Identification of novel allosteric modulators of mGlu5 acting at site

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Identification of novel allosteric modulators of mGlu5 acting at site distinct from MPEP binding Mariusz Butkiewicz, Alice L Rodriguez, Shane E. Rainey, Joshua M. Wieting, Vincent B Luscombe, Shaun R. Stauffer, Craig W Lindsley, P. Jeffrey Conn, and Jens Meiler ACS Chem. Neurosci., Just Accepted Manuscript • DOI: 10.1021/acschemneuro.8b00227 • Publication Date (Web): 27 May 2019 Downloaded from http://pubs.acs.org on May 29, 2019

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ACS Chemical Neuroscience

Identification of novel allosteric modulators of mGlu5 acting at site distinct from MPEP binding Mariusz Butkiewicz 1,3, Alice L. Rodriguez2,5, Shane E. Rainey2,5, Joshua Wieting2,5, Vincent B. Luscombe2,5, Shaun R. Stauffer1,2,5, Craig W. Lindsley1,2,5, P. Jeffrey Conn2,5, and Jens Meiler1,2,3,4* Department of Chemistry1, Department of Pharmacology2, Center for Structural Biology3, Institute of Chemical Biology4, Center for Neuroscience Drug Discovery5, Vanderbilt University, Nashville, TN Vanderbilt University, Nashville, TN 37232, USA *

Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +1-615-936-5662; Fax: +1-615-936-2211.

Abstract: As part of the G-protein coupled receptor (GPCR) family, metabotropic glutamate (mGlu) receptors play an important role as drug targets of cognitive diseases. Selective allosteric modulators of mGlu subtype 5 (mGlu5) have the potential to alleviate symptoms of numerous central nervous system disorders such as schizophrenia in a more targeted fashion. Multiple mGlu5 positive allosteric modulators (PAMs) such as DFB, CDPPB, and VU-29, exert their actions by binding to a defined allosteric site on mGlu5 located in the seven-transmembrane domain (7TM) and shared by mGlu5 negative allosteric modulator (NAM) MPEP. Actions of the PAM CPPHA are mediated by a distinct allosteric site in the 7TM domain different from the MPEP binding site. Experimental evidence confirms these findings through mutagenesis experiments involving residues F585 (TM1) and A809 (TM7). In an effort to investigate mGlu5 PAM selectivity for this alternative allosteric site distinct from MPEP binding, we employed in silico quantitative structure activity relation (QSAR) modeling. Subsequent ligand-based virtual screening prioritized a set of 63 candidate compounds predicted from a library of over 4 million commercially available compounds to bind exclusively to this novel site. Experimental validation verified the biological activity for seven of 63 selected candidates. Further, medicinal chemistry optimizations based on these molecules revealed compound VU6003586 with an experimentally validated potency of 174 nM. Radioligand binding experiments showed only partial inhibition at very high concentrations, most likely indicative of binding at a non-MPEP site. Selective positive allosteric modulators for mGlu5 have the potential for tremendous impact concerning devastating neurological disorders such as schizophrenia and Huntington's disease. These identified and validated novel selective

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compounds can serve as starting points for more specifically tailored lead and probe molecules and thus help the development of potential therapeutic agents with reduced adverse effects.

Keywords: virtual screening; machine learning; quantitative structure activity relations (QSAR); high-throughput screening (HTS); cheminformatics; PubChem; BCL; GPCR; mGlu; DFB; CDPPB; and VU-29; MPEP; PAM; CPPHA; QSAR;

Author Contributions: MB performed QSAR modeling and wrote the manuscript. ALR conducted experimental work and wrote the experimental sections of the manuscript. SER, JW, VBL helped with conducting the experimental work. SRS provided compound information for QSAR modeling. CWL, PJC, and JM designed the project and guided the writing process of the manuscript. All co-authors approved the manuscript.

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1.

Introduction

Metabotropic glutamate (mGlu) receptors are part of the G protein-coupled receptor (GPCR) family also known as seven-transmembrane (7TM) domain receptors. As the primary excitatory neurotransmitter in the mammalian nervous system, glutamate acts as an endogenous ligand to mGlu receptors binding orthosterically at a large extracellular globular domain. mGlu activation is tied to a downstream effector mechanism through guanine nucleotide binding proteins. The mGlu family consists of eight subtypes classified into three major groups [1], [2]. Group I includes mGlu1 and mGlu5, which couple primarily to Gq/11 and mediate IP3/Ca2+ signal transduction [3] whereas group II (mGlu2 and 3) and group III (mGlu4,6,7 and 8) mGlu receptors couple primarily to inhibition of adenylyl cyclase [4]. Through the diverse physiological roles, heterogeneous distribution, and wide diversity of mGlu receptor subtypes, the possibility arises for new therapeutic agents. These next generation agents have the capacity to selectively interact with a mGlu receptor subtype associated with a single or limited number of central nervous system (CNS) functions. Such targeted therapeutics could have incisive importance in the development of novel treatment strategies for a range of neurological disorders [5]–[7].

