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. 2023;24(10):684-699.
doi: 10.2174/0113892002267867231101051310.

Evaluation of Supervised Machine Learning Algorithms and Computational Structural Validation of Single Nucleotide Polymorphisms Related to Acute Liver Injury with Paracetamol

Kannan Sridharan  1 , Ambritha Balasundaram  2 , D Thirumal Kumar  3 , C George Priya Doss  2
Affiliations

Evaluation of Supervised Machine Learning Algorithms and Computational Structural Validation of Single Nucleotide Polymorphisms Related to Acute Liver Injury with Paracetamol

Kannan Sridharan et al. Curr Drug Metab. 2023.

Abstract

Aims: To identify single nucleotide polymorphisms (SNPs) of paracetamol-metabolizing enzymes that can predict acute liver injury.

Background: Paracetamol is a commonly administered analgesic/antipyretic in critically ill and chronic renal failure patients and several SNPs influence the therapeutic and toxic effects.

Objective: To evaluate the role of machine learning algorithms (MLAs) and bioinformatics tools to delineate the predictor SNPs as well as to understand their molecular dynamics.

Methods: A cross-sectional study was undertaken by recruiting critically ill patients with chronic renal failure and administering intravenous paracetamol as a standard of care. Serum concentrations of paracetamol and the principal metabolites were estimated. Following SNPs were evaluated: CYP2E1*2, CYP2E1_-1295G>C, CYP2D6*10, CYP3A4*1B, CYP3A4*2, CYP1A2*1K, CYP1A2*6, CYP3A4*3, and CYP3A5*7. MLAs were used to identify the predictor genetic variable for acute liver failure. Bioinformatics tools such as Predict SNP2 and molecular docking (MD) were undertaken to evaluate the impact of the above SNPs with binding affinity to paracetamol.

Results: CYP2E1*2 and CYP1A2*1C genotypes were identified by MLAs to significantly predict hepatotoxicity. The predictSNP2 revealed that CYP1A2*3 was highly deleterious in all the tools. MD revealed binding energy of -5.5 Kcal/mol, -6.9 Kcal/mol, and -6.8 Kcal/mol for CYP1A2, CYP1A2*3, and CYP1A2*6 against paracetamol. MD simulations revealed that CYP1A2*3 and CYP1A2*6 missense variants in CYP1A2 affect the binding ability with paracetamol. In-silico techniques found that CYP1A2*2 and CYP1A2*6 are highly harmful. MD simulations revealed CYP3A4*2 (A>G) had decreased binding energy with paracetamol than CYP3A4, and CYP3A4*2(A>T) and CYP3A4*3 both have greater binding energy with paracetamol.

Conclusion: Polymorphisms in CYP2E1, CYP1A2, CYP3A4, and CYP3A5 significantly influence paracetamol's clinical outcomes or binding affinity. Robust clinical studies are needed to identify these polymorphisms' clinical impact on the pharmacokinetics or pharmacodynamics of paracetamol.

Keywords: CYP1A2; CYP2E1; CYP3A4.; MLAs; Pharmacogenetics; bioinformatics.

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