C2 - Systems pharmacology to optimise cancer treatments

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Morgan Craig (University of Montreal, Canada) and Fahima Nekka (University of Montreal, Canada)


Faced with a declining number of molecules successfully reaching the market (despite skyrocketing expenses) and an increasing demand for more individualized treatment strategies in the clinic, a mechanistic understanding of the causal effects of drugs on their targets is progressively taking over more traditional empirical methodologies in drug research and development. Mathematics lies at the very heart of this evolution. Quantitative Systems Pharmacology (QSP) approaches leverage mathematical modelling and integrate systems biology and pharmacometrics to translate biological and pharmacological knowledge into more rational and predictable drug R&D and clinical approaches. The advent of QSP in oncology has seen a number of significant breakthroughs, from drug target identification to new therapy modalities to precision medicine in cancer treatment. This session will cover innovative quantitative approaches across the cancer drug development pipeline by highlighting cutting-edge approaches to drug candidate identification, therapy optimization, and clinical strategies using QSP in academia and in industry. Improving patient outcomes using QSP can only be accomplished through heavily interdisciplinary feedback with collaborators whose research provides the basis of the mathematical and computational models, and whose investigations are subsequently oriented by the models’ predictions. To assess the current landscape of QSP in oncology and to identify future orientations, a panel discussion will end this workshop. This session will bring together quantitative scientists, pharmaceutical scientists, clinicians, and regulators looking to have a consequential impact on how we diagnose and treat cancer.


Micropharmacology: assessing drug efficacy using mathematical oncology
Alexander Anderson (Moffitt Cancer Research Centre, USA)

Optimization of immunotherapy treatment regimens
Oleg Milberg (USA)

Developing personalized cancer treatment regimens
Paolo Vicini (UK)

Translating from benchside to bedside
Nathalie Letarte (Canada)

Learning Objectives

  1. Translate physiological mechanisms in informative and predictive cancer drug mathematical models
  2. Integrate systems biology approaches along the drug development pipeline to identify target genes and to rationalize drug therapy
  3. Quantifiably improve patient health and outcomes through the synergistic combination of multidisciplinary efforts