1.1. Modulators of mGlu5 have promising potential for treatment of schizophrenia and other diseases associated with cognitive impairment Modulators of mGlu5 are of particular interest due to an increasing body of studies providing evidence they play key roles in potential novel treatment strategies for schizophrenia, Alzheimer’s disease and other disorders linked to cognitive impairment [8], [9]. Current mGlu5 positive allosteric modulators (PAMs) have been developed based on multiple scaffolds [10]–[19]. Wellcharacterized structural classes of mGlu5 PAMs include 1-(3-fluorophenyl)-N-((3-fluorophenyl)methylideneamino)-methanimine (DFB), 3-cyano-N-(1,3-diphenyl-1H-pyrazol-5-yl)-benzamide (CDPPB),

and

N-{4-chloro-2-[(1,3-dioxo-1,3-dihydro-2H-isoindol-2-yl)methyl]phenyl}-2-

hydroxybenzamide (CPPHA) [20]. Numerous challenges encountered during the development of mGlu5 PAMs have been documented. For example, slight structural changes introduced during chemical optimization often induce a large spectrum of pharmacological responses (DFB series), making prediction of activity difficult. Also, as seen with the CPPHA series, a 'flat' structure–activity relationship (SAR) is often confronted, meaning slight structural changes result in complete loss of activity. Despite these difficulties, efforts have led to the development of positive modulators able to allosterically potentiate mGlu5-mediated electrophysiological responses in the CNS [10], [13], [20], [21]. Furthermore, antipsychotic-like effects of these compounds were confirmed by in vivo studies based on animal models [11], [22]. 3 ACS Paragon Plus Environment


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More recently, a distinct mGlu5 PAM chemotype was presented by Addex pharmaceuticals (ADX47273) [23]. The compound showed in vivo efficacy in preclinical behavioral models comparable to other known antipsychotics [24]. In general, these mGlu5 PAMs improved cognitive function in animals in which object recognition was impaired [24] and demonstrated improved behavioral flexibility [25]. These exciting discoveries provide a strong emphasis for mGlu5 PAMs having potential application as novel antipsychotic agents and cognition-enhancing therapeutics.

1.2.

Allosteric modulation of mGlu5 through multiple distinct binding sites

The primary binding site for many mGlu5 PAMs correlates with the same site for the negative allosteric modulator (NAM) 2-methyl-6-(phenylethynyl)-pyridine (MPEP) located in TM 3, 6, and 7 [8]. Evidence for a possible distinct allosteric binding site was provided by O’Brien et al. [10]. In a later study, Chen et al. [12] characterized a novel mGlu5 PAM labeled 4-nitro-N-(1,3-diphenyl1H-pyrazol-5-yl)-benzamide (VU-29) which was shown to act at an site overlapping with the binding site of MPEP. The same study demonstrated that the structurally distinct PAM CPPHA potentiates the mGlu5 response by an alternative mechanism to the one of VU-29. Additionally, a neutral ligand at the MPEP allosteric site termed 5-methyl-2-(phenylethynyl)-pyridine (5MPEP) blocks the VU-29 and CPPHA-induced potentiation of mGlu5 responses. Remarkably, an increase in 5MPEP concentration invokes a parallel rightward shift in the concentration-response curve of VU-29. In contrast, CPPHA potentiation is inhibited by 5MPEP noncompetitively [13], [21]. Furthermore, this evidence of an alternative binding site is supported by mutagenesis experiments where a mutation (A809V) decreases binding of ligands to the MPEP site. The mutation abolishes binding of VU-29 but does not influence the response to CPPHA. Likewise, a second mutation (F585I) eliminates the response to binding CPPHA but does not change binding of VU-29. Both results suggest that CPPHA does not bind at the MPEP site but rather acts at a distinct novel allosteric site on mGlu5 as a potentiator to the receptor. Additional studies have identified other mGlu5 PAMs that interact non-competitively with the MPEP site including VU0357121 [14] and (N-(4-chloro-2-((4-fluoro-1,3-dioxoisoindolin-2-yl)methyl)-phenyl)-picolinamide (NCFP) [26]. In the case of VU0357121, while similar in structure to CPPHA, radioligand binding and mutagenesis studies suggest the compound and its analogs bind to a non-MPEP and non-CPPHA site. In studies by Noetzel et al. [26], the CPPHA analog NCFP was found to likely bind to the previously mentioned CPPHA site on mGlu5. It exhibits stronger mGlu5 subtype selectivity in comparison to CPPHA, thereby increasing its suitability for studies of mGlu5 influence in the CNS. NCFP was shown to potentiate multiple responses to mGlu5 in a manner similar to CPPHA; however, it was not able to potentiate LTD and LTP in the 4 ACS Paragon Plus Environment

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hippocampus, demonstrating the differential effects mGlu5 PAMs can have on responses in the CNS. While the functional selectivity or stimulus bias demonstrated by compounds such as these may complicate drug discovery, it is possible these differential effects could result in pharmaceutical intervention tailored to individual needs.

