Computational design of improved standardized chemotherapy protocols for grade II oligodendrogliomas

Pérez-Garcı́a, Vı́ctor M.; Ayala-Hernández, Luis E.; Belmonte-Beitia, Juan; Schucht, Philippe; Murek, Michael; Raabe, Andreas; Sepúlveda, Juan Manuel

doi:10.1101/521559

Cited by 4 publications

(1 citation statement)

References 40 publications

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“…In silico clinical studies have a wide range of applicability from pediatric infectious 35 and orphan diseases 36 to diabetes 37 , inflammatory autoimmune diseases 38 , traumatic injury 39 , psychiatric illness 40 , and cancer. In oncology, several in silico clinical trials involving chemotherapy and tyrosine kinase inhibitors have been performed 41, 42 . Moreover, with the onset of checkpoint inhibitors, in silico immunotherapy trials have gained interest, leading to trials with anti-CTLA-4-antibodies and anti-PD-(L)1 antibodies 43, 44, 45 .…”

Section: Discussionmentioning

confidence: 99%

In silicocancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Creemers

Ankan²,

Roes

et al. 2021

Preprint

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Background Late-stage cancer immunotherapy trials strive to demonstrate the clinical efficacy of novel immunotherapies, which is leading to exceptional responses and long-term survival in subsets of patients. To establish the clinical efficacy of an immunotherapy, it is critical to adjust the trial's design to the expected immunotherapy-specific response patterns. Methods In silico cancer immunotherapy trials are virtual clinical trials that simulate the kinetics and outcome of immunotherapy depending on the type and treatment schedule. We used an ordinary differential equation model to simulate (1) cellular interactions within the tumor microenvironment, (2) translates these into disease courses in patients, and (3) assemble populations of virtual patients to simulate in silico late-stage immunotherapy, chemotherapy, or combination trials. We predict trial outcomes and investigate how therapy-specific response patterns affect the probability of their success. Results In silico cancer immunotherapy trials reveal that immunotherapy-derived survival kinetics — such as delayed curve separation and plateauing curve of the treatment arm — arise naturally due to biological interactions in the tumor microenvironment. In silico clinical trials are capable of translating these biological interactions into survival kinetics. Considering four aspects of clinical trial design — sample size calculations, endpoint and randomization rate selection, and interim analysis planning — we illustrate that failing to consider such distinctive response patterns can significantly reduce the power of novel immunotherapy trials. Conclusion In silico trials have three significant implications for immuno-oncology. First, they provide an economical approach to verify the robustness of biological assumptions underlying an immunotherapy trial and help to scrutinize its design. Second, the biological basis of these trials facilitates and encourages communication between biomedical researchers, doctors, and trialists. Third, its application as an educational tool can illustrate design principles to scientists in training, contributing to improved designs and higher success rates of future immunotherapy trials.

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Section: Discussionmentioning

confidence: 99%

In silicocancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Creemers

Ankan²,

Roes

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

Virtual clinical trial based on outcome modeling with iteratively redistributed extrapolation data

Oguma

Magome

Someya

et al. 2023

Radiol Phys Technol

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Advances in Research of Adult Gliomas

Finch

Solomou

Wykes

et al. 2021

IJMS

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Diffuse gliomas are the most frequent brain tumours, representing 75% of all primary malignant brain tumours in adults. Because of their locally aggressive behaviour and the fact that they cannot be cured by current therapies, they represent one of the most devastating cancers. The present review summarises recent advances in our understanding of glioma development and progression by use of various in vitro and in vivo models, as well as more complex techniques including cultures of 3D organoids and organotypic slices. We discuss the progress that has been made in understanding glioma heterogeneity, alteration in gene expression and DNA methylation, as well as advances in various in silico models. Lastly current treatment options and future clinical trials, which aim to improve early diagnosis and disease monitoring, are also discussed.

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Computational design of improved standardized chemotherapy protocols for grade II oligodendrogliomas

Cited by 4 publications

References 40 publications

In silicocancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

In silicocancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome

Virtual clinical trial based on outcome modeling with iteratively redistributed extrapolation data

Advances in Research of Adult Gliomas

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