Electrochemistry–Mass Spectrometry for Generation and Identification of Metabolites of Selected Drugs from Different Therapeutic Groups in Comparison with In Vitro and In Vivo Approaches

Abstract

The metabolism of antibiotics, antidepressants, and cardiovascular drugs has been investigated widely over the last few decades. The aim of this study was to develop an efficient analytical protocol based on the combination of electrochemistry (EC) and mass spectrometry for the identification of electrochemical products (potential pharmacologically active metabolites) of selected drugs (enalapril, metronidazole, midazolam, propranolol, venlafaxine). The electrochemical mimicry of the oxidative phase I and II metabolism was achieved in a thin-layer cell equipped with different working electrodes (magic diamond (MD), glassy carbon (GC), gold (Au), platinum (Pt)). The structures of the electrochemically generated metabolites were elucidated based on accurate mass ion data and tandem mass spectrometry (MS/MS) experiments. The in silico prediction of the main sites of selected drugs’ metabolism was performed using Biotransformer 3.0, GLORYx, and Xenosite software. Moreover, incubation with liver microsomes (LMs) was performed to examine the proposed metabolic pathways of target compounds. The data from in vitro experiments agreed with the data from electrochemical oxidation, which predicted some potential metabolites found in the real samples from patients. For enzymatic incubation, N-dealkylation, O-demethylation, and hydroxylation were the metabolic pathways involved mainly in their metabolism. Their in vitro phase II metabolites were identified as glucuronic acid conjugates. Finally, different in vivo phase I and II metabolites were identified for the studied drugs, including metabolic pathways for in vivo phase I N-demethylation, N-dealkylation, O-demethylation, and hydroxylation, while the metabolic pathways for in vivo phase II metabolites were identified as glucuronic acid conjugates.

1. Introduction

Recently, a new approach to studying the metabolic biotransformation of biologically active compounds emerged. It focuses on electrochemistry (EC) and mass spectrometry (MS). This technique allows the imitation of the course of many typical reactions of phase I and II metabolism. However, some drugs are converted into active metabolites with enhanced biological activities or reactive metabolites, often decreasing or deactivating their biological activity. Also, it enables the identification of electrochemical products that may be potential metabolites. EC/MS is most often used in the study of drug metabolism [1,2,3,4,5]. Many compounds in the body are oxidized or reduced because they have redox properties (over 90%). Hence, the explanation of redox reactions of metabolism is a key point in the field of drug pharmacology. Electrochemistry has been used in biomedical chemistry for years to study the oxidation–reduction properties of pharmaceuticals. In turn, the combination of electrochemistry with mass spectrometry EC/MS allows the generation and identification of electrochemical products for biologically active compounds with diverse physicochemical properties. The advantage of this method is that it can locate oxidation products immediately after their formation. This method can effectively predict the oxidation process initiated by single-electron oxidation, such as N-dealkylation, S-oxidation, P-oxidation, the oxidation of alcohols, the hydroxylation of aromatic systems, and dehydrogenation. Electrochemistry is not advantageous for reactions initiated by direct hydrogen atom acquisition, such as O-dealkylation or aliphatic hydroxylation of unsubstituted aromatic rings, due to the excessively high oxidation potential required for electrochemical oxidation reactions [1,2,3,4,5].

Enalapril (ENAL) is a drug belonging to the group of angiotensin-converting enzyme (ACE) inhibitors (Figure S1, Supplementary Information) [6,7].

Metronidazole (MET) is an antibiotic belonging to the nitroimidazole class (Figure S3, Supplementary Information) [8,9].

Midazolam (MID) belongs to the class of imidazobenzodiazepine derivatives (Figure S2, Supplementary Information) [10,11,12].

Propranolol (PRO) belongs to the group of drugs from the β-blockers group, blocking the action of catecholamines (adrenaline and noradrenaline) on the heart (Figure S4, Supplementary Information) [12,13].

Venlafaxine (VEN) is a bicyclic derivative of phenylethylamine. VEN belongs to the group of second-generation antidepressants (Figure S5, Supplementary Information) [14,15,16].

The described pharmaceuticals are among the most frequently prescribed medications in modern medicine. It is therefore of great interest to study the detailed metabolism of these biologically active compounds. Mostly, they undergo hepatic metabolism, forming hydroxy, acetyl-, and carboxyl derivatives. As a result of these processes, several active metabolites are formed. Some metabolites are immediately conjugated with glucuronic acid after formation, creating end products considered pharmacologically inactive [17]. Moreover, some drug metabolites possess toxicity profiles distinct from those of the parental compounds, which are essential to consider when evaluating the toxicity of a drug as a whole [18]. Consequently, based on the obtained results, the in vitro and in vivo studies conducted on the metabolism of selected medicines occurring in the presence of the metabolizing enzyme present in liver cells, along with the implementation of an electrochemical reactor for generating potential metabolites, provide a basis for understanding the possible directions of activation and/or detoxification changes that these compounds undergo in vivo in the patient’s body [17,18].

Hence, the primary objective of this research was to develop new analytical procedures for identifying electrochemical products (instrumental method) in the sense of the potential pharmacologically active metabolites in biological samples. This objective was achieved by using mass spectrometry in the identification of electrochemical products of selected drugs (in combination on-line/off-line), performing in vitro metabolic transformations of selected drugs against enzymes of the microsomal fraction of liver cells, and identifying metabolites in real samples of patients undergoing pharmacological therapy (

Table 1. Characteristics of the analytes belonging to different therapeutic groups obtained with the use of EC experiments, in vitro incubation, and real samples (with the predicted atomic sites of metabolism for the study drugs by the Xenosite web predictor).

