Identification of Novel Dopamine D2 Receptor Ligands-A Combined In Silico/In Vitro Approach

Abstract

Diseases of the central nervous system are an alarming global problem showing an increasing prevalence. Dopamine receptor D₂ (D₂R) has been shown to be involved in central nervous system diseases. While different D₂R-targeting drugs have been approved by the FDA, they all suffer from major drawbacks due to promiscuous receptor activity leading to adverse effects. Increasing the number of potential D₂R-targeting drug candidates bears the possibility of discovering molecules with less severe side-effect profiles. In dire need of novel D₂R ligands for drug development, combined in silico/in vitro approaches have been shown to be efficient strategies. In this study, in silico pharmacophore models were generated utilizing both ligand- and structure-based approaches. Subsequently, different databases were screened for novel D₂R ligands. Selected virtual hits were investigated in vitro, quantifying their binding affinity towards D₂R. This workflow successfully identified six novel D₂R ligands exerting micro- to nanomolar (most active compound K_I = 4.1 nM) activities. Thus, the four pharmacophore models showed prospective true-positive hit rates in between 4.5% and 12%. The developed workflow and identified ligands could aid in developing novel drug candidates for D₂R-associated pathologies.

Keywords: GPCR; HTRF; dopamine receptor; in silico; in vitro; pharmacophore modelling; virtual screening.

Figure 1

Overview of the highly conserved D₂R orthosteric binding pocket. Asp3.32, the serine microdomain, and the aromatic microdomain responsible for ligand binding, orientation, and receptor activation are highlighted. Picture generated in LigandScout (based on cryo-EM structure 7jvr [23]) showing bromocriptine in the orthosteric binding pocket.

Figure 2

Combined in silico/in vitro approach to identify novel D₂R ligands.

Figure 3

Overview of all clinically used D₂R agonists. All compounds were included in the active dataset used during the generation and validation of the developed pharmacophore models. The compounds were retrieved from the ChEMBL database including their in vitro K_I values (nM).

Figure 4

SB pharmacophore models M1 and M2 generated using LS and DS, respectively. M1 and M2 are based on the cryo-EM structure of D₂R in complex with agonist 2 (PDB ID 7jvr [23]). (a) Agonist 2 in the ligand-binding pocket of D₂R. (b) M1 including 60 exclusion volumes (XVOLs). (c) M1 aligned with 2. (d) M2 including 113 XVOLs. (e) M2 aligned with 2. XVOLs (grey). Hydrophobic contacts (HCs; yellow and cyan spheres). Hydrogen bond acceptor (HBA; red arrows, green spheres). Hydrogen bond donor (HBD; purple sphere). Positively ionizable interaction (PI; blue star-like shape, red sphere). All features represent characteristics of the respective structural features of the investigated molecules.

Figure 5

Ligand-based pharmacophore models M3 and M4 generated using LS and DS, respectively. Both models were based on 1, SC52, and SC59. (a) Display of M3 including 61 exclusion volumes (XVOLs). (b) M3 aligned with 1. (c) Display of M4 including 122 XVOLs. (d) M4 aligned with SC59. XVOLs (grey). Hydrophobic contacts (HCs; yellow and cyan spheres). Hydrogen bond acceptor (HBA; red and green spheres). Hydrogen bond donor (HBD; green and purple sphere). Positively ionizable interaction (PI; blue star-like shape, red sphere). Aromatic interaction (AI; blue circle, orange sphere). All features represent characteristics of the respective structural features of the investigated molecules.

Figure 6

Exemplary assessment of scaffold similarity of consensus hits (n = 3).

Figure 7

Two-dimensional structures of the compounds selected for in vitro K_I determination.

Figure 8

Comparison of the K_I values of dopamine (endogenous ligand) and compound 14 (highest activity of the identified ligands). K_I values were determined with n = 6.

Figure 9

Display of the novel D₂R ligand, compound 14, aligned with M3 and M4, identifying the compound during the screening process. (a) Three-dimensional structure of compound 14 including the pharmacophore features of M3. (b) Three-dimensional structure of compound 14 including the pharmacophore features of M4. (c) Two-dimensional structure of compound 14 highlighting the structural features recognized by the pharmacophore models.

Figure 10

Decision tree following the investigation of the compounds of interest based on a SwissTargetPrediction search.