Figure 2. (A, B) Modulation of chloride currents through GABA A receptors composed of α 1 , β 2 , and γ 2S subunits by 100 μM piperine and the indicated derivatives (dotted line indicates cutoff for highly active compounds). (C, D) Concentration-dependent I GABA (EC 3–7 ) enhancement through α 1 β 2 γ 2S GABA A receptors, (C) for 22 (▲), 23 (●), 25 (◆), and 35 (■), ranked by efficiency, and (D) for 25 ( ○ ) and 43 (●), ranked by potency, compared to piperine (dotted line). (E, F) Representative I GABA modulated by (E) 23 and (F) 25 . Data represent mean ± SEM from at least three oocytes and two oocyte batches. Asterisks indicate statistically significant differences from zero: * p < 0.05, ** p < 0.01. Data for piperine were taken from ref 31 .

First, we studied the effects of systematic modifications of the amide nitrogen onmodulation through αreceptors. As illustrated in Figure 2 A,B, 10 compounds (, and) at 100 μM induced strongermodulation than piperine (≥220%) (31) and were classified as highly active.potentiation of these compounds ranged between 294% ± 66% () and 1091% ± 257% (, see Table 1 ). At this concentration, three derivatives (, and) were less efficient, while the other compounds did not significantly modulate(see Figure 2 A,B and Table 1 ).

Modifications at the amide function were implemented in a straightforward fashion (Scheme 1 ). Piperic acid amides (, and) were synthesized by treating piperic acid chloride with the corresponding amine in the presence of triethylamine in tetrahydrofuran (THF). Compoundsandwere prepared in the same way from benzodioxolyl acryloyl chloride. Treatment of piperine with Lawesson’s reagent (35) gave thioamide. Reduction of the carbonyl group of piperine with lithium aluminum hydride afforded unsaturated amine(Scheme 2 ).

Figure 1. Piperine molecule can be structurally divided into three moieties: the 1,3-benzodioxole or aromatic function, the linker region comprising four carbon atoms, and the amide function natively constituted by a piperidine ring.

Starting with piperine as lead structure from prior biological assessment, the molecule can be structurally divided into three parts: the 1,3-benzodioxole or aromatic function, the olefinic linker region comprising four carbon atoms, and the amide function natively constituted by a piperidine ring (Figure 1 ). Within this study, we investigated modifications at the amide group as well as in the linker region.

Figure 2 D illustratesmodulation by the most potent N-substituted piperine derivative (ECfor, 13.8 ± 1.8 μM < ECfor, 23.1 ± 3.3 μM < ECfor piperine, 52.4 ± 9.4 μM (31) ). Based on the modifications at the amide group, it can be concluded that installation of noncyclic substituents bearing 3–4 carbons each at the tertiary amide improves both efficacy and potency of the analogue compounds.

Concentration–response curves ofmodulation by linker-modified derivatives, andare illustrated in Figure 3 C,D. The combination of-dipropyl amide from the serieswith the two most efficient modifications in the linker region (1,4-phenylene and naphthodioxol-5-yl) resulted in= 603% ± 87%, EC= 70.8 ± 21.1 μM),= 706% ± 58%, EC= 102.0 ± 11.2 μM), and= 480% ± 85%, EC= 31.8 ± 5.3 μM) inducing strongerenhancement than piperine (Table 3 ). These findings underscore the general validity of favorable N,N-functionalization also for this series of linker-modified compounds. However, none of the modifications led to compounds with a higher activity than the initial parent compound

Figure 3. (A, B) Modulation of chloride currents through GABA A receptors composed of α 1 , β 2 , and γ 2S subunits by 100 μM piperine and the indicated derivatives (dotted line indicates cutoff for highly active compounds). (C, D) Concentration-dependent I GABA (EC 3–7 ) enhancement through α 1 β 2 γ 2S GABA A receptors: (C) by 47 (■), 53 (▲), and 72 (●), ranked by efficiency, and (D) by 56 (▲) and 73 (●), ranked by potency, compared to piperine (dotted line). (E, F) Representative I GABA modulated by (E) 72 and (F) 73 . Data represent mean ± SEM from at least three oocytes and two oocyte batches. Asterisks indicate statistically significant differences from zero: * p < 0.05, ** p < 0.01. Data for piperine were taken from ref 31 .

