title: Artificial neural network-based pharmacogenomic algorithm for warfarin dose optimization.
abstract: OBJECTIVE
  To develop more precise pharmacogenomic algorithm for prediction of safe and effective dose of warfarin.
  
  
  METHODS
  An artificial neural network (ANN) algorithm was developed by using age, gender, BMI, plasma vitamin K levels, thyroid status and ten genetic variables as the inputs and therapeutic warfarin dose as the output. Hyperbolic tangent function was used to build an ANN architecture.
  
  
  RESULTS
  This model explained 93.5% variability in warfarin dosing and predicted warfarin dose accurately in 74.5% patients whose international normalized ratio (INR) was less than 2.0 and in 83.3% patients whose INR was more than 3.5. This algorithm reduced the out-of-range INRs (odds ratio [OR]: 0.49; 95% CI: 0.30-0.79; p = 0.003), the rate of adverse drug reactions (OR: 0.00; 95% CI: 0.00-1.21; p = 0.06) and time to reach first therapeutic INR (OR: 6.73; 95% CI: 2.17-22.31; p < 0.0001). This algorithm was found to be applicable in both euthyroid and hypothyroid status. S-warfarin/7-hydroxywarfarin ratio was found to increase in subjects with CYP2C9*2 and CYP2C9*3 justifying the warfarin sensitivity attributed to these variants.
  
  
  CONCLUSIONS
  An application of ANN for warfarin dosing improves predictability and provides safe and effective dosing.
authors.affiliation: Department of Clinical Pharmacology & Therapeutics, Nizam's Institute of Medical Sciences, Hyderabad 500082, India.
authors.forename: Addepalli
authors.initials: A
authors.lastname: Pavani
authors.affiliation: School of Chemical & Biotechnology, SASTRA University, Thanjavur 613401, India.
authors.forename: Shaik Mohammad
authors.initials: SM
authors.lastname: Naushad
authors.affiliation: School of Chemical & Biotechnology, SASTRA University, Thanjavur 613401, India.
authors.forename: Rajasekar Manoj
authors.initials: RM
authors.lastname: Kumar
authors.affiliation: School of Chemical & Biotechnology, SASTRA University, Thanjavur 613401, India.
authors.forename: Murali
authors.initials: M
authors.lastname: Srinath
authors.affiliation: Department of Cardiothoracic Surgery, Nizam's Institute of Medical Sciences, Hyderabad 500082, India.
authors.forename: Amaresh Rao
authors.initials: AR
authors.lastname: Malempati
authors.affiliation: Department of Clinical Pharmacology & Therapeutics, Nizam's Institute of Medical Sciences, Hyderabad 500082, India.
authors.forename: Vijay Kumar
authors.initials: VK
authors.lastname: Kutala
mesh_terms: D000328:Adult; D000465:Algorithms; D000925:Anticoagulants / Q000008:administration & dosage* / Q000737:chemistry / Q000493:pharmacokinetics; D065729:Cytochrome P-450 CYP2C9 / Q000235:genetics; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D016571:Neural Networks, Computer; D013237:Stereoisomerism; D014859:Warfarin / Q000008:administration & dosage* / Q000737:chemistry / Q000493:pharmacokinetics; D055815:Young Adult
chemical_list: D000925:Anticoagulants; D014859:Warfarin; D065729:Cytochrome P-450 CYP2C9
keywords: CYP2C9*2; CYP2C9*3; CYP2C9*8; artificial neural networks; warfarin/7-OH warfarin ratio