Synthesis, 3D-QSAR, and structural modeling of benzolactam derivatives with binding affinity for the D(2) and D(3) receptors

A Convenient Synthesis of Novel Isoxazolidine and Isoxazole Isoquinolinones Fused Hybrids

Isoxazolidine, isoxazole, and isoquinolinone rings are present in the structure of several natural products and/or pharmaceutically interesting compounds. In this work, facile and efficient pathways have been developed for the preparation of fused frameworks bearing those heterocycles. The successful approaches for both isoxazolidine/isoquinolinone and isoxazole/isoquinolinone hybrid syntheses relied initially on 1,3-dipolar cycloadditions of nitrones and nitrile oxides to indenone and 2-propargylbenzamide, respectively. The construction of the isoquinolinone lactam system followed by performing a selective Schmidt reaction for isoxazolidine derivatives (two steps overall), whereas the isoxazole lactams were reached via an Ullmann-type cyclisation (three steps overall). Key observations were made regarding the stereo- and regioselectivities of the reactions employed, and small libraries of the targeted hybrids were prepared, demonstrating the general applicability of these strategies.

Computational Chemistry for the Identification of Lead Compounds for Radiotracer Development

The use of computer-aided drug design (CADD) for the identification of lead compounds in radiotracer development is steadily increasing. Traditional CADD methods, such as structure-based and ligand-based virtual screening and optimization, have been successfully utilized in many drug discovery programs and are highlighted throughout this review. First, we discuss the use of virtual screening for hit identification at the beginning of drug discovery programs. This is followed by an analysis of how the hits derived from virtual screening can be filtered and culled to highly probable candidates to test in in vitro assays. We then illustrate how CADD can be used to optimize the potency of experimentally validated hit compounds from virtual screening for use in positron emission tomography (PET). Finally, we conclude with a survey of the newest techniques in CADD employing machine learning (ML).

Recent advances in dopamine D2 receptor ligands in the treatment of neuropsychiatric disorders

Dopamine is a biologically active amine synthesized in the central and peripheral nervous system. This biogenic monoamine acts by activating five types of dopamine receptors (D_1-5 Rs), which belong to the G protein-coupled receptor family. Antagonists and partial agonists of D₂ Rs are used to treat schizophrenia, Parkinson's disease, depression, and anxiety. The typical pharmacophore with high D₂ R affinity comprises four main areas, namely aromatic moiety, cyclic amine, central linker and aromatic/heteroaromatic lipophilic fragment. From the literature reviewed herein, we can conclude that 4-(2,3-dichlorophenyl), 4-(2-methoxyphenyl)-, 4-(benzo[b]thiophen-4-yl)-1-substituted piperazine, and 4-(6-fluorobenzo[d]isoxazol-3-yl)piperidine moieties are critical for high D₂ R affinity. Four to six atoms chains are optimal for D₂ R affinity with 4-butoxyl as the most pronounced one. The bicyclic aromatic/heteroaromatic systems are most frequently occurring as lipophilic appendages to retain high D₂ R affinity. In this review, we provide a thorough overview of the therapeutic potential of D₂ R modulators in the treatment of the aforementioned disorders. In addition, this review summarizes current knowledge about these diseases, with a focus on the dopaminergic pathway underlying these pathologies. Major attention is paid to the structure, function, and pharmacology of novel D₂ R ligands, which have been developed in the last decade (2010-2021), and belong to the 1,4-disubstituted aromatic cyclic amine group. Due to the abundance of data, allosteric D₂ R ligands and D₂ R modulators from patents are not discussed in this review.

Site-Selective C-H Arylation of Diverse Arenes Ortho to Small Alkyl Groups

Catalytic systems for direct C-H activation of arenes commonly show preference for electronically activated and sterically exposed C-H sites. Here we show that a range of functionally rich and pharmaceutically relevant arene classes can undergo site-selective C-H arylation ortho to small alkyl substituents, preferably endocyclic methylene groups. The C-H activation is experimentally supported as being the selectivity-determining step, while computational studies of the transition state models indicate the relevance of non-covalent interactions between the catalyst and the methylene group of the substrate. Our results suggest that preference for C(sp² )-H activation next to alkyl groups could be a general selectivity mode, distinct from common steric and electronic factors.

