Design, Synthesis, Structure–Activity Relationship Studies, and Three-Dimensional Quantitative Structure–Activity Relationship (3D-QSAR) Modeling of a Series of O-Biphenyl Carbamates as Dual Modulators of Dopamine D3 Receptor and Fatty Acid Amide Hydrolase

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.

Machine Learning and Computational Chemistry for the Endocannabinoid System

Computational methods in medicinal chemistry facilitate drug discovery and design. In particular, machine learning methodologies have recently gained increasing attention. This chapter provides a structured overview of the current state of computational chemistry and its applications for the interrogation of the endocannabinoid system (ECS), highlighting methods in structure-based drug design, virtual screening, ligand-based quantitative structure-activity relationship (QSAR) modeling, and de novo molecular design. We emphasize emerging methods in machine learning and anticipate a forecast of future opportunities of computational medicinal chemistry for the ECS.

The multitarget FAAH inhibitor/D3 partial agonist ARN15381 decreases nicotine self-administration in male rats

Tobacco use disorder is a worldwide health problem for which available medications show limited efficacy. Nicotine is the psychoactive component of tobacco responsible for its addictive liability. Similar to other addictive drugs, nicotine enhances mesolimbic dopamine transmission. Inhibition of the fatty acid amide hydrolase (FAAH), the enzyme responsible for the degradation of the endocannabinoid anandamide (AEA), palmitoylethanolamide (PEA) and oleoylethanolamide (OEA), reduces nicotine-enhanced dopamine transmission and acquisition of nicotine self-administration in rats. Down-regulation of dopamine transmission by antagonists or partial agonists of the dopamine D3 receptor (DRD3) also reduced nicotine self-administration and conditioned place preference. Based on these premises, we evaluated the effect of ARN15381, a multitarget compound showing FAAH inhibition and DRD3 partial agonist activity in the low nanomolar range, on nicotine self-administration in rats. Pretreatment with ARN15381 dose dependently decreased self-administration of a nicotine dose at the top of the nicotine dose/response (D/R) curve, while it did not affect self-administration of a nicotine dose laying on the descending limb of the D/R curve. Conversely, pretreatment with the selective FAAH inhibitor URB597 and the DRD3 partial agonist CJB090 failed to modify nicotine self-administration independent of the nicotine dose self-administered. Our data indicates that the concomitant FAAH inhibition and DRD3 partial agonism produced by ARN15381 is key to the observed reduction of nicotine self-administration, demonstrating that a multitarget approach may hold clinical importance for the treatment of tobacco use disorder.

Dopamine D3 receptor ligands: a patent review (2014-2020)

INTRODUCTION

Compelling evidence identified D3 dopamine receptor (D3R) as a suitable target for therapeutic intervention on CNS-associated disorders, cancer and other conditions. Several efforts have been made toward developing potent and selective ligands for modulating signalling pathways operated by these GPCRs. The rational design of D3R ligands endowed with a pharmacologically relevant profile has traditionally not encountered much support from computational methods due to a very limited knowledge of the receptor structure and of its conformational dynamics. We believe that recent progress in structural biology will change this state of affairs in the next decade.

AREAS COVERED

This review provides an overview of the recent (2014-2020) patent literature on novel classes of D3R ligands developed within the framework of CNS-related diseases, cancer and additional conditions. When possible, an in-depth description of both in vitro and in vivo generated data is presented. New therapeutic applications of known molecules with activity at D3R are discussed.

EXPERT OPINION

Building on current knowledge, future D3R-focused drug discovery campaigns will be propelled by a combination of unprecedented availability of structural information with advanced computational and analytical methods. The design of D3R ligands with the sought activity, efficacy and selectivity profile will become increasingly more streamlined.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

Simplified, interpretable graph convolutional neural networks for small molecule activity prediction

We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. We first conduct a hyperparameter optimization across nearly 800 protein targets that produces a simplified gCNN QSAR architecture, and we observe that such a model can yield performance improvements over both standard gCNN and RF methods on difficult-to-classify test sets. Additionally, we discuss how reductions in convolutional layer dimensions potentially speak to the “anatomical” needs of gCNNs with respect to radial coarse graining of molecular substructure. We augment this simplified architecture with saliency map technology that highlights molecular substructures relevant to activity, and we perform saliency analysis on nearly 100 data-rich protein targets. We show that resultant substructural clusters are useful visualization tools for understanding substructure-activity relationships. We go on to highlight connections between our models’ saliency predictions and observations made in the medicinal chemistry literature, focusing on four case studies of past lead finding and lead optimization campaigns.

