Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology
Abstract Clinical trials are costly and time-intensive endeavors, with a high rate of drug candidate failures. Moreover, the standard approaches often evaluate drugs under a limited number of protocols. In oncology, where multiple treatment protocols can yield divergent outcomes, addressing this iss...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-11-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80768-6 |
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| author | Emilia Kozłowska Ulla-Maija Haltia Krzysztof Puszynski Anniina Färkkilä |
| author_facet | Emilia Kozłowska Ulla-Maija Haltia Krzysztof Puszynski Anniina Färkkilä |
| author_sort | Emilia Kozłowska |
| collection | DOAJ |
| description | Abstract Clinical trials are costly and time-intensive endeavors, with a high rate of drug candidate failures. Moreover, the standard approaches often evaluate drugs under a limited number of protocols. In oncology, where multiple treatment protocols can yield divergent outcomes, addressing this issue is crucial. Here, we present a computational framework that simulates clinical trials through a combination of mathematical and statistical models. This approach offers a means to explore diverse treatment protocols efficiently and identify optimal strategies for oncological drug administration. We developed a computational framework with a stochastic mathematical model as its core, capable of simulating virtual clinical trials closely recapitulating the clinical scenarios. Testing our framework on the landmark SOLO-1 clinical trial investigating Poly-ADP-Ribose Polymerase maintenance treatment in high-grade serous ovarian cancer, we demonstrate that managing toxicity through treatment interruptions or dose reductions does not compromise treatment’s clinical benefits. Additionally, we provide evidence suggesting that further reduction of hematological toxicity could significantly improve the clinical outcomes. The value of this computational framework lies in its ability to expedite the exploration of new treatment protocols, delivering critical insights pivotal to shaping the landscape of upcoming clinical trials. |
| format | Article |
| id | doaj-art-6fe6f374bc144e598dd2f09fb985c6f3 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-6fe6f374bc144e598dd2f09fb985c6f32024-12-01T12:22:50ZengNature PortfolioScientific Reports2045-23222024-11-0114111110.1038/s41598-024-80768-6Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncologyEmilia Kozłowska0Ulla-Maija Haltia1Krzysztof Puszynski2Anniina Färkkilä3Department of Systems Biology and Engineering, Silesian University of TechnologyResearch Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of HelsinkiDepartment of Systems Biology and Engineering, Silesian University of TechnologyResearch Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of HelsinkiAbstract Clinical trials are costly and time-intensive endeavors, with a high rate of drug candidate failures. Moreover, the standard approaches often evaluate drugs under a limited number of protocols. In oncology, where multiple treatment protocols can yield divergent outcomes, addressing this issue is crucial. Here, we present a computational framework that simulates clinical trials through a combination of mathematical and statistical models. This approach offers a means to explore diverse treatment protocols efficiently and identify optimal strategies for oncological drug administration. We developed a computational framework with a stochastic mathematical model as its core, capable of simulating virtual clinical trials closely recapitulating the clinical scenarios. Testing our framework on the landmark SOLO-1 clinical trial investigating Poly-ADP-Ribose Polymerase maintenance treatment in high-grade serous ovarian cancer, we demonstrate that managing toxicity through treatment interruptions or dose reductions does not compromise treatment’s clinical benefits. Additionally, we provide evidence suggesting that further reduction of hematological toxicity could significantly improve the clinical outcomes. The value of this computational framework lies in its ability to expedite the exploration of new treatment protocols, delivering critical insights pivotal to shaping the landscape of upcoming clinical trials.https://doi.org/10.1038/s41598-024-80768-6Branching process modelVirtual clinical trialsHigh-grade serous ovarian cancerPARP inhibitors |
| spellingShingle | Emilia Kozłowska Ulla-Maija Haltia Krzysztof Puszynski Anniina Färkkilä Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology Scientific Reports Branching process model Virtual clinical trials High-grade serous ovarian cancer PARP inhibitors |
| title | Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology |
| title_full | Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology |
| title_fullStr | Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology |
| title_full_unstemmed | Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology |
| title_short | Mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology |
| title_sort | mathematical modeling framework enhances clinical trial design for maintenance treatment in oncology |
| topic | Branching process model Virtual clinical trials High-grade serous ovarian cancer PARP inhibitors |
| url | https://doi.org/10.1038/s41598-024-80768-6 |
| work_keys_str_mv | AT emiliakozłowska mathematicalmodelingframeworkenhancesclinicaltrialdesignformaintenancetreatmentinoncology AT ullamaijahaltia mathematicalmodelingframeworkenhancesclinicaltrialdesignformaintenancetreatmentinoncology AT krzysztofpuszynski mathematicalmodelingframeworkenhancesclinicaltrialdesignformaintenancetreatmentinoncology AT anniinafarkkila mathematicalmodelingframeworkenhancesclinicaltrialdesignformaintenancetreatmentinoncology |