1. Introduction
Heterogeneous photocatalysis is a type of photochemical reaction accelerated by the presence of the catalyst particle in a different phase from the reactants. The discovery of this phenomenon has marked the beginning of its versatile use in the various fields of science and the interest in the subject is reflected in a large number of reports [1]. It is assumed that the water-splitting experiment with the use of titanium dioxide made by Fujishima and Honda in 1972 was a breakthrough in the context of this technique development [2,3]. This extraordinary finding made it possible to obtain hydrogen by eco-friendly and cost-effective method and became also a promising announcement of its further use. The unique properties of this material became also the basis of its significant application especially in environmental area [4,5,6]. Particularly noteworthy is the use of TiO2 nanoparticles for the air decontamination and water purification where its ability in organic compounds degrading supports pesticides and other hazardous materials residuals removal [7,8,9,10,11]. Titanium dioxide has also medical application while being known as an effective microbiological sterilizing agent [12,13,14,15]. Moreover, it has recently become the subject of research on the use in new cancer treatment strategies [16].
The mechanism of action of photocatalysts includes their properties as a semiconductor. The absorption of photon energy equal or greater than their band gap results in electron excitation from a valence to a conduction band, generating an electron-hole pair in the valence band. The photogenerated holes can react with adsorbed water molecules forming hydroxyl radical (•OH) which is known as a powerful oxidizing agent. The additional presence of oxygen in a reaction environment results in superoxide anion radical (O2•−) formation due to its electron scavenging activity. The following chain reactions lead to formation of other reactive oxygen species (ROS). The unpaired electron of radicals with simultaneous strong reductive-oxidizing potential determines further advanced oxidation processes (AOPs) which can occur in any organic and inorganic compound present in the reaction environment [17]. Among the ideal attributes of the photocatalyst, both chemical and biological inertness, photostability and availability with low cost and toxicity are the most often mentioned [18]. Titanium dioxide is characterized by many of these properties, however it has also some limitations in use. According to the Planck equation, the band gap energy of this semiconductor (~3.2 eV–for anatase phase) allows for the absorption of radiation only below 400 nm, causing a lack of use of the remaining spectrum of sunlight [19,20]. The efforts to eliminate the significance of this problem have resulted in numerous reports of effective attempts to modify structurally titanium dioxide and attempts to use admixtures of other compounds and substances [21,22,23,24,25]. Despite the fact that TiO2 is a photocatalyst with the best-known properties and the widest application, other compounds are also gaining importance in the course of photocatalytic methods development. It is worth mentioning that zinc oxide is found to be the second most used photocatalyst due to its low-toxic, photostability and high photoreactive nature [26,27]. Zinc oxide has been the subject of many studies in which it showed even greater efficiency in relation to titanium dioxide [28,29]. It has been also shown that other photocatalysts, such as iron(II) oxide and tungsten(VI) oxide, are able to produce ROS in conditions of irradiation with the range of visible light and its photocatalytic efficiency can also be improved by using admixtures [30,31,32]. A wide range of photocatalysts and significant differences in their properties indicate a continuous need to look for the most ideal one with regard to its use.
The ability to decompose organic compounds under the influence of irradiation with appropriate energy has initiated the use of photocatalysts in the simulation of drug metabolism processes [33,34,35,36,37,38,39,40,41]. The ability to produce reactive oxygen species entering redox reactions with medicine molecules allows to mimic the biochemical reactions that the drug undergoes after administration to the patient. It was confirmed that the transformation products obtained by photocatalytic approach coincide structurally with metabolites, and the efficiency and speed of this method constitute one of its benefits. The obtained, promising results became the basis for further developing this simulation method. The understanding of the routes of drug metabolism is crucial due to possibility of toxic and reactive intermediates occurrence which may have a strong negative impact on patient health [42,43,44]. The discovery of such properties of the drug in the advanced phase of its development means enormous costs for the pharmaceutical companies [45]. For this reason, the method that engages relatively low human resources and additionally is simple and fast is highly desirable. The studies carried out so far has focused on the use of titanium dioxide for this purpose, although recent reports indicate the possible advantage of the other photocatalysts in the simulation of drug metabolism [46,47].