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.

Developing a variation of 3D-QSAR/MD method in drug design

In continuation of the previous reports on a combination of 3D-quantitative structure-activity relationships (QSAR) with computational molecular dynamics (MD) studies, a new variation of 3D-QSAR/MD method has been employed for drug-design as an alternative or supplementary for the typical experimental methods. The presented method is more cost-effective and less time-consuming than the previous methods and avoids several restrictions of experimental methods, such as validity estimation, and predictability. For this purpose, seven inhibitors for bromodomain (BRD)-containing protein, as an important protein in the development of different types of cancer and responsible for oncogenic rearrangements, have been selected to study of their interactions by docking and MD simulations using molecular mechanics/generalized born surface area (MM/GBSA) method. To build the proposed model, a common variant of 3D-QSAR methods, comparative molecular field analysis has been employed using a dataset of 100 MD-extracted ligand conformations and their corresponding MM/GBSA BRD4-binding energies. The results showed excellent predictability of the generated model for both the training set and test groups. Finally, two new inhibitors were selected among total 4000 designed derivatives (generated through evolutionary techniques) using the proposed 3D-QSAR-MD model. The potentials of these inhibitors were assessed by MD simulations, which showed the higher inhibitory of these compounds than the previous inhibitors. Therefore, this method showed high potentials for acceleration of the procedure of drug design and a basis for joining researchers in computational biology and pharmaceutical sciences.

Synthesis, in silico, and in vitro studies of novel dopamine D2 and D3 receptor ligands

Dopamine is an important neurotransmitter in the human brain and its altered concentrations can lead to various neurological diseases. We studied the binding of novel compounds at the dopamine D₂ (D₂ R) and D₃ (D₃ R) receptor subtypes, which belong to the D₂ -like receptor family. The synthesis, in silico, and in vitro characterization of 10 dopamine receptor ligands were performed. Novel ligands were docked into the D₂ R and D₃ R crystal structures to examine the precise binding mode. A quantum mechanics/molecular mechanics study was performed to gain insights into the nature of the intermolecular interactions between the newly introduced pentafluorosulfanyl (SF₅ ) moiety and D₂ R and D₃ R. A radioligand displacement assay determined that all of the ligands showed moderate-to-low nanomolar affinities at D₂ R and D₃ R, with a slight preference for D₃ R, which was confirmed in the in silico studies. N-{4-[4-(2-Methoxyphenyl)piperazin-1-yl]butyl}-4-(pentafluoro-λ6-sulfanyl)benzamide (7i) showed the highest D₃ R affinity and selectivity (pK_i values of 7.14 [D₂ R] and 8.42 [D₃ R]).

Multitarget Compounds for Bipolar Disorder: From Rational Design to Preliminary Pharmacokinetic Evaluation

Due to the complex and multifactorial nature of bipolar disorder (BD), single-target drugs have traditionally provided limited relief with no disease-modifying effects. In line with the polypharmacology paradigm, we attempted to overcome these limitations by devising two series of multitarget-directed ligands endowed with both a partial agonist profile at dopamine receptor D3 (D3R) and inhibitory activity against glycogen synthase kinase 3 beta (GSK-3β). These are two structurally unrelated targets that play independent, yet connected, roles in cognition and mood regulation. Two compounds (7 and 10) emerged as promising D3R/GSK-3β multitarget-directed ligands with nanomolar activity at D3R and low-micromolar inhibition of GSK-3β, thereby confirming, albeit preliminarily, the feasibility of our strategy. Furthermore, 7 showed promising drug-like properties in stability and pharmacokinetic studies.

Fullerene Derivatives as Lung Cancer Cell Inhibitors: Investigation of Potential Descriptors Using QSAR Approaches

Background

Nanotechnology-based strategies in the treatment of cancer have potential advantages because of the favorable delivery of nanoparticles into tumors through porous vasculature.

Materials and Methods

In the current study, we synthesized a series of water-soluble fullerene derivatives and observed their anti-tumor effects on human lung carcinoma A549 cell lines. The quantitative structure–activity relationship (QSAR) modeling was employed to investigate the relationship between anticancer effects and descriptors relevant to peculiarities of molecular structures of fullerene derivatives.

Results

In the QSAR regression model, the evaluation results revealed that the determination coefficient r² and leave-one-out cross-validation q² for the recommended QSAR model were 0.9966 and 0.9246, respectively, indicating the reliability of the results. The molecular modeling showed that the lack of chlorine atom and a lower number of aliphatic single bonds in saturated hydrocarbon chains may be positively correlated with the lung cancer cytotoxicity of fullerene derivatives. Synthesized water-soluble fullerene derivatives have potential functional groups to inhibit the proliferation of lung cancer cells.

