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Craig, Morgan

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Associate Professor

Faculty of Arts and Science - Department of Mathematics and Statistics

André-Aisenstadt Office 5243

514 343-6111 ext 7471

Courriels

Affiliations

  • Membre Centre de recherche du CHU Sainte-Justine
  • Membre Centre for Applied Mathematics in Bioscience and Medicine (CAMBAM)
  • Membre Groupe de recherche universitaire sur le médicament
  • Membre GRUM — Groupe de recherche universitaire sur le médicament
  • Membre Society for Industrial and Applied Mathematics
  • Membre Society for Mathematical Biology

Courses

  • MAT3450 A - Intro. modélisation math.

Research area

Dr. Morgan Craig is a Researcher at the Sainte-Justine University Hospital Research Centre and an Assistant Professor in the Department of Mathematics and Statistics at the Université de Montréal. She was a postdoc in the Department of Organismic and Evolutionary Biology at Harvard University working with Dr. Alison Hill after receiving her Ph.D. in Pharmaceutical Sciences from the Université de Montréal. The Quantitative and Translational Medicine Laboratory that she runs focuses on the application and implementation of quantitative approaches, particularly computational biology, to study how heterogeneity impacts on disease and treatment outcomes. Her research focuses on the development of predictive, mechanistic models in a variety of disease contexts, particularly cancers and diseases caused by viruses, to identify pathophysiological mechanisms and tailor therapeutic regimens according to patient-specific characteristics. Dr. Craig’s research is highly multidisciplinary and is conducted in close collaboration with experimentalists and clinicians.

Student supervision Expand all Collapse all

Mathematical modelling of experimental therapy for granulosa cell tumour of the ovary and mammary cell differentiation in the context of triple-negative breast cancer Theses and supervised dissertations / 2023-12
Le Sauteur-Robitaille, Justin
Abstract
Developing novel cancer drugs or therapies requires years of preclinical work before translation to clinical trials and ultimately the market. Unfortunately, an overwhelming majority of compounds will fail to make this transition and will show no benefit in trials. To reduce attrition along the drug development pipeline, mathematical modelling is increasingly used in preclinical work to investigate and optimize treatment scenarios, in the hope of improving the success rate of potential therapies. Mechanistic models aim to incorporate the mechanisms of actions of drugs and physiological/cellular interactions to provide a deeper understanding of the system and rationally investigate therapeutic effectiveness. This thesis focuses on the implementation of heterogeneous, mechanistic mathematical models in preclinical contexts in cancer drug development. The first chapter of this thesis provides an overview of mathematical oncology and the drug discovery pipeline by presenting different tumour growth models and the integration of therapeutic effect through pharmacokinetic/pharmacodynamic (PK/PD) models. The second chapter of this thesis discusses granulosa cell tumour (GCT) of the ovary and the development of a mathematical model to investigate the potential of a combination therapy using a chemotherapy and an immunotherapy that produces tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) through an oncolytic virus (OV). The model considers tumour cells throughout the phases of the cell cycle, the infection of these cancer cells by the OV, and the innate-immune pressure from the body. It also incorporates detailed PK/PD models for TRAIL and the chemotherapeutic drug, procaspase activating compound-1 (PAC-1). This includes a mechanistic receptor binding PK model for TRAIL as well as a two-compartment PK model for PAC-1 to properly integrate the concentrations of both compounds in the combination effect function applied to the cancer cell populations. Through simulations and hypothesis testing, we determined the minimal doses and ideal dosing regimens for PAC-1 that best controlled tumour growth. We also established how to successfully eradicate the tumour under the assumption of a much higher infection rate of the OV. 6 In the third chapter, we present different approaches to include inter-individual variability into mechanistic mathematical models, each with their own benefits and challenges. We describe how population PKs (PopPK) inform on cohort averages and variability due to covariates, and how to use this heterogeneity to recover the dynamics of drug treatment in patient populations. Variability in cohorts can also be generated through algorithms ensuring that virtual patients have realistic parameters and outcomes. We also touch upon in silico trials that help to predict a range of outcomes and treatment scenarios. These in silico clinical trials are highly valuable in quantitative system pharmacology (QSP) due to their predictive nature. Lastly, we present an application of PopPK using 300 generated patients in a QSP model for mammary stem cell differentiation under treatment with estrogen (estradiol). We investigate the effect of hormone therapy on mammary cell differentiation due to its potential application in triple negative breast cancer (TNBC), as prolactin has been proposed in experimental models to induce differentiation in TNBC stem cells. Our model and results serve as proof of concept for the continued investigation into pharmacological means of inducing stem cell differentiation to reduce cancer plasticity and severity.

Research projects Expand all Collapse all

Canada Research Chair in Computational Immunology SPIIE/Secrétariat des programmes interorganismes à l’intention des établissements / 2024 - 2029

Dynamiques des réseaux à travers les échelles : illustrer nos relations avec les virus et notre environnement FRQNT/Fonds de recherche du Québec - Nature et technologies (FQRNT) / 2023 - 2026

Centre de recherches mathématiques (CRM) FRQNT/Fonds de recherche du Québec - Nature et technologies (FQRNT) / 2022 - 2029

Centre de recherches mathématiques (CRM) CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2022 - 2027

Plasmonic optophysiology optogenetics SPIIE/Secrétariat des programmes interorganismes à l’intention des établissements / 2022 - 2025

La médecine quantitative au service de la personnalisation thérapeutique en oncologie FRQS/Fonds de recherche du Québec - Santé (FRSQ) / 2021 - 2025

One Health Modelling Network for Emerging Infections (OMNI)/RÉseau UNe seule santé sur la modélisation des InfectionS (REUNIS) CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2021 - 2024

La médecine quantitative au service de la personnalisation thérapeutique en oncologie COLE Foundation/Fondation Cole / 2021 - 2023

One Health Modelling Network for Emerging Infections CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2021 - 2023

One Health Modelling Network for Emerging Infections (OMNI)/RÉseau UNe seule santé sur la modélisation des InfectionS (REUNIS) CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2021 - 2023

One Health Modelling Network for Emerging Infections (OMNI)/RÉseau UNe seule santé sur la modélisation des InfectionS (REUNIS) CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2021 - 2022

Réseau Québécois de Recherche sur les Médicaments (RQRM) / Integrated quantitative approach to novel targets in triple negative breast cancer. FRQS/Fonds de recherche du Québec - Santé (FRSQ) / 2021 - 2022

La médecine quantitative en appui à la personnalisation des thérapies. Fondation de l'Hôpital Ste-Justine / 2020 - 2022

Quantitative Approaches to Understand Differential Immune Responses in COVID-19 CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2020 - 2021

Supplément COVID-19 CRSNG_Characterization of disrupted hematopoiesis by mathematical modelling CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2020 - 2021

Characterization of disrupted hematopoiesis by mathematical modelling CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2018 - 2025

Characterization of disrupted hematopoiesis by mathematical modelling CRSNG/Conseil de recherches en sciences naturelles et génie du Canada (CRSNG) / 2018 - 2024