Rationale: The aim of this study is to build a simulation framework to evaluate the number of DNA double strand breaks (DSBs) induced by in vitro targeted radionuclide therapy (TRT). This work represents the first step towards exploring underlying biological mechanisms and influence of physical/chemical parameters to enable a better response prediction in patients. We used this tool to characterize early DSB induction by [177Lu]Lu-DOTA-[Tyr3]octreotate (177Lu-DOTATATE), a commonly used TRT for neuroendocrine tumors. Methods: A multiscale approach is implemented to simulate the number of DSBs produced over 4 h by the cumulated decays of 177Lu distributed according the somatostatin receptor-binding. The approach involves 2 sequential simulations performed with Geant4/Geant4-DNA. The radioactive source is sampled according to uptake experiments on the distribution of activities within the medium and the planar cellular cluster, assuming instant and permanent internalization. A phase space (PHSP) is scored around the nucleus of the central cell. Then, the PHSP is used to generate particles entering the nucleus containing a multi-scale description of the DNA in order to score the number of DSBs per particle source. The final DSB computations are compared to experimental data, measured by immunofluorescent detection of 53BP1 foci. Results: The probability of electrons reaching the nucleus was significantly influenced by the shape of the cell compartment, causing large variance in the induction pattern of DSBs. A significant difference was found in the DSBs induced by activity distributions in cell and medium, which is explained by the specific energy (z) distributions. The average number of simulated DSBs is 14 DSBs/cell (range: 7-24 DSBs/cell) compared to 13 DSBs/cell (2-30) experimentally determined. We found a linear correlation between the mean absorbed dose to the nucleus and the number of DSBs/cell: 0.014 DSBs/cell mGy-1 for internalization in the Golgi apparatus and 0.017 DSBs/cell mGy-1 for internalization in the cytoplasm. Conclusion: This simulation tool can lead to more reliable absorbed dose to DNA correlation and help in prediction of biological response.