Protonitazepyne and metonitazepyne metabolism and pharmacology; Prediction of metabolite activity via µ-opioid receptor docking simulations

Protonitazepyne and metonitazepyne metabolism and pharmacology; Prediction of metabolite activity via µ-opioid receptor docking simulations | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 28 May 2025 V1 Latest version Share on Protonitazepyne and metonitazepyne metabolism and pharmacology; Prediction of metabolite activity via µ-opioid receptor docking simulations Authors : Diletta Berardinelli , Omayema Taoussi , Duygu Ovat , Simona Pichini , Benedikt Pulver , Volker Auwärter , Francesco Busardò [email protected] , Giuseppe Basile , Emiliano Laudadio , and Jeremy Carlier Authors Info & Affiliations https://doi.org/10.22541/au.174843951.13306235/v1 574 views 305 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Nitazenes have recently surfaced the illicit opioid market, causing numerous intoxications and fatalities. N-Pyrrolidino-derivatives protonitazepyne and metonitazepyne have circulated since 2023 and have been involved in overdose intoxications. Their pharmacological properties remain largely unknown. However, pharmacokinetic/-dynamic data are crucial for clinicians and toxicologists to manage intoxications and interpret legal cases. Methods: Protonitazepyne and metonitazepyne metabolism was assessed using human hepatocyte incubations and blood/urine from an intoxication case; samples were analyzed with liquid chromatography-high-resolution mass spectrometry and software-aided data mining. µ- (MOR), κ- (KOR), and δ- (DOR) opioid receptor activation was assessed using a GTP Gi binding assay. MOR docking was simulated with UCSF Chimera and AutoDockSuite. Pharmacological relevance of major metabolites was predicted through in silico MOR docking. Results: Major metabolites were produced through nitroreduction, pyrrolidine N-dealkylation and oxidation to N-butanoic acid, and O-dealkylation. Protonitazepyne and metonitazepyne potencies at MOR were 3.7 and 11.5 nmol L-1, respectively; efficacies were 154 and 101%. Partial agonism and low potency were observed at KOR/DOR. In silico inhibition constants at MOR for protonitazepyne, 5-amino-protonitazepyne, metonitazepyne, and 5-amino-metonitazepyne were 0.68, 11.45, 1.98, and 2,050 nmol L-1, respectively. Conclusions: Protonitazepyne and metonitazepyne are MOR-selective full agonists, with potencies about 7 and 2 times higher than fentanyl. These nitazenes present significant health risks through central nervous system/respiratory depression. Their primary metabolites showed lower/marginal in silico MOR affinity, suggesting they might be pharmacologically active, albeit to a much lesser extent than the parent compounds. We propose 5-amino derivatives (blood) and N-butanoic acid derivatives (urine) as biomarkers for detecting consumption. 1. Introduction Nitazenes (2-benzylbenzimidazole analogs) make up a class of new psychoactive substances (NPSs) inducing opioid-like euphoria, analgesia, and anesthetic effects. These compounds have been misused as substitutes for opiates or other opioids since 2019, owing to their lower cost and to circumvent drug laws and analytical detection [1]. Nitazenes are typically more potent than heroin and even fentanyl, and can cause massive central nervous system (CNS) and respiratory depression; they have already been involved in hundreds of overdose fatalities worldwide, either alone or in combination with other drugs, notably benzodiazepines (“benzo-dope” mixture) and tranquilizers (“tranq-dope”) [2–7]. Nitazenes present challenges to clinicians and toxicologists: 1) they are typically active at low blood concentrations (~ng mL -1 ) and are rapidly metabolized, making them undetectable in biological matrices after a short period of time; 2) some metabolites are active, contributing to the psychoactive effects and potentially prolonging their duration [8–10]; 3) metabolic degradation may occur through highly polymorphic enzymes, which can influence metabolite production and potentially alter their overall effects in an individual [11]. Understanding the pharmacokinetics and pharmacodynamics of nitazenes through toxicology research, is therefore crucial for accurately identifying positive cases and interpreting parent compound and metabolite concentrations in biological samples within clinical and forensic settings. This, in turn, is essential for managing intoxications, treating patients and interpreting legal cases. However, speed is critical to keep pace with the illicit market Protonitazepyne and metonitazepyne are N -pyrrolidino-substituted nitazenes that were first identified on the US drug market in 2023, and in the European Union a few months later; seizures were reported in Slovenia, Latvia, Estonia, Denmark, Germany, Austria, Italy, Greece, and Ireland [12,13]. Both compounds have been controlled as Schedule I substances in the USA since October 2024 [14]. They are not explicitly banned in most other countries, although they may fall under local analog or specific NPS legislation. The two nitazenes have been detected in several toxicology cases in the US [15,16]. However, no data are currently available regarding their concentrations in biological samples. In vitro studies of µ-opioid receptor (MOR) activation have shown that both protonitazepyne and metonitazepyne are substantially more potent than fentanyl [17,18], and concentrations in the ng/mL range are expected in blood following recreational use or intoxication [19]. There is currently no data available on the two drugs’ pharmacokinetics. The present study aimed to assess the metabolism of protonitazepyne and metonitazepyne in order to identify metabolite biomarkers of consumption applicable in clinical and forensic settings. In Addition, potentially pharmacologically active metabolites were investigated. For this purpose, human hepatocyte incubations, as well as protonitazepyne-positive blood and urine samples were analyzed using liquid chromatography-high-resolution tandem mass spectrometry (LC-HRMS/MS) combined with software-aided data mining. Protonitazepyne and metonitazepyne activity at MOR, δ- (DOR), and κ- (KOR) opioid receptors were evaluated using a GTP Gi binding assay to investigate their pharmacological effects. Finally, molecular docking of protonitazepyne, metonitazepyne, and their main metabolite in blood were conducted at MOR to predict their binding affinity and anticipate potential activity/toxicity without relying on costly, time-consuming, and analytical standard-dependent laboratory experiments. 2. Experimental 2.1. Chemicals and reagents Protonitazepyne, metonitazepyne, fentanyl, SNC-80, and U-50488 pure standards were bought from Cayman Chemical (Ann Arbor, Michigan, USA). LC-MS-grade acetonitrile, water, and formic acid (FA) were from Carlo Erba (Cornaredo, Italy). Williams’ medium E, HEPES buffer (2-[4-(2-hydroxyethyl)-1- piperazinyl]ethanesulfonic acid), l -glutamine, ammonium acetate, and β-glucuronidase from limpets ( P. vulgata ) were from Sigma Aldrich (Milan, Italy). Supplemented Williams’ Medium E (SWM) was prepared by dissolving HEPES and l -glutamine at 2 and 20 mmol/L, respectively, in Williams’ medium E. Pooled cryopreserved human hepatocytes (HEP) from ten fully anonymized donors were purchased from Lonza (Basel, Switzerland); human tissue is acquired from tissue recovery agencies, tissue suppliers, and Lonza-managed donor programs that perform tissue recovery and donor informed consent in accordance with processes approved by an Institutional Review Board. MOR, DOR, and KOR membranes and GTP Gi binding assay kits were purchased from Revvity (Milan, Italy). 2.2. Protonitazepyne and metonitzapyne metabolism 2.2.1. Hepatocyte incubation Protonitazepyne and metonitazepyne were individually incubated with HEP following our in-house protocol [3]. Briefly, 250 µL of 20 µmol L -1 protonitazepyne and metonitazepyne in SWM were incubated at 37°C for 3 h with 2×10 6 viable cells/mL in SWM in 24-well culture plates. The reactions were stopped with 500 µL of ice-cold acetonitrile and centrifugation for 10 min, 15,000g. Samples were stored at –80°C until analysis. Negative and positive controls were incubated under the same conditions for 0 and 3 h to rule out interference and non-specific reactions and confirm metabolic activity. 2.2.2. Authentic biological samples Femoral blood and urine from a fatal intoxication involving protonitazepyne were collected at the autopsy and analyzed to confirm the metabolites identified in vitro . The data were obtained as part of routine forensic investigations and fully anonymized. Therefore, in accordance with German and Italian legislation, informed consent and ethics committee approval were not required. 2.2.3. Sample preparation After thawing at room temperature, 100 µL HEP incubate was mixed with 100 µL acetonitrile and centrifuged for 10 min, 15,000g, at room temperature. The supernatants were evaporated to dryness under nitrogen at 37°C. The dried residues were reconstituted with 100 µL of 0.1% FA in water:0.1% FA in acetonitrile 90:10 (v/v), then centrifuged again under the same conditions. The supernatants were transferred into vials with glass inserts prior to analysis with LC-HRMS/MS. One hundred µL blood or urine were mixed with 200 µL acetonitrile and centrifuged for 10 min, 15,000g, at room temperature. The supernatants were evaporated to dryness under nitrogen at 37°C. The dried residues were reconstituted with 100 µL of 0.1% FA in water:0.1% FA in acetonitrile 95:5 (v/v), then centrifuged again under the same conditions. The supernatants were transferred into vials with glass inserts prior to analysis with LC-HRMS/MS. To investigate glucuronide conjugations, 100 µL urine was mixed with 10 µL of 10 mol/L ammonium acetate at pH 5.0, and 100 µL β-glucuronidase (5,000 units), and incubated for 90 min at 37°C; a negative control with 100 µL water instead of β-glucuronidase was also prepared. Four hundred µL ice-cold acetonitrile was added to the mixtures for protein precipitation. After centrifugation for 10 min, 15,000g, at room temperature, the supernatants were evaporated to dryness under nitrogen at 37 °C and reconstituted in 100 µL of 0.1% FA in water:0.1% FA in acetonitrile 95:5 (v/v). After centrifugation under the same conditions, the supernatants were transferred into vials with glass inserts prior to analysis with LC-HRMS/MS. 2.2.4. LC-HRMS/MS analysis The analyses were performed with a DIONEX UltiMate 3000 chromatographic system coupled to a Thermo Scientific Q-Exactive quadrupole-Orbitrap mass spectrometer equipped with a heated electrospray ionization (HESI) source. LC-HRMS/MS conditions were the same as those previously described for metabolite identification of isotonitazene and structural analogs to identify shared metabolites, with minor modifications [3,4] 1) The ramped normalized collision energy was optimized for the analysis of protonitazepyne and metonitazepyne to generate relevant fragments for structure elucidation (40, 55, and 80%); 2) Inclusion lists of putative metabolites were used to prioritize HRMS/MS fragmentation based on in silico predictions and postulations [3–5,10,11,20–23] (Supplemental Tables S1 and S2). 