Classification of Cytochrome P450 3A4 Ligands Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@Misc{gilbert:p450,

• author = "Richard Gilbert and Kris Birchall and William Bains",
• title = "Classification of Cytochrome {P450 3A4} Ligands Using Genetic Programming",
• year = "2002",
• email = "info@amedis-pharma.com",
• keywords = "genetic algorithms, genetic programming",
• broken = "http://www.amedis-pharma.com/Docs/3A4_ligand_poster.ppt",
• abstract = "The cytochrome P450 [CYP] family is a set of haem-containing oxidoreductase enzymes which are involved in the first-pass metabolism of xenobiotic compounds such as drug molecules. CYP 3A4 is the most abundant of these enzymes in humans, and is capable of metabolising approximately 80percent of drugs to some extent. As CYP3A4 has a limited capacity, both competing substrates and inhibitors can affect the rate at which CYP3A4 metabolises drugs, and hence the amount of the compound that reaches systemic circulation. Identifying whether a compound is metabolised by CYPs in general, and CYP3A4 in particular, is important for judging its potential as a drug. We describe an approach to the computational identification of CYP3A4 ligands (substrates and inhibitors) that is based on a type of evolutionary computing called genetic programming. The method is a supervised learning system, i.e. it is guided by past examples, in this case actual measured biological data on CYP ligand status. The GP system creates predictive models by Darwinian operations of mutation, crossover and fitness selection, operating on a population of potential solutions. Parent solutions are chosen according to their ability to explain the training data. New models are generated by mutation or crossover, and may replace less-fit individuals already in the population. After sufficient iterations, the population comprises models able to explain the observations much more effectively than the initial random population. Applying this to publicly available CYP3A4 data, we show that we can predict the ligand status of a diverse set of known drugs to approximately 90percent accuracy, and to predict whether a ligand will be a substrate or an inhibitor to approximately 85percent accuracy. The GP method also identifies structural characteristics of the molecule which it is using to build the decision algorithms, and these are consistent with more exhaustive, quantum mechanical predictions of substrate status. The evolutionary nature of GPs allows generation of multiple solutions, which allow statistical validation of the results.",
• notes = "Amedis Pharmaceuticals Limited, Upton House, Baldock Street, Royston, Herts SG8 5AY, UK",

}

Genetic Programming entries for Richard J Gilbert Kris Birchall William Bains

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