International Journal of Molecular Sciences

Article Structure-Based Approach for the Prediction of Mu- Binding Affinity of Unclassified Designer -Like Molecules

Giuseppe Floresta 1,* , Antonio Rescifina 1 and Vincenzo Abbate 2,* 1 Department of Drug Sciences, University of Catania, V.le A. Doria, 95125 Catania, Italy; arescifi[email protected] 2 King’s Forensics, School of Population Health & Environmental Sciences, King’s College London, Franklin-Wilkins Building, 150 Stamford Street, London SE1 9NH, UK * Correspondence: giuseppe.fl[email protected] (G.F.); [email protected] (V.A.)

 Received: 3 April 2019; Accepted: 8 May 2019; Published: 10 May 2019 

Abstract: Three quantitative structure-activity relationship (QSAR) models for predicting the affinity of mu- (µOR) ligands have been developed. The resulted models, exploiting the accessibility of the QSAR modeling, generate a useful tool for the investigation and identification of unclassified fentanyl-like structures. The models have been built using a set of 115 molecules using Forge as a software, and the quality was confirmed by statistical analysis, resulting in being effective for their predictive and descriptive capabilities. The three different approaches were then combined to produce a consensus model and were exploited to explore the chemical landscape of 3000 fentanyl-like structures, generated by a theoretical scaffold-hopping approach. The findings of this study should facilitate the identification and classification of new µOR ligands with fentanyl-like structures.

Keywords: QSAR; fentanyl; µOR; opioid binding affinity; designer fentanyl-like molecules; novel synthetic ; new psychoactive substances

1. Introduction Opioid receptors are the target proteins of narcotic , of which is the prototype, and their activation can produce a variety of pharmacological responses [1] that are used for the treatment of different medical conditions [2–4]. Their pharmacological action had been known long before the morphine itself was discovered, and, in early civilizations, extracts from the were widely used as a medicine [5]. Unfortunately, even at that period, the poppy plant was used as a recreational agent, and this social plague is still present all over the world representing a severe problem to societies [5]. The agents most commonly responsible for the increase in the number of drug overdose-related deaths are synthetic opioids and [6]. In fact, opioid-related overdose deaths due to synthetic opioids are rapidly increasing, augmenting an already established plague to society [7,8]. The low cost, affordable synthetic procedures, and the high potency have led to the influx of fentanyl analogs into the street-drug market as indicated by the Drug Enforcement Administration (DEA) [9]. A significant additional risk to public health is the variability in potency among fentanyl analogs; indeed, certain derivatives (e.g., carfentanyl) are 10,000 times more potent than morphine [10], resulting in a potentially fatal dose for drug-addicted persons. Fentanyl is a potent agonist of the µOR, causing the classical and euphoric pharmacological effects of this class of compounds. Unfortunately, simple modifications of the central core of the molecule (4-anilidopiperidine, Figure1) may produce ligands with increased potency, resulting in a high risk for the user. Importantly, many structural modifications to the original fentanyl chemical scaffold do not alter the primary function and binding properties to the µOR, resulting in a very large chemical space of fentanyl analogs with abuse potential.

Int. J. Mol. Sci. 2019, 20, 2311; doi:10.3390/ijms20092311 www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 2 of 11 alter the primary function and binding properties to the μOR, resulting in a very large chemical space of fentanyl analogs with abuse potential. In the USA, the DEA can schedule a substance to a legislative state regarding its physiological abuse potential; however, a complete analysis to support scheduling can take up to two years. For this reason, the Center for Drug Evaluation and Research has recently developed a docking-based virtual screening approach for the identification and hazard characterization of unclassified fentanyl- like structures [11]. Surprisingly, a ligand-based method for the classification of designer fentanyl- like structures has never been evaluated. Quantitative structure-activity relationship (QSAR) models models are frequently used to facilitate the prediction and comprehension of patterns in the chemical and biological sciences [12– 18]. In order to facilitate the investigation of unclassified fentanyl-like structures, exploiting QSAR methodology, we report the development of three different QSAR models established using a set of 115 fentanyl-like structures. The generated models, employed to evaluate a set of new potentially

μInt.OR J. Mol.binders, Sci. 2019 have, 20, 2311 been suggested employing the activity cliff analysis followed by a scaffold2 of 10 hopping study.

Figure 1. StructureStructure of fentanyl and fentanyl-like compounds.

2. ResultsIn the and USA, Discussion the DEA can schedule a substance to a legislative state regarding its physiological abuse potential; however, a complete analysis to support scheduling can take up to two years. For this 2.1.reason, Statistical the Center Analysis for Drugand Results Evaluation and Research has recently developed a docking-based virtual screening approach for the identification and hazard characterization of unclassified fentanyl-like structuresFor the [11 calculation]. Surprisingly, of the a different ligand-based models, method Forg fore uses the classificationthe SIMPLS algorithm of designer [19,20]. fentanyl-like All the experimentalstructures has vs. never predicted been evaluated. activities for the training and the test sets of the different models are presentedQuantitative in Figure structure-activity 2. The optimal number relationship of comp (QSAR)onents models in partial models least are sq frequentlyuares (PLS) used analysis to facilitate was 10-,the prediction2- and 3-component and comprehension for the field-based of patterns 3D-model, in the chemical and the and FCFP6 biological and ECFP6 sciences circular [12–18 fingerprint]. In order descriptorto facilitate 2D-models, the investigation respectively of unclassified (Figures fentanyl-likeS1–S6). All of structures, the generated exploiting models QSAR showed methodology, both good 2 2 wepredictive report theand development descriptive capabilit of threeies, diff erentdemonstrated QSAR models by the established high r and using q values a set for of both 115 fentanyl-like the training andstructures. the cross-validated The generated training models, sets employed(Table 1). The to evaluateplots of experimental a set of new vs. potentially predictedµ affinitiesOR binders, for thehave molecules been suggested included employing in the test the set activity (Figure cli 2)ff analysisshow a reasonable followed by predicti a scaffoldon demonstrated hopping study. by the cross-validated r2 (Table 1). Among the three different models, the presence of the 3D-descriptors included2. Results in and theDiscussion 3D-field model clearly increased the quality of the description, as demonstrated by the high value of r2 (0.99) for the training set. Furthermore, the predictive capabilities resulted from such2.1. Statistical model are Analysis the best and across Results the three models (r2 for the test set = 0.77, Tables S1 and S2). The results 2 2 of theFor r test the set calculation >q have been of the referred different to as models, the Kubinyi Forge paradox uses the and SIMPLS derive algorithm from the cross-validation [19,20]. All the methodexperimental (leave-one-out) vs. predicted [21]. activitiesThe reliability for the of the training models and was the also test evaluated sets of the by dithefferent measurement models are of thepresented forecast in errors. Figure 2The. The mean optimal squared number error of components(MSE), the mean in partial absolute least error squares (MAE) (PLS) and analysis the mean was 10-,absolute 2- and percentage 3-component error for (MAPE) the field-based for the three 3D-model, models and are reported the FCFP6 in and Table ECFP6 1, all circularof them fingerprintconfirmed thedescriptor statistical 2D-models, reliability respectivelyof the obtained (Figures models. S1–S6). All of the generated models showed both good predictive and descriptive capabilities, demonstrated by the high r2 and q2 values for both the training and the cross-validated training sets (Table1). The plots of experimental vs. predicted a ffinities for the molecules included in the test set (Figure2) show a reasonable prediction demonstrated by the cross-validated r2 (Table1). Among the three di fferent models, the presence of the 3D-descriptors included in the 3D-field model clearly increased the quality of the description, as demonstrated by the high value of r2 (0.99) for the training set. Furthermore, the predictive capabilities resulted from such model are the best across the three models (r2 for the test set = 0.77, Tables S1 and S2). The results of the r2 test set >q2 have been referred to as the Kubinyi paradox and derive from the cross-validation method (leave-one-out) [21]. The reliability of the models was also evaluated by the measurement of the forecast errors. The mean squared error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE) for the three models are reported in Table1, all of them confirmed the statistical reliability of the obtained models. Int. J. Mol. Sci. 2019, 20, 2311 3 of 10 Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 3 of 11

Figure 2. ExperimentalExperimental vs. predicted predicted p pKKii ofof the the compounds compounds in in the the training training and and test test set set for for the the different different QSAR models.