Analyte	Structural Formula	Molecular Formula	Molar Mass [g/mol]	Application
Enalapril		C20H28N2O5	376.45	angiotensin converting enzyme inhibitor
Enalaprilat (pharmacologically active metabolite)		C18H24N2O5	348.40	angiotensin converting enzyme inhibitor
Enalapril glucuronide (metabolite)		C26H36N2O11	552.58	angiotensin converting enzyme inhibitor
Metronidazole		C6H9N3O3	171.16	antibiotic
Hydroxymetronidazole (pharmacologically active metabolite)		C6H9N3O4	187.15	antibiotic
Metronidazole glucuronide (metabolite)		C12H17N3O9	347.28	antibiotic
Midazolam		C18H13ClFN3	325.78	anesthetic
Hydroxymidazolam (pharmacologically active metabolite)		C18H13ClFN3O	341.77	anesthetic
Propranolol		C16H21NO2	259.34	β-blocker
Hydroxypropranol (pharmacologically active metabolite)		C16H21NO3	275.35	β-blocker
Propranolol glucuronide (metabolite)		C22H29NO8	435.47	β-blocker
Venlafaxine		C17H27NO2	277.40	antidepressant
Desmethylvenlafaxine (pharmacologically active metabolite)		C16H25NO2	263.38	antidepressant

). Additionally, in silico predictions were performed using GLORYx and Biotransformer 3.0 freeware. Additionally, the predicted sites of reactive functional groups for the study molecules were obtained using the XenoSite predictor, available at https://xenosite.org/ (accessed on 28 April 2025). The outcomes were obtained as a visual image regarding the color gradient intensity, including a color scale bar. The highest probability of metabolism at an atomic site is denoted with a red color (highest score: 1), while a white color implies a zero SOM (site of metabolism) score, indicating no possibility for metabolism at an atomic site. SOMs were proposed using nine key CYP450 isoenzymes including 1A2, 2A6, 2B6, 2C8, 2C9, 2C19, 2D6, 2E1, and 3A4.

2. Experimental

2.1. Apparatus

The studies were conducted using the following apparatus: a mass spectrometer(Agilent, Waldbronn, Germany) with an ESI ionizer in the positive ionization mode. The data collection was performed using MassHunter software (Agilent, Waldbronn, Germany); a liquid chromatograph 1100 (Agilent, Waldbronn, Germany) equipped with a binary pump (G1310A), degasser, autosampler (G1313A), and UV-Vis detector (G1314A); a computer with MassHunter Qualitative Analysis software, MassHunter Quantitative Analysis; an analytical Sigma 3K30C centrifuge (Hanna Instrument, Berdfordshire, UK); an ultrasonic bath (Polsonic, Warsaw, Poland); a laboratory pH meter, which was used to prepare buffer solutions (IKA POL, Warsaw, Poland); a Sartorius Stedim solvent filtering kit (Göettingen, Germany); an AS 220/C/2 electronic analytical balance (Radwag, Radom, Poland); and an Eppendorf Research automatic pipette set (Eppendorf, Hamburg, Germany).

2.2. Reagents

The following reagents were used in the analyses: water for LC/MS, 99.9% (Sigma-Aldrich Chemie, Steinheim, Germany); methanol for LC/MS (Sigma-Aldrich Chemie, Steinheim, Germany); acetonitrile for LC/MS (Sigma-Aldrich Chemie, Steinheim, Germany); deionized water from Mili-Q apparatus (MiliporeIntertech, Bedford, MA, USA); ammonium formate for LC/MS (Sigma-Aldrich Chemie, Steinheim, Germany); ammonium acetate for LC/MS (Sigma-Aldrich Chemie, Steinheim, Germany); ammonia for LC/MS (Sigma-Aldrich Chemie, Steinheim, Germany); formic acid for LC/MS (Sigma-Aldrich Chemie, Steinheim, Germany); drugs and metabolites—enalapril (ENAL), enalaprilat (ENALAP), enalapril glucuronide (GLU ENAL), metronidazole (MET), hydroxymetronidazole (MET OH), metronidazole glucuronide (GLU MET), midazolam (MID), hydroxymidazolam (MID OH), propranolol (PRO), hydroxypropranolol (PRO OH), propranolol glucuronide (GLU PRO), venlafaxine (VEN), and desmethylvenlafaxine (VEN DEME) (Sigma-Aldrich Chemie, Steinheim, Germany) (Table 1). Chemicals and solvents were LC/MS or analytical with the highest quality. They were obtained from Sigma-Aldrich (Schnelldorf, Germany). Acetonitrile, methanol, and formic acid were LC/MS-grade, obtained from J. T. Baker (Deventer, the Netherlands). Ultra-pure water used for all analyses was obtained from Sigma-Aldrich (Schnelldorf, Germany). Ammonium formate was purchased from Fluka (Buchs, Switzerland), and ammonium hydroxide and formic acid from Sigma-Aldrich (Schnelldorf, Germany). Glucose-6-phosphate, β-NADP+, glucose-6-phosphate dehydrogenase, and uridine 5′-diphosphoglucuronic acid triammonium salt (UDPGA) were obtained from Sigma-Aldrich (Schnelldorf, Germany).

