Design, synthesis, and biological evaluation of novel 2-((2-(4-(substituted)phenylpiperazin-1-yl)ethyl)amino)-5'-N-ethylcarboxamidoadenosines as potent and selective agonists of the A2A adenosine receptor

A Survey on Impact of Internet of Medical Things Against Diabetic Foot Ulcer

INTRODUCTION: In this study, we explore the intricate domain of Diabetic Foot Ulcers (DFU) through the development of a comprehensive framework that encompasses diverse operational scenarios. The focus lies on the identification and classification assessment of diabetic foot ulcers, the implementation of smart health management strategies, and the collection, analysis, and intelligent interpretation of data related to diabetic foot ulcers. The framework introduces an innovative approach to predicting diabetic foot ulcers and their key characteristics, offering a technical solution for forecasting. The exploration delves into various computational strategies designed for intelligent health analysis tailored to patients with diabetic foot ulcers. OBJECTIVES: The primary objective of this paper is to present a technical solution for forecasting diabetic foot ulcers, utilizing computational strategies for intelligent health analysis. METHODS: Techniques derived from social network analysis are employed to conduct this research, focusing on diverse computational strategies geared towards intelligent health analysis for patients with diabetic foot ulcers. The study highlights methodologies addressing the unique challenges posed by diabetic foot ulcers, with a central emphasis on the integration of Internet of Medical Things (IoMT) in prediction strategies. RESULTS: The main results of this paper include the proposal of IoMT-based computing strategies covering the entire spectrum of DFU analysis, such as localization, classification assessment, intelligent health management, and detection. The study also acknowledges the challenges faced by previous research, including low classification rates and elevated false alarm rates, and proposes automatic recognition approaches leveraging advanced machine learning techniques to enhance accuracy and efficacy. CONCLUSION: The proposed IoMT-based computing strategies present a significant advancement in addressing the challenges associated with predicting diabetic foot ulcers. The integration of advanced machine learning techniques demonstrates promise in improving accuracy and efficiency in diabetic foot ulcer localization, marking a positive stride towards overcoming existing limitations in previous research.

Discovery of Novel Benzo[4,5]imidazo[1,2-a]pyrazin-1-amine-3-amide-one Derivatives as Anticancer Human A2A Adenosine Receptor Antagonists

The blockade of A_2A adenosine receptor (A_2AAR) activates immunostimulatory response through regulating signaling in tumor microenvironment. Thus, A_2AAR has been proposed as a promising target for cancer immunotherapy. In this work, we designed a new series of benzo[4,5]imidazo[1,2-a]pyrazin-1-amine derivatives bearing an amide substitution at 3-position to obtain potent antitumor antagonist in vivo. The structure-activity relationship studies were performed by molecular modeling and radioactive assay. The in vitro anticancer activities were evaluated by 3',5'-cyclic adenosine monophosphate (cAMP) functional and T cell activation assay. The most potent compound 12o·2HCl showed much higher affinity toward A_2AAR (K_i = 0.08 nM) and exhibited more significant in vitro immunostimulatory anticancer activity than clinical antagonist AZD4635. More importantly, 12o·2HCl significantly inhibited the growth of triple-negative breast cancer by reversing immunosuppressive tumor microenvironment in the xenograft mouse model without severe toxicity at the testing dose. These results make 12o·2HCl a promising immunotherapy anticancer drug candidate.

Binding Characterization of Agonists and Antagonists by MCCS: A Case Study from Adenosine A2A Receptor

Characterizing the structural basis of ligand recognition of adenosine A_2A receptor (AA_2AR) will facilitate its rational design and development of small molecules with high affinity and selectivity, as well as optimal therapeutic effects for pain, cancers, drug abuse disorders, etc. In the present work, we applied our reported algorithm, molecular complex characterizing system (MCCS), to characterize the binding features of AA_2AR based on its reported 3D structures of protein-ligand complexes. First, we compared the binding score to the reported experimental binding affinities of each compound. Then, we calculated an output example of residue energy contribution using MCCS and compared the results with data obtained from MM/GBSA. The consistency in results indicated that MCCS is a powerful, fast, and accurate method. Sequentially, using a receptor-ligand data set of 57 crystallized structures of AA_2ARs, we characterized the binding features of the binding pockets in AA_2AR, summarized the key residues that distinguish antagonist from agonist, produced heatmaps of residue energy contribution for clustering various statuses of AA_2ARs, explored the selectivity between AA_2AR and AA_1AR, etc. All the information provided new insights into the protein features of AA_2AR and will facilitate its rational drug design.

