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SUMMARY:Bridging the gap: What pre-clinical experiments can teach us about
  math model-guided treatment scheduling - Maximilian Strobl (Imperial Coll
 ege London)
DTSTART:20250918T095500Z
DTEND:20250918T100000Z
UID:TALK236092@talks.cam.ac.uk
DESCRIPTION:Cancers are complex and evolving diseases. To tackle this comp
 lexity there has been growing interest in developing &ldquo\;digital twins
 &rdquo\; &ndash\; personalized computational tumor models &ndash\; to bett
 er inform when and how to treat to reduce toxicity and maximize tumor cont
 rol. As this idea finds traction\, the crucial question is how do we ensur
 e efficacy and safety as we translate from bench to bedside?\nIn this stud
 y\, we test the digital twin approach to treatment scheduling in vitro\, i
 n the context of EGFR+ non-small cell lung cancer. Using fluorescent\, tim
 e-lapse microscopy we characterize the evolutionary dynamics of co-culture
 s of Gefitinib-sensitive and paired resistant cell lines (PC9) across four
  different treatment schedules: i) continuous therapy\, ii) intermittent t
 herapy (on/off)\, iii) intermittent therapy (off/on)\, iv) continuous ther
 apy at half the full dose. Our results demonstrate that both the dose and 
 the frequency of treatment influence evolutionary dynamics. Intermittent t
 herapy minimizes final resistant cell and total cell count after six treat
 ment changes (18 days total)\, across four dose levels examined (2uM\, 200
 nM\, 100nM\, 20nM Gefitinib). Moreover\, the off/on intermittent schedule 
 outperforms the on/off schedule\, suggesting a role for spatial competitio
 n in suppressing resistant cells. Next\, we test how well three commonly u
 sed mathematical models of sensitive-resistant dynamics can predict the ob
 served dynamics: 1) A simple exponential model\, 2) A logistic model which
  accounts for spatial competition\, and 3) A 3-population model which incl
 udes an additional subpopulation of drug-tolerant cells in the &ldquo\;sen
 sitive&rdquo\; population. While Models 1 and 2 can capture the dynamics u
 nder continuous treatment\, the more complex Model 3 is required to predic
 t the outcomes of intermittent treatment. Our work illustrates how in vitr
 o experiments can support the development of digital twins\, and how this 
 process can uncover new insights into drug resistance evolution in cancer.
 \n&nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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