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Identification of Biomarkers for Methamphetamine Exposure Time Prediction in Mice Using Metabolomics and Machine
Wei Sheng1,2, Runbin Sun1,2, Ran Zhang2
1China Pharmaceutical University Nanjing Drum Tower Hospital, Nanjing 210000, China.
Abstract:
Methamphetamine (METH) abuse has become a global public health and safety problem. More information is needed to identify the time of drug abuse. In this study, methamphetamine was administered to male C57BL/6J mice with increasing doses from 5 to 30 mg kg-1 (once a day, i.p.) for 20 days. Serum and urine samples were collected for metabolomics studies using gas chromatography-mass spectrometry (GC-MS). Six machine learning models were used to infer the time of drug abuse and the best model was selected to predict administration time preliminarily. The metabolic changes caused by methamphetamine were explored. As results, the metabolic patterns of methamphetamine exposure mice were quite different from the control group and changed over time. Specifically, serum metabolomics showed enhanced amino acid metabolism and increased fatty acid consumption, while urine metabolomics showed slowed metabolism of the tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism. Phenylalanine in serum and glutamine in urine increased, while palmitic acid, 5-HT, and monopalmitin in serum and gamma-aminobutyric acid in urine decreased significantly. Among the six machine learning models, the random forest model was the best to predict the exposure time (serum: MAE = 1.482, RMSE = 1.69, R squared = 0.981; urine: MAE = 2.369, RMSE = 1.926, R squared = 0.946). The potential biomarker set containing four metabolites in the serum (palmitic acid, 5-hydroxytryptamine, monopalmitin, and phenylalanine) facilitated the identification of methamphetamine exposure. The random forest model helped predict the methamphetamine exposure time based on these potential biomarkers.
Insights
This study used machine learning and metabolomics to identify methamphetamine exposure time. The random forest model accurately predicted exposure using serum and urine biomarkers.
Area of Science:
- Toxicology
- Metabolomics
- Machine Learning
Background:
- Methamphetamine (METH) abuse is a significant global public health issue.
- Accurate identification of drug abuse timing is crucial for intervention and research.
- Existing methods for determining METH exposure timelines require improvement.
Purpose of the Study:
- To investigate metabolic changes in mice following methamphetamine administration.
- To develop a machine learning model for predicting the time of methamphetamine exposure.
- To identify potential serum and urine biomarkers for methamphetamine use.
Main Methods:
- Male C57BL/6J mice were administered varying doses of METH daily for 20 days.
- Serum and urine samples were analyzed using gas chromatography-mass spectrometry (GC-MS) based metabolomics.
- Six machine learning models were evaluated to predict METH administration time.
Main Results:
- Methamphetamine exposure significantly altered metabolic profiles in both serum and urine compared to controls.
- Serum metabolomics indicated enhanced amino acid metabolism and fatty acid consumption.
- Urine metabolomics revealed slowed tricarboxylic acid (TCA) cycle, increased organic acid excretion, and abnormal purine metabolism.
- The random forest model achieved high accuracy in predicting METH exposure time.
- A panel of four serum metabolites (palmitic acid, 5-hydroxytryptamine, monopalmitin, phenylalanine) showed potential as biomarkers.
Conclusions:
- Metabolic profiling combined with machine learning offers a promising approach to determine METH exposure timing.
- Specific metabolic alterations in serum and urine can serve as indicators of METH use.
- The identified biomarkers and predictive model can aid in forensic investigations and clinical assessments of METH abuse.
