Optimized design and analysis of preclinical intervention studies in vivo

Show full item record




Laajala , T D , Jumppanen , M , Huhtaniemi , R , Fey , V , Kaur , A , Knuuttila , M , Aho , E , Oksala , R , Westermarck , J , Makela , S , Poutanen , M & Aittokallio , T 2016 , ' Optimized design and analysis of preclinical intervention studies in vivo ' , Scientific Reports , vol. 6 , 30723 . https://doi.org/10.1038/srep30723

Title: Optimized design and analysis of preclinical intervention studies in vivo
Author: Laajala, Teemu D.; Jumppanen, Mikael; Huhtaniemi, Riikka; Fey, Vidal; Kaur, Amanpreet; Knuuttila, Matias; Aho, Eija; Oksala, Riikka; Westermarck, Jukka; Makela, Sari; Poutanen, Matti; Aittokallio, Tero
Contributor organization: Institute for Molecular Medicine Finland
Faculty of Pharmacy
Pharmaceutical Design and Discovery group
Tero Aittokallio / Principal Investigator
Date: 2016-08-02
Language: eng
Number of pages: 13
Belongs to series: Scientific Reports
ISSN: 2045-2322
DOI: https://doi.org/10.1038/srep30723
URI: http://hdl.handle.net/10138/166450
Abstract: Recent reports have called into question the reproducibility, validity and translatability of the preclinical animal studies due to limitations in their experimental design and statistical analysis. To this end, we implemented a matching-based modelling approach for optimal intervention group allocation, randomization and power calculations, which takes full account of the complex animal characteristics at baseline prior to interventions. In prostate cancer xenograft studies, the method effectively normalized the confounding baseline variability, and resulted in animal allocations which were supported by RNA-seq profiling of the individual tumours. The matching information increased the statistical power to detect true treatment effects at smaller sample sizes in two castration-resistant prostate cancer models, thereby leading to saving of both animal lives and research costs. The novel modelling approach and its open-source and web-based software implementations enable the researchers to conduct adequately-powered and fully-blinded preclinical intervention studies, with the aim to accelerate the discovery of new therapeutic interventions.
3111 Biomedicine
Peer reviewed: Yes
Usage restriction: openAccess
Self-archived version: publishedVersion

Files in this item

Total number of downloads: Loading...

Files Size Format View
Optimized_design.pdf 868.9Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record