2.2.5. Software-aided metabolite identification LC-HRMS/MS data were screened with Thermo Scientific Compound Discoverer, as previously detailed [24]. Settings were the same as those described for the metabolite identification of isotonitazene and structural analogs [3,4] with a specific list of theoretical metabolites based on in silico predictions and postulations [3–5,10,11,20–23], and generated according to the settings displayed in Supplemental Table S3. 2.3. In vitro opioid receptor activation (GTP G i binding assay) MOR, KOR, and DOR activation by protonitazepyne and metonitazepyne was assessed using an HTRF ® -based GTP G i binding assay, designed to evaluate the activation of G i protein-coupled receptors with high sensitivity and specificity (low background), while avoiding the need for radioligands entailing specific regulatory and safety requirements [25]. The assay was performed following our in-house protocol [26]. Protonitazepyne, metonitazepyne, and controls (MOR, fentanyl; KOR, U-50488; DOR, SNC-80) were incubated overnight at room temperature with a supplemented stimulation buffer with optimized GDP and magnesium chloride concentrations, a detection reagent mix of equal volumes of europium cryptate and d2-labelled antibody, and human MOR, KOR, or DOR membrane preparation (total volume, 20 µL). Protonitazepyne, metonitazepyne, and controls’ concentrations ranged from 10-5 to10-11 mol L -1 ; each concentration was tested in duplicates, and the experiments were conducted in triplicates. Non-specific binding was evaluated using a non-hydrolyzable GTPγS at saturation to measure the assay background signal. The fluorescence resonance energy transfer (FRET) signal was detected using a Multilabel Plate Reader (PerkinElmer), and the fluorescence ratio at 665 and 620 nm was calculated (delay, 100 μs; total window time, 200 μs). All values were normalized to the maximum signal of the reference compounds for each receptor. Concentration-response curves were generated using GraphPad Prism (v. 10.2.3) with a three-parameter fit to determine the potency (EC 50 ) and efficacy (E max ) of the compounds. 2.4. In silico MOR docking Considering in vitro opioid receptor activation preliminary results, receptor docking was only assessed at MOR. The three-dimensional structure of protonitazepyne, metonitazepyne, their main metabolites in blood, and controls (morphine and fentanyl) were generated and minimized using UCSF Chimera. MOR crystallographic structure was obtained by the 5c1m pdb file [27,28]. Ligand-MOR interactions were investigated using AutoDock Suite 4.2 [29], with AutoDockTools to add polar hydrogen atoms and partial charges to the receptor and ligands, Addsol to assign MOR atomic solvation parameters and fragmental volumes, Autotors to assign ligands’ flexible torsions (all dihedral angles were allowed to rotate freely), and Autogrid to generate affinity grid fields. A grid field of 50×58×44 Å and the resulting docked conformations were clustered into families of similar binding modes, with a root mean square deviation (RMSD) clustering tolerance of 2 Å. The lowest and the most populated docking conformations were considered as the most stable orientations. The binding energy, representing the sum of the intermolecular contributions and the internal energy of the ligand [30], was calculated by an empirical free energy force field with a Lamarckian genetic algorithm (LGA), and can be translated into a simulated inhibition constant (K i ) through the thermodynamic law ∆G=−RT×ln(K i ). The binding poses with the highest binding affinity and population percentage were analyzed using molecular dynamic (MD) simulations to assess binding stability over time and the ligand and receptor functional groups involved in binding [30] . A membrane composed of 142 POPC (1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine) lipids was modelled to stabilize the MOR active conformation in its native environment; MOR was inserted inside the membrane using the correct coordinates obtained by Positioning of proteins in membranes (PPM) server [31]. A simulation box of 7.786×7.786×8.278 nm was generated using CHARMM-GUI, and periodic boundary conditions were used along all axes [32]. To reach physiological conditions at 0.15 mol L -1 NaCl, the simulation box was solvated by 7,100 TIP3 water molecules, 15 Na + ions, and 30 Cl – counterions [33]. Each ligand-MOR complex underwent a minimization step followed by six equilibration cycles. A total of 200 ns of MD simulations under semi-isotropic conditions were performed for the production phase, maintaining constant number of molecules, pressure, and temperature. All the simulations were performed using GROMACS 2023.3 and CHARMM36 force field. Finally, the difference between initial and final positions of a ligand within the binding site was analyzed by computing the RMSD over time, using Visual Molecular Dynamics (VMD) and UCSF Chimera software [27,34,35]. 