Predicting Skin Permeability from Complex Chemical Mixtures: Dependency of Quantitative Structure Permeation Relationships on Biology of Skin Model Used

Predicting the degree of dermal absorption of topically exposed chemicals is an important issue in both environmental and occupational risk assessment. Most risk assessment approaches are based on data obtained from dosing chemicals neat or in simple aqueous vehicles, yet most exposures are to more complex mixtures. Numerous studies have demonstrated that vehicles may have significant effects on modulating chemical absorption (Bliss, 1939; Cross et al., 2001; Idson, 1983; Qiao et al., 1996; Rosado et al., 2003; Sloan et al., 1986). In fact, this is the prime strategy behind developing pharmaceutical formulations (Augustijns and Brewster, 2007). Today, there is little doubt that vehicle and formulation components modulate dermal absorption or transdermal delivery of topically applied drugs and solutes. Despite the intellectual certainty of this effect, most attempts in risk assessment modeling to quantitate dermal absorption using quantitative structure permeability relationships (QSPR) are based on defining linear free-energy relationships (LFER) using data obtained from single compound exposure in simple aqueous solutions (Abraham and Martins, 2004; EPA, 2004; Geinoz et al., 2004; Hostynek and Magee, 1997; Neuman, 2008; Potts and Guy, 1992). Because the vast majority of occupational and environmental exposure to chemicals is in the form of mixtures, understanding the mechanism operative in modulating chemical penetration into and absorption through the skin is crucial to predicting toxicological effects. Limited work has been done to broadly incorporate the effect of mixture interactions seen in more complex but realistic formulations (Baynes et al., 2008; Gregoire et al., 2009). What is required for realistic risk assessments on topical mixtures is a general approach that at a minimum indicates whether a specific mixture would be expected to increase or decrease absorption through the skin. A quantitative indication of the extent of modulation would be optimal. We have previously reported on a straightforward approach to incorporate physiochemical properties of a mixture into existing QSPR models. These studies were focused on an initial set of 12 penetrants in 24 mixtures for 288 treatment combinations (complete factorial experimental design) based on data collected using in vitro porcine skin flow-through (PSFT) diffusion cells and the five descriptor linear free-energy QSPR relationship of Abraham and Martins (2004) (Riviere and Brooks, 2005). This approach was subsequently expanded using data from the more biologically sophisticated isolated perfused porcine skin flap (IPPSF) model (10 penetrants in five mixtures for 50 treatment combinations). The IPPSF is an ex vivo biologically intact perfused tissue preparation that has previously been shown to correlate to in vivo human dermal absorption (Riviere, 2006; Riviere et al., 1986, 1992; Riviere and Monteiro-Riviere, 1991; Wester et al., 1998). The use of this model assures that chemical mixture interactions involving the stratum corneum as well as viable epidermis and dermis would be detected because such interactions which would occur in vivo could confound solvatochromatic interactions detected using nonbiological systems, necessitating that such an approach be used if the goal is to reliably predict in vivo effects. The approach's applicability to other QSPR models was assessed by also applying it to the QSPR models of Hostynek and Magee (1997) as well as Potts and Guy (1992) (Riviere and Brooks, 2007). The present study significantly expands on this analysis by increasing the number of penetrants studied in the in vitro porcine skin diffusion cells to 16 for a total of 384 treatment combinations and in the IPPSF to 20 penetrants dosed in a total of 119 treatment combinations. These larger data sets expand the chemical inference space to which such analyses can be applied but more importantly allow for comparison between QSPR model structures across experimental systems and begin to offer validation of the concept that such an approach is applicable to modifying the dermal risk assessment paradigm now rooted in simple single chemical aqueous exposures. The goal of this analysis was to confirm that this approach is valid across a more diverse group of chemicals and determines if mixture-based QSPR models can be extrapolated across in vitro model systems of different biological complexity.