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Suzan Farhang-Sardroodi, M.Sc., Ph.D.
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Suzan Farhang-Sardroodi
Department of Pharmacology and Toxicology
Faculty of Medicine, University of Toronto
Suzan Farhang-Sardroodi is an Associate Research Scientist at the Department of Pharmacology and Toxicology, University of Toronto. With a Ph.D. in Physics, specializing in Evolutionary Dynamics from the University of Zanjan, and enhanced by an exchange semester at the University of Waterloo, she later ventured into Postdoctoral Research. Her postdoc endeavours spanned quantitative oncology at the Biomathematics and Fluids Group at Toronto Metropolitan University, and quantitative immunology at the departments of Mathematics and Statistics at York University, University of Manitoba, and Université de Montréal. Her research is focused on creating predictive and mechanistic models to understand the human immune response to various diseases and to tailor treatment strategies accordingly. Her recent ventures into clinical pharmacology have led her to explore the realms of pharmacometrics and quantitative systems pharmacology (QSP), aiming to refine drug development and therapy. Moreover, her interest in pharmacogenomics is driving her towards tailoring drug regimens based on genetic profiles, marking a significant stride in personalized medicine.
Research Interests
Quantitative Immunology
We combine the principles of immunology with quantitative sciences including mathematics, physics, and computational biology. This approach aims to decode the intricate workings of the immune system using mathematical modelling and computational methods. By quantifying how the immune system behaves, we gain a clearer picture of the body's reaction to infections, vaccines, and autoimmune disorders. Using quantitative models, we can forecast the outcomes of immune reactions and the effects of various medical interventions. This field is essential in crafting effective immunotherapies and vaccines, offering the capability to simulate different scenarios and test hypotheses computationally before proceeding to clinical trials. Progress in Quantitative Immunology is leading us towards treatments that are more tailored and precise, deepening our comprehension of the variability in individual immune systems and how this impacts disease development and the efficacy of treatments. By integrating machine learning techniques to diagnose digital twins generated based on distinct immune responses, we elevate our approach in Quantitative Immunology. Example works can include
Suzan Farhang-Sardroodi, Xiaoyan Deng, Stephanie Portet, Julien Arino, and Morgan Craig. Insights into B Cell and Antibody Kinetics Against SARS-CoV-2 Variants Using Mathematical Modelling. bioRxiv (2023): 2023-11. (Accepted by MBE)
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).
Clinical Pharmacology
Our research focuses on examining the interactions between drugs and the human body. We emphasize gaining insights into how medications are absorbed, distributed, metabolized, and excreted, along with their pharmacological impacts. This comprehension plays a significant role in establishing the right dosing, delivery techniques, and treatment schedules to maximize effectiveness and patient safety. Our ongoing efforts revolve around understanding the intricate interplays among various drugs and the human body, to optimize these interactions for each patient's benefit. In advancing healthcare, our role is important, dedicated to enhancing the effectiveness and safety of treatments.
Pharmacometrics (PK/PD)
We focus on the intricate relationship between pharmacokinetics (PK) and pharmacodynamics (PD), integral components in drug development and therapeutic management. Our work involves detailed analysis and modelling of how drugs are absorbed, distributed, metabolized, and excreted by the body (PK), as well as their biological and physiological effects (PD). This dual approach allows us to understand and predict how drugs behave in different populations, under various conditions, and in combination with other drugs. We employ sophisticated mathematical and statistical models to simulate drug behaviour and response. This modelling is crucial in the early stages of drug development, helping to predict outcomes and optimize dosages for clinical trials. It also plays a vital role in personalized medicine, enabling us to tailor drug regimens to individual patients based on their specific characteristics, thereby maximizing efficacy and minimizing adverse effects. Moreover, our work in pharmacometrics extends beyond the initial approval of drugs. As an example of our work consider the following publication
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.
Quantitative Systems Pharmacology (QSP)
We focus on an innovative and integrative approach to drug development and therapy. We combine the principles of pharmacology with computational modelling and systems biology, aiming to understand and predict how drugs affect biological systems as a whole, not just individual components or pathways. Our work in QSP involves constructing and utilizing detailed mathematical models that simulate the interactions between drugs and biological systems. These models are derived from and validated by clinical data, encompassing everything predicting the outcomes of drug interventions with greater accuracy, aiding in the design of more effective and safer drugs. Our effort is to bridge the gap between experimental pharmacology and clinical outcomes, offering a more robust and efficient pathway for drug discovery and development, and ultimately leading to more targeted and effective therapeutic strategies.
Pharmacogenomics (Recent Interest)
We study how genetic variations influence individual responses to medications. We combine pharmacology with genomics, to develop more effective medication regimens tailored to individual genetic profiles.
We support the 5R Framework to succeed in drug development, including the Right Target, Right Tissue, Right Safety, Right Patient, and Right Commercial Opportunity
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