Mathematical modelling approaches to virtual clinical trials

The virtual workshop “Mathematical modelling approaches to virtual clinical trials” will take place May 2-3, 2022 from 11:30AM – 2PM (MDT – Banff, Edmonton).

This workshop will equip participants from mathematics and related disciplines with the tools necessary to solve common drug development questions in the pharmaceutical industry. The focus will be on development of pharmacometrics skills to approach questions centred on drug discovery and in silico (virtual) clinical trials using a variety of state-of-the-art model-informed drug development (MIDD), including quantitative systems pharmacology (QSP) methodologies. The workshop’s sessions will highlight modern modeling and simulation approaches applied to diverse pre- and post-clinical drug development questions, including methodological aspects of virtual clinical trial implementation, and the use of in silico clinical trials in immuno-oncology and other key drug development spaces. 



Event Videos


    Schedule

    All times listed are MDT (GMT – 06:00)

    Monday, May 2

    11:30 – 11:40 Opening Remarks

    11:40 – 12:10 Daniel Kirouac (Notch Therapeutics)

    12:10 – 12:40 Nessy Tania (Pfizer)

    12:40 – 13:10 Jana Gevertz (The College of New Jersey)

    13:10 – 13:20 Break

    13:20 – 14:00 Panel Discussion


    Tuesday, May 3

    11:30 – 11:40 Organizers “Advice for young trainees”

    11:40 – 12:10 Alison Betts (Applied Biomath

    12:10 – 12:40 Rohit Rao (Pfizer

    12:40 – 13:10 Dean Bottino (Takeda)

    13:10 – 13:20 Break

    13:20 – 13:50 Meet the speaker session:

    Breakout rooms with each speaker

    13:50 – 14:00 Closing remarks


    Speakers

    Alison Betts, Ph.D

    Alison is Vice President of Scientific Collaborations and Fellow of Modeling & Simulation at Applied BioMath (Boston, MA USA). Here she is part of the Business Development and Sciences team providing scientific support and partner alignment based on her vast modeling experience in industry. Alison is also PI on an NIH Grant to develop a translational modeling framework for antibody drug conjugates (ADCs). Prior to joining Applied BioMath, Alison had an extensive modeling career at Pfizer, across many therapeutic areas. Most recently, she was modeling and simulation (M&S) lead supporting the Oncology Research Unit. In this role she led a team responsible for using M&S strategies to answer mechanistic questions, validate targets, select optimal compounds and translate to the clinic. Alison specializes in M&S of novel biotherapeutic modalities including bi-specific T-cell retargeting molecules, CAR-T cells, targeted nanoparticles and drug conjugates for treatment of cancer. Alison gained her PhD in ‘Quantitative Systems Pharmacology Modeling of Biotherapeutics in Oncology’ at the University of Leiden, The Netherlands.

    Use of mathematical models to predict optimal dose, regimen and patient diagnostics for ADCs

    Antibody drug conjugates (ADCs) are complex multivariate molecules which benefit from the use of quantitative system pharmacology (QSP) modeling to guide their discovery and development. In this presentation, a QSP modeling approach to select optimal dose and regimen for Besponsa (inotuzumab-ozogamicin), a CD22-ADC for B cell malignancies including non-Hodgkin’s Lymphoma (NHL) and acute lymphocytic leukemia (ALL) is discussed. The model is used to translate from preclinical studies to the clinic and validated by comparing to observed clinical data in NHL patients. The model is then applied to predict optimal dose, regimen and patient diagnostics for a new clinical indication (ALL). Finally, a next generation platform model and strategy for ADCs is presented, which integrates multiple data types and established knowledge to predict ADC efficacy and toxicity.  


    Dean Bottino, Ph.D

    Dr. Dean Bottino received his PhD in Applied Mathematics from Tulane University in 1996. His academic work at Tulane, and subsequently at University of Utah and UC Berkeley, consisted of spatiotemporal simulations of eukaryotic cell motility and chemotaxis.  Dr. Bottino then moved into industry, joining Physiome Sciences in 2001, co-founding the BioAnalytics Group LLC in 2003, then moving on to Novartis in 2005, Roche in 2011 and Millennium (Takeda) in 2013. He has specialized in preclinical and clinical modeling and simulation in oncology since 2005.

    Evaluating Strategies for Overcoming Rituximab (R) Resistance Using a Quantitative Systems Pharmacology (QSP) model of Antibody-Dependent Cell-mediated Cytotoxicity & Phagocytosis (ADCC & ADCP): An Academic/Industrial Collaboration

    Despite the impressive performance of rituximab (R) containing regimens like R-CHOP in CD20+ Non-Hodgkin’s Lymphoma (NHL), 30-60% of R-naïve NHL patients are estimated to be resistant, and approximately 60% of those patients will not respond to subsequent single agent R treatment.  Given that antibody dependent cell mediated cytotoxicity (ADCC) and phagocytosis (ADCP) are thought to be the major mechanisms of action of Rituximab, increasing the activation levels of natural killer (NK) and macrophage (MP) cells may be one strategy for overcoming R resistance.

    During (and after) the Fields Institute Industrial Problem Solving Workshop in August 2019, academic participants and industry mentors developed and calibrated to literature data a quantitative systems pharmacology (QSP) model of ADCC/ADCP to interrogate which mechanisms of R resistance could be overcome by increased NK or MP activation, and how much effector cell activation would be required to overcome a given degree and mechanism of R resistance. 

    This work was motivated by a real-world pharmaceutical drug development question, and the academic-industry interactions during and after the workshop resulted in sharknado plots as well as a published QSP model (presented at American Association of Cancer Research Annual Meeting, 2021) that was able to address some of the key questions around overcoming R resistance. The published model was then incorporated into an in-house QSP model supporting the development of a Takeda investigational drug which is being developed to restore R sensitivity in an R-resistant patient population.