The obtained results allow for an initial assessment of the suitability of the method, but do not give a broader view on the selection of the most efficient photocatalyst allowing to register the metabolic profile as similar as the natural one. In addition, the results of tests conducted on a single drug substance are also the reason for the small amount of data to be compared. In order to select the most suitable photocatalyst for the study of drug metabolism, a set of twenty model pharmaceuticals with a diverse chemical structure were used and the selection substances was limited to compounds undergoing hepatic metabolic pathway. A set of twelve photocatalysts was chosen, among which were compounds of confirmed and previously undocumented effectiveness. As a reference method, the incubation with human liver microsomes (HLM) was used, and then the obtained metabolic profiles were multivariate compared. Advanced chemometric methods, including PCA (principal component analysis) and HCA (hierarchical cluster analysis), were used to differentiate selected photocatalysts in terms of their use and suitability in further drug metabolism research.
2. Results and Discussion
The process of establishing the metabolic pathway of pharmaceutical substances transformation is one of the important elements of introducing new molecular entity (NME) for widespread use in medicine. The development of techniques allowing for a quick and uncomplicated determination of drug transformation products is based on an intensive increase in the number of designed and directed to the preliminary clinical trials, medicinal compounds. For this reason, their ability in organic substances decomposition has become a reason for the considering of metal oxides use in developing a method that allows for one to obtain intermediate products of the biological drug transformation process. The concentration a lot of attention in these studies on the use of titanium dioxide and the search for another compound with a better fit to the profile of conducted studies is also significant. Despite the fact that the photocatalysis process is successfully used in the simulation of the drug metabolism, a broader comparison assuming the use of a number of available metal oxides has not yet been performed on a set containing multiple pharmaceutical substances.
2.1. Preliminary Characterization of Metabolism Pathways
In order to collect preliminary data on the course of the metabolism process and the structures of metabolites formed by the selected for comparison pharmaceutical substances, a detailed review of the available literature was performed [36,46,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63].
Importantly, both HLM incubation and photocatalytic metabolism simulation experiments allowed the observation of numerous metabolic reactions, such as aliphatic and aromatic hydroxylation, oxidation, N-oxidation, S-oxidation, dealkylation, and dehydrogenation. Nevertheless, photocatalysis failed in case of several analyzed substances. For instance, photocatalytic transformation of toloxatone and orciprenaline did not give any of metabolites detected in HLM incubation. It should be noticed also that our previous study on photocatalytic metabolism simulation of toloxatone as a single substance allowed to obtain all metabolites formed in HLM experiment [36]. This inconsistency should be attributed to the presence of other analyzed substances in the mixture which may be responsible for inhibiting activity of applied photocatalysts. Noteworthy are cases of chlorprothixene, vardenafil, dapoxetine, and flunarizine where photocatalytic simulation experiments showed only partial efficiency in comparison to HLM experiment. Remarkable is also fact that one of trazodone metabolites was formed only in the photocatalytic metabolic simulation while absent in HLM experiment.
The summary of identified metabolism reactions is presented in Table 1.
2.2. Chemometric Analysis
The multivariate chemometric data analysis, incorporating principal component analysis (PCA) and hierarchical cluster analysis (HCA), was performed in order to compare the suitability of chosen photocatalysts in further drug metabolism research.
2.2.1. PCA
The PCA is a commonly applied method used for data exploration and visualization of the relationships between samples. Its principle relies on conversion of the original variables to the equal number of latent variables (principal components, PCs), which are uncorrelated, and explain the largest percentage of the data variability.
One of the most important arguments for the use of PCA is its ability to reduction of the data dimensionality. In this research dimensionality of the dataset is 961 (entities corresponding to their m/z detected by the MS). Because of the correlations existing between the variables, PCA enabled the significant decrease of the dimensionality. It allowed straightforward presentation of data using 2-dimensional plot.