1.3. Drug discovery guided by in silico virtual screening can prioritize modulation-specific mGlu potentiators Previous quantitative structure activity relationship (QSAR) studies were successful in identifying lead scaffolds serving as starting points for successful probe development campaigns. A research study by Mueller et al. [27] identified novel mGlu5 PAMs based on a high-throughput screen (HTS) of a diverse library of 144,475 substances which revealed 1,382 compounds as PAMs. A subsequently trained QSAR model with a theoretical enrichment ratio of up to 38 for an independent dataset was applied to screen a database of approximately 450,000 commercially available drug-like compounds. A set of 824 compounds was acquired for testing based on the highest predicted potency values. Experimental validation confirmed 28.2% (232/824) of these compounds with various activities at mGlu5. These results represent an enrichment factor of 23 for pure potentiation of the mGlu5 glutamate response and 30 for overall mGlu5 modulation activity when compared with those of the original mGlu5 experimental screening data (0.94% hit rate). A subsequent study applied the same approach to investigate the activation of metabotropic glutamate receptor subtype 4 (mGlu4) which has been shown to be efficacious in rodent models of Parkinson's disease [28]. A high-throughput screen identified 434 positive allosteric modulators of mGlu4 out of a set of approximately 155,000 compounds. A QSAR model has a theoretical enrichment of 15-fold when selecting the top 2% of compounds of an independent test dataset. The same external commercial database of approximately 450,000 drug-like compounds was screened as in the previous study. 1,100 predicted active small molecules were tested experimentally using two distinct assays of mGlu4 activity. This experiment yielded 67 positive allosteric modulators of mGlu4 that confirmed in both experimental systems. Compared to the initial hit rate of 0.3% in the primary screen, the resulting in a 22-fold enrichment. Noeske et al. [29] applied a self-organizing map to discern antagonists for mGlu1 and mGlu5. This approach employed topological pharmacophore descriptors which were applied to a compiled library of 338 compounds of non-competitive antagonists and an external library of 5,376 molecules of known drugs and lead candidates for different drug targets. The resulting selforganizing maps were able to differentiate between separate localized distributions for the two mGlu receptor targets. 5 ACS Paragon Plus Environment


1.4.

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Significance

Preclinical studies have implied the role of mGlu receptors as possible candidates for drug targets involved in treatment of a range of CNS disorders. Research efforts are focused around the development of allosteric modulators due to receptor subtype selectivity and activity reliance upon receptor activation. However, the current understanding of mechanisms that involve mGlu receptors is limited when mediated in vivo effects are considered. In addition, awareness is increasing that distinct allosteric modulators can induce differential effects on mGlu5-mediated psychological responses in the CNS, referred to as functional selectivity or stimulus bias [30]– [32]. Albeit challenging to characterize, promising evidence has demonstrated the existence of a novel mGlu5 binding site distinct from the MPEP binding site. The identification of novel allosteric potentiators binding to this alternative site will help elucidate the pharmacological significance of binding interactions and bias effects of differential binding sites. Computational techniques like virtual screening are imperative for prioritization and identification of novel selective potentiators. The present study identified 63 compounds through virtual screening and rigorous filtering which were purchased and experimentally validated (Supplementary Table S2). Approximately 11% (7/63 compounds) exhibited mGlu5 activity upon initial screening. Six compounds were PAMs. This represents a virtual screening hit rate increased by two orders of magnitude compared to an initial experimental hit rate of 0.09% = 130 CPPHA-like active molecules/(1382 PAMs + 144,475 inactive molecules). Binding studies suggest the compounds act via a site distinct from the MPEP binding pocket. These newly identified molecules have the potential to serve as lead compounds in future drug discovery campaigns and can subsequently help to establish specifically tailored therapeutics with reduced side effects.


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2.

Results

With the identification of a putative allosteric mGlu5 binding site different from the MPEP binding site, a series of CPPHA analogs was screened for mGlu5 PAM activity [14], [15], [33]. A total of 275 compounds were screened and 130 compounds found to be active (see Table 1). This dataset is comprised of CPPHA-like compounds. Structural similarity between the involved different scaffolds is shown in Figure 1. The dendrogram shows the structural similarity of each cluster scaffold with a representative structure. Additionally, for each cluster the composition of active and inactive compounds is indicated relative to the overall total number of compounds.

B

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Figure 1: Clustering of 130 active and 145 inactive compounds in the CPPHA-like series. A) Structural similarity of 17 identified clusters. B) Scaffold representation of each cluster and C) Cluster composition with respect to actives (green) and inactives (red).



