensional extracellular matrices, spatially organized compartments, and controlled fluid flows, these platforms enable the study of hematopoiesis, disease processes, and therapeutic responses under defined and physiologically relevant conditions (Figure 1).
To produce the device itself, a replication technique is applied, for which a master mold with inverted micro- or nanostructures is fabricated [110]. The resulting products are often made from polydimethylsiloxane (PDMS), silicone, polycarbonate (PC), polymethylmethacrylate (PMMA), polystyrene (PS), polyimide (PI), and polyvinyl chloride (PVC) [111], of which PDMS is the material that is most often used. The choice of material for the fabrication of microfluidic devices depends on multiple factors, including optical transparency for microscopic examination, biological and chemical compatibility, mechanical properties, ease of fabrication, and costs.
To mimic the HSPC population in microfluidic models, CD34+ cells, e.g., isolated from bone marrow, mobilized peripheral blood, or umbilical cord, are integrated [112,113,114,115,116,117]. Human umbilical vein endothelial cells or mouse endothelial C166 cells [118] are mostly used as a practical substitute for specialized bone marrow sinusoidal endothelial cells [112,113,115,116]. To represent the mesenchymal compartment, commercial cell lines can be integrated [112,113]. However, isolation of MSCs from ex vivo femoral heads obtained after hip replacement surgery has also been described [117]. Sometimes MSC-derived osteoblasts are directly integrated into the model [119].
Cells can be integrated in the microfluidic model using a hydrogel that can be designed to closely mimic the native ECM, and possesses additional desirable properties such as optical clarity, which allows for the real-time microscopic observation of cell behavior within the structures [120]. Another option to integrate cells in the microfluidic model is using a scaffold, such as hydroxyapatite, polystyrene, or decellularized bone, which is intended to mimic the in vivo trabecular bone, especially regarding its structural and mechanical properties. A key advantage of a scaffold-based approach is the close imitation of the hematopoietic niche [65]. A disadvantage is the lack of optical transparency, which may interfere with high-resolution imaging.
Traditionally, microfluidic modeling has been a labor-intensive process characterized by limited throughput, substantial experimental complexity, and challenges in model validation and standardization. However, with the emergence of artificial intelligence (AI) and automation technologies, the field is increasingly moving towards more scalable and reproducible platforms. Recent advances, including AI-assisted image analysis integrated into high-throughput bone-on-a-chip systems, highlight the potential of automated workflows to improve data acquisition, analysis, and the application of microfluidic models for drug discovery and preclinical testing [121]. However, automation and artificial intelligence do not in themselves establish biological validity. Meaningful translation will require standardized performance criteria, reproducibility across laboratories, comparison with conventional models, and validation against relevant human biological or clinical outcomes.

