4-Methoxy-α-PVP: in silico prediction, metabolic stability, and metabolite identification by human hepatocyte incubation and high-resolution mass spectrometry
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- Ellefsen, K.N., Wohlfarth, A., Swortwood, M.J. et al. Forensic Toxicol (2016) 34: 61. doi:10.1007/s11419-015-0287-4
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Novel psychoactive substances are continuously developed to circumvent legislative and regulatory efforts. A new synthetic cathinone, 4-methoxy-α-PVP, was identified for the first time in illegal products; however, the metabolism of this compound is not known. Complete metabolic profiles are needed for these novel psychoactive substances to enable identification of their intake and to link adverse effects to the causative agent. This study assessed 4-methoxy-α-PVP metabolic stability with human liver microsomes (HLMs) and identified its metabolites after HLM and hepatocyte incubations followed by high-resolution mass spectrometry (HRMS). A Thermo QExactive high-resolution mass spectrometer (HRMS) was used with full scan data-dependent mass spectrometry, with (1) and without (2) an inclusion list of predicted metabolite, and with full scan and all-ion fragmentation (3) to identify potential unexpected metabolites. In silico predictions were performed and compared to in vitro results. Scans were thoroughly mined with different data processing algorithms using WebMetabase (Molecular Discovery). 4-Methoxy-α-PVP exhibited a long half-life of 79.7 min in HLM, with an intrinsic clearance of 8.7 µL min−1 mg−1. In addition, this compound is predicted to be a low-clearance drug with an estimated human hepatic clearance of 8.2 mL min−1 kg−1. Eleven 4-methoxy-α-PVP metabolites were identified, generated by O-demethylation, hydroxylation, oxidation, ketone reduction, N-dealkylation, and glucuronidation. The most dominant metabolite in HLM and human hepatocyte samples was 4-hydroxy-α-PVP, also predicted as the #1 in silico metabolite, and is suggested to be a suitable analytical target in addition to the parent compound.
Keywords4-Methoxy-α-PVP Novel psychoactive substances Synthetic cathinones Human hepatocytes Human liver microsomes In silico prediction
In recent years, novel psychoactive substances (NPSs) appeared rapidly on the drug market in an effort to bypass controlled substance legislation. The European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) reported 41 NPSs identified for the first time across Europe in 2010, 81 in 2013, and 101 in 2014 . These NPSs are continuously developed to circumvent legislative and regulatory efforts, with limited available pharmacological and toxicological data. NPSs encompass a wide range of compounds including synthetic cannabinoids, phenethylamines, tryptamines, piperazines, ketamine, cathinones, and other plant-based psychoactive substances .
Synthetic cathinones emerged on the designer drug market as popular “legal” alternatives to illicit drugs in the late 2000s, and are marketed as “legal highs” and “not for human consumption”. They are stimulant-like drugs derived from cathinone, the active ingredient of the khat plant Catha edulis, with adverse effects including hyperthermia, agitation, confusion, psychosis, seizures, and tachycardia [3, 4, 5, 6, 7]. A variety of synthetic cathinones, alone and in combination with other illicit drugs, were detected in acute intoxications [4, 8, 9], impaired driving cases [10, 11, 12, 13], and fatalities [4, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23].
The metabolic pathways of other α-pyrrolidinophenones, including MDPV, α-PVP, α-pyrrolidinobutiophenone (α-PBP), α-pyrrolidinopropiophenone (α-PPP), 4-methyl-α-pyrrolidinopropiophenone (MPPP), and 4-methoxy-α-pyrrolidinopropiophenone (MOPPP), were previously investigated [34, 35, 36, 37, 38, 39, 40, 41, 42]. There are no metabolism studies for 4-methoxy-α-PVP. A recent review of the pharmacology of α-pyrrolidinophenones outlined the major metabolic pathways of other structurally similar compounds including reduction of the keto moiety to the corresponding alcohol, hydroxylation followed by oxidation of the pyrrolidine ring to the lactam (2″-oxo), hydroxylation and carboxylation of the 4′-methyl group, O-demethylation of the 4′-methoxy group, and demethylenation followed by O-methylation of the 3′, 4′-methylenedioxy moiety .