Conclusion

The guidelines obtained from the QSAR model might strongly facilitate the rational design of potential fullerene-based drug candidates for lung cancer therapy in the future.

Highly biodegradable fluoroquinolone derivatives designed using the 3D-QSAR model and biodegradation pathways analysis

A three-dimensional quantitative structure-activity relationship (3D-QSAR) model was established based on molecular structures and docking scores (representing the biodegradability); the scores were obtained for 23 fluoroquinolones (FQs) and the oxidoreductase (PDB ID: 1YZP) of Phanerochaete chrysosporium in the aerobic process of municipal wastewater treatment plants. In the Comparative Molecular Field Analysis (CoMFA) model, q² was 0.516 and r²_pred was 0.727, which showed that the model was reliable and robust. The modification information obtained by the contour maps showed that introducing electronegative, bulky or electropositive groups at different active sites could increase the biodegradability of fluoroquinolone derivatives. Using levofloxacin (LEV) as a modified molecule, 35 fluoroquinolone derivatives with higher biodegradability than LEV were designed. After the evaluation of genotoxicity, bioconcentration and photodegradation, Derivative-15, with higher biodegradability (increased by 27.85%), higher genotoxicity, higher photodegradation and lower bioconcentration, was identified as the most environmentally friendly fluoroquinolone derivative. The 2D-QSAR model of FQ biodegradability was established through the quantization parameters, and q⁺ was identified as the main parameter affecting the biodegradability of FQs through sensitivity analysis. In addition, the docking results of LEV and Derivative-15 with the oxidoreductase in P. chrysosporium showed that the electrostatic field force between Derivative-15 and the amino acid residues promoted the binding of the donor to the receptor protein, thereby increasing the biodegradability of Derivative-15. Additionally, molecular dynamics simulations revealed that the enhancement of the electrostatic field force with Derivative-15 could promote the binding of the ligand to the receptor, which was basically consistent with the conclusion of molecular docking. Finally, the three microbial degradation pathways of LEV and Derivative-15 were also proposed. The total energy barrier value of the pathway with the lowest total energy barrier of biodegradation was reduced by 32.07%, which was basically consistent with the enhancement of biodegradability of Derivative-15.

A perspective review on fatty acid amide hydrolase (FAAH) inhibitors as potential therapeutic agents

Fatty acid amide hydrolase (FAAH) is an important enzyme creditworthy of hydrolyzing endocannabinoids and related-amidated signalling lipids, discovery of which has pioneered novel arena of pharmacological canvasses to unwrap its curative potency in various diseased circumstances. It presents contemporary basis for understanding molecules regulating and mediating inflammatory reactions, pain, anxiety, depression, and neurodegeneration. FAAH inhibitors form vital approach for discovery of therapeutic agents that are concerned with local elevation of endocannabinoids under certain stimuli, debarring adverse/unwanted secondary effects from global activation of cannabinoid receptors by exogenous cannabimimetics. During past decades, several molecules with excellent potency developed through tailor-made approaches entered into clinical trials, but none could reach market. Hence, hunt for novel, non-toxic and selective FAAH inhibitors are on horizon. This review summarizes present perception on FAAH in conjunction with its structure, mechanism of catalysis and biological functions. It also foregrounds recent development of molecules belonging to diverse chemical classes as potential FAAH inhibitors bobbing up from in-depth chemical, mechanistic and computational studies published since 2015-November 2019, focusing on their potency. This review will assist readers to obtain rationale on FAAH as potential target for addressing various disease conditions, acquiring significant knowledge on recently established inhibitor scaffolds and their development potentials. New technologies including MD-MM simulations and 3D-QSAR studies allow mechanistic characterization of enzyme. Assessment of in-vitro and in-vivo efficacy of existing FAAH inhibitors will facilitate researchers to design novel ligands utilizing modern drug design methods. The discussions will also impose precaution in decision making process, quashing possibility of late stage failure.