Table 1. Models statistics. Table 1. Models statistics. r2 Training q2 Training r2 Test MSE a MSE a MAE b MAE b MAPE c MAPE c Model r2 q2 r2 MSE a MSE a MAE b MAPE c Set Set Set Training Set Test Set Training Set MAETest Setb Training Set MAPETest Set c Model Training Training Test Training Test Training Training 3D-field 0.99 0.68 0.77 0.005 0.22 0.05Test 0.41 Set 0.75Test 5.75 Set FCFP6 kNNSet 0.68 Set 0.65 Set 0.59 Set 0.35 Set 0.40 Set 0.44 0.53Set 6.25 7.26 3D-fieldECFP6 kNN 0.99 0.71 0.68 0.70 0.77 0.61 0.005 0.31 0.22 0.39 0.05 0.44 0.41 0.54 0.75 6.26 5.75 7.39 FCFP6 a b c Mean0.68 squared forecast 0.65 error; 0.59Mean absolute0.35 forecast0.40 error; Mean0.44 absolute percentage 0.53 forecast 6.25 error. 7.26 kNN ECFP6 0.71 0.70 0.61 0.31 0.39 0.44 0.54 6.26 7.39 kNNThe 3D visualizations, employing activity-atlas (AA), of the QSAR models superposed with the structurea Mean ofsquared fentanyl forecast are shownerror; b Mean in Figure absolute3. The forecast conclusion error; c Mean of this absolute visualization percentage highlight forecast error. that the 3D-field model is described by steric and electrostatic effects. The four colors on the 3D-map represent The 3D visualizations, employing activity-atlas (AA), of the QSAR models superposed with the the different electrostatic (red and blue), and hydrophobic and shape features (green and violet). structure of fentanyl are shown in Figure 3. The conclusion of this visualization highlight that the 3D- In the green areas, a bulk/hydrophobic interaction improves the binding affinity; opposite results are field model is described by steric and electrostatic effects. The four colors on the 3D-map represent observed within the violet areas, where a bulk/hydrophobic interaction decreases the affinity. In the the different electrostatic (red and blue), and hydrophobic and shape features (green and violet). In red areas, a more positive electrostatic field increases the receptor-affinity, whereas in the blue regions the green areas, a bulk/hydrophobic interaction improves the binding affinity; opposite results are a more negative electrostatic field increases the affinity. From this representation, it appears that the observed within the violet areas, where a bulk/hydrophobic interaction decreases the affinity. In the bulk/hydrophobic interactions have a more relevant impact on the activity of the molecules; indeed, red areas, a more positive electrostatic field increases the receptor-affinity, whereas in the blue a green area is shown near to the R2 group of the fentanyl (Figure1). This area is the hydrophobic regions a more negative electrostatic field increases the affinity. From this representation, it appears pocket formed by Ile 144, Leu 200 and Tyr 148 of the µOR. Potent opioid analogs can be derived by that the bulk/hydrophobic interactions have a more relevant impact on the activity of the molecules; simple substitution around this chemical space [22]. Moreover, another green area is located where indeed, a green area is shown near to the R2 group of the fentanyl (Figure 1). This area is the R3-substituents are usually accommodated, further indicating that the presence of a fragment moiety hydrophobic pocket formed by Ile 144, Leu 200 and Tyr 148 of the μOR. Potent opioid analogs can be in this area will produce potent fentanyl-like analogs. Two additional green areas are located near the derived by simple substitution around this chemical space [22]. Moreover, another green area is two aromatic rings. The aromatic ring linked to the R5 is located in an area near to Tyr 326, whereas located where R3-substituents are usually accommodated, further indicating that the presence of a the other one in a pocket formed by Trp 19 and Tyr 148; both interact by π–π interactions with the fragment moiety in this area will produce potent fentanyl-like analogs. Two additional green areas indicated residues [22]. A red area is located near the positively charged amine in the fentanyl structure, are located near the two aromatic rings. The aromatic ring linked to the R5 is located in an area near to Tyr 326, whereas the other one in a pocket formed by Trp 19 and Tyr 148; both interact by π–π interactions with the indicated residues [22]. A red area is located near the positively charged amine

Int.Int. J. J. Mol. Mol. Sci. Sci. 20192019, ,2020, ,x 2311FOR PEER REVIEW 44 of of 11 10 in the fentanyl structure, indicative of the presence of a negatively charged amino acid (Asp 147) in indicative of the presence of a negatively charged amino acid (Asp 147) in this region which can interact this region which can interact with the ligand group by a salt-bridge. The blue area near the fentanyl’s with the ligand group by a salt-bridge. The blue area near the fentanyl’s carbonyl group indicates a carbonyl group indicates a region able to interact with one of the oxygen atoms, presumably region able to interact with one of the oxygen atoms, presumably represented by the His 297. represented by the His 297.

Figure 3. The AA model map is superimposed to fentanyl. Molecular insight of structure-activity Figure 3. The AA model map is superimposed to fentanyl. Molecular insight of structure-activity relationshiprelationship (SAR) (SAR) mechanism mechanism models, models, revealing revealing th thee different different lead lead optimization optimization sites sites of of active active compounds.compounds. Red Red color color shows shows positive positive field field region region co controllingntrolling the the activity, activity, and and blue blue color color the the negative negative ones.ones. Green Green color color shows shows favorable favorable shape/hydrophobic shape/hydrophobic regions, regions, and and violet violet color color the the unfavorable unfavorable ones. ones. 2.2. Activity Cliffs in the Activity Landscape of the QSAR Set 2.2. Activity Cliffs in the Activity Landscape of the QSAR Set Usually, the activity cliff definition is strictly related to the activity landscape concept. An activity Usually, the activity cliff definition is strictly related to the activity landscape concept. An landscape is usually considered as a hypersurface where potency/activity of a particular compound is activity landscape is usually considered as a hypersurface where potency/activity of a particular added as a third dimension to a 2D projection of the chemical space. In this concept, discontinuous SARs compound is added as a third dimension to a 2D projection of the chemical space. In this concept, are regions termed as activity cliffs, which are formed by pairs of structurally similar compounds with discontinuous SARs are regions termed as activity cliffs, which are formed by pairs of structurally large differences in potency. In these areas, the similarity hypothesis (structurally similar compounds similar compounds with large differences in potency. In these areas, the similarity hypothesis will have similar biological activities) breaks down, and these areas are the most useful regions of (structurally similar compounds will have similar biological activities) breaks down, and these areas the activity landscape for a compound series, gaining an improved understanding of interactions are the most useful regions of the activity landscape for a compound series, gaining an improved with the target protein. To facilitate this task, a methodology known as activity miner (AM) has been understanding of interactions with the target protein. To facilitate this task, a methodology known applied to the presented dataset of fentanyl-like compounds [23]. This methodology is based on as activity miner (AM) has been applied to the presented dataset of fentanyl-like compounds [23]. This the same XED/FieldAlign technology used for the development of the QSAR model. Specifically, a methodology is based on the same XED/FieldAlign technology used for the development of the 3D-similarity metric was used, taking advantage of the same alignment produced for the development QSAR model. Specifically, a 3D-similarity metric was used, taking advantage of the same alignment of the 3D-QSAR model, which was the best performing platform for predictive and descriptive produced for the development of the 3D-QSAR model, which was the best performing platform for capabilities. Fundamental to the application of the AM calculation is the concept of disparity, defined predictive and descriptive capabilities. Fundamental to the application of the AM calculation is the as the difference between the activities of two molecules divided by the distance (similarity) between concept of disparity, defined as the difference between the activities of two molecules divided by the them. High disparity values are obtained when the similarity is high, and the difference in activity is distance (similarity) between them. High disparity values are obtained when the similarity is high, large—i.e., a small change in the molecule has made a substantial change in the activity. The seven and the difference in activity is large—i.e., a small change in the molecule has made a substantial activity cliffs with the highest values of disparity founded with the AM approach are reported in Table2. change in the activity. The seven activity cliffs with the highest values of disparity founded with the From this analysis, it is clear that the presence of a substituent on the R3 (Figure1) is fundamental AM approach are reported in Table 2. From this analysis, it is clear that the presence of a substituent for a high activity, and the presence of a simple hydrogen atom drops down the potency (Table2, on the R3 (Figure 1) is fundamental for a high activity, and the presence of a simple hydrogen atom entries 3, 5, and 7). Moreover, an alkyl chain with more than two atoms will produce less potent drops down the potency (Table 2, entries 3, 5, and 7). Moreover, an alkyl chain with more than two analogs (Table2, entries 1–3). The presence of a di fferent aromatic substituent in R5 is tolerated (Table2, atoms will produce less potent analogs (Table 2, entries 1–3). The presence of a different aromatic entry 5), whereas its removal produces less active compounds (Table2, entries 6 and 7). The results of substituent in R5 is tolerated (Table 2, entry 5), whereas its removal produces less active compounds the activity cliffs analysis suggest to further investigate the activity landscape inherent to suitable R3 (Table 2, entries 6 and 7). The results of the activity cliffs analysis suggest to further investigate the and R5 substitutions. activity landscape inherent to suitable R3 and R5 substitutions.