2.3. In Silico Metabolite Prediction

Putative metabolites of the target pharmaceuticals were predicted using selected compounds’ SMILES strings:

(ENAL: O=C(O)[C@H]2N(C(=O)[C@@H](N[C@H](C(=O)OCC)CCc1ccccc1)C)CCC2;

MET: OCCn1c(C)ncc1[N+](=O)[O-];

MID: ClC1=CC=C2C(C(C3=CC=CC=C3F)=NCC4=CN=C(C)N42)=C1;

PRO: OC(COC1=C2C=CC=CC2=CC=C1)CNC(C)C;

VEN: OC2(C(c1ccc(OC)cc1)CN(C)C)CCCCC2). Prediction was achieved through free online software GLORYx [19] and Biotransformer 3.0 [20]. Briefly, GLORYx integrates machine learning-based site of metabolism (SoM) prediction into biotransformation reaction rule sets to predict and classify the putative structures of metabolites that could be formed by phase I and/or phase II metabolism. Biotransformer 3.0 is an open access software tool that supports the rapid, accurate, and comprehensive prediction of small molecule metabolism in mammals and environmental microorganisms. The ChemDraw software (version: 21.0.0) generated all the metabolite structures (PerkinElmer, Inc., Waltham, MA, USA).

2.4. In Vitro Studies Using Liver Microsomal Fraction Enzymes

The incubation of selected drugs with liver cell microsomal fraction enzymes was carried out in an appropriate reaction buffer (BR). After the initial preincubation in a water bath (37 °C), a cofactor of the enzymatic reaction—NADPH/UDPGA at a concentration of 20 mM [16.67 mg NADPH/1 mL H₂O]—was added to the mixture of a given biologically active compound and microsome (enzyme). The resulting reaction mixture was further incubated at the same temperature for a specified time (0, 15, 30, 60, 90, and 120 min). Then, the obtained solution was cooled in an ice bath with the addition of an equivalent amount of chilled acetonitrile to stop the reaction. The obtained mixture was centrifugated (5–15 min, 5000 rpm). The resulting supernatant was analyzed using RP–HPLC with UV-Vis detection and ESI–MS/MS. BR (sodium phosphate solution used for incubation with the microsomal enzyme fraction, pH = 7.4): 100 mM Na₂HPO₄ × 7 H₂O [1.78 g/100 mL H₂O]; 2 mM EDTA(Na₃) [0.0716 g/100 solution]; mixed with magnesium chloride solution in a 1:1 (v/v) ratio of 6 mM MgCl₂ × 6 H₂O [0.122 g/100 mL H₂O]. Reactions in the presence of human microsomal enzymes were carried out in the following solution: 10 µL of a 2 mM solution of the compound (drug) in water (0.2 mM), 10 µL of 20 mM NADPH (2 mM), 10 µL of 20 mg/mL microsomal enzymes (2 mg/mL), and 35 µL of phosphorus solution (BR). Microsomes derived from mouse liver cells (MLMs) at 10 mg/vial concentrations were used in the enzymatic studies. The microsomal fractions were stored at 80 °C and thawed immediately prior to each experiment.

2.5. EC-LC-MS/MS Experiments

The electrochemical experiments were performed using the ROXY™ system (Antec, Zoeterwoude, The Netherlands) containing a potentiostat equipped with an electrochemical cell ReactorCell™, an infusion pump, and all the necessary LC connections. The instrument was controlled via the Dialog software, version 2.02 (Antec, Zoeterwoude, The Netherlands). The electrochemical system was connected to the Triple Quad MS (Agilent, Germany) for direct EC/MS analysis. The instrument was operated with the Agilent Mass Hunter software version B.04.01, which is used for data acquisition, instrument control, and data analysis. A Labconce CentriVap DNA concentrator (Kansas City, MO, USA) was used for sample evaporation and centrifugation. In our laboratory, water was obtained using Millipore’s Milli-Q RG apparatus (Millipore Intertech, Bedford, MA, USA).

The analyses in combination with LC were performed using an ACE5 C18 300 chromatographic column (150 × 4.6 mm), with the following mobile phases: (A) water + formic acid 0.1% + 2 mM ammonium formate; (B) acetonitrile + formic acid 0.1% + 2 mM ammonium formate; mobile phase flow 0.4 mL/min.

Table 2. Mass spectrometer operating parameters for the tested drugs and their metabolites (ESI(+) ionization; carrier gas: nitrogen; gas flow: 6.0 L/min, 35 psi, 4000 V).

Drug/Metabolite (Abbreviation)	Parent Ion [M+H]+	Product Ion— Qualifier Q1	Product Ion— Quantifier Q3	Drying Gas Temperature [°C]	Fragmentor Voltage [V]
ENAL	377	234	303	320	70
ENALAP	349	206	117	320	70
GLU ENAL	553	377	234	320	70
MID	326	248	291	320	110
MID OH	342	248	288	320	110
MET	172	128	111	320	70
MET OH	188	126	144	320	70
GLU MET	348	172	126	320	70
VEN	278	260	121	290	110
VEN DEME	264	246	107	290	110
PRO	260	116	183	290	150
PRO OH	276	116	199	290	150
GLU PRO	436	260	116	290	150

presents the appropriate operating parameters of the mass spectrometer, which were used during further experimental work. The selection of conditions was established using a central composition plan (CCD). The MS was operated in multiple reaction monitoring (MRM) mode. The MRM analysis was used to monitor the transitions from precursor ions to dominant product ions. Two specific transitions were used to determine each compound, with the most intense transition used for quantification (quantifier transition) and the other one being used for confirmation purposes (qualifier transition).