All obtained profiles (shown in Supplementary Figures S1–S15) recorded in time-of-flight (TOF) operation mode and treated using Mass Profiler Professional software giving 961 entities. After filtration and moderated t-Test (p ≤ 0.05, FC ≥ 7), 51 entities were obtained. The 2-D PCA allowed to explain 60.17% of the data variance (Figure 1 - squares of each color stands for one experiment). As can be seen, Control was placed close to the standard sample (working solution of model mixture before experiments) which testifies no occurrence of metabolic reactions. HLM samples were placed on the opposite side of PCA plot. All samples representing photocatalytic experiments were grouped between HLM and control, which confirms the presence of the analyzed drugs metabolites. First of all, it should be noticed that WO3 catalyzed samples were located in the nearest distance to HLM samples. On the contrary to the remaining photocatalytic samples, WO3 group was not shifted along the y-axis which clearly suggest that majority of transformation products correspond to metabolites formed during HLM incubation. The remaining photocatalytic experiments can be divided into four groups. The first of these experiments consisted of TiO2/ZnO and ZnO placed relatively close to HLM samples in comparison to other photocatalytic samples. The second group containing TiO2 and SrTiO3 was located in the middle of x-axis of the PCA plot, which indicate moderate metabolism mimicking efficiency. On the other hand, the group consisting of ZnFe2O4 and Cu2O was placed the most closely to control, which attests to their low suitability in drug metabolism simulation studies. The last cluster grouping Bi2O3, CeO2, Co3O4, ZrO2 and TiO2-CNT showed rather poor activity in the context of drugs metabolites formation. It should be also noticed that they were shifted to the greatest extent along the y-axis which suggest that significant amount of formed transformation products cannot be considered as the drugs metabolites. In order to present more detailed relationships between experiments, HCA analysis was additionally performed.
2.2.2. Hierarchical Cluster Analysis and Heatmap
Hierarchical cluster analysis is one more chemometric technique which can be used to determine the relationships between the studied samples. In general, the clustering methods depend on identification of similarities between the samples which can be represented by the distance between them. It should be noticed that the similarity between samples increases in parallel with decreasing distance between them. The results of clustering could be graphically presented on the dendrogram (Euclidean distance metrics and Ward’s method clustering algorithm were used in this study).
As was shown in Figure 2, HCA gave similar results to PCA-WO3 samples presented the highest resemblance to HLM experiment. In general, all experiments were split into two main clusters. The first group consisted of aforementioned WO3 – HLM and TiO2/ZnO–ZnO pairs. The second main cluster grouped samples corresponding to the remaining experiments. Within this group, Standard – Control pair was the most outlying. Similarly to PCA, Bi2O3, CeO2, Co3O4, ZrO2 and TiO2-CNT experiments were grouped together. Additionally, HCA showed that this cluster remained the most distant from Standard – Control pair. On the contrary to the PCA, ZnFe2O4 and SrTiO3 samples were grouped together with Bi2O3, CeO2, Co3O4, ZrO2 and TiO2-CNT cluster. Additionally, Cu2O and TiO2 were classified as a pair.
The relationships between abundances of molecular entities used for HCA construction were shown as a heatmap (Figure 2). Stripes on the map represent molecular entities (blue color: low abundance, red: high, yellow: average abundance). The heatmap clearly shows the cause of conducted experiments splitting into two clusters in HCA. The first cluster contained a high concentration of entities corresponding to metabolites which is reflected by numerous red strips. In this context, WO3 samples exhibit the highest resemblance to HLM. The second cluster contained a moderate or low concentration of entities corresponding to metabolites (yellow and blue strips). Unsuspectingly, the highest number of blue strips were present in standard and control. A low concentration of metabolites was observed in the case of ZnFe2O4 and Cu2O experiments in particular.
3. Experimental
3.1. Chemicals and Reagents
Chlorprothixene hydrochloride, quetiapine hemifumarate, loxapine succinate, toloxatone, vardenafil hydrochloride trihydrate, dapoxetine hydrochloride, salbutamol hemisulfate, orciprenaline hemisulfate, betaxolol hydrochloride, clonidine hydrochloride, perhexiline maleate, and flunarizine dihydrochloride were purchased from Sigma Aldrich Co. (St. Louis, MO, USA). Imovane 7.5 mg tablets (Sanof-Aventis, Gentilly, France)—zopiclon, Stilnox 10mg tablets (Sanofi-Aventis, Tours France)—zolpidem tartrate, Nebilet 5mg tablets (Berlin-Chemie Menarini, Glienicker Weg, Germany)—nebivolol hydrochloride, Trittico CR 75 mg tablets (Aziende Chimiche Riunite Angelini Francesco, Rome, Italy)—trazodone hydrochloride, Tranxene 5 mg capsules (Sanofi-Aventis, Paris, France)—clorazepate dipotassium, Edronax 4 mg tablets (Pfizer Europe, Sandwich Kent, Great Britain)—reboxetine methanesulfonate, Valdoxan 25 mg tablets (Les Laboratoires Servier, Suresnes, France)—agomelatine, Poltram 50 solution for injection (Polpharma, Starogard Gdański, Poland)—tramadol hydrochloride were obtained in the local pharmacy.