6. Applications of Bone Marrow-on-a-Chip Models in CML Research

Various bone-marrow-on-a-chip models have been developed, although in vitro models specifically designed to reproduce the leukemic bone marrow niche remain scarce (Table 1). Existing microfluidic models differ substantially in cellular composition, matrix materials, perfusion systems, experimental setup and experimental readouts [112,113,115,116,119]. The following section provides an overview of these published models and highlights the key differences between them.
In the model by Georgescu et al. [112], induction of vasculogenic self-assembly resulted in a 3D vascularized, perfusable tissue construct resembling the hematopoietic vascular niche of the human bone marrow that was capable of multilineage hematopoiesis. Chou et al. [115] developed a model that recapitulated aspects of bone marrow injury, including myeloerythroid toxicity after clinically relevant exposures to chemotherapeutic drugs and ionizing radiation. Moreover, regimen-specific bone marrow toxicities of the clinically used anti-cancer drug 5-fluorouracil could be observed on chip, an effect that was not reproduced in static cultures.
Aleman et al. [119] used the microfluidic bone marrow-on-a-chip model to study homing of normal and malignant hematopoietic stem cells, to elucidate niche interactions. They observed that cells of an acute myeloid leukemia (AML) cell line (MOLM13) preferentially localized to the osteoblastic compartment, whereas normal CD34+ HSPCs showed a more balanced distribution across the different niches.
Ma et al. developed an in vitro organotypic ‘leukemia-on-a-chip’ [122] to study the interactions between B-cell acute lymphoblastic leukemia (B-ALL) cells and the bone marrow niches during pharmacological treatment. This model combines control over various biological parameters, real-time visualization of physiological and pathophysiological dynamics, with compatibility with high-throughput assays [122].
The 3D microfluidic model presented by Houshmand et al. [123] combined AML cells with MSCs to evaluate the cytotoxic effects of azacitidine and cytarabine. Compared with conventional 2D cultures, leukemic cells cultured in a 3D system exhibited improved phenotypic retention and a higher proliferation rate. Additionally, cytotoxicity assays illustrated drug resistance in 3D culture conditions, demonstrating the importance of studying these leukemic cells in their niche [123]. A similar approach to study TKI treatment and LSC-niche interactions for BCR::ABL1+ cells would be highly beneficial.
Collectively, these studies demonstrate that, despite substantial variability in model design, microfluidic platforms provide a versatile approach to investigate the bone marrow niche and its role in leukemia development and progression. By enabling the controlled recapitulation of cellular, biochemical, and biophysical interactions within the bone marrow microenvironment, these models offer opportunities to address biological questions that are difficult to study using conventional two-dimensional cultures.
Table 1. Papers presenting biomimetic leukemia-on-a-chip models.
Table 1. Papers presenting biomimetic leukemia-on-a-chip models.
Disease* Year Integrated cells Main message Ref.
ALL 2015 ALL cells (SUP-B15), primary human bone marrow stromal cells, human osteoblasts. Leukemic cells in the engineered 3D microfluidic ALL model exhibit decreased chemotherapeutic drug sensitivity compared to those in 2D models. [124]
Lymphoma, promyelocytic leukemia & CML 2016 Human bone marrow stromal cells (HS5), endothelial cells (HUVECs), leukemic cell lines (U937, HL60, K562). Leukemia-induced angiogenesis in the bone marrow can be studied using a microfluidic model. [125]
AML 2017 AML cells (TF-1), primary bone marrow mesenchymal stem cells. Leukemic cells in the developed 3D microfluidic device retain their phenotype and exhibit a significantly higher proliferation rate compared to cells under 2D conditions.
AML 2018 Endothelial cells (HUVEC), stromal fibroblasts (HS-5, HS27a), marrow mesenchymal stem cells, healthy and AML patient-derived CD34+ cells. Microfluidic models can be employed to study differences in hematopoietic cell trafficking dependent on cellular niche composition. [126]
Lymphoma & AML 2019 Sinusoidal endothelial cells, arterial endothelial cells, mesenchymal stromal cells, osteoblasts, human bone marrow-derived CD34+ cells, leukemic cell lines (U937, MOLM-13). A human bone marrow-on-a-chip model was developed that enables identification and quantification of preferential interactions of circulating normal and malignant HSPCs with the niche. [119]
B-ALL 2020 Endothelial cells (HUVECs), human mesenchymal stem cells (hMSCs), human osteoblasts (hFOB), human cord blood derived CD34+ cells, B-ALL cells (various cell lines and patient-derived primary cells). Niche-derived factors maintain B-ALL cell survival and quiescence in the developed multi-niche microfluidic leukemia-on-a-chip model. The model can be used to test niche-co-targeting regimens in a preclinical setting. [122]
B-ALL 2025 Endothelial cells (HUVEC), human mesenchymal stem cells (hMSCs), bone marrow stromal cells (NHLF, hMSC-derived osteoblasts), bone marrow immune cells (patient-derived mononuclear cells), 4-1BBζ-CAR T cells, leukemic cell lines (Reh, K562). The engineered organotypic and immunocompetent microfluidic model recapitulates microarchitectural and pathophysiological characteristics. The model can be used to recreate clinically observed heterogeneous responses under CAR T cell-therapy and to map factors that drive therapeutic success or failure. [127]
B-ALL 2026 Stromal cells (HS-5), endothelial cells (EA.hy926), osteoblasts (Saos-2), B-ALL cells (Reh). The multi-niche microfluidic bone marrow model presented in the current study captures relevant aspects of niche-associated signaling and provides a platform for investigating leukemia-microenvironment interactions. [128]
*ALL, acute lymphoblastic leukemia; AML, acute myeloid leukemia; CML, chronic myeloid leukemia; B-ALL, B-cell acute lymphoblastic leukemia.