A promising approach to elucidate metabolites of NPSs includes in silico metabolite predictions, and human liver microsome (HLM) and human hepatocyte incubations, followed by analysis with high-resolution mass spectrometry (HRMS) and software-assisted data mining [44, 45]. Complete metabolic profiles are needed for 4-methoxy-α-PVP to enable identification of intake and link adverse effects to the causative agent. We evaluated 4-methoxy-α-PVP in silico metabolism predictions, assessed metabolic stability with HLMs, and identified metabolites after HLM and human hepatocyte incubations followed by HRMS.
Materials and methods
Chemicals and reagents
4-Methoxy-α-PVP HCl was purchased from Cayman Chemicals (Ann Arbor, MI, USA) and diclofenac was obtained from Toronto Research Chemicals (Toronto, Canada). Fifty-donor purpose-pooled HLMs, 10-donor purpose-pooled cryopreserved human hepatocytes, InVitro Gro HT medium for thawing, and InVitro Gro KHB (Krebs-Henseleit buffer) for washing and incubation were acquired from BioreclamationIVT (Baltimore, MD, USA), and NADPH-regenerating system (Solution A) and glucose-6-phosphate dehydrogenase (Solution B) were purchased from Corning Gentest (Woburn, MA, USA). LC–MS grade water and formic acid were from Fisher Scientific (Fair Lawn, NJ, USA), and LC–MS grade acetonitrile from Sigma Aldrich (St. Louis, MO, USA).
In silico metabolite prediction
MetaSite software (v.5.0.3; Molecular Discovery, Pinner, UK) was used to predict in silico 4-methoxy-α-PVP metabolites. The structure of 4-methoxy-α-PVP was imported into the software and predictions generated using the CYP450 liver model, reactivity correction, 39 common biotransformations, and a minimum mass threshold of 50 Da for predicted metabolites. The predicted sites of metabolism are a consensus from liver CYP isoforms and FMO3. The computational procedure considers both thermodynamic and kinetic factors. Potential metabolites were assigned a probability score representing the likelihood of being generated, with 100 % being the maximum score. In addition, the top predicted in silico metabolite (100 % probability score) was imported into the software to predict second-generation metabolites.
Metabolic stability of 4-methoxy-α-PVP in HLMs
To investigate the metabolic stability of 4-methoxy-α-PVP, 1 µmol/L drug was incubated with 50-donor purpose-pooled HLMs. Water bath incubations were performed on a Precision reciprocal shaking bath (Winchester, VA, USA). The reaction mixture contained 780 µL purified water, 100 µL 0.5 mol/L potassium phosphate buffer pH 7.4, 10 µL Solution B, and 10 µL 4-methoxy-α-PVP (100 µmol/L). Samples were vortexed to homogenize the solutions, and 50 µL HLMs (20 mg protein/mL suspension) were added to the mixture and pre-incubated at 37 °C for approximately 3 min. Organic solvent percentage (methanol) was 1 %. The reaction was initiated with the addition of 50 µL Solution A, and 100 µL samples were collected at 0, 3, 8, 13, 20, 30, 45, and 60 min. The reaction was stopped with an equal volume (100 µL) of cold acetonitrile. Samples were then centrifuged at 15,000×g for 5 min at 4 °C and subsequently stored at −80 °C prior to analysis.
