Computational electrophysiology models are beginning to emerge as digital-twin–oriented representations of cancer cells, offering mechanistic insights that complement traditional patch-clamp experiments. In this study, we evaluate the ability of the earliest in-silico cancer electrophysiology model, an ion channel model based on Hidden Markov state transitions, to reproduce drug-modulated current densities in A549 lung adenocarcinoma cells. Using independent experimental data from Glaser et al. (2021), we characterised Ca2+-activated K+ channels, KCa1.1 and KCa3.1, in wild-type (WT) and erlotinib-resistant (ER) A549 cells under baseline conditions, as well as after activation with 1-EBIO (3-ethyl-1H-benzimidazol-2-one) and inhibition with paxilline and senicapoc. The in-silico model reproduced the qualitative order of current responses under all pharmacological conditions, quantitatively matching the paxilline- and senicapoc-blocked states while remaining within biologically reasonable channel expression limits. Reproducing 1-EBIO activation required higher-than-physiological effective channel numbers, indicating that ligand-dependent gating is not fully represented. Nevertheless, the model captured the overall electrophysiological behaviour of both WT and ER cells and successfully distinguished their phenotypes. In summary, the in-silico model already enables mechanistic interpretation of electrophysiological phenotypes and drug-modulated responses. With continued refinement, including the incorporation of ligand-modulated gating, improved calcium-feedback dynamics, and formal uncertainty quantification, this model has the potential to evolve into a predictive digital twin platform supporting ion-channel pharmacology, therapy optimisation and precision oncology.
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2026 The Author(s). Clinical and Translational Discovery published by John Wiley & Sons Australia, Ltd on behalf of Shanghai Institute of Clinical Bioinformatics.
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