At 100 μM, five compounds (, and) modulatedmore efficiently than piperine (see Figure 3 A,B and Table 2 ).potentiation ranged from 280% ± 52% () to 514% ± 76% ().enhancement by, andwas less pronounced compared to piperine [potentiation range 42% ± 1% () to 178% ± 30% ()]. None of the other derivatives induced significantenhancement (see Figure 3 A,B and Table 2 ).

Naphthodioxol-5-ol triflate was also used in a palladium-catalyzed hydroxycarbonylation reaction (41) to provide access to carboxylic acid, which was further converted to products(Scheme 4 ). A different route was chosen to synthesize derivatives of naphtodioxole-6-carboxylic acid: By treating bis(bromomethyl)benzodioxole with iodide, a highly reactive diene was generated in situ, (42) which was intercepted with methyl acrylate in a Diels–Alder reaction. The resulting decaline derivativewas oxidized with 2,3-dichloro-5,6-dicyano-1,4-benzoquinone (DDQ) to afford naphthaline. Saponification of the methyl ester gave carboxylic acid, which was further converted to final products(Scheme 4 ).

Iridium-catalyzed direct borylation (39) of naphtho[2,3-]dioxole allowed direct access to the 6-position of the naphtho[2,3-]dioxole core. Boronic acid esterobtained in this step was converted into the corresponding bromide (40) and coupled under standard Heck cross-coupling conditions to afford acrylate(Scheme 4 ). The methyl ester was hydrolyzed, and acidwas converted into products(Scheme 4 ).

In order to access the 5-position of the naphtho[2,3-]dioxole core, naphtho[2,3-]dioxol-5-ol triflate was chosen as a precursor. (37) Heck coupling (38) employing methyl acrylate afforded, which gave acrylic acidafter cleavage of the methyl ester (Scheme 4 ). Amide formation yielded the final products

For the synthesis of aryl-bridged compounds, two different methods were utilized. For a number of products (, and) (Scheme 3 ), the corresponding bromo-substituted aromatic carboxylic acids were reacted with 3,4-(methylenedioxy)phenylboronic acid under Suzuki–Miyaura cross-coupling conditions. (36) The resulting bis(aryl)carboxylic acids were converted to the final amide products via the corresponding acid chloride intermediates. Alternatively, the corresponding bromobenzoic acid amides were prepared prior to the coupling step. Subsequent Suzuki–Miyaura coupling with 3,4-(methylenedioxy)phenylboronic acid afforded the final productsand(Scheme 3 ).

Three major structural modifications were envisaged (Scheme 1 ). (i) Replacement of the linker by an aryl ring (phenyl, heteroaryl, naphthyl): in this arrangement, both alkene groups of the diene system of the linker would be integrated into the rigid aromatic core. (ii) Integration of one linker double bond into a naphthyl ring: this compound class was expected to render more flexibility but still adopt a more rigidified system compared to the piperine diene structure; moreover, arrangement should allow for different angles of the aryl core relative to the amide anchoring group depending on the substitution site at the naphthyl system. (iii) “Ring closure” of the diene motif with the aryl part, consequently generating a carboxylate-substituted naphthyl lead structure: in this arrangement the double bond adopts a bent geometry, and again different angles of the aryl and amide parts can be obtained depending on the substitution site.

Based on previous reports by Zaugg et al., (31) we hypothesized that rigidification of the linker part of the structure may beneficially affect biological activity. (31) This assessment was based in particular on a decrease in modulatory capacity when partially saturated linkers were installed or when structural flexibility was increased by extending the linker length.

The influence of linker rigidity onmodulation was studied by means of a library comprising 32 linker derivatives. According to Zaugg et al. (31) and Pedersen et al., (32) a carbon chain containing at least four carbons, a conjugated double bond adjacent to the amide group, and a bulky amine moiety seem to facilitate efficient receptor binding and/ormodulation.

