Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Establishing combination PAC-1 and TRAIL regimens for treating ovarian cancer based on patient-specific pharmacokinetic profiles using in silico clinical trials

Olivia Cardinal, Chloé Burlot, Yangxin Fu, Powel Crosley, Mary Hitt, View ORCID ProfileMorgan Craig, View ORCID ProfileAdrianne L. Jenner
doi: https://doi.org/10.1101/2022.03.29.486309
Olivia Cardinal
1Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Chloé Burlot
1Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Yangxin Fu
2Department of Oncology, University of Alberta, Edmonton, AB, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Powel Crosley
2Department of Oncology, University of Alberta, Edmonton, AB, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Mary Hitt
2Department of Oncology, University of Alberta, Edmonton, AB, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Morgan Craig
1Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
3Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Morgan Craig
  • For correspondence: adrianne.jenner@qut.edu.au morgan.craig@umontreal.ca
Adrianne L. Jenner
1Department of Mathematics and Statistics, Université de Montréal, Montréal, QC, Canada
3Sainte-Justine University Hospital Research Centre, Montréal, QC, Canada
4School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Adrianne L. Jenner
  • For correspondence: adrianne.jenner@qut.edu.au morgan.craig@umontreal.ca
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Supplementary material
  • Preview PDF
Loading

Abstract

Ovarian cancer is commonly diagnosed in its late stages, and new treatment modalities are needed to improve patient outcomes and survival. We have recently established the synergistic effects of combination tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) and procaspase activating compound (PAC-1) therapies in granulosa cell tumours (GCT) of the ovary, a rare form of ovarian cancer, using a mathematical model of the effects of both drugs in a GCT cell line. Here, to understand the mechanisms of combined TRAIL and PAC-1 therapy, study the viability of this treatment strategy, and accelerate preclinical translation, we leveraged our mathematical model in combination with population pharmacokinetics (PopPK) models of both TRAIL and PAC-1 to expand a realistic heterogeneous cohort of virtual patients and optimize treatment schedules. Using this approach, we investigated treatment responses in this virtual cohort and determined optimal therapeutic schedules based on patient-specific pharmacokinetic characteristics. Our results showed that schedules with high initial doses of PAC-1 were required for therapeutic efficacy. Further analysis of individualized regimens revealed two distinct groups of virtual patients within our cohort: one with high PAC-1 elimination, and one with normal PAC-1 elimination. In the high elimination group, high weekly doses of both PAC-1 and TRAIL were necessary for therapeutic efficacy, however virtual patients in this group were predicted to have a worse prognosis when compared to those in the normal elimination group. Thus, PAC-1 pharmacokinetic characteristics, particularly clearance, can be used to identify patients most likely to respond to combined PAC-1 and TRAIL therapy. This work underlines the importance of quantitative approaches in preclinical oncology.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵† Co-senior authors

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
Back to top
PreviousNext
Posted April 03, 2022.
Download PDF

Supplementary Material

Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Establishing combination PAC-1 and TRAIL regimens for treating ovarian cancer based on patient-specific pharmacokinetic profiles using in silico clinical trials
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Establishing combination PAC-1 and TRAIL regimens for treating ovarian cancer based on patient-specific pharmacokinetic profiles using in silico clinical trials
Olivia Cardinal, Chloé Burlot, Yangxin Fu, Powel Crosley, Mary Hitt, Morgan Craig, Adrianne L. Jenner
bioRxiv 2022.03.29.486309; doi: https://doi.org/10.1101/2022.03.29.486309
Digg logo Reddit logo Twitter logo Facebook logo Google logo LinkedIn logo Mendeley logo
Citation Tools
Establishing combination PAC-1 and TRAIL regimens for treating ovarian cancer based on patient-specific pharmacokinetic profiles using in silico clinical trials
Olivia Cardinal, Chloé Burlot, Yangxin Fu, Powel Crosley, Mary Hitt, Morgan Craig, Adrianne L. Jenner
bioRxiv 2022.03.29.486309; doi: https://doi.org/10.1101/2022.03.29.486309

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Cancer Biology
Subject Areas
All Articles
  • Animal Behavior and Cognition (3686)
  • Biochemistry (7781)
  • Bioengineering (5672)
  • Bioinformatics (21253)
  • Biophysics (10565)
  • Cancer Biology (8165)
  • Cell Biology (11916)
  • Clinical Trials (138)
  • Developmental Biology (6744)
  • Ecology (10391)
  • Epidemiology (2065)
  • Evolutionary Biology (13847)
  • Genetics (9698)
  • Genomics (13060)
  • Immunology (8131)
  • Microbiology (19973)
  • Molecular Biology (7839)
  • Neuroscience (42997)
  • Paleontology (318)
  • Pathology (1276)
  • Pharmacology and Toxicology (2257)
  • Physiology (3350)
  • Plant Biology (7217)
  • Scientific Communication and Education (1309)
  • Synthetic Biology (2000)
  • Systems Biology (5529)
  • Zoology (1126)