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Virtual clinical trials identify effective combination therapies in ovarian cancer.


ABSTRACT: A major issue in oncology is the high failure rate of translating preclinical results in successful clinical trials. Using a virtual clinical trial simulations approach, we present a mathematical framework to estimate the added value of combinatorial treatments in ovarian cancer. This approach was applied to identify effective targeted therapies that can be combined with the platinum-taxane regimen and overcome platinum resistance in high-grade serous ovarian cancer. We modeled and evaluated the effectiveness of three drugs that target the main platinum resistance mechanisms, which have shown promising efficacy in vitro, in vivo, and early clinical trials. Our results show that drugs resensitizing chemoresistant cells are superior to those aimed at triggering apoptosis or increasing the bioavailability of platinum. Our results further show that the benefit of using biomarker stratification in clinical trials is dependent on the efficacy of the drug and tumor composition. The mathematical framework presented herein is suitable for systematically testing various drug combinations and clinical trial designs in solid cancers.

SUBMITTER: Kozlowska E 

PROVIDER: S-EPMC6904444 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Virtual clinical trials identify effective combination therapies in ovarian cancer.

Kozłowska Emilia E   Vallius Tuulia T   Hynninen Johanna J   Hietanen Sakari S   Färkkilä Anniina A   Hautaniemi Sampsa S  

Scientific reports 20191210 1


A major issue in oncology is the high failure rate of translating preclinical results in successful clinical trials. Using a virtual clinical trial simulations approach, we present a mathematical framework to estimate the added value of combinatorial treatments in ovarian cancer. This approach was applied to identify effective targeted therapies that can be combined with the platinum-taxane regimen and overcome platinum resistance in high-grade serous ovarian cancer. We modeled and evaluated the  ...[more]

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