Although the amino acid composition of almost all food proteins is known, estimating the amino acid intake from the diet is extremely difficult because of the lack of available data. A conservative approach would be to determine the population distribution of protein intake, select the 97.5th or higher percentile of intake, assume all comes from the target protein, and estimate exposure to some specific amino acid. Any number of dietary survey methodologies could be used to conduct such a conservative approach. However, given the great variety of brands of food supplements, estimates of amino acid intakes from supplements are problematic. Firstly, few studies include supplements in their target nutrient sources because brand-level data would need to be retained and nutritional composition data would need to be recorded. Probabilistic modeling offers some solution provided some basic data are gathered. The percentage of the population regularly taking supplements and the frequency of consumption must be known. Therefore, data on the dietary supplement market would need to be known including the percent of brands containing amino acids and if possible specific amino acids together with concentrations. A probabilistic model as follows would ensue: probability of being a consumer of amino acid supplements; probability distribution function of frequency of use of supplements; probability distribution function of dose per eating occasion; market characteristics; probability distribution function for dietary amino acid intake. Using multiple iterations and perhaps bootstrapping on some elements of the model, fully worst-case model scenarios of exposure could be computed.