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2.1. Development of QSAR model to prioritize specific mGlu5 PAMs for alternative site distinct from MPEP binding In a previous HTS campaign for mGlu5

a)

b)

c)

modulators [27], a set of 1,382 PAMs and 343 NAMs were identified to engage mGlu5 allosterically. Based on these data, we developed two specific QSAR models to predict allosteric mGlu5 PAMs [28] and NAMs [29]. In this study, a third QSAR model was

mGlu5 CPPHA like 130 actives mGlu5 CPPHA like 145 inactives mGlu5 PAM 1382 actives

mGlu5 PAM 1382 actives

mGlu5 NAM 343 actives

mGlu5 PAM 144K inactives

mGlu5 PAM 144K inactives

mGlu5 NAM 153K inactives

general mGlu5 PAM QSAR model

general mGlu5 NAM QSAR model

developed specifically to identify compounds that bind to the putative additional binding site evident through the non-competitive binding character of CPPHA-like compounds. The dataset configuration for all involved QSAR models is presented in Figure 2. To hone in on the attributes of the CPPHA-like compounds, a QSAR model was trained considering only the 130 confirmed CPPHA-

selective mGlu5 PAM CPPHA-like QSAR model

Figure 2: Schematics of dataset composition of the three QSAR models. (green – labeled active, red – labeled inactive)

like PAMs as active. The 145 CPPHA-like inactive molecules as well as all compounds screened in the HTS campaign were labeled as inactive (Figure 2a). This setup contrasts

1.0

the QSAR models used in studies [28]

0.8

and [29] (Figure 2b and 2c). Given this

0.6

QSAR model, it was possible to identify 0.4

compounds that bind to a distinct site, 0.2

different from the known MPEP binding site. Cross-validation was used to ensure

0.0 0.00001 0.0001 0.001

the QSAR model was not over-fitted. As a quality metric, we computed the

and

positive

predictive

The selective mGlu5 PAM model for CPPHA-like binders achieved a PPV of

1

Iteratively averaged until a cutoff of 0.1% FPR True Positive Rate Positive Predicted Value (PPV)

value

(PPV=TP/(TP+FP)), shown in Figure 3.

0.1

PPV: 0.73

receiver operating characteristics (ROC) curve

0.01

False Positive Rate

Ideal - Positive Predicted Value

Figure 3: ROC curve for the specific mGlu5 PAM model predicting based on CPPHA-like compounds. The ideal PPV curve is given in gray, the actual PPV curve is shown in black, and the True Positive rate (TPR) is shown in red. Plotting the TPR (y-axis) against the False Positive rate (x-axis) constitutes the ROC curve. The x-axis is on a logarithmic scale.


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73% when considering a False Positive Rate (FPR) of 0.1%. To put this into perspective, when allowing a FPR of 0.1% the model successfully predicted 73% of the active compounds in an independent dataset not used for training the model. More information on dataset composition and regarding the QSAR models for general mGlu5 PAM and NAM prediction can be found in Supplementary Section S3. While re-training the QSAR models the quality measure results improved observing PPV rates of 51% (PAM) and 19% (NAM) indicating an improvement compared to the published results when applying a similar FPR cutoff.

2.2. Virtual screening predicts site specific mGlu5 PAMs among commercially available compounds The compound library eMolecules [34] was chosen for 45

virtually screening with the aforementioned QSAR models.

40

It contains over 4 million compounds covering several large vendor compound libraries. To query for selective hits of the mGlu5 PAM CPPHA binding site, the three QSAR models were applied simultaneously to virtual screen for activity of each respective mGlu5 target. An mGlu5 PAM CPPHA site hit was defined by molecules classified as inactive by the general mGlu5 PAM model, inactive by the general mGlu5 NAM model, and active by the selective mGlu5 CPPHA-like PAM model. Through this query scheme, 4,719 compounds

35 % ordered compounds

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Frequency

30 25 20 15 10 5 0

% similarity to known PAMs

Figure 4: This distribution plot shows the similarity of the ordered compounds compared to known PAMs.

were predicted to be mGlu5 CPPHA-like PAMs.

2.3. Medicinal chemistry filters and clustering analysis narrow down selective hit compounds All 4,719 hits were assessed further by the Rapid Elimination Of Swill (REOS) filter [35] and Pan Assay Interference Compounds (PAINS) filter [36] to remove non-drug-like compounds and frequent false positives. The resulting set of compounds contained 353 molecules which were further filtered by allowing for not more than one halogen atom. The final set contained 138 molecules, 109 with one halogen atom and 29 with no halogen atoms. A subsequent hierarchical cluster analysis narrowed this set of molecules further down to 103 candidate clusters. The candidate compound with the highest predicted mGlu5 CPPHA-like PAM activity was selected for experimental verification. Taking pricing and availability into consideration, 63 of 103 identified candidate molecules were acquired. To further investigate the chemical similarity between the 63 candidate compounds and 9 ACS Paragon Plus Environment


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Table 1: List of relevant and experimentally validated compounds in this study. Potency, maximum glutamate response, and affinity for the compounds of interest. Values are an average of 898 independent experiments. N.B. denotes no binding up to highest concentration tested (300 :+"!

Structure

N

Potency

Max Glu

Binding

(EC50)

Response

Affinity (Ki)

NAM

8.4 nM

1.0%

8.2 nM

VU0603805

Potentiator

>10 M

42%

>100 M

VU0603830

Potentiator

>10 M

43%

N.B.

VU0603841

Potentiator

>10 M

53%

N.B.

