Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using in silico clinical trials
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 tumor necrosis factor‐related apoptosis‐inducing ligand (TRAIL) and procaspase activ...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-06-01
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| Series: | Computational and Systems Oncology |
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| Online Access: | https://doi.org/10.1002/cso2.1035 |
| _version_ | 1811242613158707200 |
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| author | Olivia Cardinal Chloé Burlot Yangxin Fu Powel Crosley Mary Hitt Morgan Craig Adrianne L. Jenner |
| author_facet | Olivia Cardinal Chloé Burlot Yangxin Fu Powel Crosley Mary Hitt Morgan Craig Adrianne L. Jenner |
| author_sort | Olivia Cardinal |
| collection | DOAJ |
| description | 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 tumor necrosis factor‐related apoptosis‐inducing ligand (TRAIL) and procaspase activating compound (PAC‐1) therapies in granulosa cell tumors (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 (PKs) 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 PK 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 PK 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. |
| first_indexed | 2024-04-12T13:54:35Z |
| format | Article |
| id | doaj.art-0ccd75c507b1410bba9c2f702b373273 |
| institution | Directory Open Access Journal |
| issn | 2689-9655 |
| language | English |
| last_indexed | 2024-04-12T13:54:35Z |
| publishDate | 2022-06-01 |
| publisher | Wiley |
| record_format | Article |
| series | Computational and Systems Oncology |
| spelling | doaj.art-0ccd75c507b1410bba9c2f702b3732732022-12-22T03:30:25ZengWileyComputational and Systems Oncology2689-96552022-06-0122n/an/a10.1002/cso2.1035Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using in silico clinical trialsOlivia Cardinal0Chloé Burlot1Yangxin Fu2Powel Crosley3Mary Hitt4Morgan Craig5Adrianne L. Jenner6Department of Mathematics and Statistics Université de Montréal Montréal Quebec CanadaDepartment of Mathematics and Statistics Université de Montréal Montréal Quebec CanadaDepartment of Oncology University of Alberta Edmonton Alberta CanadaDepartment of Oncology University of Alberta Edmonton Alberta CanadaDepartment of Oncology University of Alberta Edmonton Alberta CanadaDepartment of Mathematics and Statistics Université de Montréal Montréal Quebec CanadaDepartment of Mathematics and Statistics Université de Montréal Montréal Quebec CanadaAbstract 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 tumor necrosis factor‐related apoptosis‐inducing ligand (TRAIL) and procaspase activating compound (PAC‐1) therapies in granulosa cell tumors (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 (PKs) 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 PK 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 PK 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.https://doi.org/10.1002/cso2.1035granulosa cell tumor of the ovaryin silico clinical trialsmathematical modelingovarian cancerPAC‐1pharmacokinetics |
| spellingShingle | Olivia Cardinal Chloé Burlot Yangxin Fu Powel Crosley Mary Hitt Morgan Craig Adrianne L. Jenner Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using in silico clinical trials Computational and Systems Oncology granulosa cell tumor of the ovary in silico clinical trials mathematical modeling ovarian cancer PAC‐1 pharmacokinetics |
| title | Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using in silico clinical trials |
| title_full | Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using in silico clinical trials |
| title_fullStr | Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using in silico clinical trials |
| title_full_unstemmed | Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using in silico clinical trials |
| title_short | Establishing combination PAC‐1 and TRAIL regimens for treating ovarian cancer based on patient‐specific pharmacokinetic profiles using in silico clinical trials |
| title_sort | establishing combination pac 1 and trail regimens for treating ovarian cancer based on patient specific pharmacokinetic profiles using in silico clinical trials |
| topic | granulosa cell tumor of the ovary in silico clinical trials mathematical modeling ovarian cancer PAC‐1 pharmacokinetics |
| url | https://doi.org/10.1002/cso2.1035 |
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