Suzan Farhang-Sardroodi, M.Sc., Ph.D.
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Suzan Farhang-Sardroodi
Department of Pharmacology and Toxicology
Temerty Faculty of Medicine, University of Toronto
I am a Research Associate in the Department of Pharmacology and Toxicology at the University of Toronto. I received my Ph.D. in Physics from the University of Zanjan and completed an exchange semester in the Department of Applied Mathematics at the University of Waterloo. I pursued postdoctoral research in quantitative oncology at the Biomathematics and Fluids Group at Toronto Metropolitan University, and computational immunology at the Departments of Mathematics and Statistics at York University and the University of Manitoba. My current research focuses on clinical pharmacology and the pharmacogenomics of comorbid psychotic disorders.
Research Interests
Clinical Pharmacology & Toxicology
Our research in clinical pharmacology and toxicology is currently focused on pharmacokinetics, examining how drugs are absorbed, distributed, metabolized, and excreted (ADME) by the body. We study the biological factors, such as age, sex, and interspecies variations, that influence these processes, aiming to improve our understanding of drug behaviour and optimize therapeutic outcomes while remaining open to exploring other areas in the future. Example Work: Studying the pharmacokinetics of 5-fluorouracil through a two-compartment pharmacokinetic model:
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Farhang-Sardroodi, Suzan, Michael A. La Croix, and Kathleen P. Wilkie. Chemotherapy-induced cachexia and model-informed dosing to preserve lean mass in cancer treatment. PLoS Computational Biology 18.3 (2022): e1009505.
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Pharmacogenomics of the Comorbidity of Psychotic Disorders
A key aspect of our work is investigating the pharmacogenomics of psychotic disorders and their comorbidities, with a focus on understanding their genetic liability. By applying advanced genomic analysis techniques, we aim to identify novel genetic loci and pathways illuminating the biological mechanisms driving these disorders. This approach could uncover new therapeutic targets and improve precision medicine, addressing the challenges of these highly heritable and comorbid conditions. An example of our current work was presented in the abstract titled "Genetic Risk Factors for Concurrent Tobacco Use and Schizophrenia" at the Annual Scientific Meeting of the Pharmacogenomics Global Research Network (PGRN 2024).
Computational Immunology
We apply computational methods to investigate immune responses to infections (previous focus), cancer (an ongoing work), and neurodegeneration (future direction). By quantifying the immune system, we gain deeper insights into the body's reactions, enhancing our ability to predict outcomes and design effective interventions. Leveraging machine learning techniques, we simulate and analyze digital twins of distinct immune responses, enabling progress in precision medicine. in precision medicine. Example works:
Suzan Farhang-Sardroodi, Chapin S. Korosec, Samaneh Gholami, Morgan Craig, Iain R. Moyles, Mohammad Sajjad Ghaemi, Hsu Kiang Ooi, and Jane M. Heffernan. Analysis of host immunological response of adenovirus-based COVID-19 vaccines. Vaccines 9, no. 8 (2021): 861 (in collaboration with the National Research Council Canada).
Korosec, Chapin S., Suzan Farhang-Sardroodi, David W. Dick, Sameneh Gholami, Mohammad Sajjad Ghaemi, Iain R. Moyles, Morgan Craig, Hsu Kiang Ooi, and Jane M. Heffernan. Long-term durability of immune responses to the BNT162b2 and mRNA-1273 vaccines based on dosage, age and sex, Scientific Reports 12, no. 1 (2022): 21232.
Gholami, Samaneh, Chapin S. Korosec, Suzan Farhang-Sardroodi, David W. Dick, Morgan Craig, Mohammad Sajjad Ghaemi, Hsu Kiang Ooi, and Jane M. Heffernan. A mathematical model of protein subunits COVID-19 vaccines. Mathematical Biosciences 358 (2023): 108970.
Suzan Farhang-Sardroodi, Mohammad Sajjad Ghaemi, Morgan Craig, Hsu Kiang Ooi, and Jane M. Heffernan. A machine learning approach to differentiate between COVID-19 and influenza infection using synthetic infection and immune response data. medRxiv (2022): 2022-01 (in collaboration with the National Research Council of Canada).