VU6000591

Potentiator

2.4 M

86%

>10 M

174 nM

85%

>10 M

Identifier

Category

MPEP

O N H O

O

Cl N O O

O N

N Ts

S

O Br

N H

O

O

Me N H

O

O O

Me N H

VU6003586

Agonist / Potentiator

O

the 1,382 PAMs from the HTS campaign, an analysis of the similarity between every compound pair was conducted (see Figure 4). The distribution of the highest similarity of each compound pair clearly indicates a scaffold similarity peak at approximately 40%. The majority of compound pairs exhibit a scaffold similarity below 50% which is indicative of high scaffold diversity between the 63 candidate compounds and known PAMs.

2.4.

Experimental validation of identified compounds by virtual HTS

The 63 compounds were evaluated for mGlu5 activity by utilizing a cell-based functional assay that measures receptor-induced mobilization of intracellular calcium. The assay is described in detail in Rodriguez et al. 2010 [37]. In brief, test compound was applied to cells expressing rat mGlu5 in a 10-point concentration response curve (CRC) format. After an incubation period, cells were stimulated with a submaximal concentration of the mGlu5 agonist glutamate and the calcium response measured, followed by stimulation with a near maximal concentration of glutamate and the calcium response measured. In this manner both potentiator and antagonist activity can be


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assessed simultaneously. Six compounds demonstrated an increased mGlu5 response to glutamate while one compound inhibited the response to glutamate. The seven compounds that displayed activity were retested in 10-point CRC format in triplicate. Of the seven compounds, three showed significant PAM activity upon repeat (Figure 5 and Table 2). PAM activity of these three compounds reached a maximal glutamate response of 40 to 50% with potency values above 10 µM as shown in Figure 5A. Competition binding studies using [3H]methoxyPEPy, a radioligand known to bind to the MPEP

Figure 5: A) 3 compounds exhibit concentration-dependent PAM activity in calcium assay. B) mGlu5 PAMs do not compete for binding to the MPEP site in [3H]methoxyPEPy binding assay at concentrations PAM activity is present in calcium assay.

binding site, were performed to determine if the novel modulators also interacted at that site or at an alternative binding site such as the CPPHA site. Confirmed PAMs were tested in triplicate using membranes harvested from the same mGlu5 cell line used for the aforementioned calcium assay. A concentration range up to 300 µM was tested (Figure 5B). Compounds VU0603830 and VU0603841 showed no radioligand displacement up to the highest concentration tested, indicating they do not bind to the MPEP site. Compound VU0603805 exhibited weak binding at concentrations of 100 µM and 300 µM. PAM activity was observed at 10-fold lower concentrations, 10 and 30 µM, indicating a significant disconnect between potentiator activity and binding, suggesting these compounds interact via an alternate or overlapping binding site.



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While promising as non-MPEP site PAMs, the potency values of these compounds are not adequate for additional characterization. Compound VU0603805 was chosen to be investigated

Figure 6: A) VU6000591 displays significant PAM activity in calcium assay. B) VU6000591 exhibits partial inhibition in [3H]methoxyPEPy binding assay. C) VU6003586 shows a significant increase in PAM potency compared to VU6000591 in calcium assay. D) VU6003586 exhibits partial inhibition in [3H]methoxyPEPy binding assay.

further through SAR optimization. A synthesized compound series around the VU0603805 compound revealed a new promising candidate compound, VU6000591, which demonstrated enhanced potency and efficacy (2.4 µM, 86% max). When evaluated in radioligand binding assays, VU6000591 only partially inhibited [3H]methoxyPEPy at concentrations greater than 10 µM. This weak, partial inhibition suggests a non-competitive interaction with the MPEP site. (see Figure 6A and B). VU6000591 was optimized further to provide compound VU6003586 with significantly enhanced potency (174 nM, 85% max) (see Figure 6C and Table 2). This potency range is better suited for a more detailed investigation of the binding site. Subsequent radioligand binding experiments demonstrated that in a manner similar to VU6000591, VU6003586 only partially inhibited [3H]methoxyPEPy at concentrations greater than 10 µM (see Figure 6D and Table 2). This demonstrates a difference between the functional potency (EC50 = 174 nM) and


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the binding affinity for the MPEP site (Ki > 10 µM) of greater than 50-fold is consistent with a noncompetitive mechanism of action at an alternative binding site.

3.

