Bridging the gap: What pre-clinical experiments can teach us about math model-guided treatment scheduling

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• Maximilian Strobl (Imperial College London)
• Thursday 18 September 2025, 10:55-11:00
• Seminar Room 1, Newton Institute.

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OOEW07 - Mathematical Foundations of Oncological Digital Twins

Cancers are complex and evolving diseases. To tackle this complexity there has been growing interest in developing “digital twins” – personalized computational tumor models – to better inform when and how to treat to reduce toxicity and maximize tumor control. As this idea finds traction, the crucial question is how do we ensure efficacy and safety as we translate from bench to bedside? In this study, we test the digital twin approach to treatment scheduling in vitro, in the context of EGFR + non-small cell lung cancer. Using fluorescent, time-lapse microscopy we characterize the evolutionary dynamics of co-cultures of Gefitinib-sensitive and paired resistant cell lines (PC9) across four different treatment schedules: i) continuous therapy, ii) intermittent therapy (on/off), iii) intermittent therapy (off/on), iv) continuous therapy at half the full dose. Our results demonstrate that both the dose and the frequency of treatment influence evolutionary dynamics. Intermittent therapy minimizes final resistant cell and total cell count after six treatment changes (18 days total), across four dose levels examined (2uM, 200nM, 100nM, 20nM Gefitinib). Moreover, the off/on intermittent schedule outperforms the on/off schedule, suggesting a role for spatial competition in suppressing resistant cells. Next, we test how well three commonly used mathematical models of sensitive-resistant dynamics can predict the observed dynamics: 1) A simple exponential model, 2) A logistic model which accounts for spatial competition, and 3) A 3 -population model which includes an additional subpopulation of drug-tolerant cells in the “sensitive” population. While Models 1 and 2 can capture the dynamics under continuous treatment, the more complex Model 3 is required to predict the outcomes of intermittent treatment. Our work illustrates how in vitro experiments can support the development of digital twins, and how this process can uncover new insights into drug resistance evolution in cancer.  

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