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Approaches to generating virtual patient cohorts with applications in oncology

View ORCID ProfileAnudeep Surendran, Justin Le Sauteur-Robitaille, Dana Kleimeier, View ORCID ProfileJana Gevertz, View ORCID ProfileKathleen Wilkie, View ORCID ProfileAdrianne L. Jenner, View ORCID ProfileMorgan Craig
doi: https://doi.org/10.1101/2022.05.24.493265
Anudeep Surendran
1Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
2Sainte-Justine University Hospital Research Centre, Montréal, Canada
3Centre de recherches mathématiques, Montréal, Canada
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  • For correspondence: anudeep.surendran@umontreal.ca
Justin Le Sauteur-Robitaille
1Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
2Sainte-Justine University Hospital Research Centre, Montréal, Canada
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Dana Kleimeier
4Institute of Bioinformatics, University Medicine Greifswald, Germany
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Jana Gevertz
5Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, USA
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Kathleen Wilkie
6Department of Mathematics, Ryerson University, Toronto, Canada
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Adrianne L. Jenner
7School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
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Morgan Craig
1Department of Mathematics and Statistics, Université de Montréal, Montréal, Canada
2Sainte-Justine University Hospital Research Centre, Montréal, Canada
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ABSTRACT

Virtual clinical trials (VCTs) have gained popularity for their ability to rationalize the drug development process using mathematical and computational modelling, and to provide key insights into the mechanisms regulating patient responses to treatment. In this chapter, we cover approaches for generating virtual cohorts with applications in cancer biology and treatment. VCTs are an effective tool for predicting clinical responses to novel therapeutics and establishing effective treatment strategies. These VCTs allow us to capture inter-individual variability (IIV) which can lead to diversity in patient drug responses. Here we discuss three main methodologies for capturing IIV with a VCT. First, we highlight the use of population pharmacokinetic (PopPK) models, which extrapolate from empirical data population PK parameters that best fits the individual variability seen in drug disposition using non-linear mixed effects models. Next, we show how virtual patients may be sampled from a normal distribution with mean and standard deviation informed from experimental data to estimate parameters in a mechanistic model that regulates drug PKs. Lastly, we show how optimization techniques can be used to calibrate virtual patient parameter values and generate the VCT. Throughout, we compare and contrast these methods to provide a broader view of the generation of virtual patients, and to aid the decision-making process for those looking to leverage virtual clinical trials in their research.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted May 25, 2022.
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Approaches to generating virtual patient cohorts with applications in oncology
Anudeep Surendran, Justin Le Sauteur-Robitaille, Dana Kleimeier, Jana Gevertz, Kathleen Wilkie, Adrianne L. Jenner, Morgan Craig
bioRxiv 2022.05.24.493265; doi: https://doi.org/10.1101/2022.05.24.493265
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Approaches to generating virtual patient cohorts with applications in oncology
Anudeep Surendran, Justin Le Sauteur-Robitaille, Dana Kleimeier, Jana Gevertz, Kathleen Wilkie, Adrianne L. Jenner, Morgan Craig
bioRxiv 2022.05.24.493265; doi: https://doi.org/10.1101/2022.05.24.493265

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