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5 55 9.89 8.928.688.92 −14.114.1−14.1 −1.211.21−1.21 8.68 − − 5 5 9.89 −14.1−14.1 −1.21−1.21 5 5 9.899.89 8.688.688.68 −14.1−14.1 −1.21−1.21 9.89 8.688.68 5 5 9.899.89 −14.1−14.1 −1.21−1.21 9.899.89 8.688.68 5 5 8.688.68 −14.1−14.1 −1.21−1.21 6 9.899.89 −13.8 −1.02 6 8.688.68 −13.8 −1.02 9.899.89 6 66 8.688.68 −13.813.8−13.8 −1.021.02−1.02 6 6 9.899.89 −−13.8−13.8 −−1.02−1.02 10.15 9.13 6 6 9.13 −13.8−13.8 −1.02−1.02 10.15 9.13 6 6 10.1510.15 9.139.13 −13.8−13.8 −1.02−1.02 6 6 9.139.13 −13.8−13.8 −1.02−1.02 10.1510.15 6 6 10.1510.15 9.139.13 −13.8−13.8 −1.02−1.02 7 9.139.13 −12.8 −2.28 10.1510.15 77 10.1510.15 9.139.13 12.8−12.8 2.28−2.28 7 −−12.8 −−2.28 7 10.15 9.139.13 −12.8 −2.28 7 7 10.15 7.87 −12.8−12.8 −2.28−2.28 7 7 10.1510.15 7.87 −12.8−12.8 −2.28−2.28 10.15 7.87 7 7 7.877.87 −12.8−12.8 −2.28−2.28 10.1510.15 7.877.87 2.3. 7Enlarging 7 the Activity Landscape of Fentanyl-like Compounds −12.8−12.8 −2.28−2.28 10.1510.15 7.877.87 2.3.7 7 Enlarging the Activity10.1510.15 Landscape of Fentanyl-like Compounds −12.8−12.8 −2.28−2.28 2.3. Enlarging2.3.2.3. Enlarging theEnlarging Activity the the Activity Activity Landscape Landscape Landscape of Fentanyl-likeof ofFentanyl-like Fentanyl-like Compounds Compounds Compounds7.877.87 10.1510.15 7.877.87 2.3.2.3. Enlarging Enlarging the the Activity Activity10.1510.15 Landscape Landscape of ofFentanyl-like Fentanyl-like Compounds Compounds In order2.3.2.3. Enlarging to Enlarging enlarge the the Activity the Activity chemical Landscape Landscape landscape of ofFentanyl-like Fentanyl-like evaluation Compounds Compounds7.877.87 of fentanyl-like compounds, a bioisosteric 10.1510.15 2.3.2.3. Enlarging Enlarging the the Activity Activity Landscape Landscape of ofFentanyl-like Fentanyl-like Compounds Compounds and fragment 2.3.2.3. Enlarging replacementEnlarging the the Activity Activity software Landscape Landscape tool of of (SparkFentanyl-like Fentanyl-like v10.4.0, Compounds Compounds Cresset, New Cambridge House, Hertfordshire, United Kingdom) 2.3. 2.3. Enlarging Enlarging was the the adopted Activity Activity Landscape toLandscape produce of ofFentanyl-like Fentanyl-like a scaffold-hopping Compounds Compounds analysis and to generate a virtual library

3 5 of µOR ligands [24,25], investigating not only the appropriate replacement of the R and R substituents, as suggested by the AM analysis, but also the decoration of other selected portions present in the original structure of fentanyl (Figure4,R 1,R2, and R4 substituents). In particular, the molecule was divided into six different parts to produces as much series (Figure4), and 500 new virtual molecules were generated for each substitution pattern for a total of 3000 analogs (see Supplementary material, Tables S3–S8). Subsequently, each ligand was evaluated by exploiting the predictive capabilities of the 3D-field and 2D-kNN QSAR models. For each case, the replacement was performed using the same dataset of fragments already reported by us [26]. The top-scored compounds, according to the median of the three models (3D-field, 2D-ECFP6 and 2D-FCFP6), and considering only values within the domain of applicability of each model, are reported in Table3. The results outline that the Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW 6 of 11

In order to enlarge the chemical landscape evaluation of fentanyl-like compounds, a bioisosteric and fragment replacement software tool (Spark v10.4.0, Cresset, New Cambridge House, Hertfordshire, United Kingdom) was adopted to produce a scaffold-hopping analysis and to generate a virtual library of μOR ligands [24,25], investigating not only the appropriate replacement of the R3 and R5 substituents, as suggested by the AM analysis, but also the decoration of other selected portions present in the original structure of fentanyl (Figure 4, R1, R2, and R4 substituents). In particular, the molecule was divided into six different parts to produces as much series (Figure 4), Int. J. Mol. Sci.and2019 500, 20new, 2311 virtual molecules were generated for each substitution pattern for a total of 3000 analogs 6 of 10 (see Supplementary material, Tables S3–S8). Subsequently, each ligand was evaluated by exploiting the predictive capabilities of the 3D-field and 2D-kNN QSAR models. For each case, the replacement was performed using the same dataset of fragments already reported by us [26]. The top-scored replacement generated new structures with optimized chemical features for the binding to the µOR. compounds, according to the median of the three models (3D-field, 2D-ECFP6 and 2D-FCFP6), and The resultsconsidering of all the only series values demonstrate within the domain that theof applicability chemical of landscape each model, for are thisreported class in ofTable compounds 3. is still huge andThe results small outline modifications that the replacement may further generated increase new structures the activity with ofoptimized the parent chemical molecule. features Series 1–3 for the binding to the μOR. The results of all the series demonstrate that the chemical landscape for produced compounds with relatively low predicted affinities, possessing pKi 9, demonstrating that, this class of compounds is still huge and small modifications may further increase the≤ activity of the potentially, any substituent in these regions should not produce powerful and potentially harmful parent molecule. Series 1–3 produced compounds with relatively low predicted affinities, possessing compounds.pKi This≤ 9, demonstrating suggests that that, the potentially, activity any landscape substituent around in these thisregions area shou canld not be exploitedproduce powerful to prepare only novel fentanyl-likeand potentially compounds harmful compounds. with reduced This suggests potency. that Conversely, the activity landscape the results around obtained this area from can Series 4–6 confirmedbe again exploited that to theprepare R3 onlyposition novel fentanyl-like (Figure1) iscompounds the one with that reduced mostly po atency.ffects Conversely, the affinity the increase, results obtained from Series 4–6 confirmed again that the R3 position (Figure 1) is the one that mostly and that exploringaffects the theaffinity chemical increase, space and that around exploring this th area,e chemical even usingspace around a cyclic this system area, even as suggestedusing a by our results, wouldcyclic typicallysystem as suggested produce by highly our results, potent would ligands. typically While produce this highly data po couldtent ligands. be used While advantageously this for producingdata could more be eff usedective advantageously radiotracers for or producing to placate more animals effective [ 27radiotracers], these findingsor to placate are animals a double-edged sword, and[27], serious these findings risks for are humana double-edg healthed sword, must and be serious taken risks into for account. human health must be taken into account.

Table 3. Selected molecules resulted from the scaffold-hopping approach. Table 3. Selected molecules resulted from the scaffold-hopping approach. Series 1 Series 2 Series 3 Series 1 Series 2 Series 3

9.0 9.0 8.9 9.0 9.0 8.9 Table 3. Selected molecules resulted from the scaffold-hopping approach.

