细胞色素P450基因多态性在毒物分析中的应用研究进展

doi:10.3969/j.issn.1671-2072.2025.04.005

摘要/Abstract

摘要： 随着高通量测序技术与分子遗传学的显著进展，药（毒）物代谢的遗传机制在法医学领域实现了从单一化学检测到系统性分子解析的跨越式升级。通过整合基因组学、表观遗传学与毒理学的相关研究，系统性揭示了毒物暴露对基因表达调控、表观遗传修饰和代谢产物异常的影响。基于细胞色素P450（cytochrome P450，CYP450）基因多态性，分析不同个体对同一药物的敏感性差异，探讨其通过药物代谢异常导致中毒的机制。研究和分析药物代谢酶编码基因的基因型和等位基因变异，评估机体暴露于化学品、药物和环境污染物有关的风险。利用人工智能机器学习来预测和识别各种化合物以及代谢物的基因型，实现体内或体外基因表达模型分析，以提高基因表型预测的准确性。通过从毒物定性分析转向毒效动力学机制研究，为药物滥用或死亡案件的死亡原因分析和死亡方式推断提供线索。CYP450基因多态性作为一种检测手段，正在重塑法医学毒理鉴定模式，成为法医科学进入“分子时代”的核心驱动力。

关键词: 细胞色素P450, 基因多态性, 法医学, 人工智能

Abstract: With remarkable advances in high-throughput sequencing technology and molecular genetics, research on the genetic mechanisms of drug (toxicant) metabolism have made a leap from single chemical assay to systematic molecular analysis in forensic medicine. The discipline has systematically revealed the effects of toxicant exposure on gene expression regulation, epigenetic modifications, and metabolite abnormalities by integrating genomics, epigenetics, and toxicology. Based on cytochrome P450 (CYP450) gene polymorphisms, this review analyses the differences in sensitivity to the same drug in different individuals and explores the mechanisms that lead to toxicity through abnormal drug metabolism. It further investigates genotypes and allelic variants of genes encoding drug metabolising enzymes to assess risks associated with exposure to chemicals, drugs, and environmental pollutants. Artificial intelligence (AI) machine learning is employed to predict and identify genotypes of various compounds as well as metabolites, enabling in vivo or in vitro gene expression modelling analysis for enhanced accuracy of genotype prediction. By shifting the focus from qualitative toxicant analysis to toxicodynamic mechanisms, this study provides critical insights for determining the cause and manner of death in drug abuse or poisoning-related fatalities. As a detection tool, CYP450 gene polymorphisms are reshaping forensic toxicological identification paradigms, serving as a core driver propelling forensic science into the “molecular era”.

Key words: cytochrome P450 (CYP450), gene polymorphism, forensic medicine, artificial intelligence (AI)

中图分类号:

DF795.1

程忠平, 贾泽雷, 徐兴敏, 莫耀南. 细胞色素P450基因多态性在毒物分析中的应用研究进展[J]. 中国司法鉴定, 2025(4): 55-62.

CHENG Zhongping, JIA Zelei, XU Xingmin, MO Yaonan. Research Progress on the Application of Cytochrome P450 Gene Polymorphisms in Toxicological Analysis[J]. Chinese Journal of Forensic Sciences, 2025(4): 55-62.