Water (LC-MS Ultra grade), β-nicotinamide adenine dinucleotide 2′-phosphate reduced tetrasodium salt hydrate (NADPH), human liver microsomes (HLM), sodium phosphate monobasic monohydrate salt, sodium phosphate dibasic anhydrous salt, TiO2 (Aeroxide® 25), nanopowder 21 nm particle size (BET), 50 m2/g specific surface area (SSA), ZnO, nanopowder <100 nm particle size, 10–25 m2/g SSA and WO3, nanopowder <100 nm particle size (TEM), 8.3 m2/g SSA were obtained from Sigma-Aldrich (St. Louis, MO, USA). Bi2O3, nanopowder 80 nm particle size (TEM), 25 m2/g SSA, 99.9%, (Figure S16), CeO2, nanopowder 10–30nm particle size (TEM), 30–50 m2/g SSA, 99.97%, (Figure S17), Co3O4, nanopowder 10–30 nm particle size (TEM), 50–150 m2/g SSA, 99% (Supplementary Figure S18), Cu2O, nanopowder 18 nm particle size (SEM), 52.46 m2/g SSA, 99.86% (Supplementary Figure S19), SrTiO3, nanopowder, 100 nm particle size (cubic phase, TEM), 11.02 m2/g SSA, 99.9%, (Supplementary Figure S20), carbon nanotube-TiO2 consisting of CNTs 20wt%: TiO2-rutile 80wt% CNTs (outside diameter: >50nm, inside diameter: 5–15nm, length: 5-20um), TiO2 (rutile, 100~300nm, spherical, SEM), 18.5 m2/g SSA, (Figure S21), UV shielding nanopowder, 50 nm particle size consisting of ZnO/49wt% + TiO2/49wt% (TEM), 25–50 m2/g SSA, (Figure S22), ZnFe2O4, nanopowder 10–30 nm particle size (TEM), 98.5%, (Figure S23), ZrO2, nanopowder 40 nm particle size (TEM), 20-40 m2/g SSA, 99%, (Figure S24) were purchased from US Research Nanomaterials, Inc. (Houston, TX, USA). Acetonitrile (hypergrade for LC-MS) was purchased from Merck (Darmstadt, Germany) and 98% formic acid (mass spectroscopy grade) was obtained from Fluka (Taufkirchen, Germany).
3.2. Sample Preparation
Stock solutions of drug standards freely soluble in water (chlorprothixene hydrochloride, quetiapine hemifumarate, loxapine succinate, dapoxetine hydrochloride, salbutamol hemisulfate, orciprenaline hemisulfate, betaxolol hydrochloride, clonidine hydrochloride, flunarizine dihydrochloride) were prepared in water (1 mg mL−1).
Stock solutions of toloxatone and vardenafil hydrochloride trihydrate were made in water (2 mg mL−1).
Stock solution of perhexiline maleate was made in the mixture consisting of 10% acetonitrile and 90% water (1 mg mL−1).
Stock solution of tramadol hydrochloride was prepared by diluting pharmaceutical formulation with water to obtain solution (10 mg mL−1).
The substances obtained from tablets and capsules in the equivalents of 10 mg (zolpidem tartrate, reboxetine methanesulfonate, trazodone hydrochloride, quetiapine hemifumarate, nebivolol hydrochloride, clorazepate dipotassium) or 100 mg (agomelatine, zopiclone) of pharmaceuticals were weighted and swept for 5 min with 10 mL of water or acetonitrile. Semi-stock solution was obtained by mixing appropriate volumes of stock solutions and diluting with water to obtain 150 μM concentrations of each substance.