Discussion

Functional selectivity for receptors of the mGlu GPCR family is driven in part by ligand binding behavior at the receptors TM domain. With the discovery of a potential alternative allosteric mGlu5 binding site in addition to the already known MPEP binding site new possibilities to investigate more selective allosteric modulators for the mGlu5 receptor are revealed. The goal of this study is to elucidate the chemical space of ligands interacting with this distinct binding site. Thus, a series of CPPHA-like compounds was developed to investigate determinants of the alternative binding site further. The set of compounds contained 275 molecules comprised of 130 active and 145 inactive compounds that show structural similarity to CPPHA. In an effort to investigate the structural distinctiveness of these compounds a similarity analysis identified 17 clusters. Each cluster center is shown with its main scaffold and a percentage of involved active and inactive compounds (see Figure 1). A clear separation between clusters of solely active or inactive compounds was only given in clusters 3, 11, 14 and 4, 7, 10, 15, 16, respectively. This clustering scheme suggests a partially intertwined structural landscape for active and inactive molecules. Therefore, a pure structural separation is not possible. On this basis, a QSAR model was trained to identify selective allosteric mGlu5 binders for the distinct non-MPEP binding site (see Figure 3). The extreme imbalanced character of the training data set leads to a restricted chemical space coverage of the active compounds (see Figure 2), at the same time, it allows for the model to be trained to predict site-specific binders. In addition, two general prediction models for mGlu5 PAMs [27] and NAMs [38] from previous work were re-trained, observing improved PPV rates of 51% (PAM) and 19% (NAM) (see Supplementary Section S3). This improvement is due to regularization features applied to the ANN algorithm such as neuronal dropout [39], a technique that allows training the QSAR model with increased predictive generalization. These three QSAR models were combined to virtually screen the eMolecules compound library containing over 4 million molecules. A set of 4,719 molecules was predicted to be active for specific interaction with the non-MPEP site, predicted inactive by the general mGlu5 PAM QSAR model, and predicted inactive by the mGlu5 NAM model. Medicinal chemistry filters, REOS and PAINS, reduced this set further down to only 103 compounds. From this pool of compounds 63 molecules were chosen by predicted activity, structural diversity, and acquisition cost. 13 ACS Paragon Plus Environment


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A total of seven compounds were identified as exhibiting activity at mGlu5 in a functional assay measuring calcium mobilization. Six of these compounds were categorized as PAMs and one as an antagonist. The three most potent compounds reached a glutamate response of 4050% of the maximal signal with potencies above 10 µM. This relatively weak signal strength is an expected result given the primary goal of this study was to find new chemotypes through scaffoldhopping that exhibit at minimum a weak response but interact selectively with the mGlu5 site distinct from the MPEP binding site. Competition binding assays measuring the displacement of [3H]methoxyPEPy provided evidence that five of the identified seven compounds indeed interact with a different binding site distinct from the MPEP site and suggested two compounds, VU0603805, and VU0603849, interact via an alternate or overlapping site. However, these compounds exhibit only weak potentiator activity, and thus were considered as starting structures for further SAR optimization. As a result, candidate compound VU6000591 arose from the VU0603805 series. Compound optimization attempts for both other series, VU0603830 and VU0603841, yielded negative results. Though VU6000591 showed increased potency compared to previous compounds, a compound would need to have a potency roughly in the range of 300-500 nM to be considered to allow for a full exploration of its pharmacological effects. Further SAR optimization employing VU6000591 as a starting structure led to candidate compound VU6003586, showing an improved potency in comparison. Subsequent radioligand binding experiments with VU6003586 showed that the compound exhibits partial inhibition of [3H]methoxyPEPy at concentrations well above those that demonstrate functional activity (see Figure 6D). This evidence suggests that the potentiator activity of VU6003586 is most likely the result of interaction at a non-MPEP binding site, but other possibilities such as high cooperativity cannot be ruled out. While additional studies would be required to assess which specific binding site these compounds interact with, these data are significant in that they provide additional support that mGlu5 PAMs can act through multiple binding sites and present novel chemotypes from which to begin chemical optimization. Given the limited dataset used to build the site-specific QSAR model, it is impressive that novel mGlu5 PAMs were discovered, distinct in structure to known PAMs and focused on a pocket distinct from the MPEP binding site. The identified structures, specifically VU6003586, show a significant divergence from the reference CPPHA-like structures indicative of scaffold hopping through the involved QSAR modeling. This approach demonstrates that the identification of target-specific binders is possible and is a valuable method in identifying novel selective lead compounds with reduced adverse effects. 14 ACS Paragon Plus Environment

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4.

Conclusions

In summary, the mGlu5 receptor offers great potential as a possible candidate for drug targets involved in treatment of a range of CNS disorders. An alternative binding site distinct from the already known MPEP binding site was previously identified through mutagenesis and binding experiments involving residues F585 (TM1) and A809 (TM7) in the 7TM helices region. Herein, a QSAR model was employed to query the commercially available compound library ‘eMolecules’ for specific binders to the mGlu5 binding site distinct from MPEP binding. A resulting set of 4,719 candidate molecules was identified. After the application of medicinal chemistry filters a subset of 63 molecules was chosen for compound acquisition. Subsequent calcium mobilization experiments revealed a concentration-dependent modulation of the biological response to mGlu5 in vitro for seven out of the ordered 63 compounds. Further experimental validation through displacement of the radioligand [3H]methoxyPEPy confirmed five out of seven compounds fail to bind the MPEP site, indicating binding to an alternative mGlu5 allosteric pocket. This corresponds to an approximated 8% (5/63 compounds) hit rate compared to the initial 0.09%. Further medicinal chemistry optimizations revealed compound VU6003586 with an experimentally validated potency of 174 nM. Radioligand binding experiments with [3H]methoxyPEPy showed only partial inhibition at very high concentrations, which is most likely indicative of VU6003586 binding at a non-MPEP site. These are very exciting results demonstrating that QSAR modeling can identify novel chemotypes through scaffold hopping given only limited datasets. The confirmed compounds target a pocket distinct from the well-characterized known MPEP binding site, implying a separate binding site. The binding affinity can be refined through SAR optimization. As shown in this research virtual screening enables the prioritization of site-specific novel modulators, and thus can contribute to the reduction of adverse effects of drug candidates. The here identified molecules have great potential to serve as novel lead or probe compounds in subsequent drug discovery campaigns.