3. Results 3.1. In vitro and in vivo metabolism of protonitazepyne and metonitazepyne 3.1.1. LC-HRMS/MS fragmentation patterns Protonitazepyne ([M+H] + at m/z 409.2224, eluting at 17.26 min) and metonitazepyne ([M+H] + at m/z 381.1912, eluting at 14.28 min) were only detected in positive-ionization mode, and displayed a similar HRMS/MS spectrum with few fragments (Fig. 1), consistent with the fragmentation of structural analogs under the same analytical conditions [3,4]. For both compounds, the predominant fragment corresponded to the N -ethyl pyrrolidine side chain at m/z 98.0964 ±5 ppm (C 6 H 12 N + ), which further yielded a minor fragment at m/z 56.0495 ±5ppm corresponding to an N -propyl group (C 3 H 6 N + ). Fragment at m/z 121.0645 in metonitazepyne corresponded to the 1’-methyl-4’-methoxybenzyl side chain (C 8 H 9 O + ), which was further fragmented to m/z 107.0490 in protonitazepyne due to propyl loss (C 7 H 7 O + ). 3.1.2. In vitro and in vivo findings Metonitazepyne and protonitazepyne LC-HRMS peak area was 70% and 80% reduced, respectively, after 3 h incubation with HEP. Seventeen metabolites were identified in 3 h incubates with metonitazepyne (A1-A17, ordered by ascending retention time) and protonitazepyne (B1-B17). The metabolic transformations were similar for both compounds, with major reactions including pyrrolidine hydroxylation and oxidation (presumably leading to γ-lactam formation [36]), N -dealkylation of the pyrrolidine ring to form the N -butanoic acid derivative, and O -dealkylation followed by O -glucuronidation. Other reactions included hydroxylation, oxidative deamination, nitroreduction, N -glucuronidation and sulfation. Complete in vitro results are compiled in Table 1. No additional metabolites were identified in protonitazepyne-positive urine and blood samples. Nine metabolites were found in non-hydrolyzed urine; pyrrolidine N -dealkylation to N -butanoic acid and O -despropylation followed by O -glucuronidation were major transformations, N -butanoic acid-protonitazene (B16) and O -despropyl-protonitazepyne glucuronide (B2) being predominant, similar to in vitro results. The only urinary glucuronide (B2) was completely cleaved after enzymatic hydrolysis, leading to a 30-fold increase in the signal of the corresponding unconjugated metabolite, O -despropyl-protonitazepyne (B6). Four metabolites were identified in blood; unlike HEP incubation results, nitroreduction was the predominant transformation, 5-amino-protonitazepyne (B3) being the main metabolite. Complete in vivo results are compiled in Table 1. The extracted-ion chromatograms of metonitazepyne and protonitazepyne and metabolites after 3 h incubation with HEP and in the positive samples are displayed in Supplemental Figure S1. Structure elucidation of the main metabolites in protonitazepyne-positive samples is described below, with their HRMS/MS spectra being displayed in Figure 1. Considering their similar in vitro metabolism, the corresponding metabolites for metonitazepyne are also described. 3.1.3. Major metabolite structure elucidation The most intense metabolites in HEP for both metonitazepyne (A13, [M+H] + at m/z 413.1813) and protonitazepyne (B16, [M+H] + at m/z 441.2123) resulted from N -dealkylation followed by oxidation to a butanoic acid derivative (+2O, as indicated by the +31.9900 Da ±5 ppm mass shift from the parents); B16 was the second most intense metabolite in protonitazepyne-positive urine. Both A13 and B16 were also detected at a high intensity in negative-ionization mode due to the formation of a carboxylate anion ([M-H] - at m/z 411.1683 and 439.1993, respectively). This transformation follows a classic metabolic pathway for pyrrolidines, involving oxidation at carbon 2 to yield a γ-lactam intermediate, which subsequently undergoes ring opening [36]. In positive-ionization mode, cleavage at the benzimidazole core generated a major fragment at m/z 130.0860 (C 6 H 12 NO 2 + ) in both analogs, corresponding to the N -ethyl- N -butanoic side chain, and a minor one at m/z 284.1015 (C 15 H 14 N 3 O 3 + ) and 312.1131 (C 17 H 18 N 3 O 3 + ) for metonitazepyne and protonitazepyne, respectively; further neutral losses of water and formic acid from the N -ethyl- N -butanoic group produced fragments at m/z 112.0755 (C 6 H 10 NO + ) and 84.0806 (C 5 H 10 N + ), respectively. Additionally, the butanoic acid group yielded a fragment at m/z 87.0439 (C 4 H 7 O 2 + ), with subsequent water loss at m/z 69.0335 (C 4 H 5 O + ). O -Desalkylation of metonitazepyne (-CH 2 ) and protonitazepyne (-C 3 H 6 ) resulted in the same metabolite (A8=B6, [M+H] + at m/z 367.1765). A8=B6 exhibited the same fragmentation pattern as protonitazepyne, with key fragments at m/z 56.0495 ( N -propyl, C 3 H 6 N + ), 98.0961 ( N -ethylpyrrolidine, C 6 H 12 N + ), and 107.0488 (1’-methyl-4’-hydroxybenzyl, C 7 H 7 O + ), indicating that these functional groups remained unchanged. Further O -glucuronidation (+C 6 H 8 O 6 ) produced A3=B2 at m/z 543.2083 ±5 ppm. This was the third and second most intense metabolite in metonitazepyne and protonitazepyne incubations, respectively, while also being the predominant in protonitazepyne-positive urine. A3=B2 displayed a fragmentation pattern similar to its non-conjugated precursor A8=B6. The position of the glucuronide was confirmed by its susceptibility to β-glucuronidase cleavage, demonstrating that conjugation occurred at an oxygen rather than a nitrogen atom. Metonitazepyne and protonitazepyne nitroreduction (-HO 2 ) produced the 5-amino derivatives A1 ([M+H] + at m/z 351.2180) and B3 ([M+H] + at m/z 379.2492), respectively. These metabolites were minor in vitro , but B3 was preponderant in protonitazepyne-positive blood, in line with previous results from structural analogs incubated under the same conditions [3]. A1 and B3 exhibited a fragmentation pattern similar to that of the corresponding parent compound, indicating that the side chains remained unchanged. 