Highly Potent and Selective Dopamine D4 Receptor Antagonists Potentially Useful for the Treatment of Glioblastoma

To better understand the role of dopamine D₄ receptor (D₄R) in glioblastoma (GBM), in the present paper, new ligands endowed with high affinity and selectivity for D₄R were discovered starting from the brain penetrant and D₄R selective lead compound 1-(3-(4-phenylpiperazin-1-yl)propyl)-3,4-dihydroquinolin-2(1H)-one (6). In particular, the D₄R antagonist 24, showing the highest affinity and selectivity over D₂R and D₃R within the series (D₂/D₄ = 8318, D₃/D₄ = 3715), and the biased ligand 29, partially activating D₄R G_i-/G_o-protein and blocking β-arrestin recruitment, emerged as the most interesting compounds. These compounds, evaluated for their GBM antitumor activity, induced a decreased viability of GBM cell lines and primary GBM stem cells (GSC#83), with the maximal efficacy being reached at a concentration of 10 μM. Interestingly, the treatment with both compounds 24 and 29 induced an increased effect in reducing the cell viability with respect to temozolomide, which is the first-choice chemotherapeutic drug in GBM.

Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system

CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.

Synthesis and In Vitro Evaluation of Novel Dopamine Receptor D2 3,4-dihydroquinolin-2(1H)-one Derivatives Related to Aripiprazole

In this pilot study, a series of new 3,4-dihydroquinolin-2(1H)-one derivatives as potential dopamine receptor D₂ (D₂R) modulators were synthesized and evaluated in vitro. The preliminary structure-activity relationship disclosed that compound 5e exhibited the highest D₂R affinity among the newly synthesized compounds. In addition, 5e showed a very low cytotoxic profile and a high probability to cross the blood-brain barrier, which is important considering the observed affinity. However, molecular modelling simulation revealed completely different binding mode of 5e compared to USC-D301, which might be the culprit of the reduced affinity of 5e toward D₂R in comparison with USC-D301.

Predicting subtype selectivity of dopamine receptor ligands with three-dimensional biologically relevant spectrum

We applied a novel molecular descriptor, three-dimensional biologically relevant spectrum (BRS-3D), in subtype selectivity prediction of dopamine receptor (DR) ligands. BRS-3D is a shape similarity profile calculated by superimposing the objective compounds against 300 template ligands from sc-PDB. First, we constructed five subtype selectivity regression models between DR subtypes D1-D2, D1-D3, D2-D3, D2-D4, and D3-D4. The models' 10-fold cross-validation-squared correlation coefficient (Q² , for training sets) and determination coefficient (R² , for test sets) were in the range of 0.5-0.7 and 0.6-0.8, respectively. Then, four pair-wise (D1-D2, D2-D3, D2-D4, and D3-D4) and a multitype (D2, D3, and D4) classification models were developed with the prediction accuracies around or over 90% (for test sets). Lastly, we compared the performances of the models developed on BRS-3D and classical descriptors. The results showed that BRS-3D performed similarly to classical 2D descriptors and better than other 3D descriptors. Combining BRS-3D and 2D descriptors can further improve the prediction performance. These results confirmed the capacity of BRS-3D in the prediction of DR subtype-selective ligands.

C-H Activation in Primary 3-Phenylpropylamines: Synthesis of Seven-Membered Palladacycles through Orthometalation. Stoichiometric Preparation of Benzazepinones and Catalytic Synthesis of Ureas

The dimeric cyclometalated complexes [Pd2{κ(2)C,N-C6H4CH2CH2C(R)(Me)NH2-2}2(μ-Cl)2] (R = Me (1a), H (1b)) are prepared by reacting 1,1-dimethyl-3-phenylpropylammonium or 1-methyl-3-phenylpropylammonium triflate with Pd(OAc)2 in a 1:1 molar ratio and subsequent treatment with excess NaCl. The mononuclear derivatives [Pd{κ(2)C,N-C6H4CH2CH2C(R)(Me)NH2-2}Cl(L)] (L = PPh3, R = Me (3a), H (3b); L = 4-picoline (4-pic), R = Me (4a), H (4b)) were prepared from 1a,b by splitting the chloro bridges with the neutral ligands L. A conformational analysis of the mononuclear palladacycles in solution has been carried out. Insertion of CO takes place into the Pd-C bond of complexes 1a,b, affording Pd(0) and the tetrahydro-benzazepinone 5a or 5b, which possesses potential pharmacological interest. Additionally, the triflate salt A or B undergoes catalytic carbonylation with CO to afford the corresponding N,N'-dialkylurea, using Pd(OAc)2/Cu(OAc)2, in boiling acetonitrile. The crystal structures of 3a·(1)/2H2O, 3b, 4b·CHCl3, and 5a were determined by X-ray diffraction studies.

