In silico cancer immunotherapy trials uncover the consequences of therapy-specific response patterns for clinical trial design and outcome.
Journal
Nature communications
ISSN: 2041-1723
Titre abrégé: Nat Commun
Pays: England
ID NLM: 101528555
Informations de publication
Date de publication:
24 04 2023
24 04 2023
Historique:
received:
10
09
2021
accepted:
06
04
2023
medline:
26
4
2023
pubmed:
25
4
2023
entrez:
24
04
2023
Statut:
epublish
Résumé
Late-stage cancer immunotherapy trials often lead to unusual survival curve shapes, like delayed curve separation or a plateauing curve in the treatment arm. It is critical for trial success to anticipate such effects in advance and adjust the design accordingly. Here, we use in silico cancer immunotherapy trials - simulated trials based on three different mathematical models - to assemble virtual patient cohorts undergoing late-stage immunotherapy, chemotherapy, or combination therapies. We find that all three simulation models predict the distinctive survival curve shapes commonly associated with immunotherapies. Considering four aspects of clinical trial design - sample size, endpoint, randomization rate, and interim analyses - we demonstrate how, by simulating various possible scenarios, the robustness of trial design choices can be scrutinized, and possible pitfalls can be identified in advance. We provide readily usable, web-based implementations of our three trial simulation models to facilitate their use by biomedical researchers, doctors, and trialists.
Identifiants
pubmed: 37095077
doi: 10.1038/s41467-023-37933-8
pii: 10.1038/s41467-023-37933-8
pmc: PMC10125995
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
2348Informations de copyright
© 2023. The Author(s).
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