3.2. In vitro opioid receptor activation of protonitazepyne and metonitazepyne The in vitro activation profiles of metonitazepyne and protonitazepyne at MOR, DOR, and KOR, represented by the normalized FRET signal intensity as a function of drug concentration, are shown in Figure 2. Table 2 displays protonitazepyne and metonitazepyne in vitro EC 50 and E max at MOR, DOR, and KOR. Metonitazepyne and protonitazepyne potencies at MOR were approximately 2 and 7 times higher than that of fentanyl, while displaying low potency at KOR and DOR. 3.3. In silico MOR docking of protonitazepyne, metonitazepyne, and their main metabolites 3.3.1. Model validation The crystal structure 5c1m shows MOR in its active conformation bound the high-affinity agonist BU72. A focused docking approach was employed to reproduce the binding mode observed in the crystallographic structure. The resulting pose closely matched the experimental binding mode, thereby validating our docking strategy (Supplemental Fig. S2). 3.3.2. MOR docking Docking simulations were performed for metonitazepyne, protonitazepyne, 5-amino-metonitazepyne (A1), and 5-amino-protonitazepyne (B3). A mapping of the binding site is shown in Supplemental Figure S2. This site consists of 19 amino acids spanning five of the seven transmembrane helices (TMs) and appears oval, with TM1 and TM5 at the extremities; TM2 and TM4 were not involved in the binding. The site is heterogeneous, with polar, apolar, and charged amino acids. All four compounds displayed affinity for the MOR. Metonitazepyne and protonitazepyne affinity were approximately 2 and 5 times higher than that of fentanyl, but the two metabolites showed lower/marginal affinity; binding energy and K i are reported in Table 2. RMSD over time during the last 20 ns of the MD simulations, i.e., when the steady state was reached, were 1.67 ±0.19, 2.61±0.07, 0.82 ±0.05, and 1.21 ±0.63 Å for metonitazepyne, protonitazepyne, 5-amino-metonitazepyne, and 5-amino-protonitazepyne (Supplemental Fig. S2), increased RMSD together with decrease in relative error suggesting high activation capacity. Initial and final binding poses of the four compounds within the binding site are displayed in Figure 3. The various amino acids of the receptor involved in binding and the interaction types with the four compounds are reported in Table 3. 4. Discussion 4.1. Protonitazepyne and metonitazepyne metabolism Good correlation was found between protonitazepyne-positive urine and hepatocyte incubations, with N -butanoic acid-protonitazene (B16) and O -despropyl-protonitazepyne glucuronide (B2) being predominant. Although all metabolites detected in protonitazepyne-positive blood were also identified in vitro , 5-amino-protonitazepyne (B3) was the main metabolite in blood but marginal in incubations. This discrepancy between urine and blood results was previously observed with other nitazenes [3,21], and may be explained by the faster urinary elimination of O -desalkyl and N -butanoic acid metabolites compared to nitro-reduced derivatives, with the former being more polar. Protonitazepyne signal was less intense than that of the main metabolites in both blood and urine, suggesting substantial metabolization. Noteworthy, nitazenes were shown to be metabolized by highly polymorphic enzymes [11], and the metabolic profile likely varies depending on the time of sample collection after consumption. Analysis of multiple positive samples taken at varying time points after uptake is recommended to confirm the present results. The in vitro metabolism of metonitazepyne was consistent with that of protonitazepyne and other structural analogs under the same incubation conditions [3,4]. Similar results are therefore expected in vivo , with N -butanoic acid-metonitazene (A13) and O -despropyl-metonitazepyne glucuronide (A3) being major metabolites in urine, and 5-amino-metonitazepyne being major in blood. We therefore propose 5-aminoprotonitazepyne and 5-amino-metonitazepyne in blood, and N -butanoic acid derivatives in urine, as biomarkers for detecting protonitazepyne and metonitazepyne consumption, respectively. The parent compounds should be additional analytical targets. Although useful for confirming consumption in hydrolyzed urine, O -dealkyl metabolites are not specific, as they are common to protonitazepyne and metonitazepyne, but also etonitazepyne [37] . 