Effect of Substitution on the Aniline Moiety of the GPR88 Agonist 2‑PCCA: Synthesis, Structure–Activity Relationships, and Molecular Modeling Studies

GPR88, an orphan receptor richly expressed in the striatum, is implicated in a number of basal ganglia-associated disorders. In order to elucidate the functions of GPR88, an in vivo probe appropriate for CNS investigation is required. We previously reported that 2-PCCA was able to modulate GPR88-mediated cAMP production through a Gα_i-coupled pathway. Early structure–activity relationship (SAR) studies suggested that the aniline moiety of 2-PCCA is a suitable site for diverse modifications. Aimed at elucidating structural requirements in this region, we have designed and synthesized a series of analogues bearing a variety of substituents at the phenyl ring of the aniline moiety. Several compounds (e.g., <b>5j</b>, <b>5o</b>) showed improved or comparable potency, but have lower lipophilicity than 2-PCCA (clogP 6.19). These compounds provide the basis for further optimization to probe GPR88 in vivo functions. Computational studies confirmed the SAR trends and supported the notion that 4′-substituents on the biphenyl ring exit through a largely hydrophobic binding site to the extracellular loop

Design, Synthesis, and Structure–Activity Relationship Studies of Novel GPR88 Agonists (4-Substituted-phenyl)acetamides Based on the Reversed Amide Scaffold

The development of synthetic agonists for the orphan receptor GPR88 has recently attracted significant interest, given the promise of GPR88 as a novel drug target for psychiatric and neurodegenerative disorders. Examination of structure–activity relationships of two known agonist scaffolds 2-PCCA and 2-AMPP, as well as the recently resolved cryo-EM structure of 2-PCCA-bound GPR88, led to the design of a new scaffold based on the “reversed amide” strategy of 2-AMPP. A series of novel (4-substituted-phenyl)acetamides were synthesized and assessed in cAMP accumulation assays as GPR88 agonists, which led to the discovery of several compounds with better or comparable potencies to 2-AMPP. Computational docking studies suggest that these novel GPR88 agonists bind to the same allosteric site of GPR88 that 2-PCCA occupies. Collectively, our findings provide structural insight and SAR requirement at the allosteric site of GPR88 and a new scaffold for further development of GPR88 allosteric agonists

Design, Synthesis, and Structure–Activity Relationship Studies of Novel GPR88 Agonists (4-Substituted-phenyl)acetamides Based on the Reversed Amide Scaffold

The development of synthetic agonists for the orphan receptor GPR88 has recently attracted significant interest, given the promise of GPR88 as a novel drug target for psychiatric and neurodegenerative disorders. Examination of structure–activity relationships of two known agonist scaffolds 2-PCCA and 2-AMPP, as well as the recently resolved cryo-EM structure of 2-PCCA-bound GPR88, led to the design of a new scaffold based on the “reversed amide” strategy of 2-AMPP. A series of novel (4-substituted-phenyl)acetamides were synthesized and assessed in cAMP accumulation assays as GPR88 agonists, which led to the discovery of several compounds with better or comparable potencies to 2-AMPP. Computational docking studies suggest that these novel GPR88 agonists bind to the same allosteric site of GPR88 that 2-PCCA occupies. Collectively, our findings provide structural insight and SAR requirement at the allosteric site of GPR88 and a new scaffold for further development of GPR88 allosteric agonists

Discovery of Novel Proline-Based Neuropeptide FF Receptor Antagonists

The neuropeptide FF (NPFF) system has been implicated in a number of physiological processes including modulating the pharmacological activity of opioid analgesics and several other classes of drugs of abuse. In this study, we report the discovery of a novel proline scaffold with antagonistic activity at the NPFF receptors through a high throughput screening campaign using a functional calcium mobilization assay. Focused structure–activity relationship studies on the initial hit <b>1</b> have resulted in several analogs with calcium mobilization potencies in the submicromolar range and modest selectivity for the NPFF1 receptor. Affinities and potencies of these compounds were confirmed in radioligand binding and functional cAMP assays. Two compounds, <b>16</b> and <b>33</b>, had good solubility and blood–brain barrier permeability that fall within the range of CNS permeant candidates without the liability of being a P-glycoprotein substrate. Finally, both compounds reversed fentanyl-induced hyperalgesia in rats when administered intraperitoneally. Together, these results point to the potential of these proline analogs as promising NPFF receptor antagonists

DataSheet3_Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models.XLSX

IntroductionThe U. S. Environmental Protection Agency’s Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system–disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening.MethodsChemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical.Results and DiscussionPerformance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1 ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1.ConclusionOur results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.</p

Table1_Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models.docx

IntroductionThe U. S. Environmental Protection Agency’s Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system–disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening.MethodsChemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical.Results and DiscussionPerformance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1 ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1.ConclusionOur results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.</p

DataSheet1_Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models.XLSX

IntroductionThe U. S. Environmental Protection Agency’s Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system–disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening.MethodsChemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical.Results and DiscussionPerformance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1 ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1.ConclusionOur results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.</p

DataSheet2_Chemistry domain of applicability evaluation against existing estrogen receptor high-throughput assay-based activity models.XLSX