Series 1 OH Series 2 Series 3 Figure 4.FigureSelected 4. Selected portions portions of theof the fentanyl fentanyl structure for for the thescaffold-hopping scaffold-hopping approach. approach. O N N Table 3.TableSelected 3. Selected molecules molecules resulted resulted from the the scaffold-hopping scaffold-hopping approach. approach. 9.0 9.0 8.9 SeriesSeries 1 1 Series Series 2 2 Series Series3 3

OH S N N O O NN Int.Int. J. Int.Mol. J. Mol.J. Sci. Mol. Sci. 2019 NSci. 2019, 201920,, N20x, FOR,20 x ,FOR x PEERFOR PEER PEER REVIEW REVIEW REVIEW N 7 of7 11 of7 of11 11 N Int.Int. J. Int.Mol. J. Mol. J. Sci. Mol. Sci. 2019 Sci. 2019, 201920,, 20x, FOR,20 x ,FOR x PEERFOR PEER PEER REVIEW REVIEW REVIEW Cl 7 of7 11of7 of11 11 Int. J. Mol. Sci. 2019, 20, x FOR PEER REVIEW HN 7 of 11 8.9 8.9 9.0 9.0 9.09.0

O NN

HN 8.88.8 8.8 8.88.8 8.8

8.88.8 8.8 8.98.9 8.9 8.88.8 8.8 8.8 8.88.8 8.8 8.98.9 8.9 SeriesSeriesSeries 4 4 4 Series Series8.9 Series8.9 58.9 5 5 Series Series Series 6 6 6 SeriesSeriesSeries 4 4 4 Series Series Series 5 5 5 Series Series Series 6 6 6 SeriesSeriesSeries 4 4 4 Series Series Series 5 5 5 Series Series Series 6 6 6

9.59.5 9.5 9.89.8 9.8 9.5 10.810.8 10.8 9.8 9.59.5 9.5 10.8 9.89.8 9.8 9.59.5 9.5 10.810.8 10.8 9.89.8 9.8 10.810.8 10.8

