Title:Computational Structural Validation of CYP2C9 Mutations and Evaluation of
Machine Learning Algorithms in Predicting the Therapeutic Outcomes of Warfarin
Volume: 24
Issue: 6
Author(s): Kannan Sridharan*, Thirumal Kumar D, Suchetha Manikandan, Gaurav Prasanna, Lalitha Guruswamy, Rashed Al Banna and George Priya Doss C
Affiliation:
- Department of Pharmacology & Therapeutics, College of Medicine and Medical Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain.
Keywords:
Support vector machine, machine learning algorithm, CYP2C9, warfarin, molecular dynamics simulation, MLAs.
Abstract:
Aim: The study aimed to identify the key pharmacogenetic variable influencing the therapeutic outcomes
of warfarin using machine learning algorithms and bioinformatics tools.
Background: Warfarin, a commonly used anticoagulant drug, is influenced by cytochrome P450 (CYP) enzymes,
particularly CYP2C9. MLAs have been identified to have great potential in personalized therapy.
Objective: The purpose of the study was to evaluate MLAs in predicting the critical outcomes of warfarin therapy
and validate the key predictor genotyping variable using bioinformatics tools.
Methods: An observational study was conducted on adults receiving warfarin. Allele discrimination method was
used for estimating the single nucleotide polymorphisms (SNPs) in CYP2C9, VKORC1, and CYP4F2. MLAs were
used for identifying the significant genetic and clinical variables in predicting the poor anticoagulation status (ACS)
and stable warfarin dose. Advanced computational methods (SNPs' deleteriousness and impact on protein destabilization,
molecular dockings, and 200 ns molecular dynamics simulations) were employed for examining the influence
of CYP2C9 SNPs on structure and function.
Results: Machine learning algorithms revealed CYP2C9 to be the most important predictor for both outcomes compared
to the classical methods. Computational validation confirmed the altered structural activity, stability, and impaired
functions of protein products of CYP2C9 SNPs. Molecular docking and dynamics simulations revealed significant
conformational changes with mutations R144C and I359L in CYP2C9.
Conclusion: We evaluated various MLAs in predicting the critical outcome measures associated with warfarin and
observed CYP2C9 as the most critical predictor variable. The results of our study provide insight into the molecular
basis of warfarin and the CYP2C9 gene. A prospective study validating the MLAs is urgently needed.