The electrochemical cell consisted of a classical three-electrode arrangement, including a working electrode, a counter electrode (inlet block), and a reference electrode (HyREF electrode) (Antec, The Netherlands). A 50 mm spacer (a cell volume of 0.7 μL) separated the working and the counter electrode inlet block. In addition, the scan was performed using different working electrode materials such as glassy carbon (GC), a boron-doped diamond electrode (MD) consisting of an ultra-thin crystalline diamond layer deposited on top of a silicon substrate, gold (Au), and platinum (Pt). The accessible area of the working electrode was 15 mm2. The applied working electrodes without MD were cleaned with a polishing disk and diamond slurry provided by the manufacturer before each experiment.

To simulate phase I metabolism, a 5 μM solution of a tested compound in one of the above mobile phases was prepared. Then, the solution with added acetonitrile was introduced via an injection pump into the reaction chamber (kept at 36.7 °C), equipped with a working electrode (GC, Pt, Au, or MD) at the rate of 10 μL/min. The recorded mass voltammograms were obtained by linearly increasing the working electrode potential from 0 to 2000 mV (for GC, Pt, Au) or 3000 mV (for MD) at 10 mV/s.

Potential phase II metabolism occurred similarly to potential phase I metabolism. Then, the solution containing the derivatives of the studied drug (created during phase I) left the chamber, mixing with 25 μM glucuronic acid solution, with enzymatic catalysis required for the spontaneous reaction of UDPGA (uridine 5′-diphosphoglucuronic acid triammonium salt) in the same mobile phase as the analyzed substance, which was achieved using a 100 μL reaction coil placed between the ReactorCell and the ESI source. The defined volumes (100 μM UDPGA solution and 25 μM glucuronic acid solution) of both reactants were injected into the electrochemical system via a syringe pump. Having passed through a reaction coil, the reaction products were directly detected using an ESI-MS ion trap mass analyzer in positive ion mode for identifying potential adducts. The optimization of EC conditions was accomplished for each target compound by the infusion of the native form (Figures S6 and S7, Supplementary Information).

The efficiency of a performed conversion was calculated as a ratio between the signal intensity of the target compound with no potential applied (Cell_Off, no oxidation/reduction, control measurement) and with applied voltages (Cell_On, oxidation/reduction) according to the following equation: 100% × (Cell_Off − Cell_On)/Cell_Off. Accordingly, the values of Cell_Off and Cell_On correspond to the intensity of the average mass spectra characterized for the target compound.

2.6. Real Samples from Patients

The study material consisted of urine and plasma samples from patients from various clinical departments. The Nicolaus Copernicus University Bioethics Committee approved the study in Toruń (decision no. 477/2021). All the study participants were informed verbally and in writing about the purpose of the study and completed a questionnaire and a declaration of voluntary consent to participate in the study. Samples were collected in test tubes after the oral administration of the drug (minimum 2 weeks). Then, after centrifugation, they were frozen (−80 °C) and thawed immediately before the developed analytical procedure was performed. The study group of patients also had other comorbidities, including hypertension, diabetes, osteoporosis, obesity, bronchial asthma, circulatory failure, thyroid disease, or urinary incontinence, and therefore, these people took various medicinal preparations that could affect the body’s metabolism.

2.7. Microextraction in Packed Syringe Procedure

Microextraction in a packed syringe (MEPS) was performed using a 250 L volume syringe coupled with barrel insert and needle assemblies (BINs) packed with the relevant sorbent. C₁₈ MEPS sorbent material was obtained from SGE Analytical (Melbourne, Australia). Before being used for the first time, a BIN was conditioned with 100 mL of MeOH/H₂O (v/v, 9:1) followed by 100 L 0.1% formic acid. After that, 5 × 50 mL of human plasma diluted with water (1:1, v/v) was drawn through a BIN very slowly to provide sufficient interaction between the analytes and the sorbent (urine without dilution). Then, to remove interfering agents, the BIN was washed with 2 × 50 mL of 0.1% formic acid and dried by blowing 2 × 50 mL of air. Then, 250 mL of MeOH eluted the analyzed drugs to Eppendorf vials (1.5 mL) in the following step. The MEPS cartridge was rinsed with 2 × 250 mL of MeOH and then 2 × 250 mL of 0.1% formic acid to prepare for the following sample. The eluate evaporated, and the dry residue was dissolved in 50 mL of the mobile phase.

2.8. Method Validation

The method was validated according to ICH guidelines. The following validation characteristics were addressed: linearity, detection and quantitation limits, accuracy, precision, robustness, selectivity, and stability (Tables S1–S3, Supplementary Information).