9.7 9.4 10.6 9.7 9.49.4 9.4 10.610.6 10.6 9.79.7 9.49.4 10.610.6 10.6 9.79.7 9.7 9.49.4 10.610.6 10.6 9.7 9.4 2.4.2.4. Designer2.4. Designer Designer Drugs Drugs Drugs from from fromthe the Scaffold-Hopping the Scaffold-Hopping Scaffold-Hopping Results Results Results 2.4.2.4. Designer2.4. Designer Designer Drugs Drugs Drugs from from fromthe the Scaffold-Hopping the Scaffold-Hopping Scaffold-Hopping Results Results Results 2.4.2.4. Designer2.4.Newly-identified DesignerNewly-identified DesignerNewly-identified Drugs Drugs Drugs from from fentanyl from the fentanyl the fentanylScaffold-Hopping the Scaffold-Hopping Scaffold-Hoppinganalogs analogs analogs often often Resultsoften lackResults Resultslack lack any any anyin invitro in vitro vitro and/or and/or and/or in invivo in vivo vivo pharmacology pharmacology pharmacology data. data. data. Newly-identifiedNewly-identifiedNewly-identified fentanyl fentanyl fentanyl analogs analogs analogs often often often lack lack lackany any anyin invitro invitro vitro and/or and/or and/or in invivo invivo vivo pharmacology pharmacology pharmacology data. data. data. SomeSomeSomeNewly-identified ofNewly-identified oftheNewly-identified of the identified the identified identified moleculesfentanyl moleculesfentanyl moleculesfentanyl analogs are analogs are analogstemporarily are temporarily oftentemporarily often often lack lackpl lack acedanypl placedany acedany inby invitroby the inbyvitro the DEAvitro theand/or DEA and/or DEA inand/or inthe in in the inUSvivo thein vivoUS Schedule vivoUS pharmacology Schedule pharmacologySchedule pharmacology I waiting I waiting I waiting data. fordata. data.for for SomeSomeSome of ofthe ofthe identified the identified identified molecules molecules molecules are are temporarily are temporarily temporarily pl acedpl placedaced by by the by the DEA the DEA DEA in inthe in the US the US Schedule US Schedule Schedule I waiting I waiting I waiting for for for completeSomeSomecompleteSomecomplete of ofthe analysesof the identified analysesthe identifiedanalyses identified and andmolecules andmoleculesdata, moleculesdata, data, which arewhich arewhich temporarily are cantemporarily temporarilycan canbe be abe time-consumingpla acedapltime-consuming placedtime-consumingaced by by the by the DEA the DEA exercise.DEA inexercise. intheexercise. in the US the VirtualUS Schedule USVirtual ScheduleVirtual Schedule screening screeningI waitingscreening I waiting I waiting may formay for may for completecompletecomplete analyses analyses analyses and and anddata, data, data, which which which can can canbe be abe time-consuminga atime-consuming time-consuming exercise. exercise. exercise. Virtual Virtual Virtual screening screening screening may may may representcompletecompleterepresentcompleterepresent aanalyses validaanalyses validaanalyses valid and andand andfaster andfasterdata, fasterdata, alternative,data, alternative,which alternative,which which can andcan canandbe anditbe a wasbeit atime-consuming it was a time-consumingwas alreadytime-consuming already already reported reported reported exercise. exercise. thatexercise. that thatstructure-based Virtualstructure-based Virtualstructure-based Virtual screening screening screening methods methods methodsmay may may representrepresentrepresent a valida valida valid and and andfaster faster faster alternative, alternative, alternative, and and andit wasit itwas wasalready already already reported reported reported that that thatstructure-based structure-based structure-based methods methods methods mightrepresentrepresentmightrepresentmight allow allow a allow validspeeding-upa valid aspeeding-up validspeeding-up and and andfaster faster the faster the alternative,identification the alternative,identification alternative,identification and and andandit classificationandwasit itclassificationwas classificationwasalready already already reportedof reportedofpotentially reportedof potentially potentially that that thattoxicstructure-based toxicstructure-based toxicstructure-based compounds compounds compounds methods [11]. methods [11].methods [11].To To To mightmightmight allow allow allow speeding-up speeding-up speeding-up the the identification the identification identification and and classificationand classification classification of ofpotentially of potentially potentially toxic toxic toxic compounds compounds compounds [11]. [11]. [11].To To To furthermightmightfurthermightfurther allow validate allow validateallow validatespeeding-up speeding-up speeding-upthe the predictingthe predicting thepredicting the identification the identification capabilities identification capabilities capabilities and ofand andclassificationofour of classificationour classification ourconsensus consensus consensus of ofpotentiallyQSAR of potentiallyQSAR potentiallyQSAR ligand-based ligand-based toxic ligand-based toxic toxic compounds compounds compounds approach, approach, approach, [11]. [11]. [11]. weTo weTo weTo furtherfurtherfurther validate validate validate the the predictingthe predicting predicting capabilities capabilities capabilities of ofour of our ourconsensus consensus consensus QSAR QSAR QSAR ligand-based ligand-based ligand-based approach, approach, approach, we we we decidedfurtherfurtherdecidedfurtherdecided validateto validate toinvestigate validateto investigate investigatethe the thepredicting predictingpreviously predictingpreviously previously capabilities capabilitiesreported capabilitiesreported reported ofdesi ofdesiour gnerof desiour gner ourconsensusgner drugs—alsoconsensus consensusdrugs—also drugs—also QSAR QSAR knownQSAR known ligand-basedknown ligand-based ligand-basedas asNovel as Novel Novel approach,Psychoactive approach, Psychoactive approach,Psychoactive we we we decideddecideddecided to toinvestigate toinvestigate investigate previously previously previously reported reported reported desi desi gnerdesignergner drugs—also drugs—also drugs—also known known known as asNovel as Novel Novel Psychoactive Psychoactive Psychoactive SubstancesdecideddecidedSubstancesdecidedSubstances to to(NPS)—amonginvestigate to(NPS)—amonginvestigate (NPS)—amonginvestigate previously previously thepreviously the resultsthe results resultsreported reportedof reported ofthe of the scaffoldthedesi scaffolddesi scaffoldgnerdesigner hoppinggner drugs—also hopping drugs—alsohopping drugs—also approach. approach. approach. known known knownInterestingly, Interestingly,as Interestingly, asNovel as Novel Novel Psychoactivea numberPsychoactivea Psychoactivenumbera number of of of SubstancesSubstancesSubstances (NPS)—among (NPS)—among (NPS)—among the the resultsthe results results of ofthe of the scaffoldthe scaffold scaffold hopping hopping hopping approach. approach. approach. Interestingly, Interestingly, Interestingly, a numbera numbera number of of of alreadySubstancesSubstancesalreadySubstancesalready reported reported(NPS)—among reported(NPS)—among (NPS)—among designer designer designer the fentanyl-likethe resultsthefentanyl-like resultsfentanyl-like results of ofthe of thecompounds scaffoldthecompounds scaffoldcompounds scaffold hopping hopping werehopping were were approach.found, approach. found, approach.found, including Interestingly,including Interestingly,including Interestingly, , furanylfentanyl, afuranylfentanyl, numbera numbera number of of of alreadyalreadyalready reported reported reported designer designer designer fentanyl-like fentanyl-like fentanyl-like compounds compounds compounds were were were found, found, found, including including including furanylfentanyl, furanylfentanyl, furanylfentanyl, ,alreadyalreadyacetylfentanyl,alreadyacetylfentanyl, reported reported reported designer designer designeracrylfentanyl,, acrylfentanyl,fentanyl-like fentanyl-like fentanyl-like compounds compounds ,compoundsbenzodioxolefentanyl,benzodioxolefentanyl, were were were found, found, found, including including includingcyclopentylfentanyl,, furanylfentanyl,cyclopentylfentanyl, furanylfentanyl, furanylfentanyl, acetylfentanyl,acetylfentanyl,acetylfentanyl, acrylfentanyl,acrylfentanyl,acrylfentanyl, benzodioxolefentanyl,benzodioxolefentanyl,benzodioxolefentanyl, cyclopentylfentanyl,cyclopentylfentanyl,cyclopentylfentanyl, tetramethylcyclopropylfentanyl,acetylfentanyl,acetylfentanyl,tetramethylcyclopropylfentanyl,acetylfentanyl,tetramethylcyclopropylfentanyl, acrylfentanyl,acrylfentanyl,acrylfentanyl, tetrahyd tetrahyd tetrahyd rofuranylfentanyl,rofuranylfentanyl,rofuranylfentanyl,benzodioxolefentanyl,benzodioxolefentanyl,benzodioxolefentanyl, 3-furanylfentanyl, 3-furanylfentanyl, 3-furanylfentanyl, cyclopentylfentanyl, cyclopentylfentanyl,a -methylfentanyl,cyclopentylfentanyl,a -methylfentanyl,a-methylfentanyl, tetramethylcyclopropylfentanyl,tetramethylcyclopropylfentanyl,tetramethylcyclopropylfentanyl, tetrahyd tetrahyd tetrahydrofuranylfentanyl,rofuranylfentanyl,rofuranylfentanyl, 3-furanylfentanyl, 3-furanylfentanyl, 3-furanylfentanyl, a -methylfentanyl,a -methylfentanyl,a-methylfentanyl, acryloylfentanyl,tetramethylcyclopropylfentanyl,tetramethylcyclopropylfentanyl,acryloylfentanyl,acryloylfentanyl,tetramethylcyclopropylfentanyl, , cyclopropylfentanyl, cyclopropylfentanyl, tetrahyd tetrahyd tetrahyd4-fluorofent 4-fluorofent rofuranylfentanyl,4-fluorofentrofuranylfentanyl,rofuranylfentanyl,anyl,anyl,anyl, methoxyacetylfentanyl, methoxyacetylfentanyl, methoxyacetylfentanyl, 3-furanylfentanyl, 3-furanylfentanyl, 3-furanylfentanyl, 3-methylfentanyl, 3-methylfentanyl,a -methylfentanyl, 3-methylfentanyl,a -methylfentanyl,a-methylfentanyl, 4- 4- 4- acryloylfentanyl,acryloylfentanyl,acryloylfentanyl, cyclopropylfentanyl, cyclopropylfentanyl, cyclopropylfentanyl, 4-fluorofent 4-fluorofent 4-fluorofentanyl,anyl,anyl, methoxyacetylfentanyl, methoxyacetylfentanyl, methoxyacetylfentanyl,β β3-methylfentanyl,β 3-methylfentanyl, 3-methylfentanyl, 4- 4- 4- fluoroisobutyrylfentanyl,acryloylfentanyl,acryloylfentanyl,fluoroisobutyrylfentanyl,acryloylfentanyl,fluoroisobutyrylfentanyl, cyclopropylfentanyl, cyclopropylfentanyl, cyclopropylfentanyl, 4-chlorisobu 4-chlorisobu 4-chlorisobu tyrfentanyl,4-fluorofent 4-fluorofenttyrfentanyl, 4-fluorofenttyrfentanyl, anyl,4-methoxybutyrfentanyl, anyl,4-methoxybutyrfentanyl, anyl, 4-methoxybutyrfentanyl,methoxyacetylfentanyl, methoxyacetylfentanyl, methoxyacetylfentanyl,Int. J.