Artificial neural network-based pharmacogenomic algorithm for warfarin dose optimization. (January 2016)

• Record Type: Journal Article
• Authors: Pavani, Addepalli; Naushad, Shaik Mohammad; Kumar, Rajasekar Manoj; Srinath, Murali; Malempati, Amaresh Rao; Kutala, Vijay Kumar
• Is Part Of: Pharmacogenomics. Volume 17:Number 2(2016)
• Page Start: 121
• Page End: 131
• Publication Date: 2016-01
• Subjects: artificial neural networks -- CYP2C9*2 -- CYP2C9*3 -- CYP2C9*8 -- warfarin/7-OH warfarin ratio; Pharmacogenomics -- Periodicals; 615.1
• DOI: 10.2217/pgs.15.161
• Languages: English
• ISSNs: 1462-2416

Abstract

Aim: To develop more precise pharmacogenomic algorithm for prediction of safe and effective dose of warfarin.Materials & 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.Conclusion: An application of ANN for warfarin dosing improves predictability and provides safe and effective dosing.