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A multi-batch design to deliver robust estimates of efficacy and reduce animal use - a syngeneic tumour case study.
Karp, Natasha A; Wilson, Zena; Stalker, Eve; Mooney, Lorraine; Lazic, Stanley E; Zhang, Bairu; Hardaker, Elizabeth.
Afiliación
  • Karp NA; Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK. Natasha.Karp@astrazeneca.com.
  • Wilson Z; Early Oncology TDE, R&D Oncology, AstraZeneca, Alderley Park, UK.
  • Stalker E; Early Oncology TDE, R&D Oncology, AstraZeneca, Alderley Park, UK.
  • Mooney L; Precision Medicine, R&D Oncology, AstraZeneca, Cambridge, UK.
  • Lazic SE; Early Oncology TDE, R&D Oncology, AstraZeneca, Alderley Park, UK.
  • Zhang B; Preclinical Science Services, Alderley park Limited, Macclesfield, UK.
  • Hardaker E; Data Sciences & Quantitative Biology, Discovery Sciences, R&D, AstraZeneca, Cambridge, UK.
Sci Rep ; 10(1): 6178, 2020 04 10.
Article en En | MEDLINE | ID: mdl-32277094
ABSTRACT
Phenotypic plasticity, the ability of a living organism to respond to the environment, can lead to conclusions from experiments that are idiosyncratic to a particular environment. The level of environmental responsiveness can result in difficulties in reproducing studies from the same institute with the same standardised environment. Here we present a multi-batch approach to in-vivo studies to improve replicability of the results for a defined environment. These multi-batch experiments consist of small independent mini-experiments where the data are combined in an integrated data analysis to appropriately assess the treatment effect after accounting for the structure in the data. We demonstrate the method on two case studies with syngeneic tumour models which are challenging due to high variability both within and between studies. Through simulations and discussions, we explore several data analysis options and the optimum design that balances practical constraints of working with animals versus sensitivity and replicability. Through the increased confidence from the multi-batch design, we reduce the need to replicate the experiment, which can reduce the total number of animals used.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Modelos Animales de Enfermedad / Análisis de Datos / Neoplasias Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: Reino Unido

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Modelos Animales de Enfermedad / Análisis de Datos / Neoplasias Tipo de estudio: Prognostic_studies Límite: Animals Idioma: En Revista: Sci Rep Año: 2020 Tipo del documento: Article País de afiliación: Reino Unido