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A multi-scale systems pharmacology approach for anticancer chronotherapy personalisation.

Cancer management personalisation requires to reliably account for molecular pathways of patient’s response to drug administration. In a context where clinical molecular data is usually minimally available in individual patients, multi-scale physiologically-based modelling appears as an adapted solution to describe underlying gene and protein networks ultimately responsible for treatment antitumor efficacy and side effects. Basing mathematical models on physiology allows the use of in vitro studies to design whole-body preclinical rodent models, to be further scaled to patient population data. The resulting human model, that describes an average cancer patient, may then be used in global parameter sensitivity analyses to generate specific predictions on relevant biomarkers. Partial re-calibration of the population human model for a given cancer patient according to individual biomarker recordings, patient’s genetic background and therapeutic history further allow for chemotherapy personalisation. The patient-specific models then appeal for clinical validation, thus initiating a novel trial design, where each individual patient receives personalised drug combinations/scheduling computed via mathematical models, informed with a continuous flow of multidimensional information obtained and tele-transmitted from patients.

I will present how this approach was undertaken for personalising chronotherapy against digestive cancers with a particular focus on the anticancer drug irinotecan [1-3] . Multi-scale models representing irinotecan pharmacokinetics-pharmacodynamics (PK-PD) were designed. While PK quantifies the transport and metabolism of the drug and its metabolites that are driving exposure concentration over time, PD quantifies drug interactions with cellular targets and subsequent cytotoxicity.

References

1. Dulong, S., et al., Identification of Circadian Determinants of Cancer Chronotherapy through In Vitro Chronopharmacology and Mathematical Modeling. Mol Cancer Ther, 2015.

2. Ballesta, A., et al., A systems biomedicine approach for chronotherapeutics optimization: focus on the anticancer drug irinotecan, in New Challenges for Cancer Systems Biomedicine. 2012, Springer.

3. Ballesta, A., et al., A combined experimental and mathematical approach for molecular-based optimization of irinotecan circadian delivery. PLoS Comput Biol, 2011. 7(9): p. e1002143.

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