Design, synthesis and biological evaluation of novel scaffold benzo[4,5]imidazo [1,2-a]pyrazin-1-amine: Towards adenosine A2A receptor (A2A AR) antagonist

Antagonists of adenosine receptor are under exploration as potential drug candidates for treatment of neurological disorders, depression, certain cancers and potentially used as a cancer immunotherapy. Herein, we describe design and synthesis of novel scaffold benzo[4,5]imidazo [1,2-a]pyrazin-1-amine (6) derivatives. All the compounds were evaluated for A_2A AR antagonist activity and displayed encouraging results (IC₅₀ 9-300 nM) of A_2A AR antagonist binding affinity in biochemical assay. Compound 27 exhibits good activity in A_2A AR antagonist cAMP functional assay (IC₅₀ 31 nM) and further this compound shows T-cell activation at the IL-2 production assay (EC₅₀ 165 nM). Molecular docking studies were carried out to rationalize the observed binding affinity of compound 27.

Identification of Novel Chemical Entities for Adenosine Receptor Type 2A Using Molecular Modeling Approaches

Adenosine Receptor Type 2A (A_2AAR) plays a role in important processes, such as anti-inflammatory ones. In this way, the present work aimed to search for compounds by pharmacophore-based virtual screening. The pharmacokinetic/toxicological profiles of the compounds, as well as a robust QSAR, predicted the binding modes via molecular docking. Finally, we used molecular dynamics to investigate the stability of interactions from ligand-A_2AAR. For the search for A_2AAR agonists, the UK-432097 and a set of 20 compounds available in the BindingDB database were studied. These compounds were used to generate pharmacophore models. Molecular properties were used for construction of the QSAR model by multiple linear regression for the prediction of biological activity. The best pharmacophore model was used by searching for commercial compounds in databases and the resulting compounds from the pharmacophore-based virtual screening were applied to the QSAR. Two compounds had promising activity due to their satisfactory pharmacokinetic/toxicological profiles and predictions via QSAR (Diverset 10002403 pEC₅₀ = 7.54407; ZINC04257548 pEC₅₀ = 7.38310). Moreover, they had satisfactory docking and molecular dynamics results compared to those obtained for Regadenoson (Lexiscan^®), used as the positive control. These compounds can be used in biological assays (in vitro and in vivo) in order to confirm the potential activity agonist to A_2AAR.

The In Silico Drug Discovery Toolbox: Applications in Lead Discovery and Optimization

BACKGROUND

Discovery and development of a new drug is a long lasting and expensive journey that takes around 20 years from starting idea to approval and marketing of new medication. Despite R&D expenditures have been constantly increasing in the last few years, the number of new drugs introduced into market has been steadily declining. This is mainly due to preclinical and clinical safety issues, which still represent about 40% of drug discontinuation. To cope with this issue, a number of in silico techniques are currently being used for an early stage evaluation/prediction of potential safety issues, allowing to increase the drug-discovery success rate and reduce costs associated with the development of a new drug.

METHODS

In the present review, we will analyse the early steps of the drug-discovery pipeline, describing the sequence of steps from disease selection to lead optimization and focusing on the most common in silico tools used to assess attrition risks and build a mitigation plan.

RESULTS

A comprehensive list of widely used in silico tools, databases, and public initiatives that can be effectively implemented and used in the drug discovery pipeline has been provided. A few examples of how these tools can be problem-solving and how they may increase the success rate of a drug discovery and development program have been also provided. Finally, selected examples where the application of in silico tools had effectively contributed to the development of marketed drugs or clinical candidates will be given.