Beyond small-molecule SAR: using the dopamine D3 receptor crystal structure to guide drug design

The dopamine D3 receptor is a target of pharmacotherapeutic interest in a variety of neurological disorders including schizophrenia, restless leg syndrome, and drug addiction. The high protein sequence homology between the D3 and D2 receptors has posed a challenge to developing D3 receptor-selective ligands whose behavioral actions can be attributed to D3 receptor engagement, in vivo. However, through primarily small-molecule structure-activity relationship (SAR) studies, a variety of chemical scaffolds have been discovered over the past two decades that have resulted in several D3 receptor-selective ligands with high affinity and in vivo activity. Nevertheless, viable clinical candidates remain limited. The recent determination of the high-resolution crystal structure of the D3 receptor has invigorated structure-based drug design, providing refinements to the molecular dynamic models and testable predictions about receptor-ligand interactions. This chapter will highlight recent preclinical and clinical studies demonstrating potential utility of D3 receptor-selective ligands in the treatment of addiction. In addition, new structure-based rational drug design strategies for D3 receptor-selective ligands that complement traditional small-molecule SAR to improve the selectivity and directed efficacy profiles are examined.

Homology modeling of dopamine D2 and D3 receptors: molecular dynamics refinement and docking evaluation

Dopamine (DA) receptors, a class of G-protein coupled receptors (GPCRs), have been targeted for drug development for the treatment of neurological, psychiatric and ocular disorders. The lack of structural information about GPCRs and their ligand complexes has prompted the development of homology models of these proteins aimed at structure-based drug design. Crystal structure of human dopamine D₃ (hD₃) receptor has been recently solved. Based on the hD₃ receptor crystal structure we generated dopamine D₂ and D₃ receptor models and refined them with molecular dynamics (MD) protocol. Refined structures, obtained from the MD simulations in membrane environment, were subsequently used in molecular docking studies in order to investigate potential sites of interaction. The structure of hD₃ and hD_2L receptors was differentiated by means of MD simulations and D₃ selective ligands were discriminated, in terms of binding energy, by docking calculation. Robust correlation of computed and experimental K_i was obtained for hD₃ and hD_2L receptor ligands. In conclusion, the present computational approach seems suitable to build and refine structure models of homologous dopamine receptors that may be of value for structure-based drug discovery of selective dopaminergic ligands.

A two-step target binding and selectivity support vector machines approach for virtual screening of dopamine receptor subtype-selective ligands

Target selective drugs, such as dopamine receptor (DR) subtype selective ligands, are developed for enhanced therapeutics and reduced side effects. In silico methods have been explored for searching DR selective ligands, but encountered difficulties associated with high subtype similarity and ligand structural diversity. Machine learning methods have shown promising potential in searching target selective compounds. Their target selective capability can be further enhanced. In this work, we introduced a new two-step support vector machines target-binding and selectivity screening method for searching DR subtype-selective ligands, which was tested together with three previously-used machine learning methods for searching D1, D2, D3 and D4 selective ligands. It correctly identified 50.6%–88.0% of the 21–408 subtype selective and 71.7%–81.0% of the 39–147 multi-subtype ligands. Its subtype selective ligand identification rates are significantly better than, and its multi-subtype ligand identification rates are comparable to the best rates of the previously used methods. Our method produced low false-hit rates in screening 13.56 M PubChem, 168,016 MDDR and 657,736 ChEMBLdb compounds. Molecular features important for subtype selectivity were extracted by using the recursive feature elimination feature selection method. These features are consistent with literature-reported features. Our method showed similar performance in searching estrogen receptor subtype selective ligands. Our study demonstrated the usefulness of the two-step target binding and selectivity screening method in searching subtype selective ligands from large compound libraries.

Progress in the structural prediction of G protein-coupled receptors: D3 receptor in complex with eticlopride

Predicting the three-dimensional structure of ligand-receptor complexes involving G protein-coupled receptors (GPCRs) is still a challenging task in rational drug design. To evaluate the reliability of the GPCR structural prediction, only a couple of community-wide assessments have been carried out. Our participation in the last edition, DOCK2010, involved the blind prediction of the dopaminergic D(3) receptor in complex with the D(2)/D(3) selective antagonist eticlopride for which the crystal structure has been recently released. Here, we describe a methodology that succeeded to produce a correctly predicted eticlopride-D(3) receptor complex out of three submitted models. Ranking the obtained models in the correct order is the main challenge due to subtle structural differences in the complex that are not sufficiently captured by conventional scoring functions. Importantly, our work reveals that a correct ranking is obtained by including a more sophisticated description of conformational ligand energy on binding. All in all, this case study highlights the current progress in modeling GPCR complexes and underlines that in silico modeling can be a valuable complement in GPCR drug discovery.