The working solutions were prepared by diluting semi-stock solution with gradient grade water, to obtain 7.5 μM concentration of each pharmaceutical substance in the case of photocatalytic experiments and 15 μM in the case of HLM incubation.
3.3. Photocatalytic Simulation of Metabolism
The photocatalytic simulation of metabolism was performed at concentration 7.5 µM of tested drugs. The applied catalysts loadings were set to 100 mg L−1. For all experiments, suspensions were transferred into 3.5 mL quartz caped cells (l = 1 cm) and stirred at 500 rpm (microstirrer Cimarel: Telemodul, Thermo Electron LED GmbH, Germany) in the dark for 30 min to achieve adsorption-desorption equilibrium. Next, reaction cells were mounted horizontally in Atlas Suntest CPS+ photostability chamber with D65 filter (Linsengericht, Germany), and irradiated simultaneously with stirring. The irradiance was set to 750 W m−2 which corresponds to energy dose of 2700 kJ m−2 h−1. The temperature in the chamber was controlled and kept below 35 °C. Aliquots (100 µL) were collected after 60 min of irradiation. After centrifuging at 13,500 rpm for 5 minutes, 50 μL of samples were subjected to UHPLC-ESI-Q-TOF analysis.
3.4. HLM Metabolism Simulation
The phase I transformation process was conducted using microsomes fraction. Total volume of reaction suspension was 200 μL including 0.5 mg mL−1 microsomes, 50 mM phosphate buffer (pH 7.4) and 15 μM substrate. The reaction mixture was pre-incubated at 37 °C for 2 min and then the metabolic reactions were initiated by addition of 10 μL NADPH (50 mM). The reaction was terminated after 120 min of incubation with 200 μL of ice-cold acetonitrile-methanol mixture (1:1). The final concentration of each studied pharmaceutical was equal to 7.5 μM. Next, the precipitated samples were centrifuged at 13,500 rpm for 10 minutes at 4 °C and the supernatants (50 μL) were transferred into autosampler vials for LC-MS analysis. The negative control samples (Control) were prepared as described above without addition of NADPH solution.
3.5. LC-MS Analysis
The LC-MS analysis was performed with the use of Agilent Accurate-Mass Q-TOF LC/MS G6520B system with dual electrospray (DESI) ionization source and Infinity 1290 ultra-high-pressure liquid chromatography system consisting of: binary pump G4220A, FC/ALS thermostat G1330B, autosampler G4226A, DAD detector G4212A, TCC G1316C module (Agilent Technologies, Santa Clara, CA, USA) and Kinetex C18 (2.1 × 50 mm, dp = 1.7 μm) column with C18 precolumn guard (Phenomenex, Torrance, CA, USA). A mixture of ultrapure water with addition of 0.1% solution of formic acid (A) and acetonitrile (B) was used as a mobile phase. The gradient elution was carried out at constant flow 0.3 mL min−1 from 95% A (5% B) to 70% A (30% B) 0–7.50 min and then 55% A (45% B) to 5% A (95% B) 7.50–12.50 min for all analysis. One-and-a-half-minute equilibrium time was performed to return to initial conditions. The injection volume was 4 µl and the column temperature was maintained at 35 °C. MassHunter workstation software in version B.08.00 was used for the control of the system, data acquisition, qualitative and quantitative analysis.
The optimization of the instrument conditions started from the proper tuning of Q-TOF detector in a positive mode with the use of Agilent ESI-L tuning mix in the extended dynamic range (2 GHz). The following instrument settings were applied: gas temperature: 325 °C, drying gas: 10 L/min, nebulizer pressure: 40 psig, capillary voltage: 3000 V, fragmentor voltage: 175 V, skimmer voltage: 65 V, octopole 1 RF voltage: 750 V.
Data acquisition was performed in centroids with the use of TOF (MS) and auto MS/MS mode. The spectral parameters for both modes were: mass range: 80–950 m/z and the acquisition rate: 1.5 spectra/s. To ensure accuracy in masses measurements, a reference mass correction was used and masses 121.050873 and 922.009798 were used as lock masses.