Incubation of 4-methoxy-α-PVP with cryopreserved primary human hepatocytes
For the human hepatocyte experiments, 10 µmol/L 4-methoxy-α-PVP was incubated at 37 °C with pooled cryopreserved human hepatocytes. Hepatocytes were incubated in a Forma™ Steri-Cycle™ CO2 incubator from Thermo Scientific (Fremont, CA, USA). The cells were thawed in and washed with InVitro Gro HT medium and centrifuged at 50×g for 5 min at room temperature. The supernatant was aspirated, and InVitro Gro KHB was added to wash the hepatocytes again. After centrifugation and removal of the supernatant, the cell pellet was re-suspended in 2 mL buffer. Cell viability was assessed with the Trypan blue exclusion method assuring >80 % viability. The reaction mixture contained 250 µL 4-methoxy-α-PVP in buffer (20 µmol/L) and 250 µL cell suspension, yielding a final drug concentration of 10 µmol/L and final cell concentration of 1 × 106 cells/mL. Samples (500 µL) were collected at 0, 1, and 3 h based on HLM half-life calculations, and the reaction stopped with 500 µL cold acetonitrile. Specimens were stored at −80 °C prior to analysis. Diclofenac also was incubated as a positive control for human hepatocyte functional viability.
Liquid chromatography–high resolution mass spectrometry (LC-HRMS) was performed on a Thermo Scientific NCS-3500RS Ultimate 3000 Binary Rapid system coupled to a Thermo Scientific QExactive mass spectrometer (Thermo Scientific). The Ultimate 3000 RSLCnano system consisted of a degasser, a tertiary loading pump, a binary eluting pump, a column oven, and an RS Autosampler. The QExactive contained a heated electrospray ionization source (HESI-II) and was operated in positive ionization mode. The spray voltage was 3 kV, capillary temperature 350 °C, heater temperature 425 °C, S-lens RF level 50, sheath gas flow rate 50, auxiliary gas flow rate 13 and sweep gas 3 (manufacturer’s units). Nitrogen was used for spray stabilization, for collision-induced dissociation experiments in the HCD cell, and as the damping gas in the C-trap. The instrument was calibrated in the positive and negative modes every 25 h.
LC-HRMS for HLM samples
Chromatographic separation of HLM samples, diluted 100-fold with mobile phase A (0.1 % formic acid in water), was achieved with an Accucore C18 column (2.6 µm, 100 × 2.1 mm) and C18 guard cartridge (4 × 2.0 mm) with a 0.4 mL/min flow rate at 35 °C. Gradient elution was performed with 2 % B (0.1 % formic acid in acetonitrile) for 2 min, increased to 10 % B at 10 min, then ramped to 30 % B at 14 min, from 30 to 95 % in 2 min, held at 95 % for 1 min, and returned to initial conditions over 1 min. A 2-min equilibration followed, yielding a total run time of 20 min.
Full scan data-dependent MSMS (ddMS2) data were collected from m/z 100 to 600 at 70,000 resolution with automatic gain control (AGC) target 1.0 × 106, and maximum injection time 250 ms. Dd MS2 scans were triggered at the apex of the peak (3–8 s) when an underfill ratio of 5 % was reached (intensity 8.3 × 104) for the 5 most intense peaks per cycle (TopN of 5). MS scans were acquired at a resolution of 17,500 with AGC of 2 × 105, maximum injection time of 120 ms, and stepped normalized collision energy (NCE) 50 ± 30 %.
LC-HRMS for human hepatocytes
Hepatocyte samples were diluted fivefold with mobile phase A and chromatographically separated using a Synergi 4 Hydro-RP column (80Å, 150 × 2 mm) and C18 guard cartridge (4 × 2.0 mm) within 30 min at 0.4 mL/min flow rate. Initial conditions (2 % B) were held for 2 min, increased to 95 % B for 18 min, held at 95 % B for 5 min, and returned to initial conditions over 1 min. A 4-min equilibration followed, yielding a total run time of 30 min.
Hepatocytes incubations were analyzed with three different MS methods: full scan and ddMS2, with (1) and without (2) an inclusion list of predicted metabolites, and with a full scan and all-ion-fragmentation (AIF) method (3). Full scan and ddMS2 data (without an inclusion list) were acquired as previously described for HLM samples. In addition, an inclusion list was generated by MetaSite software based on its in silico metabolism predictions and imported into the full scan ddMS2 acquisition method. DdMS2 scans were triggered if the precursor ions from the inclusion list were detected above 8.3 × 104 intensity threshold. To identify potential unexpected metabolites, full scan and AIF data were acquired. AIF experiments were performed at 35,000 resolution over a scan range of m/z 100–600, with AGC target 5 × 105, maximum injection time of 120 ms, and a stepped NCE 50 ± 30 %.
