Multivariate chemical mapping of antibiotics and identification of structurally representative substances

Antibiotics used in human and veterinary medicine have been found in samples from diverse environments in many parts of the world. To assess the environmental risks associated with them, data regarding their toxicity, occurrence, and fate are needed, but gathering such data is time-consuming and expensive. An efficient approach to address these difficulties would be to select a small subset of antibiotics with a wide variation in chemical characteristics, perform experimental tests on this subset, and then extrapolate the results to larger numbers of antibiotics, including the most potentially hazardous compounds. To assess the potential utility of such an approach, a set of 92 antibiotics for human use was studied and their structural properties were described with 24 chemical descriptors that included information on their steric, lipophilic, and electronic properties. Principal component analysis in combination with statistical experimental design was used to map the chemical diversity of the antibiotics and to select a small subset, a "training set", of 20 antibiotics. The chemical representativity of the training set was assessed in a quantitative structure-activity model established to predict ultimate biodegradation. The selected antibiotics showed to cover the chemical variation of the studied antibiotics and are suggested for use in future testing programs to assess antibiotics' fate and effects in the environment.