Consuming step associated to the use of model membranes. This model does not explicitly take into account possible interactions of the peptide with other system components besides the cell membrane. However, for such interactions to influence the bound concentrations�Cnamely by significantly reducing the unbound amount of peptide�Cthey would have to be extremely strong or the interacting components would have to be in a very high concentration. One other cellular constituent present in enough quantity to potentially sequester a significant amount of peptide is the anionic Gram-positive peptidoglycan wall. Even so, this structure has at most only 20 times the volume of the membrane and, despite not being the subject of many studies, a proportionally lower affinity towards it was 3,4,5-Trimethoxyphenylacetic acid reported for the peptide omiganan, meaning that the presence of peptidoglycan is roughly equivalent to having a second membrane for the peptide to interact with. This is well within the allowable error margin discussed above and it also means that the presence of an outer membrane in Gram-negative bacteria will not significantly influence the binding model. Likewise, bacterial DNA and RNA molecules, being markedly anionic, could bind a significant portion of the peptide and render the above conclusions invalid. However, while cellular components seem to be unable to prevent high peptide accumulation in the membrane, the same might not be true for bulk phase constituents, which are often present in milimolar concentrations: one can expect high ionic strengths to reduce the degree of peptide interaction with the membrane by neutralizing the effective charge of both the peptide and the membrane surface, especially if the involved counterions are not easily displaced. This effect should be compensated for by using physiological ionic strengths when measuring partition constants. On the other hand, this proportion might actually better approximate the charge density of the Gram-negative outer membrane. See the Supporting Information for an analysis of the possible impact of using this model system on the conclusions of this work. The predictive model was tested using Equation 6 with the published parameters and activities of the peptides omiganan and BP100. Good agreement between predicted and Riociguat (BAY 63-2521) observed activities was obtained for both, as summarized in Table 1. Equation 7 was then tested with published threshold data for the same peptides, also with good approximations of the actual MICs. This simple approach was further tested using threshold points of BP100 interaction with multilamellar vesicles, determined from the optical density of the system. This prediction is in good agreement with that from Figure 1 and the observed MICs, the method being indeed robust to the use of multilamellar vesicles. Equations 6 and 7 may also be used to estimate other relevant limits, such as the minimum hemolytic concentration of a peptide.