7. Potential Applications of Microfluidic Technologies in CML

Lab-on-a-chip technologies have emerged as promising tools for leukemia diagnostics because they require only small sample volumes, enable high-throughput parallel processing and multiplexed readouts, and can be produced and operated at relatively low cost [129]. Bone marrow specimens from patients with leukemia, including CML, have previously been analyzed using biochip-based fluorescence in situ hybridization [130]. Similar approaches have been developed for antibody- and DNA-based analyses in AML and CLL [131,132]. More recently, a microfluidic chip combining amplification of BCR::ABL1 transcripts with gold-nanoprobe colorimetric-based detection was described as a rapid and potentially low-cost alternative to conventional molecular assays [133]. Although these approaches do not reconstruct the in vivo bone marrow microenvironment and should therefore not be presented as bone marrow-on-a-chip models, they do illustrate the value of microfluidic models for processing small clinical samples and generate rapid measurements.
Microfluidic models can be used to monitor drug response. Faley et al. [134] presented a microfluidic model that served to trap individual live cells, so that intracellular signaling mechanisms underlying CML and TKI treatment could be studied on a single-cell level. Jeon et al. [135] subsequently introduced a device that supports the characterization of cell viability across various TKI types and concentrations, yielding comprehensive metrics to assess drug efficacy in both CML cell lines and patient-derived CML marrow cells. A similar approach was taken by Rocci et al. [136], where a microfluidic platform was used to profile drug response in acute myeloid leukemia. The model showed good predictive power by comparing ex vivo drug response with clinical outcomes. This model is thought to hold potential to guide personalized treatment in hematologic malignancies, and is seen as a bedside analysis platform requiring only a small sample of bone marrow aspirate and simple pre-processing, with results provided within 24 hours [136]. In addition to applications aimed at rapid assessment of patient-specific treatment responses, microfluidic bone marrow models have also been developed to investigate the efficacy of emerging immunotherapies. For example, Ma et al. [127] developed an organotypic bone marrow microfluidic model to monitor the efficacy of CAR T-cell immunotherapy.
Microfluidic models have also been applied to monitor drug response to prodrug treatment in pediatric leukemia. As these prodrugs require metabolic activation to have their effect, and these metabolites can be short-lived and unstable, a microfluidic device was developed that enables co-culturing of patient-derived leukemia cells, MSCs and human liver microtissues. This approach allows for the multiplex ex vivo testing of patient-derived leukemia samples, offering great potential for the selection and optimization of treatments for leukemia patients [137].

