Russell and Burch first described the ‘3Rs’ concept in 1959. It acknowledges that animals are a valuable resource through which great discoveries can be made, but it is up to you to use them ethically and judiciously. The ultimate benefit is that people and animals will be able to live longer, happier, healthier lives!
So how do you make sure that your sample sizes are not too little (which results in insufficient and incomplete data; having to repeat experiments; wasteful), not too large (unnecessary and also wasteful), but just right? Here’s a brief look at how to use the minimum amount of animal resources whilst still collecting the maximum amount of data.
Step 1: Reducing Sample Size Numbers per Experiment
There are two common ways to select the right sample size so that you are able to see significant differences between treatment groups:
1. Approximating with the Resource Equation
This method is useful when you want to test a drug that hasn’t been used before, need preliminary data, and want to explore a variety of dose groups.
E = Total number of animals – Total number of groups
Where E represents degrees of freedom of analysis of variance (ANOVA). The target for E is between 10 and 20; so, adjust the total number of animals or total number of groups to land in this range if you seek significant results.
2. A Power Analysis Best Determines Sample Size
Most studies rely on a power analysis to estimate the sample size needed to detect the effect of interest. There is quite a bit of background information that goes into this to make it more substantial than the resource equation. Power analysis takes into account the typical items like effect size, standard deviation, and type 1 error (or p-value). Your power will be approximated with the percentage of an expected effect, whether you choose one- two-tailed directions, and the choice of statistical test used to process the data (among a few other items). Lastly, the calculation also takes into account the correction for attrition, or how many animals are expected to “exit” a study due to death or other factors.
Because this analysis draws from a lot of statistical analysis to determine the “just right” sample size, it’s worth a chat with a bioinformatician, statistician, or an IACUC scientist when beginning to plan that next experiment.
3. Efficient Colony Management
Now that you know the numbers, it is time to acquire your cohorts. For occasional studies, it makes more sense to purchase from a vendor as needed. In other cases, resource-sharing with a colleague who has the strain or line you want to work with is the way to go.
For larger studies and rare models that may be difficult to acquire or produce, it’s invaluable to establish an in-house breeding program. Then, when you have one or two scientists dedicated to all things animal husbandry, the breeding schedules are set on time and maintained, the right number of animals are produced and delivered to the right people for experimentation. That way only the models in demand are kept in stock, helping to minimize the total numbers over the life of the colony.
4. Up-and-Down Method for ED50s
It takes no more than six animals to determine the ED50, or the effective dose that elicits a response in half of a sample population. The up-and-down method begins with a dose that is believed to show an effect. If it does, step the dose down one log value and treat again. If it doesn’t show effect, increase the dose by one log value and repeat up/down based on subsequent tests. Then reference tabulated data of effect versus no effect to calculate your final ED50 (Dixon, 1965).
5. Multiple Experiments, a Single Sample Set
One other great way to keep usage numbers low is to use the same sample set across multiple experiments. At the end, if a variety of samples are not collected for storage and analysis, consider donating animals for handling and technique practice for the next group of incoming scientists.
Step 2: Replacing Your Models
In a lab that often relies on mice and rats, for example, it may seem convenient to use the same model just because of its availability. But is that the best choice? Depending on your goals there may be a better starting point to delay using these models, or by replacing them altogether.
1. In Vitro Instead of In Vivo
Cell cultures grow fast and are in plentiful supply (or can be after establishing a primary culture), which makes new experimentation fast too. Begin with this model to collect preliminary data, hunt for gene expression, and even pilot test drug treatments and transfections in specific cell types to see what works and what doesn’t. When your technique is fully validated, then move into the animal model.
2. Sample and Tissue Banks
While some projects may require the use of naive or untreated animals for fresh samples, other projects only need a specific genetic sample. Turn to sample banks – and your colleagues’ freezers – to inquire about archived tissue slices, organs, DNA, RNA, protein, and more for use in your assays.
3. C. elegans, Fruit Flies, and Zebrafish
Otherwise, consider taking a step or two down on the phylogenetic tree to select an organism that will provide the same data, but in a less complex system. When discovery unlocks the genes and gene networks, validate in a higher organism.
4. Computer Modeling
And while we may still be some steps away from replacing living animal models with whole biological systems virtualization, there are databases waiting with ready-to-use information. Take your pick of behavioral, strain or line, histological, genotype, gene expression, or RNA-seq data and dig in! Where there are massive data, resource atlases are not far behind.
Step 3: Refining Your Experiments
Regularly evaluating your lab’s techniques will help identify ways to do things better to reduce and eliminate the chances that an animal may experience discomfort pain or even distress. Refinement is also the place to incorporate present-day technologies to increase efficiency, adopt less-invasive or non-invasive methods of measurement and add automation.
Do remember that the care and well-being of your animals are more important than the data to be collected! If your lab does utilize them, how do you embrace the 3Rs?
- National Research Council (US) Committee for the Update of the Guide for the Care and Use of Laboratory Animals. Guide for the Care and Use of Laboratory Animals. 8th edition. Washington (DC): National Academies Press (US); 2011. Available from: https://www.ncbi.nlm.nih.gov/books/NBK54050/ doi: 10.17226/12910
- Charan J, Kantharia ND. How to calculate sample size in animal studies? Journal of Pharmacology & Pharmacotherapeutics. 2013;4(4):303-306.
- Dixon, W. J. “The Up-and-Down Method for Small Samples.” Journal of the American Statistical Association, vol. 60, no. 312, 1965, pp. 967–978. jstor.org/stable/2283398.
- Lichtman, A. H. “The up-and-down method substantially reduces the number of animals required to determine antinociceptive ED50 values.” Journal of Pharmacological and Toxicological Methods, vol. 40, Issue 2, 1998, pp. 81-8.