Title:Comparative Analysis of Machine Learning Algorithms Evaluating the Single
Nucleotide Polymorphisms of Metabolizing Enzymes with Clinical Outcomes
Following Intravenous Paracetamol in Preterm Neonates with Patent Ductus
Arteriosus
Volume: 25
Issue: 2
Author(s): Kannan Sridharan*, George Priya Doss C, Hephzibah Cathryn R, Thirumal Kumar D and Muna Al Jufairi
Affiliation:
- Department of Pharmacology & Therapeutics, College of Medicine & Medical Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain
Keywords:
Acetaminophen, PDA, genetic polymorphisms, MLA, paracetamol, CYP2D6.
Abstract:
Aims: Pharmacogenomics has been identified to play a crucial role in determining drug response.
The present study aimed to identify significant genetic predictor variables influencing the therapeutic effect of
paracetamol for new indications in preterm neonates.
Background: Paracetamol has recently been preferred as a first-line drug for managing Patent Ductus Arteriosus
(PDA) in preterm neonates. Single Nucleotide Polymorphisms (SNPs) in CYP1A2, CYP2A6, CYP2D6,
CYP2E1, and CYP3A4 have been observed to influence the therapeutic concentrations of paracetamol.
Objectives: The purpose of this study was to evaluate various Machine Learning Algorithms (MLAs) and
bioinformatics tools for identifying the key genotype predictor of therapeutic outcomes following paracetamol
administration in neonates with PDA.
Methods: Preterm neonates with hemodynamically significant PDA were recruited in this prospective, observational
study. The following SNPs were evaluated: CYP2E1*5B, CYP2E1*2, CYP3A4*1B, CYP3A4*2,
CYP3A4*3, CYP3A5*3, CYP3A5*7, CYP3A5*11, CYP1A2*1C, CYP1A2*1K, CYP1A2*3, CYP1A2*4,
CYP1A2*6, and CYP2D6*10. Amongst the MLAs, Artificial Neural Network (ANN), C5.0 algorithm, Classification
and Regression Tree analysis (CART), discriminant analysis, and logistic regression were evaluated
for successful closure of PDA. Generalized linear regression, ANN, CART, and linear regression were used to
evaluate maximum serum acetaminophen concentrations. A two-step cluster analysis was carried out for both
outcomes. Area Under the Curve (AUC) and Relative Error (RE) were used as the accuracy estimates. Stability
analysis was carried out using in silico tools, and Molecular Docking and Dynamics Studies were carried
out for the above-mentioned enzymes.
Results: Two-step cluster analyses have revealed CYP2D6*10 and CYP1A2*1C to be the key predictors of
the successful closure of PDA and the maximum serum paracetamol concentrations in neonates. The ANN
was observed with the maximum accuracy (AUC = 0.53) for predicting the successful closure of PDA with
CYP2D6*10 as the most important predictor. Similarly, ANN was observed with the least RE (1.08) in predicting
maximum serum paracetamol concentrations, with CYP2D6*10 as the most important predictor. Further
MDS confirmed the conformational changes for P34A and P34S compared to the wildtype structure of
CYP2D6 protein for stability, flexibility, compactness, hydrogen bond analysis, and the binding affinity when
interacting with paracetamol, respectively. The alterations in enzyme activity of the mutant CYP2D6 were
computed from the molecular simulation results.
Conclusion: We have identified CYP2D6*10 and CYP1A2*1C polymorphisms to significantly predict the
therapeutic outcomes following the administration of paracetamol in preterm neonates with PDA. Prospective
studies are required for confirmation of the findings in the vulnerable population.