-hydroxythiofentanyl Mol.3-methylfentanyl, -hydroxythiofentanyl 3-methylfentanyl,-hydroxythiofentanyl Sci. 3-methylfentanyl, 2019, 20, x; doi: 4- FOR 4- 4- PEER REVIEW www.mdpi.com/journal/ijms fluoroisobutyrylfentanyl,fluoroisobutyrylfentanyl,fluoroisobutyrylfentanyl, 4-chlorisobu 4-chlorisobu 4-chlorisobutyrfentanyl,tyrfentanyl,Int.tyrfentanyl, J. Mol. Sci.4-methoxybutyrfentanyl, 4-methoxybutyrfentanyl,2019 4-methoxybutyrfentanyl,, 20, x; doi: FOR PEER REVIEW β-hydroxythiofentanyl β -hydroxythiofentanylβ -hydroxythiofentanyl www.mdpi.com/journal/ijms andfluoroisobutyrylfentanyl,fluoroisobutyrylfentanyl,and andfluoroisobutyrylfentanyl,isobutyrylfentanyl, isobutyrylfentanyl, isobutyrylfentanyl, predicted 4-chlorisobupredicted predicted4-chlorisobu 4-chlorisobu to tohave to tyrfentanyl,have tyrfentanyl,have ptyrfentanyl,K pi KofpiK of8.5,i of 8.5,4-methoxybutyrfentanyl, 8.3,8.5,4-methoxybutyrfentanyl, 8.3,4-methoxybutyrfentanyl, 8.5,8.3, 8.5, 8.4,8.5, 8.4, 8.3,8.4, 8.3, 7.9,8.3, 7.9, 8.2,7.9, 8.2, 8.4,8.2,β -hydroxythiofentanyl8.4, 8.7,-hydroxythiofentanyl8.4, -hydroxythiofentanyl8.7, 8.5,8.7, 8.5, 8.6,8.5, 8.6, 7.9,8.6, 7.9, 8.1,7.9, 8.1, 8.1, andand andisobutyrylfentanyl, isobutyrylfentanyl, isobutyrylfentanyl, predicted predicted predicted to tohave to have have pK pi K ofpiK of8.5,i of 8.5, 8.3,8.5, 8.3, 8.5,8.3, 8.5, 8.4,8.5, 8.4, 8.3,8.4, 8.3, 7.9,8.3, 7.9, 8.2,7.9, 8.2, 8.4,8.2, 8.4, 8.7,8.4, 8.7, 8.5,8.7, 8.5, 8.6,8.5, 8.6, 7.9,8.6, 7.9, 8.1,7.9, 8.1, 8.1, 8.5,andand8.5, 7.8,and8.5,isobutyrylfentanyl, 7.8,isobutyrylfentanyl, 8.0, 7.8,isobutyrylfentanyl, 8.0, 8.0,8.0, 8.0, 8.18.0, 8.1 and 8.1 and and8.6,predicted predicted8.6, respectively.8.6,predicted respectively. respectively. to tohave to have haveAll p KAll pof iAll Kofp ioftheseK of 8.5,iof theseof 8.5, these 8.3,8.5,compounds 8.3, compounds 8.5, 8.3,compounds 8.5, 8.4,8.5, 8.4, 8.3,8.4, are 8.3, are 7.9, 8.3, potentare 7.9, potent 8.2,7.9, potent 8.2, μ8.4,8.2,OR 8.4,μ OR8.7,8.4,μ ligands OR8.7, ligands8.5,8.7, ligands8.5, 8.6,8.5, and 8.6, and7.9,8.6, andsome7.9, 8.1,7.9,some 8.1,some 8.1, 8.5,8.5, 7.8,8.5, 7.8, 8.0,7.8, 8.0, 8.0,8.0, 8.0, 8.18.0, 8.1 and 8.1 and and8.6, 8.6, respectively.8.6, respectively. respectively. All All of All ofthese of these these compounds compounds compounds are are potentare potent potent μOR μ ORμ ligandsOR ligands ligands and and andsome some some of8.5, 8.5,ofthem 7.8,8.5,of them 7.8,them 8.0,have7.8, 8.0,have 8.0,have already 8.0, already8.18.0, already8.1 and 8.1 beenand andbeen8.6, been 8.6,attributed respectively.8.6, attributed respectively. attributed respectively. to toa Alllargeto a Alllargea of Alllarge numberofthese of numberthese numberthese compounds ofcompounds compoundsoffatalities of fatalities fatalities are worldwide are worldwidepotentare worldwidepotent potent μ OR[28,29].μ OR μ[28,29]. ligandsOR [28,29]. ligands ligandsAll All suchand All andsuch andsuchsome NPS some NPS some NPS of ofthemof them them have have have already already already been been been attributed attributed attributed to toa largeto a largea large number number number of offatalities of fatalities fatalities worldwide worldwide worldwide [28,29]. [28,29]. [28,29]. All All such All such such NPS NPS NPS wereof ofwerethemofwere themidentified them identifiedhave identified have have already alreadyas already aspotent as potentbeen potent been beenligands attributed ligands attributed ligands attributed by by ourto by tooura methodlargeto oura largemethoda largemethod number ology,number numberology,ology, ofstrengthening offatalitiesstrengthening offatalitiesstrengthening fatalities worldwide worldwide the worldwide the qualitythe quality [28,29].quality [28,29]. of [28,29]. ofour Allof our Allconsensusour such All consensussuch consensus such NPS NPS NPS werewerewere identified identified identified as aspotent as potent potent ligands ligands ligands by by our by our methodour method methodology,ology,ology, strengthening strengthening strengthening the the qualitythe quality quality of ofour of our consensusour consensus consensus methodology.wereweremethodology.weremethodology. identified identified identified as aspotent as potent potent ligands ligands ligands by by our by our methodour method methodology,ology,ology, strengthening strengthening strengthening the the qualitythe quality quality of ofour of our consensusour consensus consensus methodology.methodology.methodology.Int. J. Mol. Sci. 2019, 20, x; doi: FOR PEER REVIEW www.mdpi.com/journal/ijms methodology.methodology.methodology. 3. Materials3. 3.Materials Materials and and andMethods Methods Methods 3. Materials3. 3.Materials Materials and and andMethods Methods Methods 3. Materials3. 3.Materials Materials and and andMethods Methods Methods 3.1.3.1. Biological3.1. Biological Biological Data Data Data 3.1.3.1. Biological3.1. Biological Biological Data Data Data 3.1.3.1. Biological3.1.The BiologicalThe Biological chemicalThe chemical chemicalData Data Data structures structures structures of allof of allthe all the molecules the molecules molecules were were were retrieved retrieved retrieved from from from the the ChEMBL the ChEMBL ChEMBL database database database selecting selecting selecting TheThe chemicalThe chemical chemical structures structures structures of ofall of all the all the molecules the molecules molecules were were were retrieved retrieved retrieved from from from the the ChEMBL the ChEMBL ChEMBL database database database selecting selecting selecting onlyonlyonly The Thethose Thechemicalthose chemicalthose chemical tested structurestested testedstructures structures for for offor oftheirall of alltheir the alltheir the molecules theaffinity molecules affinity moleculesaffinity were for were for were retrievedfor the retrieved retrievedthe the human fromhuman fromhuman from the the μChEMBL the OR ChEMBLμ ORChEMBLμ OR (available database(available database(available database selecting online: selecting online:selectingonline: onlyonlyonly those thosethose tested testedtested for for for their theirtheir affinity affinityaffinity for for for the the the human humanhuman μ ORμORμ OR (available (available(available online: online:online: https://www.ebi.ac.uk/chembl/target/inspect/onlyonlyhttps://www.ebi.ac.uk/chembl/target/inspect/onlyhttps://www.ebi.ac.uk/chembl/target/inspect/ those thosethose tested testedtested for for for their theirtheir affinity affinityaffinityCHEMBL233). CHEMBL233). CHEMBL233).for for for the the the human humanDatawarriorhumanDatawarriorDatawarrior μ ORμORμ OR (available (5.0.0,(available(5.0.0,(available(5.0.0, Idorsiaonline: Idorsiaonline:Idorsiaonline: https://www.ebi.ac.uk/chembl/target/inspect/https://www.ebi.ac.uk/chembl/target/inspect/https://www.ebi.ac.uk/chembl/target/inspect/CHEMBL233).CHEMBL233).CHEMBL233). DatawarriorDatawarriorDatawarrior (5.0.0,(5.0.0,(5.0.0, IdorsiaIdorsiaIdorsia Pharmaceuticalshttps://www.ebi.ac.uk/chembl/target/inspect/https://www.ebi.ac.uk/chembl/target/inspect/Pharmaceuticalshttps://www.ebi.ac.uk/chembl/target/inspect/Pharmaceuticals Ltd., Ltd., Ltd., Allschwil, Allschwil, Allschwil, Switzerland) Switzerland) Switzerland)CHEMBL233). CHEMBL233).[30]CHEMBL233). [30] [30]was was wasused used used Datawarriorfor Datawarriorfor handlingDatawarriorfor handling handling the the(5.0.0, selectionthe(5.0.0, selection(5.0.0, selection Idorsiaof Idorsia of theIdorsiaof the the PharmaceuticalsPharmaceuticalsPharmaceuticals Ltd., Ltd., Ltd., Allschwil, Allschwil, Allschwil, Switzerland) Switzerland) Switzerland) [30] [30] [30]was was wasused used used for for handlingfor handling handling the the selectionthe selection selection of of theof the the moleculesPharmaceuticalsPharmaceuticalsmoleculesPharmaceuticalsmolecules with with with fentanyl-like Ltd.,fentanyl-like fentanyl-likeLtd., Ltd., Allschwil, Allschwil, Allschwil, structures structures structures Switzerland) Switzerland) amongSwitzerland) among among the [30]the entire the[30] entire [30]was entire wasdownloaded was downloadedused downloadedused used for for handlingfordataset. handling dataset. handlingdataset. Only the Only theOnly moleculestheselection moleculesselection moleculesselection whereof ofwhere the of wherethe the moleculesmoleculesmolecules with with with fentanyl-like fentanyl-like fentanyl-like structures structures structures among 3among among3 3 the the entire the entire entire downloaded downloaded downloaded dataset. dataset. dataset. Only Only Only molecules molecules molecules where where where themoleculesmoleculesthe moleculesthedisplacement displacement displacement with with with fentanyl-like fentanyl-likeof fentanyl-like of theof the theradioligand structuresradioligand structuresradioligand structures among[ among H]DAMGO[ amongH]DAMGO[ H]DAMGOthe the entire the entire entirefrom downloadedfrom downloadedfrom downloadedthe the thehuman human dataset.human dataset. dataset.μ ORμ OnlyORμ OnlyORwas Only moleculeswas wasmoleculesused moleculesused used for wherefor wherethefor wherethe the 3 3 thethe thedisplacement displacement displacement of of theof the theradioligand radioligand radioligand [ H]DAMGO[ 3H]DAMGO[3H]DAMGO from from from the the thehuman human human μ ORμ ORμ ORwas was wasused used used for for thefor the the thethe thedisplacement displacement displacement of of theof the theradioligand radioligand radioligand [3 H]DAMGO[ H]DAMGO[ H]DAMGO from from from the the thehuman human human μ ORμ ORμ ORwas was wasused used used for for forthe the the

