Reduction in laboratory animal use by factorial design

Area of Science:

• * Pharmacology and Toxicology
• * Experimental Design and Biostatistics

Background:

• * Factorial experimental designs (FEDs) are widely employed in manufacturing but underutilized in optimizing animal experiments for drug discovery.
• * Traditional
• one-variable-at-a-time
• methods are less efficient than FEDs.
• * FEDs allow for the study of multiple controllable variables (e.g., treatment, sex, age, diet) and their effects on experimental outcomes.

Purpose of the Study:

• * To highlight the benefits and applications of factorial experimental designs in optimizing animal experiments within drug discovery.
• * To demonstrate how FEDs can provide more comprehensive information compared to traditional experimental approaches.
• * To showcase the successful implementation of FEDs in reducing animal usage in drug screening.

Main Methods:

• * Application of factorial experimental designs to study the effects of multiple variables simultaneously.
• * Utilizing two-level factorial designs for initial exploration of many factors.
• * Employing fractional factorial designs to manage complex experiments with numerous variables.
• * Case study of FED implementation in optimizing in vivo drug screening at AstraZeneca.

Main Results:

• * FEDs yield more information per animal by assessing multiple factors and their interactions.
• * The use of FEDs in drug discovery enables the identification of significant interactions between variables.
• * Successful application of FEDs in optimizing in vivo drug screening has led to a reduction in the number of animals used.

Conclusions:

• * Factorial experimental designs are highly effective for optimizing animal experiments in drug discovery and development.
• * FEDs offer a more efficient and informative alternative to traditional experimental methods, leading to reduced animal use.
• * The strategic implementation of FEDs can significantly enhance the efficiency and ethical considerations of preclinical research.