Towards a digital cancer cell twin: External pharmacological validation of a mechanistic A549 electrophysiology model

Abstract 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 Ca 2 + ‐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.