In vitro microsomal half-life (T1/2) and microsomal intrinsic clearance (CLint, micr) of 4-methoxy-α-PVP were calculated on the model described by Baranczewski et al. . This microsomal intrinsic clearance was then scaled to whole liver dimensions to calculate the intrinsic clearance (CLint) by multiplying two factors: the content of microsomal protein/g liver tissue (~45 mg/g) and liver weight/kg body weight (~20 g/kg), according to McNaney et al. . Human hepatic clearance (CLH) and extraction ratio were calculated  without consideration of plasma protein binding.
Mass spectra acquired by the QExactive were analyzed with WebMetaBase software (v.2.0.2, Molecular Discovery) for metabolite candidates in the HLM and hepatocyte incubations. Raw data were submitted as a batch, which included a substrate (parent compound, 4-methoxy-α-PVP) structure mol file, a blank file (mobile phase), a substrate file (neat standard) to analyze the substrate fragmentation pattern, and raw data files for each incubation time point for hepatocyte incubations (t = 0, 1, 3 h) and for HLMs (t = 0, 3, 8, 13, 20, 30, 45, 60 min). Processing parameters for hepatocyte incubations were as follows: “hepatocyte metabolic system”; retention time window 2–25 min; chromatogram, MS, and MS/MS autofilter thresholds of 0.98; same peak tolerance of 0.010; three metabolite generations, minimum metabolite generation mass of 50 Da, and expected metabolites were rescue enabled (selects peaks with an equivalent m/z to a known metabolite regardless of peak area, provided there is MS2 data associated with the peaks). For the HLM incubations, similar processing parameters were chosen, except the metabolic system selected was HLMs and the retention time window was 2–18 min due to the different chromatography. The software auto-detects chromatographic peaks related to the parent compound and its metabolites, proposes potential structures based on fragmentation patterns for each detected peak and Markush structures (chemical structures with functional groups highlighted based on site reactivity), and ranks potential structures compatible with extracted fragment information. Structures of potential metabolite candidates were assigned based on retention times, mass shift between theoretical mass and observed mass (<5 ppm), peak abundance and fragmentation pattern. Metabolites were thoroughly examined against literature reports of structurally similar compounds and were eventually compared to the in silico predictions.
In silico metabolite prediction
In silico predicted metabolites for 4-methoxy-α-PVP in decreasing order based on score
Metabolic stability assessment with HLMs
4-Methoxy-α-PVP exhibited an in vitro T1/2 of 79.7 ± 1.3 min in HLMs, with a microsomal intrinsic clearance CLint, micr of 8.7 µL min−1 mg−1 and intrinsic clearance CLint of 8.2 mL min−1 kg−1. Hepatic clearance was calculated to 5.8 mL min−1 kg−1 and the extraction ratio was 0.29.
Identification of HLM metabolites
In the HLM samples, the O-demethylated metabolite (M3 in the hepatocyte samples) was the only metabolite detected. Peak areas increased from the 20 to 60 min incubation times.
Metabolite profiling with human hepatocytes
4-Methoxy-α-PVP phase I metabolites identified after incubation with human hepatocytes, sorted by retention time (RT). Rank was based on mass spectrometric peak areas. Fragments are expressed in nominal mass
Mass error (ppm)
MS % peak areas
O-Demethylation + N-dealkylation
135, 107, 58
Ketone reduction + O-demethylation
189, 232, 107, 70
107, 126, 121, 72, 177
121, 142, 135, 191
Pyrrolidine ring opening + hydroxylation
121, 190, 262, 148, 135, 144
121, 87, 140, 276, 135, 191
246, 203, 188, 121, 175, 72
126, 86, 135, 163, 121
Iminium ion formation
175, 86, 121, 70
140, 98, 121, 135, 69, 191
121, 126, 135. 191, 84, 163, 219, 72, 70
All metabolites were identified with full scan and ddMS2, with the exception of M8. This glucuronidated metabolite was identified with our full scan and dd-MS2 with the in silico generated inclusion list. Other than M8, this acquisition method did not detect any additional metabolites and only found 5/10 metabolites (M3, M4, M6, M7, and M11) identified with full scan and ddMS2 . The full scan and AIF method did not detect any additional unexpected metabolites. Compared to full scan and ddMS2, the full scan and AIF did not identify M5 (pyrrolidine ring opening + hydroxylation), the lowest prevalence metabolite other than M8.
