Three-Dimensional Quantitative Structure-Activity Relationships (3D-QSAR) on a Series of Piperazine-Carboxamides Fatty Acid Amide Hydrolase (FAAH) Inhibitors as a Useful Tool for the Design of New Cannabinoid Ligands

Endocannabinoid Hydrolase Inhibitors: Potential Novel Anxiolytic Drugs

Over the past decade, the idea of targeting the endocannabinoid system to treat anxiety disorders has received increasing attention. Previous studies focused more on developing cannabinoid receptor agonists or supplementing exogenous cannabinoids, which are prone to various adverse effects due to their strong pharmacological activity and poor receptor selectivity, limiting their application in clinical research. Endocannabinoid hydrolase inhibitors are considered to be the most promising development strategies for the treatment of anxiety disorders. More recent efforts have emphasized that inhibition of two major endogenous cannabinoid hydrolases, monoacylglycerol lipase (MAGL) and fatty acid amide hydrolase (FAAH), indirectly activates cannabinoid receptors by increasing endogenous cannabinoid levels in the synaptic gap, circumventing receptor desensitization resulting from direct enhancement of endogenous cannabinoid signaling. In this review, we comprehensively summarize the anxiolytic effects of MAGL and FAAH inhibitors and their potential pharmacological mechanisms, highlight reported novel inhibitors or natural products, and provide an outlook on future directions in this field.

Motifs in Natural Products as Useful Scaffolds to Obtain Novel Benzo[d]imidazole-Based Cannabinoid Type 2 (CB2) Receptor Agonists

The endocannabinoid system (ECS) constitutes a broad-spectrum modulator of homeostasis in mammals, providing therapeutic opportunities for several pathologies. Its two main receptors, cannabinoid type 1 (CB1) and type 2 (CB2) receptors, mediate anti-inflammatory responses; however, their differing patterns of expression make the development of CB2-selective ligands therapeutically more attractive. The benzo[d]imidazole ring is considered to be a privileged scaffold in drug discovery and has demonstrated its versatility in the development of molecules with varied pharmacologic properties. On the other hand, the main psychoactive component of Cannabis sativa, delta-9-tetrahydrocannabinol (THC), can be structurally described as an aliphatic terpenoid motif fused to an aromatic polyphenolic (resorcinol) structure. Inspired by the structure of this phytocannabinoid, we combined different natural product motifs with a benzo[d]imidazole scaffold to obtain a new library of compounds targeting the CB2 receptor. Here, we synthesized 26 new compounds, out of which 15 presented CB2 binding and 3 showed potent agonist activity. SAR analysis indicated that the presence of bulky aliphatic or aromatic natural product motifs at position 2 of the benzo[d]imidazoles ring linked by an electronegative atom is essential for receptor recognition, while substituents with moderate bulkiness at position 1 of the heterocyclic core also participate in receptor recognition. Compounds 5, 6, and 16 were further characterized through in vitro cAMP functional assay, showing potent EC50 values between 20 and 3 nM, and compound 6 presented a significant difference between the EC50 of pharmacologic activity (3.36 nM) and IC50 of toxicity (30-38 µM).

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.

Combined 3D-QSAR, molecular docking and dynamics simulations studies to model and design TTK inhibitors

Tyrosine threonine kinase (TTK) is the key component of the spindle assembly checkpoint (SAC) that ensures correct attachment of chromosomes to the mitotic spindle and thereby their precise segregation into daughter cells by phosphorylating specific substrate proteins. The overexpression of TTK has been associated with various human malignancies, including breast, colorectal and thyroid carcinomas. TTK has been validated as a target for drug development, and several TTK inhibitors have been discovered. In this study, ligand and structure-based alignment as well as various partial charge models were used to perform 3D-QSAR modelling on 1H-Pyrrolo[3,2-c] pyridine core containing reported inhibitors of TTK protein using the comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) approaches to design better active compounds. Different statistical methods i.e., correlation coefficient of non-cross validation (r2), correlation coefficient of leave-one-out cross-validation (q2), Fisher's test (F) and bootstrapping were used to validate the developed models. Out of several charge models and alignment-based approaches, Merck Molecular Force Field (MMFF94) charges using structure-based alignment yielded highly predictive CoMFA (q2 = 0.583, Predr2 = 0.751) and CoMSIA (q2 = 0.690, Predr2 = 0.767) models. The models exhibited that electrostatic, steric, HBA, HBD, and hydrophobic fields play a key role in structure activity relationship of these compounds. Using the contour maps information of the best predictive model, new compounds were designed and docked at the TTK active site to predict their plausible binding modes. The structural stability of the TTK complexes with new compounds was confirmed using MD simulations. The simulation studies revealed that all compounds formed stable complexes. Similarly, MM/PBSA method based free energy calculations showed that these compounds bind with reasonably good affinity to the TTK protein. Overall molecular modelling results suggest that newly designed compounds can act as lead compounds for the optimization of TTK inhibitors.

