Short communication Biopartitioning micellar chromatography to predict mutagenicity of aromatic amines S. Torres-Cartas b , Y. Martı ´n-Biosca a , R.M. Villanueva-Cama~ nas a , S. Sagrado a , M.J. Medina-Herna ´ndez a, * a Departamento de Quı ´mica Analı ´tica, Universidad de Valencia, C/Vicente Andre ´s Estelle ´s s/n, 46100 Burjassot, Valencia, Spain b Departamento de Quı ´mica, Escuela Polite ´cnica Superior de Gandia, Ctra. Nazaret-Oliva, s/n, 46730 Grao de Gandia, Valencia, Spain Received 29 January 2007; received in revised form 26 February 2007; accepted 27 February 2007 Available online 18 March 2007 Abstract Mutagenicity is a toxicity endpoint associated with the chronic exposure to chemicals. Aromatic amines have considerable industrial and environmental importance due to their widespread use in industry and their mutagenic capacity. Biopartitioning micellar chromatography (BMC), a mode of micellar liquid chromatography that uses micellar mobile phases of Brij35 in adequate experimental conditions, has demonstrated to be useful in mimicking the drug partitioning process into biological systems. In this paper, the usefulness of BMC for predicting mutagenicity of aromatic amines is demonstrated. A multiple linear regression (MLR) model based on BMC retention data is proposed and compared with other ones reported in bibliography. The proposed model present better or similar descriptive and predictive capability. Ó 2007 Elsevier Masson SAS. All rights reserved. Keywords: Mutagenicity; Biopartitioning micellar chromatography; Aromatic amines; Quantitative retentioneactivity relationships 1. Introduction The advent of combinatorial chemistry has made possible to synthesize hundreds of new chemicals every year. The de- velopment of efficient and inexpensive technologies for testing and predicting the physical, chemical and biological properties of new compounds, which would enable the estimation of the potential dangers of old and new organic compounds, and al- low effective risk assessment, is thus of major significance. Carcinogenicity and mutagenicity are important toxicity pa- rameters associated with the chronic exposure to chemicals. They are closely related, and ca. 70% of carcinogenic com- pounds are potential mutagens [1e3]. These toxicity parame- ters are of interest both for environmental pollutants and potential therapeutic agents. While the experimental assess- ment of carcinogenicity is complex and time consuming [4], several tests allow easy detection of mutagenicity [5]. Probably the most widely used is a bacterial test, based on the Salmonella typhimurium strain, introduced by Ames et al. [1,5,6]. Mutagenicity is used for screening of substances, poten- tially hazardous to human and environmental health. The importance of this endpoint and availability of high quality databases support the postulations of predictive structureemu- tagenicity relationships. These studies are potentially suitable for investigating mechanisms of action and for estimating the toxicity of compounds lacking experimental determinations. Quantitative structureeproperty relationships (QSPR) and quantitative structureeactivity relationships (QSAR) have been used over the years to develop models to estimate/predict toxicity by relating it to chemical structure. Many different statistical models have been derived for the estimation and prediction of mutagenicity [7e9]. * Corresponding author. Tel.: þ34 96 354 4899; fax: þ34 96 354 4953. E-mail address: maria.j.medina@uv.es (M.J. Medina-Herna ´ndez). 0223-5234/$ - see front matter Ó 2007 Elsevier Masson SAS. All rights reserved. doi:10.1016/j.ejmech.2007.02.022 Available online at www.sciencedirect.com European Journal of Medicinal Chemistry 42 (2007) 1396e1402 http://www.elsevier.com/locate/ejmech