A variety of different product ions were observed for 4-methoxy-α-PVP (Fig. 1), with m/z 121 the most intense fragment ion at the given collision energy conditions. The base peak is generated by cleavage between the β-keto (C1′) and aromatic ring. Cleavage of the bond between the β-keto (C1′) and alpha (C2′) carbon generated the next highest intensity ions at m/z 126 and 135, similar to cleavage that was previously reported for α-PVP . Fragmentation of the bond between the pyrrolidine ring and alpha carbon (C2′) produced m/z 191 and 72 ions. Other distinct product ions included m/z 219 (loss of C3H7 radical) and 84. The unique fragmentation pattern of 4-methoxy-α-PVP was used for metabolite structure elucidation.
Identification of O-demethylated metabolites
Identification of hydroxylated metabolites
Metabolite generated by ketone reduction
Other identified metabolites
Prevalence of 4-methoxy-α-PVP metabolites
Metabolic stability assessment in HLMs
4-Methoxy-α-PVP exhibited a long half-life (79.7 ± 1.3 min) in vitro, identified by the slow 9 and 21.3 % decrease of parent compound at 1 and 3 h in the hepatocyte samples, respectively. Based on the intrinsic clearance, extraction ratio and half-life observed in this study, 4-methoxy-α-PVP is predicted to be slowly metabolized. Low clearance compounds typically have CLint < 15 mL min−1 kg−1  and extraction ratios <0.3 . It is important to note that predicted hepatic clearance can vary among individuals based on enzymatic polymorphisms.
Comparison with other synthetic cathinones
In general, 4-methoxy-α-PVP followed the metabolic patterns identified in other structurally similar synthetic cathinones, i.e. multiple hydroxylation products, carbonylation, ring opening and oxidation reactions, ketone reduction, and O-demethylation . The major metabolic pathway identified in our study was O-demethylation of the 4-methoxy group, similar to MOPPP (a 4-methoxy-α-pyrrolidinophenone with a three-carbon alkyl chain) in HLMs by Springer et al. .
Several studies investigated the metabolism of α-PVP, the closest analog to 4-methoxy-α-PVP. An in vivo study examining rat urine following a 20 mg/kg α-PVP dose reported multiple hydroxylation products of α-PVP, carbonylation of the 2′-position of the pyrrolidine ring, ring opening and oxidation reactions, as well as degradation of the pyrrolidine ring to the corresponding primary amine . For α-PVP and α-PBP in authentic human urine specimens, Uralets et al.  reported N-dealkylation to the primary amine metabolite that subsequently underwent ketone reduction. The authors suggested that the pyrrolidine ring hinders direct ketone reduction. No information was provided about the time of urine collection and how long after dosing the specimens were collected. However, ketone reduction was identified by Tyrkko et al.  while investigating the phase I metabolites of α-PVP in vitro using HLMs and in authentic human urine. Similarly, Shima et al.  found that the two major metabolic pathways of α-PVP identified in human urine were the reduction of the ketone to the corresponding alcohol and the carbonylation of the 2′-position of the pyrrolidinophenone ring to produce the 2″-oxo metabolite, which was also confirmed by Namera et al. . Alpha-PVP and α-PBP do not contain the 4-methoxy moiety, and therefore, cannot produce O-demethylated metabolites.