3. Results and Discussion

3.1. Selection of Mass Spectrometer Operating Parameters

The first stage of the study was the selection of the operating parameters of the mass spectrometer (Table 2). For this purpose, the ionization mode and the fragmentor voltage were changed for selected compounds at a constant pressure of 35 psi. No signals were observed, or their intensity was much lower for the negative ionization mode than for the ionization in the positive mode. Therefore, the ionization in the positive ESI(+) mode was selected for further studies. The determination of analytes using the mass spectrometer was carried out with positive polarization in the so-called full-spectrum sweep mode (Full Scan), with single-ion monitoring (SIM), and product ion search (Product Ion). Scanning in full mode was performed to determine the m/z quotient value of the pseudomolecular ion; i.e., the analyte molecule mass was increased by the mass of the attached proton. Using the selected ion monitoring mode, the voltage value on the fragmentor was chosen—the voltage at the end of the capillary directing ions to the Q1 analyzer. Using the Product Ion mode, daughter ions were obtained for each parent ion due to its fragmentation. At this stage, the previously selected voltage value on the fragmentor and the drying gas temperature were used, and the appropriate collision energy values were chosen depending on the tested compound.

3.2. Application of the Central Composition Plan

At the stage of selecting the mass spectrometer operating parameters for the tested analytes, a central compositional design (CCD) was used, which significantly facilitated the planning of the experiment. The drying gas temperature was set in the range of 290–350 °C, while the voltage value on the fragmentor was from 70 to 150 V. The plan consisted of nine experiments (

Table 3. Central composition plan used to select MS operating parameters.

Input Variables	Levels
	Low (−1)	Middle (0)	High (+1)
(X1) Fragmentor voltage [V]	70	110	150
(X2) Drying gas temperature [°C]	290	320	350
Experiment number	X1	X2
5	290 (−1)	110 (+1)
1	350 (+1)	110 (−1)
4	320 (0)	110 (0)
8	290 (0)	150 (+1)
3	290 (0)	70 (−1)
7	350 (−1)	150 (−1)
9	320 (−1)	150 (0)
2	320 (+1)	70 (0)
6	350 (+1)	70 (+1)

), and the parameter values were in the normalized form of −1, 0, and +1.

The obtained results were presented graphically in spatial response surface plots for ENAL, MID, VEN, and their metabolites (Figure 1). The determined values of the drying gas temperature and the fragmentor voltage are presented in Table 3.

The graphs show that the best matching of conditions for ENAL and its metabolites depends on X2. The higher the drying gas temperature, the better the matching of the system and the larger the surface area. The value of X1, i.e., the voltage on the fragmentor, is not essential here. The results for MID and its metabolite are similar. However, for VEN and its metabolite, the opposite is true. The matching depends on the voltage value on the fragmentor. Initially, the higher the value of X1, the better the matching, but after reaching the optimal value to determine the best matching, a further increase in X1 causes the result to deteriorate. However, the value of the drying gas temperature is not significant here. The conclusion is that with the exact parameters of the drying gas temperature and the voltage on the fragmentor for the drug, its metabolites can be simultaneously marked and identified.

The fragmentor voltage is a key parameter that controls the degree of in-source fragmentation, influencing the intensity and fragmentation patterns of ions. It affects the sensitivity and limit of the detection of target compounds in the LC-MS analysis. Optimizing the fragmentor voltage is crucial for achieving the best analytical performance and obtaining reliable data in the targeted analysis. It constitutes a compromise between maximizing the signal intensity and ensuring the sufficient fragmentation for identification. Such a specific range (70–150 eV) was used to generate some fragmentation for the structural analysis, to maximize the number of fragment ions for the analysis and improvement of the signal-to-noise ratio by reducing the background interference. Drying gas flow (N2) is crucial for effective ionization and detection. It helps remove solvent vapor from the samples, preventing it from interfering with the ionization process and ensuring accurate measurements. Optimizing drying gas flow in mass spectrometry involves finding a balance between maximizing the analyte signal and maintaining a good peak shape. Such a specific range (290–350 °C) was used to find a balance between efficient desolvation and maintaining the analyte stability.

3.3. In Silico Prediction of Metabolism

The in silico prediction of metabolism was performed using GloryX (Table S4, Table S6, Supplementary Information) and Biotransformer3.0 free ware and results are shown in Table S3, Supplementary Information. Moreover, a reliable tool for classifying predicted metabolites was obtained by using SoM (site of metabolism) probabilities predicted by FAME 3 machine learning models (Table S5, Supporting Information) to evaluate predicted metabolites [19,20]. Predicted derivatives were classified as probable, minor, and less probable with appropriate numbering. Phase I and phase II metabolites were proposed in the study. Primary phase I metabolites included hydroxylated and oxidized derivatives such as enalaprilat, hydroxymidazolam, hydroxymetronidazole, desmethylvenlafaxine, and hydroxypropranolol. Major phase II metabolites included glucuronic conjugated, sulfo conjugated, and methylated derivatives such as enalapril glucuronide, metronidazole glucuronide, and propranolol glucuronide.

3.4. Electrochemical Simulation of the Metabolism of Selected Drugs

During the study, four types of working electrodes were tested: platinum (Pt), gold (Au), carbon (GC), and Magic Diamond^TM. The platinum and carbon electrodes are adapted to work in the range of 0–2000 mV. The potential range of the Magic Diamond^TM electrode is higher than in the case of previous electrodes and is 0–3000 mV. The electrochemical processes of the tested analytes were carried out with an increasing potential value (10 mV/s) and a mobile phase flow (FR) of 10 µL/min. The criteria for selecting the working electrode was the number and intensity of signals in the mass spectrum. Analyzing the obtained MS spectra of the tested compounds, it can be stated that the most significant number of signals is possible using a diamond electrode (MD). This working electrode’s efficiency is probably related to the possibility of conducting electrochemical processes across a broader range of potentials. The MD electrode is a type of electrode material used in electrochemistry due to its exceptional properties. These include a wide potential window, low background currents, and corrosion resistance, making it suitable for various applications. In comparison to the different electrodes (GC, Au, Pt) it can be used to apply a wide range of voltages in electrochemical reactions without causing the water to decompose or the electrode to corrode, allowing for the study of a broader range of target compounds. Moreover, the MD electrode produces very little current when no reaction is taking place, which allows for more accurate measurements of electrochemical reactions. It is stable in harsh chemical environments, making it ideal for long-term experiments.