8. Potential Contribution to Personalized CML Treatment

The currently recommended TKI doses were largely established in early phase I studies, following a ‘one-dose-fits-all’ strategy. However, accumulating clinical and real-world evidence suggests that these dosing strategies may not be optimal for long-term therapy, and thus carefully monitored dose reduction can reduce treatment-related toxicity and treatment burden while maintaining disease control [138]. In addition, treatment-free remission (TFR), the maintenance of a deep molecular response after TKI therapy discontinuation, has recently emerged as a new goal of CML treatment. Therefore, personalization of TKI therapy through dose optimization is receiving increasing attention. Currently, dose reductions are implemented based on clinical monitoring of treatment response and adverse effects. Whereas the current approach works for the majority of patients, very rare instances of disease progression to blast phase or development of kinase domain mutations has been reported upon TKI dose reduction [139]. A patient-specific microfluidic model could provide a complementary in vitro platform to assess the effects of TKI dose reductions on treatment efficacy, potentially supporting personalized dose optimization. Despite this, future application of a CML-on-a-chip in the selection of patients for TKI dose reduction or TKI withdrawal would require prospective demonstration of its predictive power beyond currently established clinical and molecular factors.
Beyond mechanistic studies, diagnostic purposes, and drug response monitoring, CML-on-a-chip models may facilitate investigation of interpatient variability in TKI response. By incorporating primary CML cells together with autologous stromal and vascular components, these platforms can recapitulate individualized leukemic niches. Such models may help identify niche-mediated mechanisms of resistance and support optimization of patient-specific treatment strategies.

9. Limitations and Open Challenges

Despite the significant advances in microfluidic platforms for modeling complex biological environments, several limitations remain. The complexity and limited accessibility of the in vivo bone marrow hamper its characterization, thereby complicating the development of in vitro models. Moreover, factors such as the variability in fabrication methods, the lack of standardization, and technical limitations remain challenging for initiatives aiming to recapitulate organ function using microfluidic models. To integrate CML-on-a-chip models in drug development pipelines and patient care, with the ultimate goal of using them as a personalized tool to predict patient drug responses, pharmaceutical companies and regulatory agencies will require rigorous demonstration of equivalent or superior performance compared to current models. This will require the definition of specific validation methods and performance criteria, in addition to successful completion of statistically well-powered validation studies that demonstrate that a specific organ chip model can generate human-relevant results in a reproducible and statistically robust manner, independent of where the work is carried out [102]. This may require both advances in (AI-driven) automation and increased commercial involvement to reach this point [140]. Importantly, a clear commercial opportunity has emerged, as it was predicted that incorporation of predictive organ-on-a-chip systems could substantially improve drug discovery and development, allowing manufacturers to bring safer, more effective medicines to market in less time and at lower costs [141].

10. Conclusions

Interactions between CML LSCs and the bone marrow microenvironment may contribute to leukemia cell persistence and variability in TKI response. Bone marrow-on-a-chip technology has emerged as a promising platform for more accurate recapitulation of the bone marrow microenvironment in CML. This platform may therefore hold potential in CML research, diagnostics, drug response monitoring, and personalized medicine. A major advantage of a bone marrow-on-a-chip model is its potential to capture interindividual variation in leukemic niche composition and function. This may enable identification of patient-specific mechanisms of TKI resistance and provide a foundation for personalized therapeutic decision-making in CML. Future studies should focus on prospective validation, in which patient-specific bone marrow-on-a-chip responses to TKIs are compared with subcellular distribution, molecular responses, treatment failure rates, and long-term clinical outcomes. Demonstration of predictive value will be essential before these systems can be incorporated into personalized treatment strategies.

Author Contributions

Writing—original draft preparation, N.E.V.; writing—review and editing, J.B.K., J.J.W.M.J. and F.G.M.R. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Preprints 237079 g001
Figure 1. Schematic representation of a conceptual microfluidic leukemia-on-a-chip model. The model incorporates relevant cellular and microenvironmental components of the leukemic bone marrow niche within a controlled microfluidic environment. Created in BioRender. Verhagen, N. (2026) https://BioRender.com/ifzb75s.
Figure 1. Schematic representation of a conceptual microfluidic leukemia-on-a-chip model. The model incorporates relevant cellular and microenvironmental components of the leukemic bone marrow niche within a controlled microfluidic environment. Created in BioRender. Verhagen, N. (2026) https://BioRender.com/ifzb75s.
Preprints 237079 g001
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