参考文献

[ 1 ] ZHAO M Z，MA J S，LI M，et al. Cytochrome P450 enzymes and drug metabolism in humans[J]. International Journal of Molecular Sciences，2021，22（23）：12808.
[ 2 ] DAVID R. The promise of toxicogenomics for genetic toxicology：Past，present and future[J]. Mutagenesis，2020，35（2）：153-159.
[ 3 ] 沈敏，严慧，施妍，等. 系统毒理学——法医毒物学发展的机遇与挑战[J]. 中国司法鉴定，2016（5）：57-65.
[ 4 ] CHOWDHURY H A，BHATTACHARYYA D K，KALITA J K. （Differential）co-expression analysis of gene expression：A survey of best practices[J]. IEEE/ACM Transactions on Computational Biology and Bioinformatics，2020，17（4）：1154-1173.
[ 5 ] SYVÄNEN A C. From early methods for DNA diagnostics to genomes and epigenomes at high resolution during four decades—a personal perspective[J]. Upsala Journal of Medical Sciences，2024，129：129.
[ 6 ] FUERST R，BREINBAUER R. Activity-based protein profiling （ABPP） of oxidoreductases[J]. ChemBioChem，2021，22（4）：630-638.
[ 7 ] ZANGER U M，TURPEINEN M，KLEIN K，et al. Functional pharmacogenetics/genomics of human cytochromes P450 involved in drug biotransformation[J]. Analytical and Bioanalytical Chemistry，2008，392（6）：1093-1108.
[ 8 ] PIRAIANU A I，FULGA A，MUSAT C L，et al. Enhancing the evidence with algorithms：How artificial intelligence is transforming forensic medicine[J]. Diagnostics （Basel），2023，13（18）：2992.
[ 9 ] HICKS J K，SWEN J J，GAEDIGK A. Challenges in CYP2D6 phenotype assignment from genotype data：A critical assessment and call for standardization[J]. Current Drug Metabolism，2014，15（2）：218-232.
[10] DORJI P W，TSHERING G，NA-BANGCHANG K. CYP2C9，CYP2C19，CYP2D6 and CYP3A5 polymorphisms in South-East and East Asian populations：A systematic review[J]. Journal of Clinical Pharmacy and Therapeutics，2019，44（4）：508-524.
[11] MANIKANDAN P，NAGINI S. Cytochrome P450 structure，function and clinical significance：A review[J]. Current Drug Targets，2018，19（1）：38-54.
[12] 钱建畅. 中国汉族人群细胞色素P4502D6（CYP2D6）基因多态性研究[D]. 温州：温州医科大学，2012.
[13] 施妍. 法医毒理学中药物代谢与中国汉族人群CYP基因多态性的关联分析[D]. 上海：复旦大学，2013.
[14] United Nations Office on Drugs and Crime. World drug report 2024[R/OL].[2025-02-06].https：//www.unodc.org/unodc/en/data-and-analysis/world-drug-report-2024.
[15] GASSE A，VENNEMANN M，KÖHLER H，et al. Toxicogenetic analysis of Δ9-THC-metabolizing enzymes[J]. International Journal of Legal Medicine，2020，134（6）：2095-2103.
[16] WOLOWICH W R，GREIF R，KLEINE-BRUEGGENEY M，et al. Minimal physiologically based pharmacokinetic model of intravenously and orally administered delta-9-tetrahydrocannabinol in healthy volunteers[J]. European Journal of Drug Metabolism and Pharmacokinetics，2019，44（5）：691-711.
[17] VEVELSTAD M，ØIESTAD E L，NEREM E，et al. Studies on para-methoxymethamphetamine （PMMA） metabolite pattern and influence of CYP2D6 genetics in human liver microsomes and authentic samples from fatal PMMA intoxications[J]. Drug Metabolism and Disposition，2017，45（12）：1326-1335.
[18] CISZKOWSKI C，MADADI P，PHILLIPS M S，et al. Codeine，ultrarapid-metabolism genotype，and postoperative death[J]. New England Journal of Medicine，2009，361（8）：827-828.
[19] KOREN G，CAIRNS J，CHITAYAT D，et al. Pharmacogenetics of morphine poisoning in a breastfed neonate of a codeine-prescribed mother[J]. Lancet，2006，368（9536）：704.
[20] WU A H B，KEARNEY T. Lack of impairment due to confirmed codeine use prior to a motor vehicle accident：Role of pharmacogenomics[J]. Journal of Forensic and Legal Medicine，2013，20（8）：1024-1027.
[21] GLOOR Y，LLORET-LINARES C，BOSILKOVSKA M，et al. Drug metabolic enzyme genotype-phenotype discrepancy：High phenoconversion rate in patients treated with antidepressants[J]. Biomedicine & Pharmacotherapy，2022，152：113202.
[22] NEUKAMM M A，VOGT S，HERMANNS-CLAUSEN M，et al. Fatal doxepin intoxication—Suicide or slow gradual intoxication?[J]. Forensic Science International，2013，227（1/2/3）：82-84.
