Designing and optimising sunscreens from the desk

Developing successful sunscreens, in particular sunscreens with high SPF numbers is not an easy task. A modern sunscreen should be of highest protection performance and should deliver an acceptable sensory experience in order to appeal to consumers. It should be affordable and, last but not least, most compatible with our ecosystem. Formulating a sunscreen means therefore making the best out of those sometimes contradicting parameters. On top, the design process must be quick and inexpensive, meaning it should not consume too much time and laboratory work on trial and error and should in particular not require too many Sun Protection Factor (SPF) in vivoclaim iterations. An in silicopredictive tool can become very handy here. 

Basics for calculation of sunscreen performance values

Calculating performance parameters like SPF for a given sunscreen composition seems at first glance easy if the absorption spectra are known utilising Lambert-Beers Law and combining this with the concentration applied. However, sunscreen films on human skin are not regular and show quite a variation in film thicknesses. A first model had been developed by O’Neill1 to explain and to take care of the marked discrepancies between calculated values implying a regular constant film thickness and a more realistic rough film model. The model has later been refined to create an SPF-in-vivo-calibrated calculation tool for sunscreen performance prediction.2, 3 Basically the transmission spectrum is modified by a mathematical fitting function that ideally contains the effects of the rough film distribution and modulates the spectral performance accordingly. Once this function is established by calibration with in vivoSPF data, it can be applied to many other compositions and thus enable formulators to estimate the in vivoSPF by calculation based on UV filter concentrations.