4.2. In vitro opioid receptor activation of protonitazepyne and metonitazepyne It is important to consider that the GTP G i binding assay, like other in vitro approaches, is a limited model that does not measure the ultimate functional outcomes and does not account for various parameters such as cell types, receptor expression levels, or physiological states, which may impact drug activity. However, it provides quick results for clinical and forensic toxicologists to support clinical diagnoses, overdose management, and legal investigations. Metonitazepyne and protonitazepyne were shown to act as full MOR agonists, with potencies approximately 2- and 7-fold higher than fentanyl, respectively. These findings are consistent with studies by De Vrieze et al. and Kozell et al., which demonstrated that N -pyrrolidine-substituted nitazenes, such as protonitazepyne, metonitazepyne, etonitazepyne, and isotonitazepyne, exhibit greater MOR potency compared to their classic N , N -diethyl-substituted analogs [17,18]. Using different analytical approaches (β-arrestin2 recruitment assay, bioluminescent cAMP reporter assay, and [ 35 S]GTPγS functional assay), the authors obtained similar values for protonitazepyne (EC 50 =0.09-0.94 nmol L -1 , E max =93.8-198%) and metonitazepyne (EC 50 =9.32-18.2 nmol L -1 , E max =95.3-174%); apparent potencies and efficacies may vary depending on the assay principle (analytical technique, point of signalling cascade assessed). These results suggest that both compounds, particularly protonitazepyne, may induce potent analgesic and euphoric effects, but also carry a significant risk of fatal respiratory depression and dependence. Metonitazepyne and protonitazepyne also exhibited low potencies and only partial agonism at DOR and KOR, suggesting that their pharmacological effects are primarily driven by MOR activation. These results are comparable with the in vitro experiments by Kozell et al. [18]. The MOR selectivity may contribute to their high toxicity and abuse liability, due to the lack of counterbalancing KOR-mediated aversive signalling. 4.3. In silico MOR docking of protonitazepyne, metonitazepyne, and their main metabolites Considering the marginal effects of metonitazepyne and protonitazepyne at DOR and KOR, as demonstrated by in vitro receptor activation studies, in silico docking was performed only at MOR. Both compounds showed high affinity at the receptor, with K i approximately 2- and 5-fold higher than fentanyl, respectively. These results are consistent with the in vitro results by Kozell et al., who measured K i of 0.92, 0.29, and 1.25 nM for metonitazepyne, protonitazepyne, and fentanyl at MOR [18], i.e. ratio of 2.2, 2.3, and 2.9 when compared to in silico K i . The in silico receptor docking results are also in line with the present in vitro receptor activation data, hinting a strong correlation. Although receptor affinity does not equal activation, and further investigation with multiple analogs is warranted, docking simulations may be used to estimate the activity of nitazenes and their metabolites, helping to guide experimental research. Both compounds docked to the classic opioid binding pocket. At this site, ligand binding induces a conformational shift in the TM helices, with TM6 undergoing an outward movement, which creates a cavity for G-protein coupling and downstream signaling [38]. In particular, ionic interactions between the protonated opioid form and Asp147 (TM3) are crucial for MOR docking and serve as a key determinant of binding affinity and activation. Morphine-type opioids typically bind Tyr148 (TM3) via H bonding and π–π stacking and Trp318 (TM7) via π–π stacking and hydrophobic interactions. In contrast, fentanyl-like opioids tend to interact more prominently with Val300 (TM6) via hydrophobic and van der Waals interactions and His297 (TM6) (analogous to rat His319) via H bonding. Tyr148, Val300, and His297 are important for stabilizing the opioid within the pocket, while Trp318 is critical for morphine-like ligands [38–40]. In our experiments, although metonitazepyne and protonitazepyne exhibited different interaction patterns, with 9 and 11 amino acids involved, respectively, both compounds interacted with Asp147, Tyr148, and Val300, suggesting a similar binding mode to MOR compared to classic opioids; both also interacted with extra-helical residues His54 and Ser55. RMSD over time indicated good stability at the receptor, and the two compounds showed comparable oscillation values after 90 ns. A translational movement of TM1, 2, 3, and 7 was observed upon binding, with protonitazepyne inducing more significant movements than metonitazepyne, indicating stronger binding. In vitro MOR activation and in silico docking results were congruent, and binding simulations were therefore used to predict metabolite activity. Considering the results of the metabolite identification experiments, which showed that the 5-amino derivatives of metonitazepyne and protonitazepyne are predominant in the bloodstream and therefore the most pharmacologically relevant metabolites, MOR docking was simulated for A1 and B3. Both compounds showed affinity for MOR, but their K i values were substantially higher than those of their parent compounds and even morphine, especially for 5-amino-metonitazepyne, whose affinity for MOR appeared marginal. The patterns of oscillations during MD simulations confirmed this trend. Notably, both compounds interacted with Asp147 and Val300 (and also Tyr148 for 5-amino-protonitazepyne), but showed limited interactions with other amino acids classically involved in opioid binding. TM translational movements also were limited upon binding. Together, these results indicate that 5-amino-metonitazepyne and 5-amino-protonitazepyne may be pharmacologically active, albeit to a substantially lesser extent than their parent compounds. The results are consistent with the in vitro experiments by Vandeputte et al. showing that 5-amino-isotonitazene is active, but more than 200 less potent than its parent compound isotonitazene [8]. 