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.

Journal: Current drug metabolism

PMID: 38445694

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.

Authors

Kannan Sridharan

Department of Pharmacology & Therapeutics, College of Medicine & Medical Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain.
George Priya Doss C

Laboratory of Integrative Genomics, School of BioSciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India.
Hephzibah Cathryn R

Laboratory of Integrative Genomics, School of BioSciences and Technology, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India.
Thirumal Kumar D

Faculty of Allied Health Sciences, Meenakshi Academy of Higher Education and Research, Chennai, Tamil Nadu, India.
Muna Al Jufairi

Department of Pediatrics, Salmaniya Medical Complex, Manama, Kingdom of Bahrain.

Keywords

Acetaminophen Administration, Intravenous Algorithms Analgesics, Non-Narcotic Cytochrome P-450 Enzyme System Ductus Arteriosus, Patent Female Humans Infant, Newborn Infant, Premature Machine Learning Male Polymorphism, Single Nucleotide Prospective Studies Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (38445694)

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.

Abstract

Authors

Keywords

External Resources

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