3.6. Chemometric Analysis
Fourteen metabolism experiments: HLM (after 120 min of incubation), Control sample (HLM without NADPH), twelve photocatalytic experiments (each after 60 min of exposition) were made in five replications for each one experiment. Additionally, standard sample (working solution before experiments) was added in chemometric analysis also. In this manner a set of seventy-five samples for fifteen different experiments was obtained. For all samples, high resolution liquid chromatography–mass spectrometry analysis was performed in TOF mode and their profiles were recorded. Data background ion noise cleaning and extraction of the list of the ions characteristic for metabolite profiles of analyzed substances were made by molecular feature extraction (MFE) algorithm provided by Mass Hunter Qualitative Analysis software version B.06.00 (Agilent). The following settings of MFE parameters were chosen as: isotope model: common organic molecules with peak spacing tolerance 0.0025 m/z, more than 2000 counts for the compound filter and single charge state of the analyzed ions. In order to perform PCA and HCA, MPP software in version 12.61 (Agilent and Strand Life Sciences Pvt. Ltd.) was used.
4. Conclusions
The aim of the study was to compare the performance of a set of twelve metal oxides in the context of their use in the simulation of drug metabolism. For this purpose, a mixed solution of twenty model pharmaceutical substances was used. Separate photocatalytic experiments and incubation with human liver microsomes as a reference method were performed. The samples were subjected to UHPLC-ESI-Q-TOF MS analysis and the obtained metabolic profiles were compared using advanced chemometric methods due to the multidimensionality of data. Graphical presentation of the study results in the form of a PCA plot and cluster dendrogram allowed for their interpretation.
All the used photocatalysts confirmed their effectiveness, while the exact outcome of the study indicates a clear advantage of WO3-assisted photocatalysis over other metal oxides experiments used in this comparison. WO3-assisted photocatalytic experiment showed the best fit in terms of quantity and quality to the metabolic profile obtained from HLM incubation. On this basis, ZnFe2O4 and Cu2O showed the lowest efficiency in relation to the key test assumptions. However, satisfactory results have also been achieved with the use of zinc oxide. Moderate metabolism mimicking efficiency in the case of TiO2 experiment is somewhat surprising. On the other hand, despite the rather poor result of a single photocatalyst in this case, its coupling with the zinc oxide is also relevant as it significantly improves the efficiency of both, and this combination clearly gains its usefulness in the context of drug metabolism studies.
For this reason, it seems promising to study various combinations of metal oxides, e.g., zinc oxide and tungsten oxide, or other combinations of zinc oxide and titanium dioxide to further the development of photocatalytic methods of the drug metabolism simulation.
Supplementary Materials
The following are available online at
Author Contributions
Conceptualization, R.S.; data curation, R.S.; formal analysis, M.G. and J.T.; investigation, M.G., J.T. and R.S.; methodology, R.S.; supervision, R.S.; writing—original draft preparation, M.G., J.T. and R.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research received no external funding.
Conflicts of Interest
The authors declare no conflict of interest.
Figures and Table
Enlarge this image.Figure 1. 2D PCA plot of the standard sample (STD), Control (Cont), HLM after 120 min of incubation and photocatalytic experiments samples after 60 min of irradiation.
Enlarge this image.Figure 2. Cluster dendrogram and heatmap of the standard sample (STD), Control (Cont), HLM after 120 min of incubation and photocatalytic experiments samples after 60 min of irradiation.
Metabolites observed during HLM incubation and photocatalytic experiments.