These data support the previous observation that when the cyclic piperidine residue is replaced by-dialkyl moieties such as-dipropyl (),-diisopropyl (), (34) or-dibutyl (), efficiency and potency can be significantly enhanced. However, while (34) lost its ability to distinguish between the β-subunit isoforms, preferential modulation of βreceptors bywas comparable to piperine, and it was even more pronounced for(see Figure 4 B,D and Tables 4 6 ). Thus,anddisplay—compared to classical GABAreceptor modulators such as benzodiazepines—a distinct subunit selectivity profile. Unlike benzodiazepines,andalso modulate GABAreceptors containing αsubunits with high efficiency and are not dependent on the presence of a γsubunit (data not shown). Whether this subunit selectivity profile has any pharmacological relevance has to be clarified in further studies.

Like, derivativemost efficiently enhancedthrough GABAreceptors composed of αsubunits (I= 760% ± 47%; see Table 4 and Figure 4 C,D). Replacing the αsubunit by αsubunits significantly reducedpotentiation by(see Table 4 and Figure 4 C). Notably,displayed a more pronounced βpreference compared to piperine or[inducing a 3.9-fold (α) to 5-fold (α) strongerenhancement compared to αreceptors]. Compoundshowed comparable potency for most of the tested receptor subtypes ranging from 13.8 ± 1.8 μM to 56.7 ± 21.0 μM; significantly higher ECvalues were estimated for αreceptors (see Tables 4 and 6 ).

Figure 4. Analysis of subunit preferential I GABA enhancement by (A, B) the most efficient ( 23 ) and (C, D) the most potent ( 25 ) piperine derivatives. (E, F) Representative I GABA through seven GABA A receptor subtypes by 23 at 100 μM. Data represent mean ± SEM from at least three oocytes and two oocyte batches.

In the present study, analysis of the most efficient piperine derivative () revealed that GABAreceptors composed of α= 1673% ± 146%) and α= 1624% ± 156%) subunits were more efficiently modulated than receptors containing αsubunits (= 1284.6% ± 142%; see Table 4 ). Significantly weaker potentiation was observed for receptors composed of α= 980% ± 129%) and αsubunits (= 1316% ± 55%). Replacing the βsubunits by βsubunits did not significantly alter the strength ofpotentiation, whereas modulation of GABAreceptors containing βsubunits was significantly less pronounced (= 1157% ± 69%;< 0.05). In comparison with αreceptors,displayed an increased potency for αreceptors, followed by α, α, and αreceptors. ECvalues for the other receptor subtypes did not differ from those for α(see Figure 4 A,B and Tables 4 and 5 ).

Previously, we have shown that (34) [(2,4)-5-(1,3-benzodioxol-5-yl)--diisobutyl-2,4-pentadienamide] similarly modulates GABAreceptors containing either βor βsubunits, in contrast to the preferential modulation of βreceptors by piperine. (34)

With respect to the linker region, shortening the distance by removing one vinylene unit significantly reducedenhancement (piperine vsandvs). All the other modifications, such as rigidification by inserting benzene, thiophene, or naphthalene moieties, reducedpotentiation by at least a factor of 5 compared to. Interestingly, the modulatory activity did not seem to be related to distance of pharmacophoric substructures, such as the benzodioxole and the amide moiety. For naphthalene analoguesand, an increase in distance led to a decrease of activity, whereas in the case ofand, a decrease of distance led to a decrease of activity. Comparingand, which show identical distance of these two moieties,completely lacks activity (32% ± 12%, Table 1 ). In conclusion, the best compounds achieved in terms of efficiency were the piperine analoguesand

Replacement of the tertiary nitrogen atom for a secondary one, irrespective of alkyl or aryl substitution, led to a complete loss of activity (aryl-substituted N,, and; alkyl-substituted N,, and). Reducing the H-bond acceptor strength of the amide by synthesizing the respective thioamide () abolished the modulatory activity. Reduction of the amide to the analogous amine changed the profile of the compound from potentiation (piperine at 100 μM, 226% ± 26%) (31) to inactive (at 100 μM, −16% ± 14%; Table 1 ).

Figure 5. Relation between log(potentiation of I GABA ) of dialkyl-substituted piperine derivatives at the amide nitrogen and number of carbon atoms at this region. Data for 24* were taken from ref 34 .