    Jana Gevertz, Ph.D

    Jana Gevertz is a professor in the Department of Mathematics and Statistics at The College of New Jersey. Her interest in mathematical biology began as an undergraduate student at Rutgers University, and she began researching in the area of mathematical oncology as a PhD student in applied and computational mathematics at Princeton University. Beyond her research in data-driven modeling of cancer treatment, Dr. Gevertz is passionate about undergraduate mathematics education and research. She has received national and state-level recognition for her teaching, with the Mathematical Association of America (MAA) awarding her the 2016 Henry L. Alder Award for Distinguished Teaching by a Beginning College or University Mathematics Faculty Member, and the New Jersey section of the MAA awarding her the 2016 Distinguished College or University Teaching of Mathematics Award. Dr. Gevertz has served as the research mentor for over twenty undergraduate students, with several student projects resulting in peer-reviewed publications. She is currently the treasurer of the Society for Mathematical Biology. 

    Virtual Expansion of Populations for the Analysis of Robustness of Therapies

    Mathematical models of biological systems are often validated by fitting to the average behavior in an often-small experimental dataset. Here we ask the question of whether mathematical predictions for the average are actually applicable in samples that deviate from the average. We explore this question in the context of immunotherapy-treated melanoma by first introducing a virtual population method. Using nonparametric statistics, a small sample population is amplified to create a large number of virtual populations. Using these virtual populations, we demonstrate how a mathematically optimal protocol for treating the average can lack robustness, meaning the “best treatment for the average” can fail to be optimal (and in fact, can be far from optimal) in individuals that differ from the average. We also show how these virtual populations can be leveraged to identify an optimal treatment protocol that is robust to perturbations from the average. 


    Daniel Kirouac, Ph.D

    Dr. Kirouac leads the Systems Biology department at Notch Therapeutics, integrating bioinformatics, dynamical systems modelling and machine learning to design and deliver the next generation of T cell therapies.  Prior to joining Notch in 2020, he held scientific positions in large pharma (Genentech), mid-size biotech (Merrimack Pharmaceuticals) and consulting (Applied BioMath), and has been developing mathematical models of biological systems for almost 20 years.  He has published 25 research and opinion articles, has organized and spoken at multiple conferences focused on the nascent field of Quantitative Systems Pharmacology (QSP), and in 2019 received an award for his research from the American Society for Clinical Pharmacology.  Dr. Kirouac did post-doctoral training at MIT and Harvard Medical School, holds a PhD in Biomedical Engineering from the University of Toronto, and Bachelors’ in both Chemical Engineering and Genetics from the University of Western Ontario.  

    Systems pharmacology model development and validation 

    Quantitative systems pharmacology (QSP) approaches strive to link clinical outcomes to underlying cell biology and physiology.  While scientifically appealing, the multi-scale nature of QSP models prove challenging to validate in comparison to empirical pharmacometrics approaches.  I will highlight three specific challenges; model structure identification, code verification, and assessment of predictive accuracy.  Regarding the later, QSP models may be assessed both by their pharmacological predictions, as well as the accuracy of biological inferences.   While approaches to assess of pharmacological predictions can be borrowed from empirical pharmacometrics, there are no standards for assessing the accuracy of biological insights derived from mathematical models.   Integrating Next Generation Sequencing technologies and bioinformatics pipelines with modelling workflows provides an avenue for doing so.  I will provide examples from published literature demonstrating the challenges of QSP model development and validation, and highlight recent progress towards understanding the pharmacology of T cell-based therapies using a combination of dynamic systems analysis, RNA-sequencing and machine learning.    


    Rohit Rao, Ph.D

    Rohit Rao supports quantitative systems pharmacology efforts in immunology, anti-invectives and immuno-oncology related disease areas at Pfizer. Prior to joining Pfizer he received his PhD in Chemical & Biochemical Engineering from Rutgers University in 2018 and subsequently completed a postdoctoral fellowship in the School of Engineering and Applied Sciences at Harvard University. 

    Facilitating the Clinical Development of Nirmatrelvir with Quantitative Systems Pharmacology

    A quantitative systems pharmacology (QSP) model of the pathogenesis and treatment of SARS-CoV-2 infection can streamline and accelerate the development of novel medicines to treat COVID-19. Moreover, simulation of clinical trials allows in silico exploration of the uncertainties of clinical trial design and can rapidly inform their protocols. To this end, we will discuss the development of a QSP model of COVID-19 that was used to accelerate the clinical development of nirmatrelvir, a novel oral anti-viral for the treatment of COVID-19


    Nessy Tania, Ph.D

     Nessy Tania is a Principal Quantitative Systems Pharmacologist at Pfizer. Prior to transitioning to industry and joining Pfizer, she was an Associate Professor in the Department of Mathematics and Statistics at Smith College She was trained as a math biologist: she obtained her Ph.D. in Mathematics at the University of Utah followed by a postdoctoral fellowship at the University of British Columbia. Since joining Pfizer in 2019, she has developed and utilized QSP models to support clinical development of programs in the Rare Disease Research Unit.

    Simulating Clinical Trials with QSP Models and Virtual Populations: Philosophy, Challenges, and Application

    Abstract coming soon!


    Organizers

    Morgan Craig

    Researcher, Sainte-Justine University Hospital Research Centre | Link to Morgan’s Lab

    Assistant Professor of Mathematics & Statistics, Université de Montréal


    Anna Sher

    Senior Principal Scientist, Pfizer


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