5. Conclusion Protonitazepyne and metonitazepyne are MOR-selective full agonists, presenting significant health risks through CNS and respiratory depression. Both compounds undergo extensive metabolism, with 5-amino derivatives in blood and N -butanoic acid derivatives in urine, as major metabolite biomarkers of consumption. In vitro MOR activation and in silico docking results were congruent, and metabolite activity can therefore be anticipated through in silico receptor docking, avoiding the need for laboratory experiments, which are often costly, time-consuming, and dependent on the synthesis or commercial availability of analytical standards. Docking simulations showed that 5-amino-protonitazepyne and 5-amino-metonitazepyne might be active, although much less than their parent compounds. MD might become a critical tool to keep pace with the highly dynamic NPS market, and these preliminary results warrant further investigation. CRediT author contribution statement Diletta Berardinelli: Methodology, Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Funding acquisition. Omayema Taoussi: Validation, Formal analysis, Investigation, Data curation, Writing – original draft, Visualization, Funding acquisition. Duygu Yeşim Ovat: Investigation, Writing – review & editing. Simona Pichini: Writing – review & editing. Benedikt Pulver: Writing – review & editing. Volker Auwärter: Resources, Writing – review & editing, Funding acquisition. Francesco Paolo Busardò: Resources, Writing – review & editing, Supervision, Project administration, Funding acquisition. Giuseppe Basile: Writing – review & editing. Emiliano Laudadio: Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing – original draft, Visualization. Jeremy Carlier: Conceptualization, Methodology, Investigation, Writing – original draft, Visualization, Supervision, Project administration. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Declaration of competing interest The authors declare no competing interests Acknowledgements This study was conducted within the framework of the Italian “National plan to prevent misuse of fentanyl and other synthetic opioids”, Chapter 6 “Preclinical evaluation of synthetic opioids’ pharmaco-toxicological effects and interactions with new substances”. Data availability The original contributions presented in the study are included in the article and supplemental material. Further inquiries can be directed to the corresponding author. Fundings: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. References [1] European Monitoring Centre for Drugs and Drug Addiction. European Drug Report 2024: Trends and Developments. https://www.euda.europa.eu/publications/european-drug-report/2024_en (Accessed: 12 January 2025). [2] E. Montanari, G. Madeo, S. Pichini, F.P. Busardò, J. 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Metabolic transformation, elemental composition, retention time (RT), experimental accurate mass of molecular ion, deviation from theoretical accurate mass, and liquid chromatography-high-resolution mass spectrometry peak area of metonitazepyne and metabolites (A1-17), as well as protonitazepyne and metabolites (B1-17) in positive- and negative-ionization mode after 3 h Incubation with human hepatocytes and blood and urine from an intoxication case. without hydrolysis with hydrolysis A1 Nitro reduction C 21 H 26 N 4 O 4.84 351.2180 ND a 0.18 ND 3.5 x 10 6 ND NA b NA NA A2 O-Dealkylation + Nitro reduction + Oxidation (pyrrolidine) C 20 H 23 N 4 O 2 6.89 351.1816 ND 0.13 ND 1.2 x 10 6 ND NA NA NA A3 O -Dealkylation + O -Glucuronidation C 26 H 31 N 4 O 9 8.37 543.2083 541.1957 -0.65 3.14 2.1 x 10 7 9.4 x 10 6 NA NA NA A4 Hydroxylation + O -Glucuronidation C 27 H 32 N 4 O 10 8.81 573.2195 ND 0.66 ND 5.7 x 10 5 ND NA NA NA A5 O -Dealkylation + Hydroxylation (pyrrolidine) C 20 H 23 N 4 O 4 9.57 383.1714 ND 0.05 ND 3.1 x 10 6 ND NA NA NA A6 N -Dealkylation to N -butanoic acid + O -Dealkylation C 20 H 23 N 4 O 5 9.82 399.1660 397.1533 -0.74 3.67 1.1 x 10 7 4.3 x 10 6 NA NA NA A7 Nitro reduction + Oxidation (pyrrolidine) C 21 H 25 N 4 O 2 10.44 365.1974 ND 0.54 ND 1.1 x 10 7 ND NA NA NA A8 O -Dealkylation C 20 H 23 N 4 O 3 10.49 367.1765 365.1631 0.09 2.97 1.9 x 10 7 3.6 x 10 6 NA NA NA A9 O -Dealkylation + Oxidative deamination C 16 H 15 N 3 O 4 12.01 314.1128 ND -2.33 ND 7.4 x 10 6 ND NA NA NA A10 Hydroxylation (pyrrolidine) + O -Glucuronidation C 27 H 32 N 4 O 10 12.14 573.2181 ND -1.78 ND 1.8 x 10 6 ND NA NA NA A11 N , N -Didealkylation C 17 H 18 N 4 O 3 12.25 327.1447 ND -1.43 ND 4.3 x 10 6 ND NA NA NA A12 Hydroxylation (pyrrolidine) C 21 H 25 N 4 O 4 13.12 397.1865 ND -1.34 ND 6.3 x 10 7 ND NA NA NA A13 N -Dealkylation to N -butanoic acid C 21 H 25 N 4 O 5 13.46 413.1813 411.1683 -1.56 2.21 8.5 x 10 7 3.0 x 10 7 NA NA NA Parent Metonitazepyne C 21 H 25 N 4 O 3 14.28 381.1912 ND -2.41 ND 2.2 x 10 8 ND NA NA NA A14 N -Dealkylation to N -butanal C 21 H 25 N 4 O 4 14.39 399.2024 ND 0.35 ND 6.2 x 10 5 NA NA NA A15 Hydroxylation C 21 H 25 N 4 O 4 14.85 397.1872 ND 0.42 ND 9.4 x 10 6 ND NA NA NA A16 O -Dealkylation + Oxidation (pyrrolidine) C 20 H 21 N 4 O 4 15.33 381.1561 ND 0.97 ND 1.2 x 10 7 ND NA NA NA A17 Oxidation (pyrrolidine) C 21 H 23 N 4 O 4 17.99 395.1706 ND -1.98 ND 7.0 x 10 7 ND NA NA NA B1 O -Dealkylation + Sulfation C 20 H 22 N 4 O 6 S 3.27 447.1340 ND 1.61 