Data-Mining Methods as Useful Tools for Predicting Individual Drug Response: Application to <i>CYP2D6</i> Data

Abstract

Objectives: Selecting a maximally informative subset of polymorphisms to predict a clinical outcome, such as drug response, requires appropriate search methods due to the increased dimensionality associated with looking at multiple genotypes. In this study, we investigated the ability of several pattern recognition methods to identify the most informative markers in the CYP2D6 gene for the prediction of CYP2D6 metabolizer status. Methods: Four data-mining tools were explored: decision trees, random forests, artificial neural networks, and the multifactor dimensionality reduction (MDR) method. Marker selection was performed separately in eight population samples of different ethnic origin to evaluate to what extent the most informative markers differ across ethnic groups. Results: Our results show that the number of polymorphisms required to predict CYP2D6 metabolic phenotype with a high accuracy can be dramatically reduced owing to the strong haplotype block structure observed at CYP2D6. MDR and neural networks provided nearly identical results and performed the best. Conclusion: Data-mining methods, such as MDR and neural networks, appear as promising tools to improve the efficiency of genotyping tests in pharmacogenetics with the ultimate goal of pre-screening patients for individual therapy selection with minimum genotyping effort.

Prediction and visualization of CYP2D6 genotype-based phenotype using clustering algorithms

“…[16,17] To predict CYP2D6 phenotype, a combination of genetic variations were selected as markers and four data mining techniques were employed to predict IM and PM groups in Hong Kong-Chinese data. [18] Two pieces of genetic information, 188C>T and deletion genes, were sufficient to predict IM and PM groups that had clinical significance. The 188C>T mutation was not previously identified in a study of 51 Korean subjects using direct DNA sequencing, [11] suggesting that different ethnic groups within Asian populations are genetically diverse.…”

Estimation Strategies for Reacting to the Identification of an Association Between the Genome and Adverse Drug Reactions

“…That said, there are many studies that have used machine learning approaches which are able to capture interaction effects. For example, Sabbagh (2006) compared the performance of a number of machine learning tools when applied to the identification of genetic markers within an already identified gene. That is a situation in which interaction between multiple markers is plausible and should be taken into account.…”

Desenvolvimento de metodologia para a determinação dos genótipos principais dos genes CYP2D6, CYP2C19 e CYP2C9: aplicação na farmacogenética

“…Entre os genes que codificam enzimas metabolizadoras de medicamentos, CYP2D6 (citocromo P450, família 2, subfamília D, polipeptídeo 6) é um dos melhores caracterizados (Sabbagh et al, 2006;Naveen et al, 2006a). Este gene codifica a enzima CYP2D6 que é responsável pelo metabolismo de aproximadamente 25% dos medicamentos mais utilizados que incluem os betabloqueadores, antiarrítmicos, opióides e um grande número de antidepressivos (TCAs, SSRIs), antipsicóticos e fármacos utilizados na quimioterapia do câncer (Ingelman-Sundberg, 2005;Sabbagh et al, 2006;Goetz et al, 2008).…”

“…Este gene codifica a enzima CYP2D6 que é responsável pelo metabolismo de aproximadamente 25% dos medicamentos mais utilizados que incluem os betabloqueadores, antiarrítmicos, opióides e um grande número de antidepressivos (TCAs, SSRIs), antipsicóticos e fármacos utilizados na quimioterapia do câncer (Ingelman-Sundberg, 2005;Sabbagh et al, 2006;Goetz et al, 2008).…”

“…As freqüências alélicas do gene CYP2D6 mostram-se bastante diferentes dentro de uma mesma população e entre diferentes grupos étnicos (Sistonen et al, 2007) (Tabela 1). Essas variações alélicas são resultantes de alterações pontuais (SNPs), deleções ou inserções, deleção ou multiplicação do gene inteiro (Sabbagh et al, 2006). O efeito causado por tais polimorfismos depende do medicamento e da variante alélica envolvida.…”