Currently in Europe, sun protection products must follow many recommendations and although they are not law, compliance is expected from the cosmetic industry by regulators, consumers and the industry itself. For example, the European Commission and Colipa recommend that products provide both UVB and UVA protection, with an SPF ratio of at least one-third. SPF claims are organized into four categories: low SPFs of 6 and 10; medium SPFs of 15, 20 and 25; high SPFs of 30 and 50; and very high SPFs of 50+.1, 2
In relation, in the United States in 2007, the US Food and Drug Administration (FDA) proposed an amendment to its Final Monograph relating to sun protection.3 The proposed rules are different than Europe’s for UVA but are similar in terms of SPF. The FDA has proposed: replacing its current category descriptors of minimal and moderate respectively with the terms low, meaning SPF 2 to < 15, and medium, meaning SPF 15 to < 30; and increasing the labeled SPF value to 50 for SPF 30–50, and 50+ for SPF 50+.
SPF is assessed based on the biological marker erythema, which is caused by a certain portion of UV radiation and is a generally accepted benchmark by which consumers choose sunscreen products. So while companies should test sunscreen products throughout the development process to ensure the desired SPF level is maintained, many also measure SPF in vivo during the development phase since the final SPF claimed is obtained in vivo. Oftentimes, in order to cut the costs of in vivo testing, companies reduce the number of volunteers from 10 to 5.
The purpose of this article is to provide product developers with a guide for interpreting in vivo SPF results. Values including the mean, standard deviation and confidence intervals are related to probability concepts that are often misunderstood or misinterpreted, which can lead to serious consequences since the screening value of the in vivo SPF has a direct effect on the product’s further development. Product developers should thus consider whether two SPF results being compared are similar.
All of these parameters will be explained and discussed here. From the SPF mean and standard deviation, the authors will show how to calculate the confidence percentage if a result obtained is different from the claimed SPF mean. Step by step calculation will be given in order to compare two means, and the impact of a decrease from 10 to 5 volunteers on the results will be discussed. Tools for decision support also will be communicated.