When the whole data set was analyzed, several distinct SARs could be deduced. They are mostly related to the substitution pattern at the amide nitrogen atom, as this was the main point of variation in the data set. Thus, concerning-dialkyl-substituted amides, there is evidence thatenhancement is related in a nonlinear (parabolic) function to the number of carbon atoms (Figure 5 ), with the optimum being dipropyl (). This type of parabolic relationship is quite common, especially when it refers to a parameter that is linked to lipophilicity of the compounds and activity data obtained in a cellular assay. It has, for example, also been observed for a series of capsaicin analogues with respect to their TRPV1 activation. (43) Interestingly, whether the alkyl chains are linear or branched does not reverse the order:(dimethyl) (34) (diisobutyl)

Computational Analysis

I GABA potentiation does not allow classical QSAR analysis, binary classification models were built from five methods and three descriptor sets. For these studies, all 76 piperine derivatives described above were employed. Sixteen compounds showing ≥200% I GABA potentiation were assigned to an active class, since they were at least as active as the lead compound piperine. The remaining 60 ligands were assigned to an inactive class. Classification methods comprised instance-based classifier (IBk), J48 decision tree (J48), naïve-Bayes classifier (NB), random forest (RF), and support vector machine (SMO) implemented in the software package WEKA. In order to rationalize the trends observed in the SAR with respect to physicochemical properties and chemical substructures, we explored the possibility to apply quantitative structure–activity relationship (QSAR) methods. Aspotentiation does not allow classical QSAR analysis, binary classification models were built from five methods and three descriptor sets. For these studies, all 76 piperine derivatives described above were employed. Sixteen compounds showing ≥200%potentiation were assigned to an active class, since they were at least as active as the lead compound piperine. The remaining 60 ligands were assigned to an inactive class. Classification methods comprised instance-based classifier (IBk), J48 decision tree (J48), naïve-Bayes classifier (NB), random forest (RF), and support vector machine (SMO) implemented in the software package WEKA. (44) The software package Molecular Operating Environment (MOE) was used for calculation of 2D descriptors and fingerprints. The three descriptor sets used comprised six 2D descriptors obtained after applying a feature selection algorithm on the whole panel of 125 2D MOE descriptors (6D), 11 physical chemical properties (PHYSCHEM), and MACCS fingerprints (MACCS).

The statistical parameters obtained for the 15 best classification models are listed in Table 7 . Most of the models possess reliable quality (except models 11 and 13); that is, values of the Matthews correlation coefficient (MCC) are higher than 0.4 and total accuracy varies from 0.7 to 0.9.

Table 7. Statistical Parameters of the 15 Best Models Obtained after 10-Fold Cross-Validation model classification method TP, TN, FP, FNa sensitivity specificity accuracy MCC, ROC Descriptor Set 6D 1 IBk 12, 52, 8, 5 0.706 0.867 0.831 0.542, 0.825 2 J48 15, 46, 14, 2 0.882 0.767 0.792 0.556, 0.818 3 NB 16, 49, 11, 1 0.941 0.817 0.844 0.659, 0.831 4 RF 13, 52, 8, 4 0.765 0.867 0.844 0.588, 0.838 5 SMO 16, 39, 21, 1 0.941 0.650 0.714 0.491, 0.796 Descriptor Set PHYSCHEM 6 IBk 10, 52, 8, 7 0.588 0.867 0.805 0.446, 0.749 7 J48 15, 46, 14, 2 0.882 0.767 0.792 0.556, 0.828 8 NB 15, 40, 20, 2 0.882 0.667 0.714 0.457, 0.828 9 RF 15, 46, 14, 2 0.882 0.767 0.792 0.556, 0.811 10 SMO 15, 36, 24, 2 0.882 0.600 0.662 0.400, 0.741 Descriptor Set MACCS 11 IBk 9, 45, 15, 8 0.529 0.750 0.701 0.250, 0.619 12 J48 12, 48, 12, 5 0.706 0.800 0.779 0.453, 0.797 13 NB 12, 42, 18, 5 0.706 0.700 0.701 0.345, 0.713 14 RF 13, 43, 17, 4 0.765 0.717 0.727 0.409, 0.730 15 SMO 10, 56, 4, 7 0.588 0.933 0.857 0.561, 0.761

Models 3 and 4, although possessing the best statistical performance parameters, are not discussed further, as they are difficult to interpret. Instead, models 7 and 12 are discussed in more detail, because these models (i) show almost equal performance, (ii) were built using descriptors of physical chemical properties and MACCS fingerprints, (iii) provide clear separation between active and inactive instances, and (iv) allow us to trace back the decisive chemical and structural descriptors for the data set.