In connection with the attempt to simulate human metabolism, it was assumed that the experiment would be performed in an environment corresponding to physiological pH (pH = 7.4). Lower and higher pH decreased the amount of electrochemical products obtained, which is probably related to changes in the solubility of analytes in the mobile phase. The obtained results prove that electrochemistry and mass spectrometry effectively simulate drug metabolism. Further development of this technique will probably allow it to also be used as an alternative in vitro method for studying oxidation–reduction reactions. It should be expected that in the future, the popularization of research in this field will allow for the conducting of standard screening tests, enabling the prediction of its further course, and consequently will allow individualized and more effective treatment among patients. Detailed information regarding the optimization steps is presented in the Supplementary Information [Part 2. Electrochemical simulation of selected drug metabolism].

3.5. Analysis of Mass Spectrometric Fragmentation Patterns of Selected Drugs and Their Pharmacologically Active Metabolites for EC/ESI-MS/MS Experiments

Based on the MS spectra and signal intensities for characteristic m/z, the proposed structures of the products obtained by electrochemical oxidation were presented (Table 1). The structures were proposed based on EC/ESI-MS/MS experiments, the Mass Hunter Metabolite ID computer program, and information available in the literature. Using this analytical tool, the signals from the tested analyte regarding the control samples were determined by comparing their retention times, MS spectra, and surface areas. With the previously selected conditions of analysis using a mass spectrometer, the signals for the tested drugs were identified as [M+H]⁺ with characteristic m/z and retention coefficients (k). The multi-stage fragmentation of the tested drugs was performed to obtain data to interpret MS/MS spectra for potential metabolites of selected compounds (Figure 2). In phase II, there were no signals of free glucuronic acid because its molar mass (194.1 g/mol) or m/z was below the detected range. Because the ESI mass spectra presented a mixture of the resulting oxidation products and their conjugation with glucuronic acid, only proposed chemical structures could be provided. Separating the products using a preparative scale for further NMR experiments could contribute to structural identification. Nevertheless, it can be assumed that the determined m/z values represent known metabolites from the phase II metabolism of the studied drugs. Figure S8 shows the MS/MS fragmentation patterns of selected drugs and their pharmacologically active metabolites.

The fragmentation spectrum for enalapril (Figure 2a) shows the signal m/z = 377, which corresponds to the structure of the tested compound, the signal m/z = 303, which was generated by the cleavage of the ethyl ester, and the signal m/z = 234, which was generated by the cleavage of 1-carboxyproline. The fragmentation spectrum for enalaprilat (Figure 2b) reveals the signal m/z = 349, which corresponds to the structure of the tested compound, the signal m/z = 206, which was generated by the cleavage of 1-carboxyproline, and styrene, and the signal m/z = 160, which was generated by the cleavage of 1-carboxyproline and formic acid. The fragmentation spectrum for enalapril glucuronide (Figure 2c) shows the signal m/z = 553, which corresponds to the structure of the tested compound, the signal m/z = 377, which was caused by the cleavage of glucuronic acid, the signal m/z = 303, which was caused by the cleavage of glucuronic acid and ethyl ester, and the signal m/z = 234, which was caused by the cleavage of glucuronic acid and 1-carboxyproline. The fragmentation spectrum for midazolam (Figure 2d) shows the signal m/z = 326, which corresponds to the structure of the tested compound, and the fragment signal m/z = 291. The fragmentation spectrum for hydroxymidazolam (Figure 2e) shows the signal m/z = 342, which corresponds to the structure of the tested compound, and the fragment signal m/z = 291. The fragmentation spectrum for metronidazole (Figure 2f) shows the signal m/z = 172, which corresponds to the structure of the tested compound, and the signal m/z = 128, which was created by the cleavage of the C₂H₄O molecule. The fragmentation spectrum for hydroxymetronidazole (Figure 2g) shows the signal m/z = 188, which corresponds to the structure of the tested compound, the signal m/z = 144, which was created by the cleavage of the C₂H₄O molecule, and the signal m/z = 126, which is likely a protonated 2-methylene-4-nitroimidazole. The fragmentation spectrum for metronidazole glucuronide (Figure 2h) shows the signal m/z = 348, which corresponds to the structure of the tested compound, and the signal m/z = 172, which was created by the cleavage of glucuronic acid. m/z= 126 corresponds to protonated 2-methylene-4-nitroimidazole, m/z = 141 is a dehydrated gluconic acid oxonium ion, and m/z = 113 is a further fragment from m/z = 141. The fragmentation spectrum for venlafaxine (Figure 2i) shows the signal m/z = 278, which corresponds to the structure of the tested compound, the signal m/z = 260, which was caused by the cleavage of the OH group, the signal m/z = 215, which was caused by the cleavage of the OH group and dimethylamine, the signal m/z = 121, which is likely (4-methoxyphenyl)methylium, and the signal m/z = 147, which was caused by the cleavage of dimethylamine and phenol. The fragmentation spectrum for desmethylvenlafaxine (Figure 2j) shows the signal m/z = 264, which corresponds to the structure of the tested compound, the signal m/z = 246, which was generated by the cleavage of the OH group, the signal m/z = 201, which was generated by the cleavage of the OH group and dimethylamine, the signal m/z = 107, which is likely (4-hydroxyphenyl)methylium, and the signal m/z = 132, which was generated by the cleavage of dimethylamine and phenol. The fragmentation spectrum for propranolol (Figure 2k) shows the signal m/z = 260, which corresponds to the structure of the tested compound, the signal m/z = 116, which was generated by the cleavage of 1-hydroxynaphthalene, the signal m/z = 157, which was generated by the cleavage of methoxynaphthalene, and the signal m/z = 183, which was generated by the cleavage of 1-isopropylamine and the -OH group. The fragmentation spectrum for hydroxypropranolol (Figure 2l) shows the signal m/z = 276, which corresponds to the structure of the tested compound, the signal m/z = 116, which was generated by the cleavage of 4,8-dihydroxynaphthalene, the signal m/z = 173, which was generated by the cleavage of 4-hydroxy-8-methoxynaphthalene, and the signal m/z = 199, which was generated by the cleavage of the -OH group and isopropylamine. The fragmentation spectrum for propranolol glucuronide (Figure 2m) shows the signal m/z = 436, which corresponds to the structure of the tested compound, the signal m/z = 260, which was caused by the cleavage of glucuronic acid, the signal m/z = 115, which results from the loss of CO₂ and H₂O from the glucuronic acid oxonium ion, and the signal m/z = 183, which was caused by the cleavage of isopropylamine and glucuronic acid.