ND 4.9 x 10 6 ND ND ND ND ND ND ND B2 O -Dealkylation + O -Glucuronidation C 26 H 31 N 4 O 9 8.38 543.2084 541.1943 -0.28 0.55 6.4 x 10 7 3.1 x 10 7 ND ND 2.6 x 10 7 9.4 x 10 6 ND ND B3 Nitro reduction C 23 H 30 N 4 O 8.70 379.2492 ND -0.10 ND 2.3 x 10 6 ND 6.7 x 10 6 ND 3.5 x 10 6 ND 4.5 x 10 6 ND B4 O -Dealkylation + Oxidative deamination + O -Glucuronidation C 22 H 23 N 3 O 10 9.29 490.1459 ND 0.57 ND 3.6 x 10 6 ND ND ND ND ND ND ND B5 N -Dealkylation to N -butanoic acid + O -Dealkylation C 20 H 23 N 4 O 5 9.85 399.1662 397.1532 -0.24 3.67 9.6 x 10 6 3.8 x 10 6 ND ND ND ND ND ND B6 O -Dealkylation C 20 H 23 N 3 O 4 10.52 367.1765 ND 0.09 ND 1.8 x 10 7 ND ND ND 1.6 x 10 6 ND 5.2 x 10 7 ND B7 O -Dealkylation + Oxidative deamination C 16 H 15 N 3 O 4 12.04 314.1129 312.0994 -2.01 1.35 1.4 x 10 7 4.2 x 10 6 ND ND 6.8 x 10 5 ND 9.5 x 10 6 ND B8 N -Dealkylation to N -butanoic acid + Hydroxylation C 23 H 29 N 4 O 6 12.04 457.2075 ND -1.45 ND 1.5 x 10 7 ND ND ND 3.9 x 10 6 ND 4.2 x 10 6 ND B9 O -Dealkylation + Oxidation (pyrrolidine) + O -Glucuronidation C 26 H 28 N 4 O 10 12.32 557.1881 ND 0.50 ND 2.6 x 10 6 ND ND ND ND ND ND ND B10 Hydroxylation C 23 H 29 N 4 O 4 12.56 425.2185 ND 0.39 ND 1.8 x 10 7 ND ND ND 3.5 x 10 6 ND 3.5 x 10 6 ND B11 Nitro reduction + Oxidation (pyrrolidine) C 23 H 29 N 4 O 2 14.69 393.2287 ND 0.50 ND 2.4 x 10 7 ND 3.3 x 10 6 ND 2.5 x 10 6 ND 2.9 x 10 6 ND B12 O -Dealkylation + Oxidation (pyrrolidine) C 20 H 20 N 4 O 4 15.35 381.1558 ND 0.18 ND 8.9 x 10 6 ND ND ND ND ND 5.5 x 10 6 ND B13 N , N -Didealkylation C 19 H 23 N 4 O 3 16.29 355.1766 ND 0.37 ND 1.8 x 10 7 ND ND ND ND ND ND ND B14 N -Glucuronidation C 29 H 37 N 4 O 9 16.46 585.2558 ND 0.50 ND 9.5 x 10 6 ND ND ND ND ND ND ND B15 Hydroxylation (Pyrrolidine) C 23 H 29 N 4 O 4 16.62 425.2181 ND -0.54 ND 2.6 x 10 7 ND ND ND ND ND ND ND B16 N -Dealkylation to N -butanoic acid C 23 H 29 N 4 O 5 16.75 441.2123 439.1993 -2.14 1.38 3.2 x 10 8 1.4 x 10 8 1.1 x 10 5 6.0 x 10 4 1.1 x 10 7 ND 2.6 x 10 7 5.7 x 10 6 Parent Protonitazepyne C 23 H 29 N 4 O 3 17.26 409.2224 ND -2.49 ND 3.4 x 10 8 ND 1.2 x 10 6 ND 1.4 x 10 7 ND 8.7 x 10 6 ND B17 Oxidation (pyrrolidine) C 23 H 27 N 4 O 4 19.78 423.2022 ND -1.14 ND 9.8 x 10 7 ND 5.8 x 10 5 ND 8.7 x 10 5 ND 7.8 x 10 5 ND a ND, not detected. b NA, not applicable. Table 2. In vitro MOR, KOR, and DOR activation using a HTRF ® GTP Gi binding assay, represented by their potency (EC 50 ) and efficacy (E max ) values (relative to fentanyl [MOR], U-50488 [KOR], SNC-80 [DOR]), and in silico binding energy, represented by their simulated inhibition constant (K i ). 95% confidence intervals are given between parentheses. EC 50 , nmol L -1 E max , % Binding energy, kcal/mol Simulated K i , nM EC 50 , nmol L -1 E max , % EC 50 , nmol L -1 E max , % Fentanyl 25.6 (10.1- 70.2) 100 (98-119) -11.7 3.62 NA a NA NA NA Morphine NA NA -11.2 3.85 NA NA NA NA U-50488 NA NA NA NA 49.4 (29.6-80.1) 100 (98-113) NA NA SNC-80 NA NA NA NA NA NA 4.5 (2.1-9.7) 100 (89-108) Protonitazepyne 3.7 (2.2-8.7) 154 (152-186) -13.2 0.68 2530 (2311.2-2781.5) 14 (10-24) 425 (202.3-790.5) 56 (54-72) 5-Aminoprotonitazepyne NA NA -10.3 11.4 NA NA NA NA Metonitazepyne 11.5 (6.8-14.6) 101 (84-120) -12.3 1.98 257 (196.9-311.6) 22 (16-33) 266 (96.8-742.7) 36 (30-46) 5-Aminometonitazepyne NA NA -7.76 2.05 x 10 3 NA NA NA NA a NA, not applicable. Table 3. Amino acids of the MOR transmembrane helices (TM) involved in interactions with metonitazepyne, protonitazepyne, and main metabolites. The type of interaction is described with a symbol: □, H bond; ◊, dipole-induced dipole; ○, π stacking; ●, CH-π; ▪, CH-CH. Meto-nitazepyne 5-Amino-meto-nitazepyne Proto-nitazepyne 5-Amino-proto-nitazapyne His54 ○ □ Ser55 ● □ ○ Tyr75 (TM1) ● Val143 (TM3) ▪ Ile144 (TM3) ▪ Asp147 (TM3) ◊ □ ◊ ◊ Tyr148 (TM3) ● ● ○ Phe152 (TM3) ● Phe221 (TM5) ● Leu232 (TM5) ● Lys233 (TM5) □ ◊ Trp293 (TM6) His297 (TM6) □ Val300 (TM6) ○ ● □ ● Trp318 (TM7) □ Ile322 (TM7) ▪ Gly325 (TM7) ◊ Ile322 (TM7) Tyr326 (TM7) □ Supplementary Material File (figure 1_protopynemetopyne_spectrum_06052025_db-jc.tif) Download 29.66 MB File (figure 2_pyne_mudeltakappa_06052025_db-jc.tif) Download 129.67 MB Information & Authors Information Version history V1 Version 1 28 May 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords addiction bioinformatics drug metabolism opioids pharmacokinetics Authors Affiliations Diletta Berardinelli Polytechnic University of Marche View all articles by this author Omayema Taoussi Polytechnic University of Marche View all articles by this author Duygu Ovat Ege Universitesi View all articles by this author Simona Pichini Istituto Superiore di Sanità View all articles by this author Benedikt Pulver University of Freiburg Faculty of Medicine View all articles by this author Volker Auwärter University of Freiburg Faculty of Medicine View all articles by this author Francesco Busardò [email protected] Polytechnic University of Marche View all articles by this author Giuseppe Basile Polytechnic University of Marche View all articles by this author Emiliano Laudadio Polytechnic University of Marche View all articles by this author Jeremy Carlier Polytechnic University of Marche View all articles by this author Metrics & Citations Metrics Article Usage 574 views 305 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Diletta Berardinelli, Omayema Taoussi, Duygu Ovat, et al. 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