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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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Genetic variations significantly influence drug response through pharmacokinetics, receptor interactions, and biologic milieu modifications. Pharmacokinetic alterations impact drug metabolism and clearance, affecting efficacy and toxicity. Variants in drug-metabolizing enzymes, such as CYP2C9 and CYP2C19, alter drug activation and elimination. For example, CYP2C9 loss-of-function variants require lower warfarin doses to prevent excessive bleeding, while CYP2C19 variants reduce clopidogrel...
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Artificial neural network-based pharmacogenomic algorithm for warfarin dose optimization.

Addepalli Pavani1, Shaik Mohammad Naushad2, Rajasekar Manoj Kumar2

  • 1Department of Clinical Pharmacology & Therapeutics, Nizam's Institute of Medical Sciences, Hyderabad 500082, India.

Pharmacogenomics
|December 16, 2015
PubMed
Summary

A new artificial neural network (ANN) algorithm precisely predicts warfarin dosage, improving patient safety. This pharmacogenomic approach enhances warfarin dosing predictability and reduces adverse drug reactions.

Keywords:
CYP2C9*2CYP2C9*3CYP2C9*8artificial neural networkswarfarin/7-OH warfarin ratio

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Area of Science:

  • Pharmacogenomics
  • Computational Biology
  • Clinical Pharmacology

Background:

  • Warfarin dosing requires careful management due to its narrow therapeutic index.
  • Existing dosing algorithms have limitations in predicting optimal warfarin doses.
  • Pharmacogenetic factors significantly influence warfarin metabolism and response.

Purpose of the Study:

  • To develop a precise pharmacogenomic algorithm for predicting safe and effective warfarin dosage.
  • To enhance the accuracy of warfarin dose prediction using artificial neural networks (ANN).

Main Methods:

  • An artificial neural network (ANN) algorithm was developed using demographic data (age, gender, BMI), clinical factors (vitamin K, thyroid status), and ten genetic variables.
  • The ANN model's output was the therapeutic warfarin dose.
  • A hyperbolic tangent function was employed to construct the ANN architecture.

Main Results:

  • The ANN model explained 93.5% of the variability in warfarin dosing.
  • Accurate warfarin dose prediction was achieved in 74.5% of patients with international normalized ratio (INR) < 2.0 and 83.3% with INR > 3.5.
  • The algorithm significantly reduced out-of-range INRs, adverse drug reactions, and the time to reach therapeutic INR.

Conclusions:

  • Application of ANN for warfarin dosing significantly improves predictability.
  • The developed pharmacogenomic algorithm provides a safer and more effective approach to warfarin dosing.
  • The algorithm demonstrated applicability across different thyroid statuses and elucidated the impact of CYP2C9 variants on warfarin sensitivity.