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Related Concept Videos

Pharmacogenetics of Phase I Enzymes: Cytochrome P450 Isozymes01:28

Pharmacogenetics of Phase I Enzymes: Cytochrome P450 Isozymes

Cytochrome P450 (CYP450) enzymes are a superfamily of heme-containing monooxygenases that play a pivotal role in Phase I drug metabolism by catalyzing oxidation and reduction reactions.These enzymes transform lipophilic xenobiotics into more hydrophilic metabolites, facilitating subsequent Phase II conjugation and eventual excretion. The CYP450 family is classified into families (e.g., CYP1–CYP3) and subfamilies (e.g., CYP2A, CYP2C), based on amino acid sequence homology.CYP450 isoenzymes,...
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The pharmacogenetics of drug transporters is increasingly recognized as a critical factor influencing interindividual variability in drug absorption, distribution, and elimination. These membrane-bound proteins regulate drugs' movement across cellular barriers by actively pumping them out (efflux) or facilitating their uptake (influx). Among the major transporter families, ATP-binding cassette (ABC) and solute carrier (SLC) transporters play particularly prominent roles. Genetic polymorphisms...
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Genetic polymorphism in drug metabolism is crucial to the inter-individual variability observed in drug responses. Drug metabolism primarily involves the chemical modification of drugs and other xenobiotics to enhance their elimination by increasing their polarity. Two main classes of enzymes mediate this biotransformation process: Phase I enzymes, primarily cytochrome P450s, catalyze oxidation and reduction reactions, while other enzymes, such as esterases, mediate hydrolysis, and Phase II...
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The human genome is over 99.9% identical between individuals, yet genetic differences exist at millions of bases. The human genome contains approximately 3 million variant positions per individual, many of which are heterozygous, contributing to genetic diversity and individual traits. Genetic variations include single-nucleotide polymorphisms (SNPs), insertions, deletions, and copy number variations (CNVs).SNPs, the most common variation, involve single-base changes in DNA. These can be...
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Organisms are capable of detecting and fixing nucleotide mismatches that occur during DNA replication. This sophisticated process requires identifying the new strand and replacing the erroneous bases with correct nucleotides. Mismatch repair is coordinated by many proteins in both prokaryotes and eukaryotes.
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Mutation probability of cytochrome P450 based on a genetic algorithm and support vector machine.

Yu Yao1, Tao Zhang, Yi Xiong

  • 1State Key Laboratory of Microbial Metabolism, Luc Montagnier Biomedical Research Institute, College of Life Science and Biotechnology, Shanghai Jiaotong University, Shanghai, China.

Biotechnology Journal
|July 2, 2011
PubMed
Summary

A new GA-SVM program optimizes features and parameters for mutation prediction in human cytochrome P450s. This approach improves accuracy for non-synonymous single nucleotide polymorphisms (nsSNPs) impacting drug metabolism.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Support Vector Machines (SVM) are effective for mutation prediction.
  • Feature selection and parameter optimization significantly impact SVM performance.
  • Human cytochrome P450s (CYP450s) are crucial for drug metabolism.

Purpose of the Study:

  • To develop a GA-SVM program for simultaneous optimization of features and parameters in mutation prediction.
  • To apply the GA-SVM program to predict the impact of non-synonymous single nucleotide polymorphisms (nsSNPs) in human CYP450s.
  • To enhance the accuracy and efficiency of mutation prediction models.

Main Methods:

  • Developed a Genetic Algorithm-Support Vector Machine (GA-SVM) program.
  • Applied GA-SVM to optimize feature selection and parameter settings for SVM classification.
  • Focused on predicting the effects of nsSNPs in human CYP450 protein sequences.

Main Results:

  • The GA-SVM program achieved simultaneous optimization of features and parameters.
  • Reduced feature usage and improved overall prediction accuracy.
  • The final predictive model demonstrated a prediction accuracy of 61% and cross-validation accuracy of 73%.

Conclusions:

  • The GA-SVM program is a powerful tool for optimizing mutation predictive models.
  • This approach effectively improves the prediction of nsSNPs in human CYP450s.
  • The optimized models contribute to a better understanding of drug metabolism variations.