Int. J. Mol. Sci. 2019, 20, 2311 7 of 10

2.4. Designer Drugs from the Scaffold-Hopping Results Newly-identified fentanyl analogs often lack any in vitro and/or in vivo pharmacology data. Some of the identified molecules are temporarily placed by the DEA in the US Schedule I waiting for complete analyses and data, which can be a time-consuming exercise. Virtual screening may represent a valid and faster alternative, and it was already reported that structure-based methods might allow speeding-up the identification and classification of potentially toxic compounds [11]. To further validate the predicting capabilities of our consensus QSAR ligand-based approach, we decided to investigate previously reported designer drugs—also known as Novel Psychoactive Substances (NPS)—among the results of the scaffold hopping approach. Interestingly, a number of already reported designer fentanyl-like compounds were found, including furanylfentanyl, acetylfentanyl, acrylfentanyl, benzodioxolefentanyl, cyclopentylfentanyl, tetramethylcyclopropylfentanyl, tetrahydrofuranylfentanyl, 3-furanylfentanyl, a-methylfentanyl, acryloylfentanyl, cyclopropylfentanyl, 4-fluorofentanyl, methoxyacetylfentanyl, 3-methylfentanyl, 4-fluoroisobutyrylfentanyl, 4-chlorisobutyrfentanyl, 4-methoxybutyrfentanyl, β-hydroxythiofentanyl and isobutyrylfentanyl, predicted to have pKi of 8.5, 8.3, 8.5, 8.4, 8.3, 7.9, 8.2, 8.4, 8.7, 8.5, 8.6, 7.9, 8.1, 8.5, 7.8, 8.0, 8.0, 8.1 and 8.6, respectively. All of these compounds are potent µOR ligands and some of them have already been attributed to a large number of fatalities worldwide [28,29]. All such NPS were identified as potent ligands by our methodology, strengthening the quality of our consensus methodology.

3. Materials and Methods

3.1. Biological Data The chemical structures of all the molecules were retrieved from the ChEMBL database selecting only those tested for their affinity for the human µOR (available online: https://www.ebi.ac.uk/ chembl/target/inspect/CHEMBL233). Datawarrior (5.0.0, Idorsia Pharmaceuticals Ltd., Allschwil, Switzerland) [30] was used for handling the selection of the molecules with fentanyl-like structures among the entire downloaded dataset. Only molecules where the displacement of the radioligand 3 [ H]DAMGO from the human µOR was used for the determination of all of the Ki values, were selected. The binding affinity data were converted into their negative decimal logarithm pK (pK = logK ). i i − i 3.2. Molecular Modeling The two-dimensional structures of the dataset were built using Marvin Sketch (18.24, ChemAxon Ltd., Budapest, Hungary). The protonation states of the molecules were calculated assuming a neutral pH, and the Merck molecular force field (MMFF94) was used for a first 3D geometry optimization. Subsequently, the geometry of the resulting 3D structures was optimized at a semi-empirical level using the parameterized model number 3 (PM3) Hamiltonian as implemented in the MOPAC package (MOPAC2016 v. 18.151, Stewart Computational Chemistry, Colorado Springs, CO, USA) [31,32].

3.3. Compound Alignment for the 3D Model and kNN Models Information All the fentanyl-like 3D-optimized structures were imported into the software Forge (v10.4.2, Cresset, New Cambridge House, Hertfordshire, UK) [23] to set the field-based 3D-QSAR model and the 2D k-Nearest Neighbor (kNN) models. Out of the 115 structures, 94 molecules were randomly selected as a training set while the remaining 21 were used as an external validation (test set) to evaluate the models [33] (Tables S1 and S2). The molecules in the training set and the test set covered a pKi range from 10.1 to 5.3. Ten different splits were tried and the results were consistent to the reported ones. All the fentanyl-like molecules were aligned using fentanyl in its previously reported conformation [22]. The field points (negative and positive electrostatic, van der Waals shape, and hydrophobic description of the molecules) were generated using the extended electron distribution (XED) force field included in Forge. All the software parameters used for the conformation hunt, alignment, and model building calculations are presented in the Supplementary material (Figure S1–S6, Int. J. Mol. Sci. 2019, 20, 2311 8 of 10

Tables S1 and S2). As an alternative to the 3D-field QSAR, we also developed other two QSAR models using the kNN method with the same software. The kNN approach is well-known, robust and has an effective distance learning approach [34,35]. The two kNN models were developed using two different 2D-fingerprint similarities: the ECFP6 and the FCFP6 circular fingerprint descriptors. More detailed information for the kNN models generations is reported in the Supplementary material.

4. Conclusions The present study explores the development of three QSAR models exploitable for the prediction of a ligand affinity to the µOR, and for the identification of new molecules that could efficiently interact with such receptor class. Forge was employed to build a statistically robust QSAR evaluating methodology using a set of 115 fentanyl-analogs covering a wide range of known µOR ligands. An activity cliff analysis followed by a scaffold-hopping approach has been performed to provide a theoretical route for the exploration of novel unidentified fentanyl-analogs with high potency. The QSAR models reported here will guarantee, prospectively, fruitful applications to speed up the design and the identification process of new µOR ligands with tunable activities. Since µOR fentanyl-like ligands currently represent a well-explored class of synthetic opioids, often associated with fatal cases worldwide, this proposed ligand-based tool could be considered by the DEA, the European Monitoring Centre for Drugs and Drug Addiction (EMCDDA), and other regulatory bodies for speeding-up the classification of novel fentanyl-like NPS. Likewise, the newly identified libraries may potentially aid the interpretation of toxicological analyses where the presence of novel synthetic opioids is postulated.

Supplementary Materials: Supplementary materials can be found at http://www.mdpi.com/1422-0067/20/9/2311/s1. Author Contributions: Conceptualization, G.F. and V.A.; methodology, G.F., A.R. and V.A.; software, G.F., A.R. and V.A.; validation, G.F., A.R. and V.A.; formal analysis, G.F., A.R. and V.A.; investigation, G.F. and V.A.; resources, G.F., A.R. and V.A.; data curation, G.F.; writing—original draft preparation, G.F.; writing—review and editing, G.F., A.R. and V.A.; visualization, G.F., A.R. and V.A.; supervision, G.F., A.R. and V.A.; project administration, G.F. and V.A.; funding acquisition, G.F., A.R. and V.A. Funding: This research received no external funding. Conflicts of Interest: The authors declare no conflict of interest.

References

1. Wang, S. Historical Review: Opiate Addiction and Opioid Receptors. Cell Transpl. 2018.[CrossRef] 2. Gracies, J.M.; O’Dell, M.; Vecchio, M.; Hedera, P.; Kocer, S.; Rudzinska-Bar, M.; Rubin, B.; Timerbaeva, S.L.; Lusakowska, A.; Boyer, F.C.; et al. Effects of Repeated Abobotulinumtoxina Injections in Upper Limb Spasticity. Muscle Nerve 2018, 57, 245–254. [CrossRef] 3. Vecchio, M.; Gracies, J.M.; Panza, F.; Fortunato, F.; Vitaliti, G.; Malaguarnera, G.; Cinone, N.; Beatrice, R.; Ranieri, M.; Santamato, A. Change in Coefficient of Fatigability Following Rapid, Repetitive Movement Training in Post-Stroke Spastic Paresis: A Prospective Open-Label Observational Study. J. Stroke Cerebrovasc. Dis. 2017, 26, 2536–2540. [CrossRef] 4. Vecchio, M.; Malaguarnera, G.; Giordano, M.; Malaguarnera, M.; Volti, G.L.; Galvano, F.; Drago, F.; Basile, F.; Malaguarnera, M. A musician’s dystonia. Lancet 2012, 379, 2116. [CrossRef] 5. Lake, S.; Kennedy, M.C. Health outcomes associated with illicit prescription opioid injection: A systematic review. J. Addict. Dis. 2016, 35, 73–91. [CrossRef] 6. Frank, R.G.; Pollack, H.A. Addressing the Fentanyl Threat to Public Health. N. Engl. J. Med. 2017, 376, 605–607. [CrossRef] 7. Rudd, R.A.; Seth, P.; David, F.; Scholl, L. Increases in Drug and Opioid-Involved Overdose Deaths—United States, 2010–2015. MMWR Morb. Mortal. Wkly. Rep. 2016, 65, 1445–1452. [CrossRef] 8. Rudd, R.A.; Aleshire, N.; Zibbell, J.E.; Gladden, R.M. Increases in Drug and Opioid Overdose Deaths–United States, 2000–2014. MMWR Morb. Mortal. Wkly. Rep. 2016, 64, 1378–1382. [CrossRef] Int. J. Mol. Sci. 2019, 20, 2311 9 of 10