5.

Methods

5.1.

Machine learning applied to develop QSAR models

A large fraction of proteins targeted by therapeutics have no suitable structural model for structure-based virtual screening. Quantitative structure activity relationship (QSAR) modeling is an area of computational research that builds in silico models to predict the biological activity for 15 ACS Paragon Plus Environment


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small organic molecules with respect to a protein target [40]. The models seek to quantitatively correlate complex non-linear relations between chemical and physical properties of a molecule with its biological response for a specific biological target. The application of QSAR models have impact on a variety of fields like drug discovery [41] and toxicity prediction [42]. Knowledge acquired from compounds already biologically tested has impact on the design of novel molecules. Molecular descriptors play a fundamental role in encoding chemical information of a molecule [43]. Chemical properties and structural information of a molecule are encoded in a numerical representation or descriptor. A list of all applied molecular descriptors is available in supplementary materials of previous work [44]. Each encoding function is independent of translation or rotation of the molecule. They can be readily applied to conformational ensembles and yield a numerical description of constant length independent from size and composition of the compound. Machine Learning algorithms, such as Artificial Neural Networks (ANNs), have shown exciting potential in estimating biological target data through approximation of highly non-linear relations between molecules and biological targets [45]. The algorithms learn to recognize complex patterns and make intelligent decisions based on an established compound library. Imposing such acquired sets of patterns obtained by a learning process, the algorithms are able to recognize not yet tested molecules and categorize them towards a given outcome. ANNs consist of an interconnected group of artificial neurons and process information adaptively using restricted set of transformations. ANNs have been used for several years in chemistry and biochemistry to describe QSARs [46]–[48]. Their critical advantage compared to linear methods such as multiple linear regression lies in the flexibility of the models. ANN models can adapt to complex interrelations and are capable of detecting even small signals at high noise levels. Here, ANNs are trained by means of 5-fold cross-validation and prediction performance metrics [49], such as ROC Curve, Enrichment, Positive predicted value (PPV) and accuracy, were applied to an independent dataset.

5.2.

Clustering of identified hits from virtual screening

To assess the similarity of predicted compounds a hierarchical clustering approach based on average linkage as cluster distance measure was chosen. The similarity calculation among all compound pairs was based on common occurring small molecule fragments. The fragment library was established by determining the largest common substructure between pairs of molecule fragments keeping ring systems intact. Each fragment had a minimum of four atoms. Pairwise 16 ACS Paragon Plus Environment

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compound similarities were calculated based on molecule fragment overlap using the tanimoto coefficient [50] between molecular fragment sets. All atoms were considered equivalent by element type. Bonds were compared by order (single, double, triple), ring membership, and aromaticity.

5.3. Calcium mobilization assay assesses ligands for mGlu5 modulation response HEK 293 cells expressing rat mGlu5 were plated in black-walled, clear-bottomed, poly-D-lysine coated 384-well plates in 20 S? of assay medium (DMEM containing 10% dialyzed FBS, 20 mM HEPES, and 1 mM sodium pyruvate) at a density of 20K cells/well. The cells were grown overnight at 37 °C in the presence of 5% CO2. The next day, medium was removed and the cells incubated with 20 S? of 2 S

Fluo-4, AM prepared as a 2.3 mM stock in DMSO and mixed in a 1:1 ratio with

10% (w/v) pluronic acid F-127 and diluted in assay buffer (Hank’s balanced salt solution, 20 mM HEPES, and 2.5 mM probenecid) for 45 minutes at 37 °C. Dye was removed, 20 S? of assay buffer was added, and the plate was incubated for 10 minutes at room temperature. Compounds were serially diluted 1:3 in DMSO into 10 point concentration response curves and transferred to daughter plates using the Echo acoustic plate reformatter (Labcyte, Sunnyvale, CA) followed by further dilution into assay buffer to a 2x stock using a Thermo Fisher Combi (Thermo Fisher, Waltham, MA). Ca2+ flux was measured using the Functional Drug Screening System (FDSS7000, Hamamatsu, Japan). After establishment of a fluorescence baseline for about 3 seconds, the test compounds were added to the cells, and the response in cells was measured. 2.3 minutes later an EC20 concentration of the mGlu5 receptor agonist glutamate was added to the cells, and the response of the cells was measured for 1.9 minutes; an EC80 concentration of agonist was added and readings taken for an additional 1.7 minutes. Data were collected at 1 Hz. Concentration response curves were generated using a four point logistical equation with XLfit curve fitting software for Excel (IDBS, Guildford, U.K.) or GraphPad Prism (GraphPad Software, Inc., La Jolla, CA).