Multi-target dopamine D3 receptor modulators: Actionable knowledge for drug design from molecular dynamics and machine learning

Local changes in the structure of G-protein coupled receptors (GPCR) binders largely affect their pharmacological profile. While the sought efficacy can be empirically obtained by introducing local modifications, the underlining structural explanation can remain elusive. Here, molecular dynamics (MD) simulations of the eticlopride-bound inactive state of the Dopamine D3 Receptor (D3DR) have been clustered using a machine learning-based approach in the attempt to rationalize the efficacy change in four congeneric modulators. Accumulating extended MD trajectories of receptor-ligand complexes, we observed how the increase in ligand flexibility progressively destabilized the crystal structure of the inactivated receptor. To prospectively validate this model, a partial agonist was rationally designed based on structural insights and computational modeling, and eventually synthesized and tested. Results turned out to be in line with the predictions. This case study suggests that the investigation of ligand flexibility in the framework of extended MD simulations can assist and inform drug design strategies, highlighting its potential role as a powerful in silico counterpart to functional assays.

Multi-target-directed-ligands acting as enzyme inhibitors and receptor ligands

In this review, we present the latest advances in the field of multi-target-directed ligand (MTDL) design for the treatment of various complex pathologies of multifactorial origin. In particular, latest findings in the field of MTDL design targeting both an enzyme and a receptor are presented for different diseases such as Alzheimer's disease (AD), depression, addiction, glaucoma, non-alcoholic steatohepatitis and pain and inflammation. The ethology of the diseases is briefly described, with special emphasis on how the MTDL can evolve into novel therapies that replace the classic pharmacological dogma "one target one disease". Considering the current needs for therapy adherence improvement, it is exposed as from the medicinal chemistry, different molecular scaffolds are studied. With the use of structure activity relationship studies and molecular optimization, new hybrid molecules are generated with improved biological properties acting at two biologically very distinct targets.

Combined pharmacophore modeling, 3D-QSAR and docking studies to identify novel HDAC inhibitors using drug repurposing

Histone deacetylases (HDACs), a critical family of epigenetic enzymes, has emerged as a promising target for antitumor drugs. Here, we describe our protocol of virtual screening in identification of novel potential HDAC inhibitors through pharmacophore modeling, 3D-QSAR, molecular docking and molecular dynamics (MD) simulation. Considering the limitation of current virtual screening works, drug repurposing strategy was applied to discover druggable HDAC inhibitor. The ligand-based pharmacophore and 3D-QSAR models were established, and their reliability was validated by different methods. Then, the DrugBank database was screened, followed by molecular docking. MD simulation (100 ns) was performed to further study the stability of ligand binding modes. Finally, results indicated the hit DB03889 with high in silico inhibitory potency was suitable for further experimental analysis.Communicated by Ramaswamy H. Sarma.

A computationally designed binding mode flip leads to a novel class of potent tri-vector cyclophilin inhibitors

Cyclophilins (Cyps) are a major family of drug targets that are challenging to prosecute with small molecules because the shallow nature and high degree of conservation of the active site across human isoforms offers limited opportunities for potent and selective inhibition. Herein a computational approach based on molecular dynamics simulations and free energy calculations was combined with biophysical assays and X-ray crystallography to explore a flip in the binding mode of a reported urea-based Cyp inhibitor. This approach enabled access to a distal pocket that is poorly conserved among key Cyp isoforms, and led to the discovery of a new family of sub-micromolar cell-active inhibitors that offer unprecedented opportunities for the development of next-generation drug therapies based on Cyp inhibition. The computational approach is applicable to a broad range of organic functional groups and could prove widely enabling in molecular design.

Design and Potency of Dual Soluble Epoxide Hydrolase/Fatty Acid Amide Hydrolase Inhibitors

Fatty acid amide hydrolase (FAAH) is responsible for regulating concentrations of the endocannabinoid arachidonoyl ethanolamide. Multiple FAAH inhibitors have been developed for clinical trials and have failed to demonstrate efficacy at treating pain, despite promising preclinical data. One approach toward increasing the efficacy of FAAH inhibitors is to concurrently inhibit other targets responsible for regulating pain. Here, we designed dual inhibitors targeting the enzymes FAAH and soluble epoxide hydrolase (sEH), which are targets previously shown to synergize at reducing inflammatory and neuropathic pain. Exploration of the sEH/FAAH inhibitor structure-activity relationship started with PF-750, a FAAH inhibitor (IC₅₀ = 19 nM) that weakly inhibited sEH (IC₅₀ = 640 nM). Potency was optimized resulting in an inhibitor with improved potency on both targets (11, sEH IC₅₀ = 5 nM, FAAH IC₅₀ = 8 nM). This inhibitor demonstrated good target selectivity, pharmacokinetic properties (AUC = 1200 h nM, t _1/2 = 4.9 h in mice), and in vivo target engagement.