9. Fentanyl Remains the Most Significant Synthetic Opioid Threat and Poses the Greatest Threat to the Opioid User Market in the United States. Report Number: DEA-DCT-DIB-003-18. Available online: https://ndews.umd.edu/sites/ndews.umd.edu/files/fentanyl-remains-most-significant-synthetic- opioid-threat-2018.pdf (accessed on 22 January 2019). 10. Fomin, D.; Baranauskaite, V.; Usaviciene, E.; Sumkovskaja, A.; Laima, S.; Jasulaitis, A.; Minkuviene, Z.N.; Chmieliauskas, S.; Stasiuniene, J. Human deaths from drug overdoses with carfentanyl involvement-new rising problem in forensic medicine: A STROBE-compliant retrospective study. Medicine (Baltimore) 2018, 97, e13449. [CrossRef] 11. Ellis, C.R.; Kruhlak, N.L.; Kim, M.T.; Hawkins, E.G.; Stavitskaya, L. Predicting opioid receptor binding affinity of pharmacologically unclassified designer substances using molecular docking. PLoS ONE 2018, 13, e0197734. [CrossRef] 12. Floresta, G.; Amata, E.; Dichiara, M.; Marrazzo, A.; Salerno, L.; Romeo, G.; Prezzavento, O.; Pittala, V.; Rescifina, A. Identification of Potentially Potent Heme Oxygenase 1 Inhibitors through 3D-QSAR Coupled to Scaffold-Hopping Analysis. ChemMedChem 2018.[CrossRef] 13. Floresta, G.; Rescifina, A.; Marrazzo, A.; Dichiara, M.; Pistara, V.; Pittala, V.; Prezzavento, O.; Amata, E. Hyphenated 3D-QSAR statistical model-scaffold hopping analysis for the identification of potentially potent and selective sigma-2 receptor ligands. Eur. J. Med. Chem. 2017, 139, 884–891. [CrossRef] 14. Rescifina, A.; Floresta, G.; Marrazzo, A.; Parenti, C.; Prezzavento, O.; Nastasi, G.; Dichiara, M.; Amata, E. Sigma-2 receptor ligands QSAR model dataset. Data Brief 2017, 13, 514–535. [CrossRef] 15. Rescifina, A.; Floresta, G.; Marrazzo, A.; Parenti, C.; Prezzavento, O.; Nastasi, G.; Dichiara, M.; Amata, E. Development of a Sigma-2 Receptor affinity filter through a Monte Carlo based QSAR analysis. Eur. J. Pharm. Sci. 2017, 106, 94–101. [CrossRef] 16. Floresta, G.; Cilibrizzi, A.; Abbate, V.; Spampinato, A.; Zagni, C.; Rescifina, A. FABP4 inhibitors 3D-QSAR model and isosteric replacement of BMS309403 datasets. Data Brief 2019, 22, 471–483. [CrossRef] 17. Floresta, G.; Cilibrizzi, A.; Abbate, V.; Spampinato, A.; Zagni, C.; Rescifina, A. 3D-QSAR assisted identification of FABP4 inhibitors: An effective scaffold hopping analysis/QSAR evaluation. Bioorg. Chem. 2019, 84, 276–284. [CrossRef] 18. Cherkasov, A.; Muratov, E.N.; Fourches, D.; Varnek, A.; Baskin, I.I.; Cronin, M.; Dearden, J.; Gramatica, P.; Martin, Y.C.; Todeschini, R.; et al. QSAR modeling: where have you been? Where are you going to? J. Med. Chem. 2014, 57, 4977–5010. [CrossRef] 19. De Jong, S. SIMPLS: An alternative approach to partial least squares regression. Chemom. Intell. Lab. Syst. 1993, 18, 251–263. [CrossRef] 20. Wold, S.; Sjöström, M.; Eriksson, L. PLS-regression: a basic tool of chemometrics. Chemom. Intell. Lab. Syst. 2001, 58, 109–130. [CrossRef] 21. Kubinyi, H.; Hamprecht, F.A.; Mietzner, T. Three-dimensional quantitative similarity-activity relationships (3D QSiAR) from SEAL similarity matrices. J. Med. Chem. 1998, 41, 2553–2564. [CrossRef] 22. Jiang, H.L.; Huang, X.Q.; Rong, S.B.; Luo, X.M.; Chen, J.Z.; Tang, Y.; Chen, K.X.; Zhu, Y.C.; Jin, W.Q.; Chi, Z.Q.; et al. Theoretical studies on opioid receptors and ligands. I. Molecular modeling and QSAR studies on the interaction mechanism of fentanyl analogs binding to µ-opioid receptor. Int. J. Quantum Chem. 2000, 78, 285–293. [CrossRef] 23. Cheeseright, T.; Mackey, M.; Rose, S.; Vinter, A. Molecular field extrema as descriptors of biological activity: Definition and validation. J. Chem. Inf. Model. 2006, 46, 665–676. [CrossRef] 24. Olesen, P.H. The use of bioisosteric groups in lead optimization. Curr. Opin. Drug Discov. Dev. 2001, 4, 471–478. 25. Floresta, G.; Pittala, V.; Sorrenti, V.; Romeo, G.; Salerno, L.; Rescifina, A. Development of new HO-1 inhibitors by a thorough scaffold-hopping analysis. Bioorg. Chem. 2018, 81, 334–339. [CrossRef] 26. Floresta, G.; Apirakkan, O.; Rescifina, A.; Abbate, V. Discovery of High-Affinity Cannabinoid Receptors Ligands through a 3D-QSAR Ushered by Scaffold-Hopping Analysis. Molecules 2018, 23, 2183. [CrossRef] 27. Clarke, D.L.; Drobatz, K.J.; Korzekwa, C.; Nelson, L.S.; Perrone, J. Trends in Opioid Prescribing and Dispensing by Veterinarians in Pennsylvania. JAMA Netw. Open 2019, 2, e186950. [CrossRef] 28. Armenian, P.; Vo, K.T.; Barr-Walker, J.; Lynch, K.L. Fentanyl, fentanyl analogs and novel synthetic opioids: A comprehensive review. Neuropharmacology 2018, 134, 121–132. [CrossRef] Int. J. Mol. Sci. 2019, 20, 2311 10 of 10

29. Mounteney, J.; Giraudon, I.; Denissov, G.; Griffiths, P. : Are we missing the signs? Highly potent and on the rise in Europe. Int. J. Drug Policy 2015, 26, 626–631. [CrossRef] 30. Lopez-Lopez, E.; Naveja, J.J.; Medina-Franco, J.L. DataWarrior: An evaluation of the open-source drug discovery tool. Expert Opin. Drug Discov. 2019, 1–7. [CrossRef] 31. Stewart, J.J.P. Optimization of parameters for semiempirical methods IV: Extension of MNDO, AM1, and PM3 to more main group elements. J. Mol. Model. 2004, 10, 155–164. [CrossRef] 32. Alemán, C.; Luque, F.J.; Orozco, M. Suitability of the PM3-derived molecular electrostatic potentials. J. Comput. Chem. 1993, 14, 799–808. [CrossRef] 33. Roy, P.P.; Leonard, J.T.; Roy, K. Exploring the impact of size of training sets for the development of predictive QSAR models. Chemom. Intell. Lab. Syst. 2008, 90, 31–42. [CrossRef] 34. Choudhari, P.B.; Bhatia, M.S.; Jadhav, S.D. Pharmacophore Identification and QSAR Studies on Substituted Benzoxazinone as Antiplatelet Agents: kNN-MFA Approach. Sci. Pharm. 2012, 80, 283–294. [CrossRef] 35. Gupta, S.P.; Samanta, S.; Patil, V.M. A 3D-QSAR study on a series of benzimidazole derivatives acting as hepatitis C virus inhibitors: Application of kNN-molecular field analysis. Med. Chem. 2010, 6, 87–90. [CrossRef]

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