We identified all α-PVP metabolic biotransformations, as discussed above, in our study of the novel 4-methoxy-α-PVP, with the exception of N-dealkylation of the pyrrolidine ring to the corresponding primary amine. Since formation of the primary amine metabolite was one of the major pathways for α-PVP, we carefully checked all our raw data, and although we did not identify this metabolite, we did find the metabolite resulting from degradation of the pyrrolidine ring in combination with O-demethylation (M1). In agreement with Tyrkko et al.  and Shima et al. , we also identified a metabolite resulting from direct reduction of the ketone moiety for 4-methoxy-α-PVP (M7) as also observed for α-PVP. Ketone reduction and carbonylation of the pyrrolidine ring were ranked 3rd and 4th among the most prevalent metabolites, respectively.
It is important to note that although incubation with hepatocytes reflects liver metabolism, it cannot reflect additional processes involved in drug elimination including extrahepatic metabolism, enterohepatic circulation, and renal filtration, re-absorption, and secretion. In addition, distribution processes can alter parent and metabolite patterns and concentrations that hepatocytes cannot simulate. Metabolites identified in these experiments may be present in human urine and blood after 4-methoxy-α-PVP intake; however, it is important to confirm these metabolites in authentic specimens as hepatocytes only reflect one aspect of drug elimination.
In silico prediction of 4-methoxy-α-PVP metabolites
In silico metabolite prediction software was used to predict 4-methoxy-α-PVP metabolic candidates and evaluate them against metabolites identified in vitro. Eleven phase I metabolites with a probability score >20 % were predicted in silico, while ten phase I metabolites were identified in the 3 h hepatocyte samples. Three in silico metabolites (>20 %) matched metabolites identified in the 3 h hepatocyte samples, namely M3, which corresponds to P1 scoring 100 % in silico and also the predominant hepatocyte metabolites, M10 (→P7) and M11 (→P8) (Table 1). Metabolites resulting from hydroxylation of the pyrrolidine ring (M4) and ketone reduction (M7) also were predicted in silico, although scores were less than 20 % (19.1 and 8.2 %, respectively). Five hepatocyte biotransformations were not predicted in silico; however, these metabolites involved combinations of phase I biotransformations, which the software does not readily predict. Manufacturers suggest analyzing the top predicted metabolites (100 % scores) in the software in order to identify potential second-generation metabolites. In our case, none of the predicted second-generation metabolites were identified in vitro in this study; a possible reason might be the low clearance of the parent compound. Although in silico prediction software has limitations, it is a valuable investigative tool for metabolite identification, especially for novel psychoactive substances. Limited information for these compounds exists; thus in silico prediction software can aid in developing a chromatographic method as it suggests metabolites that may be less polar than the parent. In addition, the software also can be used to import an inclusion list for MS acquisition methods.
4-Methoxy-α-PVP followed the metabolic patterns comparable to other pyrrolidinophenones, including O-demethylation, ketone reduction, multiple hydroxylation products, ring opening and oxidation reactions, N-dealkylation, and carbonylation to the corresponding lactam. The combination of different HRMS acquisition methods proved useful for identifying potential metabolites, as these are the first data identifying 4-methoxy-α-PVP metabolites that are good targets for documenting drug intake in forensic and clinical investigations. Based on our results, 4-hydroxy-α-PVP, the predominant metabolite, and the parent compound are good markers to identify consumption. This study demonstrated the applicability of using in vitro techniques coupled with HRMS methods to elucidate the metabolic profiles of emerging NPSs and comparison with in silico metabolite predictions. It is necessary to elucidate the metabolic pathways of these NPSs to enable linkage to adverse effects and recent intake.
The authors would like to acknowledge Timothy Moeller from BioreclamationIVT for his assistance with the human hepatocyte incubations, and Ismael Zamora from Molecular Discovery for his help with MetaSite and WebMetabase software. This research was supported by the Intramural Research Program of the National Institute on Drug Abuse, National Institutes of Health.
Compliance with ethical standards
Conflict of interest
There are no financial or other relations that could lead to a conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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|National Institutes of Health|