3.6. Comparison of the Obtained Results

The results obtained for experiments conducted by the electrochemical simulation (EC) and incubation of the test drugs with liver cell microsomal fraction (LM) enzymes with the results of biological sample analysis for the five selected drugs (ENAL, MET, MID, PRO, and VEN) are presented in

Table 4. The experimental m/z values for product ions of selected drugs and their electrochemical products for EC experiments, potential metabolites in cases of in vitro incubation, and biological samples.

Compound	Ion ID	Experimental m/z	Chemical Formula
Enalapril Product ions	D1	377	C20H29N2O5
	P1	303	C17H26N2O3
	P2	234	C13H16NO3
	P3	160	C7H14NO3
	P4	130	C5H8NO3
Enalaprilat Product ions	M1	349	C18H25N2O5
	M1P1	303	C17H26N2O3
	M1P2	206	C11H12NO3
	M1P3	160	C7H14NO3
	M1P4	116	C4H4NO3
Enalapril glucuronide Product ions	M2	553	C26H36N2O11
	M2P1	377	C20H29N2O5
	M2P2	303	C17H26N2O3
	M2P3	234	C13H16NO3
Midazolam Product ions	D2	326	C18H14ClFN3
	P1	291	C18H14FN3
Hydroxymidazolam Product ions	M1	342	C18H14ClFN3O
	M1P1	291	C18H14FN3
	M1P2	178	C11H18N2
Metronidazole Product ions	D3	172	C6H10N3O3
	P1	128	C4H6N3O2
Hydroxymetronidazole Product ions	M1	188	C6H10N3O4
	M1P1	144	C4H6N3O3
	M1P2	126	C4H4N3O2
Metronidazole glucuronide Product ions	M2	348	C12H18N3O9
	M2P1	172	C6H10N3O3
	M2P2	141	C4H3N3O3
	M2P3	126	C4H4N3O2
	M2P4	113	C3H3N3O2
Venlafaxine Product ions	D4	278	C17H28NO2
	P1	260	C17H26NO
	P2	215	C15H19O
	P3	159	C11H11O
	P4	147	C10H11O
	P5	121	C8H7O
Desmethylvenlafaxine Product ions	M1	264	C16H26NO2
	M1P1	246	C16H24NO
	M1P2	201	C14H16O
	M1P3	159	C10H11O
	M1P4	133	C9H9O
	M1P4	107	C7H7O
Propranolol Product ions	D5	260	C16H22NO2
	P1	183	C13H11O
	P2	157	C11H9O
	P3	141	C10H5O
	P4	116	C8H4O
Hydroxypropranolol Product ions	M1	276	C16H22NO3
	M1P1	199	C13H11O2
	M1P2	173	C11H9O2
	M1P3	116	C8H4O
Propranolol glucuronide Product ions	M2	436	C22H30NO8
	M2P1	260	C16H22NO2
	M2P2	183	C13H11O
	M2P3	141	C10H5O
	M2P4	116	C8H4O

. All the proposed metabolites that were observed in the electrochemical cell (EC), liver microsomes (LMs), or real samples are summarized, including their ion ID, experimental m/z, and MS/MS fragmentation pattern, along with the chemical formula. It should be mentioned that the results obtained by the in vitro route show remarkable similarity to those obtained by the electrochemical method, since it was possible to generate all metabolites in vitro in the EC reaction cell. Table 4 summarizes the key similarities in the case of study drugs and their pharmacologically active metabolites. Moreover, the data from the biological samples originating from patients receiving medication were studied in detail. They were compared with the results obtained via the proposed EC-MS method using an electrochemical cell and the microsomal approach. Figure 3 presents MRM MS chromatograms for the selected drugs and their pharmacologically active metabolites. MRM MS spectra further confirmed the presence of the drug and its potential metabolites in biological samples collected from patients. The obtained results and the adopted methods are consistent with the literature, and reciprocally, as the same potential phase I and II metabolites were identified in all modeling approaches. The results demonstrate that combining electrochemistry and mass spectrometry is reliable for simulating drug metabolism. Development of this technique will likely enable its use as an alternative in vitro method for studying oxidation–reduction reactions.