IntroductionThe U. S. Environmental Protection Agency’s Endocrine Disruptor Screening Program (EDSP) Tier 1 assays are used to screen for potential endocrine system–disrupting chemicals. A model integrating data from 16 high-throughput screening assays to predict estrogen receptor (ER) agonism has been proposed as an alternative to some low-throughput Tier 1 assays. Later work demonstrated that as few as four assays could replicate the ER agonism predictions from the full model with 98% sensitivity and 92% specificity. The current study utilized chemical clustering to illustrate the coverage of the EDSP Universe of Chemicals (UoC) tested in the existing ER pathway models and to investigate the utility of chemical clustering to evaluate the screening approach using an existing 4-assay model as a test case. Although the full original assay battery is no longer available, the demonstrated contribution of chemical clustering is broadly applicable to assay sets, chemical inventories, and models, and the data analysis used can also be applied to future evaluation of minimal assay models for consideration in screening.MethodsChemical structures were collected for 6,947 substances via the CompTox Chemicals Dashboard from the over 10,000 UoC and grouped based on structural similarity, generating 826 chemical clusters. Of the 1,812 substances run in the original ER model, 1,730 substances had a single, clearly defined structure. The ER model chemicals with a clearly defined structure that were not present in the EDSP UoC were assigned to chemical clusters using a k-nearest neighbors approach, resulting in 557 EDSP UoC clusters containing at least one ER model chemical.Results and DiscussionPerformance of an existing 4-assay model in comparison with the existing full ER agonist model was analyzed as related to chemical clustering. This was a case study, and a similar analysis can be performed with any subset model in which the same chemicals (or subset of chemicals) are screened. Of the 365 clusters containing >1 ER model chemical, 321 did not have any chemicals predicted to be agonists by the full ER agonist model. The best 4-assay subset ER agonist model disagreed with the full ER agonist model by predicting agonist activity for 122 chemicals from 91 of the 321 clusters. There were 44 clusters with at least two chemicals and at least one agonist based upon the full ER agonist model, which allowed accuracy predictions on a per-cluster basis. The accuracy of the best 4-assay subset ER agonist model ranged from 50% to 100% across these 44 clusters, with 32 clusters having accuracy ≥90%. Overall, the best 4-assay subset ER agonist model resulted in 122 false-positive and only 2 false-negative predictions compared with the full ER agonist model. Most false positives (89) were active in only two of the four assays, whereas all but 11 true positive chemicals were active in at least three assays. False positive chemicals also tended to have lower area under the curve (AUC) values, with 110 out of 122 false positives having an AUC value below 0.214, which is lower than 75% of the positives as predicted by the full ER agonist model. Many false positives demonstrated borderline activity. The median AUC value for the 122 false positives from the best 4-assay subset ER agonist model was 0.138, whereas the threshold for an active prediction is 0.1.ConclusionOur results show that the existing 4-assay model performs well across a range of structurally diverse chemicals. Although this is a descriptive analysis of previous results, several concepts can be applied to any screening model used in the future. First, the clustering of the chemicals provides a means of ensuring that future screening evaluations consider the broad chemical space represented by the EDSP UoC. The clusters can also assist in prioritizing future chemicals for screening in specific clusters based on the activity of known chemicals in those clusters. The clustering approach can be useful in providing a framework to evaluate which portions of the EDSP UoC chemical space are reliably covered by in silico and in vitro approaches and where predictions from either method alone or both methods combined are most reliable. The lessons learned from this case study can be easily applied to future evaluations of model applicability and screening to evaluate future datasets.</p

Effect of 1‑Substitution on Tetrahydroisoquinolines as Selective Antagonists for the Orexin‑1 Receptor

Selective blockade of the orexin-1 receptor (OX₁) has been suggested as a potential approach to drug addiction therapy because of its role in modulating the brain’s reward system. We have recently reported a series of tetrahydroisoquinoline-based OX₁ selective antagonists. Aimed at elucidating structure–activity relationship requirements in other regions of the molecule and further enhancing OX₁ potency and selectivity, we have designed and synthesized a series of analogues bearing a variety of substituents at the 1-position of the tetrahydroisoquinoline. The results show that an optimally substituted benzyl group is required for activity at the OX₁ receptor. Several compounds with improved potency and/or selectivity have been identified. When combined with structural modifications that were previously found to improve selectivity, we have identified compound <b>73</b> (RTIOX-251) with an apparent dissociation constant (<i>K</i>_e) of 16.1 nM at the OX₁ receptor and >620-fold selectivity over the OX₂ receptor. In vivo, compound <b>73</b> was shown to block the development of locomotor sensitization to cocaine in rats

Blocking Alcoholic Steatosis in Mice with a Peripherally Restricted Purine Antagonist of the Type 1 Cannabinoid Receptor

Type 1 cannabinoid receptor (CB1) antagonists have demonstrated promise for the treatment of obesity, liver disease, metabolic syndrome, and dyslipidemias. However, the inhibition of CB1 receptors in the central nervous system can produce adverse effects, including depression, anxiety, and suicidal ideation. Efforts are now underway to produce peripherally restricted CB1 antagonists to circumvent CNS-associated undesirable effects. In this study, a series of analogues were explored in which the 4-aminopiperidine group of compound <b>2</b> was replaced with aryl- and heteroaryl-substituted piperazine groups both with and without a spacer. This resulted in mildly basic, potent antagonists of human CB1 (hCB1). The 2-chlorobenzyl piperazine, <b>25</b>, was found to be potent (<i>K</i>_i = 8 nM); to be >1000-fold selective for hCB1 over hCB2; to have no hERG liability; and to possess favorable ADME properties including high oral absorption and negligible CNS penetration. Compound <b>25</b> was tested in a mouse model of alcohol-induced liver steatosis and found to be efficacious. Taken together, <b>25</b> represents an exciting lead compound for further clinical development or refinement