In vitro metabolic studies and machine learning analysis of mass spectrometry data: A dual strategy for differentiating alpha-pyrrolidinohexiophenone (α-PHP) and alpha-pyrrolidinoisohexanophenone (α-PiHP) in urine analysis

• Published in: Forensic science international Vol. 361; p. 112134
• Main Authors: Yeh, Ya-Ling, Wen, Che-Yen, Hsieh, Chin-lin, Chang, Yu-Hsiang, Wang, Sheng-Meng
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.08.2024; Elsevier Limited
• Subjects: Accuracy; Alkaloids - metabolism; Alkaloids - urine; Chromatography; Chromatography, Liquid; Classification; Europe; Feature selection; forensic sciences; Humans; Hydroxylation; in vitro metabolic study; In vitro methods and tests; In Vitro Techniques; Incubation; Isomer; Isomerism; Isomers; Ketones; Learning algorithms; Liquid chromatography; liver microsomes; Low concentrations; Machine Learning; Machine learning analysis; Mass spectra; Mass spectrometry; Mass Spectrometry - methods; Mass spectroscopy; Metabolism; Metabolites; Microsomes; Microsomes, Liver - metabolism; New psychoactive substance; principal component analysis; Principal components analysis; Psychotropic drugs; Psychotropic Drugs - metabolism; Psychotropic Drugs - urine; Pyrrolidines - urine; Quadrupoles; Regression analysis; Retention; Scientific imaging; Sodium; Synthetic cathinone; Taiwan; Tandem Mass Spectrometry; urinalysis; Urine; United States > US; Europe; Taiwan; Synthetic cathinone; in vitro metabolic study; New psychoactive substance; Mass spectrometry; Machine learning analysis; Isomer
• ISSN: 0379-0738; 1872-6283; 1872-6283
• DOI: 10.1016/j.forsciint.2024.112134

Synthetic cathinones are some of the most prevalent new psychoactive substances (NPSs) globally, with alpha-pyrrolidinoisohexanophenone (α-PiHP) being particularly noted for its widespread use in the United States, Europe, and Taiwan. However, the analysis of isomeric NPSs such as α-PiHP and alpha-pyrrolidinohexiophenone (α-PHP) is challenging owing to similarities in their retention times and mass spectra. This study proposes a dual strategy based on in vitro metabolic experiments and machine learning-based classification modelling for differentiating α-PHP and α-PiHP in urine samples: (1) in vitro metabolic experiments using pooled human liver microsomes and liquid chromatography tandem quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) were conducted to identify the key metabolites of α-PHP and α-PiHP from the high-resolution MS/MS spectra. After 5 h incubation, 71.4 % of α-PHP and 64.7 % of α-PiHP remained unmetabolised. Nine phase I metabolites were identified for each compound, including primary β-ketone reduction (M1) metabolites. Comparing the metabolites and retention times confirmed the efficacy of in vitro metabolic experiments for differentiating NPS isomers. Subsequently, analysis of seven real urine samples revealed the presence for various metabolites, including M1, that could be used as suitable detection markers at low concentrations. The aliphatic hydroxylation (M2) metabolite peak counts and metabolite retention times were used to determine α-PiHP use. (2) Classification models for the parent compounds and M1 metabolites were developed using principal component analysis for feature extraction and logistic regression for classification. The training and test sets were devised from the spectra of standard samples or supernatants from in vitro metabolism experiments with different incubation times. Both models had classification accuracies of 100 % and accurately identified α-PiHP and its M1 metabolite in seven real urine samples. The proposed methodology effectively distinguished between such isomers and confirmed their presence at low concentrations. Overall, this study introduces a novel concept that addresses the complexities in analysing isomeric NPSs and suggests a path towards enhancing the accuracy and reliability of NPS detection. [Display omitted] •Challenges in analyzing isomeric NPS samples.•Duel strategies are proposed for identifying α-PHP and α-PiHP in urine samples.•Key metabolites were identified using LC-QTOF-MS spectral fragmentation.•Innovative classification models based on PCA and LR were developed.•Accurate identification of α-PiHP and its M1 metabolite in 7 urine samples.