CONCLUSION

The in silico toolbox finds great application in every step of early drug discovery: (i) target identification and validation; (ii) hit identification; (iii) hit-to-lead; and (iv) lead optimization. Each of these steps has been described in details, providing a useful overview on the role played by in silico tools in the decision-making process to speed-up the discovery of new drugs.

Unbinding of Translocator Protein 18 kDa (TSPO) Ligands: From in Vitro Residence Time to in Vivo Efficacy via in Silico Simulations

Translocator protein 18 kDa (TSPO) is a validated pharmacological target for the development of new treatments for neurological disorders. N,N-Dialkyl-2-phenylindol-3-ylglyoxylamides (PIGAs) are effective TSPO modulators and potentially useful therapeutics for the treatment of anxiety, central nervous system pathologies featuring astrocyte loss, and inflammatory-based neuropathologies. For this class of compounds, no correlation exists between the TSPO binding affinity and the corresponding functional efficacy. Rather, their biological effectiveness correlates with the kinetics of the unbinding events and more specifically with the residence time (RT). So far, the structural reasons for the different recorded RT of congeneric PIGAs remain elusive. Here, to understand the different kinetics of PIGAs, their unbinding paths were studied by employing enhanced-sampling molecular dynamics simulations. Results of these studies revealed how subtle structural differences between PIGAs have a substantial effect on the unbinding energetics. In particular, during the egress from the TSPO binding site, slow-dissociating PIGAs find tight interactions with the protein LP1 region thereby determining a long RT. Further support to these findings was achieved by in vivo studies, which demonstrated how the anxiolytic effect observed for the inspected PIGAs correlated with their RT to TSPO.

Pharmacological relationships and ligand discovery of G protein-coupled receptors revealed by simultaneous ligand and receptor clustering

Conventional ligand and receptor similarity methods have been extensively used for exposing pharmacological relationships and drug lead discovery. They may in some cases neglect minor relationships useful for target hopping particularly against the remote family members. To complement the conventional methods for capturing these minor relationships, we developed a new method that uses a SLARC (Simultaneous Ligand And Receptor Clustering) 2D map to simultaneously characterize the ligand structural and receptor binding-site sequence relationships of a receptor family. The SLARC maps of the rhodopsin-like GPCR family comprehensively revealed scaffold hopping, target hopping, and multi-target relationships for the ligands of both homologous and remote family members. Their usefulness in new ligand discovery was validated by guiding the prospective discovery of novel indole piperazinylpyrimidine dual-targeting adenosine A2A receptor antagonist and dopamine D2 agonist compounds. The SLARC approach is useful for revealing pharmacological relationships and discovering new ligands at target family levels.

Homology Modeling, Validation and Dynamics of the G Protein-coupled Estrogen Receptor 1 (GPER-1)

Estrogens exert their action mainly by binding three receptors, namely estrogen receptors α and β (ERα and ERβ) and GPER-1 (G-protein coupled estrogen receptor 1). While the patho-physiological role of both ERα and ERβ has been deeply investigated, the role of GPER-1 in estrogens' signaling has not been clearly defined yet. Unfortunately, only few GPER-1 selective ligands were discovered so far, and the real efficiency of such compounds is still matter of debate. To better understand the physiological relevance of GPER-1, new selective chemical probes are higly needed. In this scenario, we report herein the generation and validation of a three-dimensional (3-D) GPER-1 homology model by means of docking studies and molecular dynamics simulations. The model thus generated was employed to (i) decipher the structural basis underlying the ability of estrogens and some Selective Estrogen Receptor Modulators (SERMs) to bind GPER-1 and classical ERα and ERβ, and (ii) generate a reliable G1/GPER-1 complex useful in rationalizing the pharmacological profile of G1 reported in the literature. The G1/GPER-1 complex herein reported could be further exploited in drug design approaches aimed at improving the pharmacological profile of G1 or at identifying new chemical entities (NCEs) as potential modulators of GPER-1.