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Adaptive therapy for ovarian cancer: An integrated approach to PARP inhibitor scheduling. | LitMetric

AI Article Synopsis

  • Toxicity and resistance to medication are significant issues in treating ovarian cancer with PARP inhibitors (PARPi), but research suggests adaptive therapy could help.
  • The study combines mathematical modeling and lab experiments to create a model that predicts how cancer cells respond to different treatment schedules, emphasizing the importance of timing in treatment modifications.
  • The findings indicate that continuous treatment—with adjustments rather than complete pauses—may lead to better outcomes for patients, highlighting the need for further exploration in adaptive therapy approaches.

Article Abstract

Toxicity and emerging drug resistance are important challenges in PARP inhibitor (PARPi) treatment of ovarian cancer. Recent research has shown that evolutionary-inspired treatment algorithms which adapt treatment to the tumor's treatment response (adaptive therapy) can help to mitigate both. Here, we present a first step in developing an adaptive therapy protocol for PARPi treatment by combining mathematical modelling and wet-lab experiments to characterize the cell population dynamics under different PARPi schedules. Using data from Incucyte Zoom time-lapse microscopy experiments and a step-wise model selection process we derive a calibrated and validated ordinary differential equation model, which we then use to test different plausible adaptive treatment schedules. Our model can accurately predict the treatment dynamics, even to new schedules, and suggests that treatment modifications need to be carefully timed, or one risks losing control over tumour growth, even in the absence of any resistance. This is because our model predicts that multiple rounds of cell division are required for cells to acquire sufficient DNA damage to induce apoptosis. As a result, adaptive therapy algorithms that modulate treatment but never completely withdraw it are predicted to perform better in this setting than strategies based on treatment interruptions. Pilot experiments confirm this conclusion. Overall, this study contributes to a better understanding of the impact of scheduling on treatment outcome for PARPis and showcases some of the challenges involved in developing adaptive therapies for new treatment settings.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10055330PMC
http://dx.doi.org/10.1101/2023.03.22.533721DOI Listing

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