[23] PELLETTI G，LEONE O，GAVELLI S，et al. Sudden unexpected death after a mild trauma：The complex forensic interpretation of cardiac and genetic findings[J]. Forensic Science International，2021，328：111004.
[24] FOTI F，DE-GIORGIO F，VETRUGNO G，et al. A de novo ryanodine receptor 2 gene variant in a case of sudden cardiac death[J]. International Journal of Legal Medicine，2020，134（2）：619-623.
[25] JORNIL J，NIELSEN T S，ROSENDAL I，et al. A poor metabolizer of both CYP2C19 and CYP2D6 identified by mechanistic pharmacokinetic simulation in a fatal drug poisoning case involving venlafaxine[J]. Forensic Science International，2013，226（1/2/3）：e26-31.
[26] RAMÍREZ-SANTANA M，ZÚÑIGA-VENEGAS L，CORRAL S，et al. Reduced neurobehavioral functioning in agricultural workers and rural inhabitants exposed to pesticides in northern Chile and its association with blood biomarkers inhibition[J]. Environmental Health，2020，19（1）：84.
[27] SINGH S，KUMAR V，VASHISHT K，et al. Role of genetic polymorphisms of CYP1A1，CYP3A5，CYP2C9，CYP2D6，and PON1 in the modulation of DNA damage in workers occupationally exposed to organophosphate pesticides[J]. Toxicology and Applied Pharmacology，2011，257（1）：84-92.
[28] CHEN L，QIAN Y Z，JIA Q，et al. A national-scale distribution of organochlorine pesticides （OCPs） in cropland soils and major types of food crops in China：Co-occurrence and associated risks[J]. Science of the Total Environment，2023，861：160637.
[29] GARSHIN A，ALTYNOVA N，DJANGALINA E，et al. Individual risk assessment for population living on the territories long-term polluted by organochlorine pesticides[J]. Toxics，2023，11（6）：482.
[30] DE OLIVEIRA A F B，DE SOUZA M R，BENEDETTI D，et al. Investigation of pesticide exposure by genotoxicological，biochemical，genetic polymorphic and in silico analysis[J]. Ecotoxicology and Environmental Safety，2019，179：135-142.
[31] BI Q F，GOODMAN K E，KAMINSKY J，et al. What is machine learning? a primer for the epidemiologist[J]. American Journal of Epidemiology，2019，188（12）：2222-2239.
[32] GUPTA R，SRIVASTAVA D，SAHU M，et al. Artificial intelligence to deep learning：Machine intelligence approach for drug discovery[J]. Molecular Diversity，2021，25（3）：1315-1360.
[33] MAS S，GASSÓ P，TORRA M，et al. Intuitive pharmacogenetic dosing of risperidone according to CYP2D6 phenotype extrapolated from genotype in a cohort of first episode psychosis patients[J]. European Neuropsychopharmacology，2017，27（7）：647-656.
[34] WENDT F R，NOVROSKI N M M，RAHIKAINEN A L，et al. A pathway-driven predictive model of tramadol pharmacogenetics[J]. European Journal of Human Genetics，2019，27（7）：1143-1156.
[35] WENDT F R，NOVROSKI N M M，RAHIKAINEN A L，et al. Supervised classification of CYP2D6 genotype and metabolizer phenotype with postmortem tramadol-exposed finns[J]. The American Journal of Forensic Medicine and Pathology，2019，40（1）：8-18.
[36] SINGH A V，CHANDRASEKAR V，PAUDEL N，et al. Integrative toxicogenomics：Advancing precision medicine and toxicology through artificial intelligence and OMICs technology[J]. Biomedicine & Pharmacotherapy，2023，163：114784.
[37] O’DONOVAN S D，CAVILL R，WIMMENAUER F，et al. Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data[J]. PLoS One，2023，18（11）：e0292030.
[38] STOYANOVA R，KATZBERGER P M，KOMISSAROV L，et al. Computational predictions of nonclinical pharmacokinetics at the drug design stage[J]. Journal of Chemical Information and Modeling，2023，63（2）：442-458.
[39] O’DONOVAN S D，DRIESSENS K，LOPATTA D，et al. Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes[J]. PLoS One，2020，11，15（8）：e0236392.
[40] DI NUNNO N，ESPOSITO M，ARGO A，et al. Pharmacogenetics and forensic toxicology：A new step towards a multidisciplinary approach[J]. Toxics，2021，9（11）：292.

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