Department of Pharmacology & Therapeutics, College of Medicine and Medical Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain.
School of BioSciences and Technology, Vellore Institute of Technology, Vellore, India.
Curr Drug Metab. 2023;24(10):684-699. doi: 10.2174/0113892002267867231101051310.
To identify single nucleotide polymorphisms (SNPs) of paracetamol-metabolizing enzymes that can predict acute liver injury.
Paracetamol is a commonly administered analgesic/antipyretic in critically ill and chronic renal failure patients and several SNPs influence the therapeutic and toxic effects.
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.
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: . 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.
and genotypes were identified by MLAs to significantly predict hepatotoxicity. The predictSNP2 revealed that 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 , and against paracetamol. MD simulations revealed that and missense variants in affect the binding ability with paracetamol. In-silico techniques found that and are highly harmful. MD simulations revealed (A>G) had decreased binding energy with paracetamol than , and (A>T) and both have greater binding energy with paracetamol.
Polymorphisms in , and 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.
鉴定可预测急性肝损伤的扑热息痛代谢酶单核苷酸多态性（SNPs）。
扑热息痛是危重病和慢性肾衰竭患者常用的镇痛/解热剂，几种 SNPs 影响治疗效果和毒性。
评估机器学习算法（MLAs）和生物信息学工具在描绘预测性 SNPs 以及了解其分子动力学方面的作用。
通过招募患有慢性肾衰竭的危重病患者并给予静脉内扑热息痛作为标准治疗，进行了一项横断面研究。估计扑热息痛和主要代谢物的血清浓度。评估了以下 SNPs：. 使用 MLAs 确定急性肝衰竭的预测遗传变量。采用 Predict SNP2 和分子对接（MD）等生物信息学工具，评估上述 SNPs 对扑热息痛结合亲和力的影响。
MLAs 确定 和 基因型可显著预测肝毒性。Predict SNP2 显示 在所有工具中均高度有害。MD 显示与扑热息痛的结合能分别为-5.5 Kcal/mol、-6.9 Kcal/mol 和-6.8 Kcal/mol。MD 模拟表明 和 中的错义变异会影响与扑热息痛的结合能力。基于计算机的技术发现 和 是高度有害的。MD 模拟显示 （A>G）与扑热息痛的结合能比 （A>G）和 （A>T）都要低，而 （A>T）和 （A>T）与扑热息痛的结合能则更高。
和 中的多态性显著影响扑热息痛的临床结果或结合亲和力。需要进行强有力的临床研究，以确定这些多态性对扑热息痛药代动力学或药效学的临床影响。