Identifying FAAH Inhibitors as New Therapeutic Options for the Treatment of Chronic Pain through Drug Repurposing

Chronic pain determines a substantial burden on individuals, employers, healthcare systems, and society. Most of the affected patients report dissatisfaction with currently available treatments. There are only a few and poor therapeutic options-some therapeutic agents are an outgrowth of drugs targeting acute pain, while others have several serious side effects. One of the primary degradative enzymes for endocannabinoids, fatty acid amide hydrolase (FAAH) attracted attention as a significant molecular target for developing new therapies for neuropsychiatric and neurological diseases, including chronic pain. Using chemical graph mining, quantitative structure-activity relationship (QSAR) modeling, and molecular docking techniques we developed a multi-step screening protocol to identify repurposable drugs as FAAH inhibitors. After screening the DrugBank database using our protocol, 273 structures were selected, with five already approved drugs, montelukast, repaglinide, revefenacin, raloxifene, and buclizine emerging as the most promising repurposable agents for treating chronic pain. Molecular docking studies indicated that the selected compounds interact with the enzyme mostly non-covalently (except for revefenacin) through shape complementarity to the large substrate-binding pocket in the active site. A molecular dynamics simulation was employed for montelukast and revealed stable interactions with the enzyme. The biological activity of the selected compounds should be further confirmed by employing in vitro and in vivo studies.

Identification of new target proteins of a Urotensin-II receptor antagonist using transcriptome-based drug repositioning approach

Drug repositioning research using transcriptome data has recently attracted attention. In this study, we attempted to identify new target proteins of the urotensin-II receptor antagonist, KR-37524 (4-(3-bromo-4-(piperidin-4-yloxy)benzyl)-N-(3-(dimethylamino)phenyl)piperazine-1-carboxamide dihydrochloride), using a transcriptome-based drug repositioning approach. To do this, we obtained KR-37524-induced gene expression profile changes in four cell lines (A375, A549, MCF7, and PC3), and compared them with the approved drug-induced gene expression profile changes available in the LINCS L1000 database to identify approved drugs with similar gene expression profile changes. Here, the similarity between the two gene expression profile changes was calculated using the connectivity score. We then selected proteins that are known targets of the top three approved drugs with the highest connectivity score in each cell line (12 drugs in total) as potential targets of KR-37524. Seven potential target proteins were experimentally confirmed using an in vitro binding assay. Through this analysis, we identified that neurologically regulated serotonin transporter proteins are new target proteins of KR-37524. These results indicate that the transcriptome-based drug repositioning approach can be used to identify new target proteins of a given compound, and we provide a standalone software developed in this study that will serve as a useful tool for drug repositioning.

Docking-Based 3D-QSAR Studies for 1,3,4-oxadiazol-2-one Derivatives as FAAH Inhibitors