1–16 with monosubstituted amide function and compounds 29, 31, 32, 36, 37, 40–42, and 44 containing several heteroatoms (e.g., OH groups or an additional nitrogen as in piperazines or both). Thus, application of a single filter decreased the number of inactive ligands in the data set almost by half, from 60 to 35 compounds. In the next branch of the decision tree, 10 compounds with less than four rotatable bonds were excluded from the data set. These included highly rigid piperine derivatives with linker regions modified to either a single double bond (17) or to an aromatic system (46, 50, 54, 58, 62, 65, 68, 71, and 75). Furthermore, 11 compounds with high lipophilicity (log P > 5.2) were filtered out: 26 and 27 with n-hexyl and cyclohexyl sustituents at the amide nitrogen, as well as 48, 52, 56, 60, 64, 67, 70, 77, and 63, which have dibutyl and dipropyl substituents in the same region. The fact that the top-ranked compounds are either N,N-dipropyl-, N,N-dibutyl-, or N,N-diisobutyl-substituted is reflected in the next leaf, which assigns five compounds (23, 24,(34)25, 43, and 73) with more than seven rotatable bonds to the active class. The last two branches of the decision tree filter out compounds on the basis of their molecular weight and refractivity. The decision tree obtained in model 7 with PHYSCHEM descriptors (Figure 6 ) uses as a first criterion for separation of active and inactive piperine derivatives: the topological polar surface area. By applying a threshold of 39, 25 inactive ligands exhibiting polar substituents at the amide nitrogen were filtered out. These include compoundswith monosubstituted amide function and compounds, andcontaining several heteroatoms (e.g., OH groups or an additional nitrogen as in piperazines or both). Thus, application of a single filter decreased the number of inactive ligands in the data set almost by half, from 60 to 35 compounds. In the next branch of the decision tree, 10 compounds with less than four rotatable bonds were excluded from the data set. These included highly rigid piperine derivatives with linker regions modified to either a single double bond () or to an aromatic system (, and). Furthermore, 11 compounds with high lipophilicity (log> 5.2) were filtered out:andwith-hexyl and cyclohexyl sustituents at the amide nitrogen, as well as, and, which have dibutyl and dipropyl substituents in the same region. The fact that the top-ranked compounds are either-dipropyl-,-dibutyl-, or-diisobutyl-substituted is reflected in the next leaf, which assigns five compounds (, and) with more than seven rotatable bonds to the active class. The last two branches of the decision tree filter out compounds on the basis of their molecular weight and refractivity.

Figure 6 Figure 6. Decision tree obtained for the data set of 76 piperine derivatives with PHYSCHEM descriptor set.

1–16, 31, 32, 40, 42, and 45), most of which were those showing high polar surface area (TPSA). The next branching filter was presence of a sulfur atom, which removes six inactive ligands (30, 44, and 58–61) from the data set. The next leaf separates compounds that do not have a six-membered ring as in piperidinyl, cyclohexyl, and morpholinyl, which led to seven correctly classified active ligands (21–23, 24,(34)25, 28, and 43) and three missclassified inactives (18, 20, and 26). This criterion is in line with the filter “b_rotN > 7” for active compounds in the PHYSCHEM model. The decision tree obtained for model 12 with MACCS fingerprints (Figure 7 ) is fully in line with the one based on the PHYSCHEM descriptor set. The first filtering criterion was presence or absence of an NH group. It filtered 21 derivatives (, and), most of which were those showing high polar surface area (TPSA). The next branching filter was presence of a sulfur atom, which removes six inactive ligands (, and) from the data set. The next leaf separates compounds that do not have a six-membered ring as in piperidinyl, cyclohexyl, and morpholinyl, which led to seven correctly classified active ligands (, and) and three missclassified inactives (, and). This criterion is in line with the filter “b_rotN > 7” for active compounds in the PHYSCHEM model.

Figure 7 Figure 7. Decision tree obtained for the data set of 76 piperine derivatives with MACCS fingerprints.

To summarize, active piperine analogues are mainly characterized by a topological polar surface smaller than 39, have at least three rotatable bonds (better more than 7), and show a log P value smaller than 5.2.