Numerous approaches have been used in the literature to simulate the biotransformation of selected compounds in liver microsomes using enzyme-independent chemical systems. The results obtained in this study demonstrate that systems using an EC reaction cell can mimic these metabolic reactions and, in some cases, can generate the same metabolites observed in conventional enzymatic systems using CYP450 enzymes. However, no previous literature reports have compared the similarity of the metabolic profiles between mimic systems and conventional in vitro metabolism systems with real biological samples from patients. Therefore, a metabolic prediction approach using an EC/MS system was developed, demonstrating the ability to mimic the metabolite profiles generated by traditional microsomal incubation systems. All proposed phase I and II metabolic reactions of biotransformation are typical of processes catalyzed by cytochrome P450 isoenzymes. It can be concluded that liver microsomes exhibiting enzymatic activity can be very useful in studying the metabolism of drugs belonging to various therapeutic groups. A flowchart for the approach used in the experimental section is presented in Figure S8. Potential metabolites were identified during biotransformation simulations, consistent with the literature data and those resulting from oxidation using cytochrome P450 isoenzymes in the liver microsomal fraction. Based on the results obtained, it can be concluded that this approach can effectively predict oxidation processes initiated by single-electron oxidation, such as N-dealkylation, S-oxidation, β-oxidation, alcohol oxidation, hydroxylation of aromatic systems, and dehydrogenation. Due to the high oxidation potential required to initiate electrochemical oxidation reactions, it does not yield results for reactions initiated by direct hydrogen addition, such as O-dealkylation or aliphatic hydroxylation of unsubstituted aromatic rings.

The EC-MS approach determines and identifies electrochemical products (ETPs) in many electrochemical systems. EC-MS applications are divided into two groups: volatile and non-volatile ETPs. This method has been used for decades to mimic biochemical transformations and obtain reliable information on oxidation and reduction processes. EC-MS is an interesting approach to studying redox processes and identifying oxidation and reduction products. Studies using EC-MS described in the scientific literature involve electrochemical simulation of oxidative metabolism, oxidative stress, or degradation processes. Although the instrumentation of EC-MS and EC-HPLC-MS is well established and commercially available, new research in recent years has focused on interfacing strategies and the development of real-time online analytical systems and electrochemical interfaces combining electrochemistry and ionization for MS. Studies using tandem mass spectrometry (MS/MS) can be used to identify chemical structures based on characteristic fragments. However, structures can still only be proposed in many cases because analytical standards for comparison are often lacking or prohibitively expensive. Another challenge is the small amount of product the reaction cell (EC) generates. Therefore, obtaining sufficient samples for chromatographic separation and offline analysis, for example, by NMR, is difficult if longer electrolysis times are used. This results in the loss of one significant advantage: the short analysis time. Many subsequent reactions involving the main oxidation products take place, and the composition of the reaction mixture will change significantly over time, considerably complicating analysis. This can be a problem, especially in studies with pharmaceutical or biomedical applications, as there is often interest in reactive intermediates. In the future, additional structural identification techniques will be needed to characterize the oxidation and reduction products reliably.

4. Conclusions

The obtained results allowed the development of an alternative method based on electrochemistry and mass spectrometry (EC-MS) for studying the metabolism of drugs (enalapril, metronidazole, midazolam, propranolol, venlafaxine) belonging to different therapeutic groups (antibiotics, antidepressants, cardiovascular drugs). The oxidation process of the studied drugs in the electrochemical system was influenced by the buffer’s type, concentration, and pH and the type of working electrode used. The central composite design (CCD) was successfully used to predict MS parameters for the studied drugs and their pharmacologically active metabolites for identification based on the multiple reaction monitoring mode (MRM).

In silico tools (Biotransformer 3.0, GLORYx, and Xenosite) predicted the most vulnerable sites of metabolism (SOMs) in the structure of the studied drugs. Finally, the suggested atomic sites for metabolism in their chemical structure were the same as seen in the generated metabolites in phases I and II of biotransformation. Hence, these probabilities were used as a guide during the experimental part. Moreover, the results were correlated with data obtained from in vitro enzymatic incubation and samples taken from patients in clinical departments (Table 4).

During in vitro experiments, different phase I and II metabolites were identified for the compounds studied. For enzymatic incubation, N-dealkylation, O-demethylation, and hydroxylation were the metabolic pathways involved mainly in their metabolism. Their in vitro phase II metabolites were identified as glucuronic acid conjugates. Finally, different in vivo phase I and II metabolites were identified for the studied drugs, including metabolic pathways for in vivo phase I N-demethylation, N-dealkylation, O-demethylation, and hydroxylation, while the metabolic pathways for in vivo phase II metabolites were identified as glucuronic acid conjugates.