Which is a borderline situation regarding the validity of Equation 5. The method is then more likely to estimate a lower bound of an MHC than a central value. Application of Equation 7 to published threshold data on the interaction of the AMP melittin with different erythrocyte membrane models predicts MHC values from 220.02 to 15:3mM. Notwithstanding the high ?L MICassay and the wide prediction interval, the values do overlap with the observed MHC50 range, between 0:9 and 2:5mM. The successful application of the method to BP100 and omiganan forebodes a good predictive power, in spite of all the simplifications and approximations in the model. Hopefully, along with an increasing awareness of the relevance of partition and threshold events to the activity of AMPs, more datasets will become available against which our method can be applied and validated. Finally, more than a theoretical exercise in bridging biology with physical-chemistry, the presented Folic acid methodology provides a basis for fast, cost-effective alternatives for screening libraries of peptide drug leads before actual biological testing. The predictive relationships can also be coupled with drug design algorithms, further improving the process. This work demonstrates that it is possible to use a purely physical-chemical reasoning to understand, model, and predict the mechanisms of complex biological interactions such as AMP-mediated bacterial death, with applications that, in this case, may ultimately lead to a faster, more efficient antibiotic drug development. It must be remarked that although our model performed well with omiganan and BP100 it is too simple to precisely predict the activity of all AMPs against all types of bacteria. The use of the partition constant implies the assumption of equilibrium in membrane binding; this might never be attained in practical timescales for cases where bacteria present effective barriers to free diffusion towards the membrane. Another limitation to the applicability of the model stems from the working hypothesis that peptide action depends on a critical membrane-bound concentration threshold: peptides like the apidaecins that exert their action independently of some sort of cooperativity in the membrane are not contemplated. Still, membrane disruption by either lysis or 4-(Aminomethyl)benzoic acid poration is not a requirement of the model; the activity of peptides that target intracellular components can still be modelled as long as translocation into the cytoplasm is a threshold-dependent step. Multiple disruptive thresholds are often observed with model membranes, which may complicate analysis if identification of the relevant threshold is not possible. Such is the case in Figure 2 and in one of the data sources used for predicting the MHC of melittin. Lacking further information on the relationship between these disruptive points and the in vivo activity of the peptides, we opted to combine predictions from the different thresholds into a single range.