Investigation of structural and electronic biases in mutagenic compounds

The use of high throughput screening as the main means to discover lead compounds continues to require the design of libraries for screening. In this work some insights that may permit building chemical libraries that are biased toward compounds that are less likely to be mutagenic are shown. Toward this goal, the study of the distribution of substructural and electronic properties found in compounds that have been reported to be mutagens and nonmutagens is discussed. Mutagenic data retrieved for each of four Salmonella strains, TA-98, TA-100, TA-1535, and TA-1537, were compiled and divided depending on whether the compounds underwent metabolic activation. For each of the eight compound databases, an exhaustive enumeration of all molecular substructures within it was carried out. Comparison of the fragment distributions in sets of molecules that were Ames positive to a set of compounds found to be negative and vice versa allowed the deduction of moieties that are statistically overrepresented in active versus inactive or inactive versus active compounds for each strain and metabolic state. Our results show that compounds containing nitro functionalities are overrepresented in the Ames test in all strains whether the compounds had been metabolically activated or not. However, presence of a nitro functionality does not ensure that the compounds being Ames positive. Therefore, we computed a series of steric and electronic properties for 270 nitroaromatic compounds using semiempirical techniques. The electron affinity of the compounds offers a statistically significant discrimination between positive and negative compounds. © 2002 Wiley Periodicals, Inc. Int J Quantum Chem 88: 107–117, 2002

[1]  H O Villar,et al.  Toward the design of chemical libraries for mass screening biased against mutagenic compounds. , 2001, Journal of medicinal chemistry.

[2]  Hugo O. Villar,et al.  Quantum mechanical parametrization of a conformationally dependent hydrophobic index , 1992 .

[3]  Lahana,et al.  How many leads from HTS? , 1999, Drug discovery today.

[4]  Tudor I. Oprea,et al.  Property distribution of drug-related chemical databases* , 2000, J. Comput. Aided Mol. Des..

[5]  E Benfenati,et al.  Computational predictive programs (expert systems) in toxicology. , 1997, Toxicology.

[6]  J. Stewart Optimization of parameters for semiempirical methods I. Method , 1989 .

[7]  Ajay,et al.  Can we learn to distinguish between "drug-like" and "nondrug-like" molecules? , 1998, Journal of medicinal chemistry.

[8]  G H Loew,et al.  Computer-assisted mechanistic structure-activity studies: application to diverse classes of chemical carcinogens. , 1985, Environmental health perspectives.

[9]  T Ohta,et al.  Recommendations for the performance of bacterial mutation assays. , 1994, Mutation research.

[10]  Roger E. Critchlow,et al.  Beyond mere diversity: tailoring combinatorial libraries for drug discovery. , 1999, Journal of combinatorial chemistry.

[11]  D. Sanderson,et al.  Computer Prediction of Possible Toxic Action from Chemical Structure; The DEREK System , 1991, Human & experimental toxicology.

[12]  R. Mannhold,et al.  6-Substituted benzopyrans as potassium channel activators: synthesis, vasodilator properties, and multivariate analysis. , 1999, Journal of medicinal chemistry.

[13]  Evans,et al.  Approaches to higher-throughput pharmacokinetics (HTPK) in drug discovery. , 2000, Drug discovery today.

[14]  G. Klopman Artificial intelligence approach to structure-activity studies. Computer automated structure evaluation of biological activity of organic molecules , 1985 .

[15]  H. Kubinyi,et al.  A scoring scheme for discriminating between drugs and nondrugs. , 1998, Journal of medicinal chemistry.

[16]  J. Ferrell,et al.  Quantum chemical studies of polycyclic aromatic hydrocarbons and their metabolites: correlations to carcinogenicity. , 1979, Chemico-biological interactions.

[17]  T. Nohmi,et al.  Recent advances in the construction of bacterial genotoxicity assays. , 1997, Mutation research.

[18]  G. Loew,et al.  Metabolism and relative carcinogenic potency of chloroethylenes: a quantum chemical structure-activity study. , 1983, Chemico-biological interactions.

[19]  Hugo O. Villar,et al.  Exhaustive enumeration of molecular substructures , 1997 .

[20]  G. Bemis,et al.  The properties of known drugs. 1. Molecular frameworks. , 1996, Journal of medicinal chemistry.

[21]  A. Beresford,et al.  ADME/PK as part of a rational approach to drug discovery. , 2000, Drug discovery today.

[22]  K. Enslein,et al.  Use of SAR in computer-assited prediction of carcinogenicity and mutagenicity of chemicals by the TOPKAT program , 1994 .