| Name |
m/z |
Metabolism |
HLM | Bi2O3 | CeO2 | Co3O4 | Cu2O | SrTiO3 | TiO2 | TiO2-CNTs | TiO2-ZnO | WO3 | ZnFe2O4 | ZnO | ZrO2 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Chlorprothixene |
302.0764 | demethylation | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 332.0870 | S-oxidation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| 318.0714 | demethylation |
+ | - | - | - | - | - | + | - | + | + | - | - | - | |
| 348.0819 | S-oxidation |
+ | - | - | - | - | - | - | - | - | - | - | - | - | |
| Reboxetine |
286.1437 | deethylation | + | - | + | - | - | + | + | + | + | + | - | + | + |
| 330.1699 | hydroxylation | + | - | - | - | - | - | - | - | - | + | - | - | - | |
| 328.1543 | oxidation | + | - | - | - | - | - | + | - | + | + | - | - | - | |
| 344.1492 | hydroxylation |
+ | + | + | + | + | + | + | + | + | + | + | + | + | |
| Trazodone |
192.0767 | dealkylation |
- | + | + | - | + | + | + | + | + | + | - | + | + |
| 388.1534 | hydroxylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| 406.1640 | dihydroxylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| Quetiapine |
340.1478 | dealkylation | + | + | + | - | + | - | + | + | + | + | + | + | + |
| 296.1215 | dealkylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| 400.1689 | hydroxylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| Loxapine |
344.1160 | hydroxylation | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 314.1054 | demethylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| Agomelatine |
260.1287 | hydroxylation | + | - | - | + | - | - | + | + | + | - | - | + | - |
| 230.1181 | demethylation | + | - | - | - | - | - | + | - | - | - | + | + | - | |
| 246.1130 | demethylation |
+ | + | + | + | + | + | + | + | + | + | + | + | + | |
| 276.1236 | dihydroxylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| 262.1079 | demethylation |
+ | - | - | - | - | - | + | - | + | - | - | + | - | |
| Salbutamol |
254.1386 | N-oxidation | + | + | - | - | + | + | + | + | + | + | - | - | + |
| Orciprenaline |
228.1230 | hydroxylation | + | - | - | - | - | - | - | - | - | - | - | - | - |
| Nebivolol |
420.1671 | oxidation | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 422.1773 | hydroxylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| 212.1081 | N-dealkylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| Toloxatone |
224.0917 | aliphatic |
+ | - | - | - | - | - | - | - | - | - | - | - | - |
| 224.0917 | aromatic |
+ | - | - | - | - | - | - | - | - | - | - | - | - | |
| 224.0917 | N-oxidation | + | - | - | - | - | - | - | - | - | - | - | - | - | |
| Vardenafil |
461.1966 | N-dealkylation | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 393.1227 | dealkylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| 477.1915 | N-dealkylation |
+ | - | - | - | - | - | - | - | - | - | - | - | - | |
| Dapoxetine |
322.1802 | N-oxidation | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 322.1802 | aromatic hydroxylation | + | - | - | - | - | - | - | - | - | + | - | - | - | |
| 292.1696 | N-dealkylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| 308.1645 | oxidation | + | - | - | - | - | - | - | - | - | - | - | - | - | |
| 278.1539 | N,N-dealkylation | + | + | + | + | + | + | + | + | + | + | - | + | + | |
| Tramadol |
250.1801 | N-demethylation | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 250.1801 | O-demethylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| Zopiclone |
375.0966 | demethylation | + | + | + | + | + | + | - | + | + | + | + | - | + |
| 405.1072 | N-oxidation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| Zolpidem |
324.1706 | aliphatic hydroxylation | + | + | + | + | + | + | + | + | + | + | + | + | + |
| Betaxolol |
254.1750 | O-dealkylation | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 266.1750 | N-dealkylation | + | + | + | + | + | + | + | + | + | + | + | + | + | |
| Clorazepate |
271.0632 | decarboxylation | - | - | - | - | - | - | - | - | - | - | - | - | - |
| 287.0581 | decarboxylation |
+ | - | + | - | + | - | + | + | + | + | - | + | - | |
| Clonidine |
228.0089 | dehydrogenation | + | + | + | + | - | + | + | + | + | + | + | + | + |
| Perhexiline |
294.2791 | aliphatic hydroxylation | + | + | + | + | + | + | + | + | + | + | + | + | + |
| 292.2634 | oxidation | + | + | + | + | + | + | + | + | + | + | - | + | + | |
| Flunarizine |
421.2085 | aromatic hydroxylation | + | - | - | - | - | - | - | - | - | - | - | - | - |
| 439.2191 | dihydroxylation | + | - | - | - | - | - | - | - | - | - | - | - | - | |
| 289.1510 | dealkylation | + | + | + | + | + | + | + | + | + | + | + | + | + |
© 2019 by the authors.
Details
; Trawiński, Jakub