Use of Factorial Designs to Optimize Animal Experiments and Reduce Animal Use

• Published in: ILAR journal Vol. 43; no. 4; pp. 223 - 232
• Main Authors: Shaw, Robert, Festing, Michael F. W., Peers, Ian, Furlong, Larry
• Format: Journal Article
• Language: English
• Published: England Institute for Laboratory Animal Research 2002
• Subjects: Allyl Compounds - therapeutic use; Animal Testing Alternatives; Animals; Animals, Laboratory; Chloramphenicol - toxicity; Data Interpretation, Statistical; Dose-Response Relationship, Drug; Factor Analysis, Statistical; Female; Leukocyte Count; Leukocytes - drug effects; Leukocytes - pathology; Male; Mice; Models, Animal; Models, Statistical; Neoplasms, Experimental - drug therapy; Neoplasms, Experimental - pathology; Research Design - standards; Sample Size; Sulfides - therapeutic use; models; animal testing alternatives; factorial analysis; research design; case report; animal; animal use alternatives; statistical methods
• ISSN: 1084-2020; 1930-6180
• DOI: 10.1093/ilar.43.4.223

Optimization of experiments, such as those used in drug discovery, can lead to useful savings of scientific resources. Factors such as sex, strain, and age of the animals and protocol-specific factors such as timing and methods of administering treatments can have an important influence on the response of animals to experimental treatments. Factorial experimental designs can be used to explore which factors and what levels of these factors will maximize the difference between a vehicle control and a known positive control treatment. This information can then be used to design more efficient experiments, either by reducing the numbers of animals used or by increasing the sensitivity so that smaller biological effects can be detected. A factorial experimental design approach is more effective and efficient than the older approach of varying one factor at a time. Two examples of real factorial experiments reveal how using this approach can potentially lead to a reduction in animal use and savings in financial and scientific resources without loss of scientific validity.