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 KI = 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.

Identification of Novel β-Tubulin Inhibitors Using a Combined In Silico/In Vitro Approach

“…The calculated quality metrics included sensitivity (eq ), specificity (eq ), accuracy (eq ), yield of actives (YoA) (eq ), and enrichment factor (EF) (eq ). This refinement process was guided by continuous observation of the quality enrichment metrics calculated during each step as described in previous reports . Pharmacophore models which failed to reach good performance (i.e., EF < 4) were discarded.…”

“…This refinement process was guided by continuous observation of the quality enrichment metrics calculated during each step as described in previous reports. 47 Pharmacophore models which failed to reach good performance (i.e., EF < 4) were discarded. Graphical depictions of the final optimized models and detailed descriptions outlining their individual optimization and features can be found in the SI (Section 2, Figures S1–S16) .…”

“…It has also been demonstrated that using several modeling programmes for model hit prediction is advantageous . Moreover, workflows employing both LigandScout (LS) and Discovery Studio (DS) software with in vitro validation of potential lead candidates has been successfully applied. − Meanwhile pharmacophore models have been developed in CBS antimitotic discovery efforts. − However, to our knowledge, none have yet integrated a combined SB-LB pharmacophore workflow with more than one modeling software coupled with experimental validation. Coupled LB pharmacophore modeling allows for inclusion of features from inhibitors where the exact binding mode is not elucidated in crystallography.…”

“… 44 Moreover, workflows employing both LigandScout (LS) and Discovery Studio (DS) software with in vitro validation of potential lead candidates has been successfully applied. 45 − 47 Meanwhile pharmacophore models have been developed in CBS antimitotic discovery efforts. 48 − 50 However, to our knowledge, none have yet integrated a combined SB-LB pharmacophore workflow with more than one modeling software coupled with experimental validation.…”

In Silico Design of Potential Small-Molecule Antibiotic Adjuvants against Salmonella typhimurium Ortho Acetyl Sulphydrylase Synthase to Address Antimicrobial Resistance

“…Compound 155773276 showed the third best binding affinity, with three HBAs and two HBDs making three hydrogen bonds with Lys41, Asn71, and Thr72 (Figure 2). The strength of the complex produced is significantly increased by the intermolecular hydrogen bonds formed between the ligand and amino acids in the active sites, and this improves the docking scores [17]. Therefore, Lipinski's rule of five, which states that a drug candidate should contain HBA ≤ 10 and HBD ≤ 5, emphasizes the importance of hydrogen bond acceptors (HBAs) and donors (HBDs) in the ligand structure.…”