Testing Non-Additivity of Biological Activity in a Combinatorial Library

Title: Testing Non-Additivity of Biological Activity in a Combinatorial Library

Volume: 5 Issue: 2

Author(s): Nanxiang Ge, Sung Jin Cho, Mark Hermsmeier, Michael Poss and C. Frank Shen

Affiliation:

Keywords: Combinatorial library

Abstract: Combinatorial chemistry offers new opportunities to generate and analyze QSAR data. Traditional QSAR attempts to correlate activity with structure. With combinatorial chemistry, it is possible to correlate activity directly with the reagents used in a combinatorial library. If one can determine which reagents lead to the compounds of highest activities, it may then be possible to predict active compounds in virtual libraries of 10 6 to 10 10 compounds. This would greatly facilitate library design and provide confidence that the best compounds are being considered for synthesis. An important question is whether the activity of a product molecule can be considered as a sum of its components. This is referred to as additivity between reagents. If there is non-additivity, it is necessary to identify and include the non-additive terms in the model in order to improve QSAR models. Presented here are methods for developing QSAR models relating compound activity to reagents and a method for detecting the second effects of side-chain non-additivity. If the reagents in a library are shown to be additive in their contribution to activity, simple QSAR based on additive models can be applied confidently to reagents. Testing non-additivity can also guide the synthesis of the library. If the contributions are shown to be additive then the strategy for library synthesis may be shifted to include many reagents of a given type but not to make all combinations. The result is more efficient use of resources. In the analysis of percent inhibition data of a combinatorial library an additive model using reagents as descriptors yields a R2 of 0.43. Application of this method is probably appropriate for HTS single point data while methods employing topological or pharmacophore based descriptors would be necessary to adequately model IC50 data.

Cite this article as:

Ge Nanxiang, Cho Jin Sung, Hermsmeier Mark, Poss Michael and Shen Frank C., Testing Non-Additivity of Biological Activity in a Combinatorial Library, Combinatorial Chemistry & High Throughput Screening 2002; 5 (2) . https://dx.doi.org/10.2174/1386207024607347