Multiscale computational modeling of circulating tumor cells: From separation and detection to targeted drug delivery.

Abstract

Circulating tumor cells (CTCs) provide a minimally invasive window into cancer progression and metastatic dissemination; however, their extreme rarity, phenotypic heterogeneity, and mechanical adaptability make their isolation, identification, and therapeutic targeting challenging. Although computational methods have been applied to individual steps of CTC research, the field lacks a unified framework connecting molecular recognition, cell mechanics, microfluidic separation, detection, vascular transport, and targeted drug delivery. This review addresses that gap by organizing computational approaches into a multiscale digital thread spanning quantum chemistry, molecular docking and molecular dynamics, coarse-grained and dissipative-particle simulations, membrane and whole-cell mechanics, computational fluid dynamics, lattice-Boltzmann and immersed-boundary methods, physiologically based pharmacokinetic and pharmacokinetic-pharmacodynamic models, and machine learning. Direct CTC-specific studies are distinguished from enabling evidence derived from related cancer and nanomedicine systems; because computational studies of CTC-directed delivery are scarce, the delivery sections rest mainly on enabling evidence. Emphasis is placed on quantities transferred between scales, including receptor accessibility, binding, force-dependent adhesion, deformability, encounter probability, uptake, exposure, and therapeutic response. The analysis shows that CTC heterogeneity is not simply experimental noise but a governing variable shaping enrichment, detection, and treatment outcomes. It also reveals a major translational gap: device-scale separation and image-based detection are comparatively mature, whereas receptor-to-carrier modeling and end-to-end prediction of CTC-directed therapy remain fragmented. By identifying cross-scale dependencies, validation requirements, and missing links, and following one transfer contract from binding parameters to exposure, this review provides a framework for designing more reliable CTC technologies and testing computational predictions of CTC-directed delivery.

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