5.4. site

Radioligand binding assay determines interaction of ligands with MPEP

The allosteric antagonist MPEP analog [3H]methoxyPEPy was used to evaluate the ability of test compounds to interact with the MPEP site on mGlu5. Membranes were prepared from rat mGlu5 HEK 293 cells as previously described [21]. Compounds were serially diluted in DMSO then added to assay buffer (50 mM Tris/0.9% NaCl, pH 7.4) to reach a 5x stock and 50 µL test compound was added to each well of a 96 deep-well assay plate. 150 µL aliquots of membranes 17 ACS Paragon Plus Environment


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diluted in assay buffer (20 µg/well) were added to each well. 50 µL [3H]methoxyPEPy (2 nM final concentration) was added and the reaction was incubated at room temperature for 1 hour with shaking. After the incubation period, the membrane-bound ligand was separated from free ligand by filtration through glass-fiber 96 well filter plates (Unifilter-96, GF/B, PerkinElmer Life and Analytical Sciences, Boston, MA). The contents of each well were transferred simultaneously to the filter plate and washed 3-4 times with assay buffer using a cell harvester (Brandel Cell Harvester, Brandel Inc., Gaithersburg, MD). 40 µL scintillation fluid was added to each well and the membrane-bound radioactivity determined by scintillation counting (TopCount, PerkinElmer Life and Analytical Sciences). Non-specific binding was estimated using 5 µM MPEP. Concentration response curves were generated using a four parameter logistical equation in GraphPad Prism (GraphPad Software, Inc., La Jolla, CA).

5.5.

Medicinal Chemistry Experimental Details

All reactions were carried out employing standard chemical techniques under inert atmosphere. Solvents used for extraction, washing, and chromatography were HPLC grade. All reagents were purchased from commercial sources and were used without further purification. Analytical HPLC was performed on an Agilent 1200 LCMS with UV detection at 215 nm and 254 nm along with ELSD detection and electrospray ionization, with all final compounds showing > 95% purity and a parent mass ion consistent with the desired structure. All NMR spectra were recorded on a 400 MHz Brüker AV-400 instrument. 1H chemical shifts are reported as V values in ppm relative to the residual solvent peak (CDCl3 = 7.26). Data are reported as follows: chemical shift, multiplicity (br = broad, s = singlet, d = doublet, t = triplet, q = quartet, m = multiplet), coupling constant (Hz), and integration. 13C chemical shifts are reported as V values in ppm relative to the residual solvent peak (CDCl3 = 77.16). Low resolution mass spectra were obtained on an Agilent 1200 LCMS with electrospray ionization, with a gradient of 5-95% 0.1% TFA water in MeCN over a run time of 90 seconds. High resolution mass spectra were recorded on a Waters QToF-API-US plus Acquity system with electrospray ionization. Automated purification of library compounds was performed on a Gilson 215 preparative LC system. More experimental details can be found in Supplementary Section S2 and synthesis information for compound VU6003586 in Supplementary Section S4.

5.6.

Implementation

Developed in the Meiler laboratory, the BioChemistryLibrary (BCL) is an object-oriented C++ programming library hosting an array of applications to simulate, interact, and modify biological 18 ACS Paragon Plus Environment

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molecules, such as small organic molecules and proteins. Specifically, the BCL::ChemInfo software suite was applied in this study providing implementations of machine learning algorithms, molecular descriptors, and a cross-validation scheme for in silico virtual screening. The BCL is free of charge for the academic community and can be downloaded from www.meilerlab.org.

Acknowledgments We thank the Advanced Computing Center for Research and Education (ACCRE) at Vanderbilt University for use of their cluster. Work in the Meiler laboratory is supported through NIH (R01 MH090192, R01 DK097376) and NSF (CHE 1305874).

Supporting Information:

• List of abbreviations used in the manuscript and its corresponding explanations • NMR peak information of experimental determination of key compounds for this study • Description of the dataset composition used to create the QSAR model and its applied quality metrics • Step by step synthesis information for the key compound of this manuscript (VU6003586) • Chemical structures and IUPAC names of 63 compounds identified through virtual screening with the developed QSAR model, experimentally evaluated, and a subset was considered as starting structures for subsequent SAR optimization

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Identification of novel allosteric modulators of mGlu 5 acting at site distinct from MPEP binding Mariusz Butkiewicz 1,3, Alice L. Rodriguez2,5, Shane E. Rainey2,5, Joshua Wieting2,5, Vincent B. Luscombe2,5, Shaun R. Stauffer1,2,5, Craig W. Lindsley1,2,5, P. Jeffrey Conn2,5, and Jens Meiler1,2,3,4*

Department of Chemistry1, Department of Pharmacology2, Center for Structural Biology3, Institute of Chemical Biology4, Center for Neuroscience Drug Discovery5, Vanderbilt University, Nashville, TN Vanderbilt University, Nashville, TN 37232, USA *

Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +1-615-936-5662; Fax: +1-615-936-2211.

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