This work aimed to construct 3D-QSAR CoMFA and CoMSIA models for a series of 31 FAAH inhibitors, containing the 1,3,4-oxadiazol-2-one moiety. The obtained models were characterized by good statistical parameters: CoMFA Q2 = 0.61, R2 = 0.98; CoMSIA Q2 = 0.64, R2 = 0.93. The CoMFA model field contributions were 54.1% and 45.9% for steric and electrostatic fields, respectively. In the CoMSIA model, electrostatic, steric, hydrogen bond donor, and hydrogen acceptor properties were equal to 34.6%, 23.9%, 23.4%, and 18.0%, respectively. These models were validated by applying the leave-one-out technique, the seven-element test set (CoMFA r2test-set = 0.91; CoMSIA r2test-set = 0.91), a progressive scrambling test, and external validation criteria developed by Golbraikh and Tropsha (CoMFA r20 = 0.98, k = 0.95; CoMSIA r20 = 0.98, k = 0.89). As the statistical significance of the obtained model was confirmed, the results of the CoMFA and CoMSIA field calculation were mapped onto the enzyme binding site. It gave us the opportunity to discuss the structure-activity relationship based on the ligand-enzyme interactions. In particular, examination of the electrostatic properties of the established CoMFA model revealed fields that correspond to the regions where electropositive substituents are not desired, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one moiety. This highlights the importance of heterocycle, a highly electronegative moiety in this area of each ligand. Examination of hydrogen bond donor and acceptor properties contour maps revealed several spots where the implementation of another hydrogen-bond-donating moiety will positively impact molecules' binding affinity, e.g., in the neighborhood of the 1,3,4-oxadiazol-2-one ring. On the other hand, there is a large isopleth that refers to the favorable H-bond properties close to the terminal phenoxy group of a ligand, which means that, generally speaking, H-bond acceptors are desired in this area.

Synthesis, Docking, 3-D-Qsar, and Biological Assays of Novel Indole Derivatives Targeting Serotonin Transporter, Dopamine D2 Receptor, and Mao-A Enzyme: In the Pursuit for Potential Multitarget Directed Ligands

A series of 27 compounds of general structure 2,3-dihydro-benzo[1,4]oxazin-4-yl)-2-{4-[3-(1H-3indolyl)-propyl]-1-piperazinyl}-ethanamides, Series I: 7(a-o) and (2-{4-[3-(1H-3-indolyl)-propyl]-1-piperazinyl}-acetylamine)-N-(2-morfolin-4-yl-ethyl)-fluorinated benzamides Series II: 13(a-l) were synthesized and evaluated as novel multitarget ligands towards dopamine D₂ receptor, serotonin transporter (SERT), and monoamine oxidase-A (MAO-A) directed to the management of major depressive disorder (MDD). All the assayed compounds showed affinity for SERT in the nanomolar range, with five of them displaying Ki values from 5 to 10 nM. Compounds 7k, Ki = 5.63 ± 0.82 nM, and 13c, Ki = 6.85 ± 0.19 nM, showed the highest potencies. The affinities for D₂ ranged from micro to nanomolar, while MAO-A inhibition was more discrete. Nevertheless, compounds 7m and 7n showed affinities for the D₂ receptor in the nanomolar range (7n: Ki = 307 ± 6 nM and 7m: Ki = 593 ± 62 nM). Compound 7n was the only derivative displaying comparable affinities for SERT and D₂ receptor (D₂/SERT ratio = 3.6) and could be considered as a multitarget lead for further optimization. In addition, docking studies aimed to rationalize the molecular interactions and binding modes of the designed compounds in the most relevant protein targets were carried out. Furthermore, in order to obtain information on the structure-activity relationship of the synthesized series, a 3-D-QSAR CoMFA and CoMSIA study was conducted and validated internally and externally (q² = 0.625, 0.523 for CoMFA and CoMSIA and r²_ncv = 0.967, 0.959 for CoMFA and CoMSIA, respectively).

The recent application of 3D-QSAR and docking studies to novel HIV-protease inhibitor drug discovery

INTRODUCTION

Despite the availability of FDA approved inhibitors of HIV protease, numerous efforts are still ongoing to achieve 'near-perfect' drugs devoid of characteristic adverse side effects, toxicities, and mutational resistance. While experimental methods have been plagued with huge consumption of time and resources, there has been an incessant shift towards the use of computational simulations in HIV protease inhibitor drug discovery.

AREAS COVERED

Herein, the authors review the numerous applications of 3D-QSAR modeling methods over recent years relative to the design of new HIV protease inhibitors from a series of experimentally derived compounds. Also, the augmentative contributions of molecular docking are discussed.

EXPERT OPINION

Efforts to optimize 3D QSAR and molecular docking for HIV-1 drug discovery are ongoing, which could further incorporate inhibitor motions at the active site using molecular dynamics parameters. Also, highly predictive machine learning algorithms such as random forest, K-means, decision trees, linear regression, hierarchical clustering, and Bayesian classifiers could be employed.

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.