N-Adamantyl-1-alkyl-4-oxo-1,4-dihydroquinoline-3-carboxamide Derivatives as Fluorescent Probes to Detect Microglia Activation through the Imaging of Cannabinoid Receptor Subtype 2 (CB2R).

Cannabinoid receptor subtype 2 (CB2R) is emerging as a pivotal biomarker to identify the first steps of inflammation-based diseases such as cancer and neurodegeneration. There is an urgent need to find specific probes that may result in green and safe alternatives to the commonly used radiative technologies, to deepen the knowledge of the CB2R pathways impacting the onset of the above-mentioned pathologies. Therefore, based on one of the CB2R pharmacophores, we developed a class of fluorescent N-adamantyl-1-alkyl-4-oxo-1,4-dihydroquinoline-3-carboxamide derivatives spanning from the green to the near-infrared (NIR) regions of the light spectrum. Among the synthesized fluorescent ligands, the green-emitting compound 55 exhibited a favorable binding profile (strong CB2R affinity and high selectivity). Notably, this ligand demonstrated versatility as its use was validated in different experimental settings such as flow cytometry saturation, competitive fluorescence assays, and in vitro microglia cells mimicking inflammation states where CB2R are overexpressed.

Structure-based design of multitargeting ChEs-MAO B inhibitors based on phenyl ring bioisosteres: AChE/BChE selectivity switch and drug-like characterization.

A structure-based drug design approach was focused on incorporating phenyl ring heterocyclic bioisosteres into coumarin derivative 1, previously reported as potent dual AChE-MAO B inhibitor, with the aim of improving drug-like features. Structure-activity relationships highlighted that bioisosteric rings were tolerated by hMAO B enzymatic cleft more than hAChE. Interestingly, linker homologation at the basic nitrogen enabled selectivity to switch from hAChE to hBChE. In the present work, we identified thiophene-based isosteres 7 and 15 as dual AChE-MAO B (IC50 = 261 and 15 nM, respectively) and BChE-MAO B (IC50 = 375 and 20 nM, respectively) inhibitors, respectively. Both 7 and 15 were moderately water-soluble and membrane-permeant agents by passive diffusion (PAMPA-HDM). Moreover, they were able to counteract oxidative damage induced by both H2O2 and 6-OHDA in SH-SY5Y cells and predicted to penetrate into CNS in a cell-based model mimicking blood-brain barrier. Molecular dynamics (MD) simulations shed light on key differences in AChE and BChE recognition processes promoted by the basic chain homologation from 7 to 15.

Structural basis for specific inhibition of salicylate synthase from Mycobacterium abscessus.

Blocking iron uptake and metabolism has been emerging as a promising therapeutic strategy for the development of novel antimicrobial compounds. Like all mycobacteria, M. abscessus (Mab) has evolved several countermeasures to scavenge iron from host carrier proteins, including the production of siderophores, which play a crucial role in these processes. In this study, we solved, for the first time, the crystal structure of Mab-SaS, the first enzyme involved in the biosynthesis of siderophores. Moreover, we screened a small, focused library and identified a compound exhibiting a potent inhibitory effect against Mab-SaS (IC50 ≈ 2 µM). Its binding mode was investigated by means of Induced Fit Docking simulations, performed on the crystal structure presented herein. Furthermore, cytotoxicity data and pharmacokinetic predictions revealed the safety and drug-likeness of this class of compounds. Finally, the crystallographic data were used to optimize the model for future virtual screening campaigns. Taken together, the findings of our study pave the way for the identification of potent Mab-SaS inhibitors, based on both established and unexplored chemotypes.

AMALPHI: A Machine Learning Platform for Predicting Drug-Induced PhospholIpidosis.

Drug-induced phospholipidosis (PLD) involves the accumulation of phospholipids in cells of multiple tissues, particularly within lysosomes, and it is associated with prolonged exposure to druglike compounds, predominantly cationic amphiphilic drugs (CADs). PLD affects a significant portion of drugs currently in development and has recently been proven to be responsible for confounding antiviral data during drug repurposing for SARS-CoV-2. In these scenarios, it has become crucial to identify potential safe drug candidates in advance and distinguish them from those that may lead to false in vitro antiviral activity. In this work, we developed a series of machine learning classifiers with the aim of predicting the PLD-inducing potential of drug candidates. The models were built on a high-quality chemical collection comprising 545 curated small molecules extracted from ChEMBL v30. The most effective model, obtained using the balanced random forest algorithm, achieved high performance, including an AUC value computed in validation as high as 0.90. The model was made freely available through a user-friendly web platform named AMALPHI (https://www.ba.ic.cnr.it/softwareic/amalphiportal/), which can represent a valuable tool for medicinal chemists interested in conducting an early evaluation of PLD inducer potential.

ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators.

The development of small molecules that selectively target the cannabinoid receptor subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat neurodegeneration, as well as to contrast the onset and progression of cancer. In this context, in-silico tools able to predict CB2R affinity and selectivity with respect to the subtype 1 (CB1R), whose modulation is responsible for undesired psychotropic effects, are highly desirable. In this work, we developed a series of machine learning classifiers trained on high-quality bioactivity data of small molecules acting on CB2R and/or CB1R extracted from ChEMBL v30. Our classifiers showed strong predictive power in accurately determining CB2R affinity, CB1R affinity, and CB2R/CB1R selectivity. Among the built models, those obtained using random forest as algorithm proved to be the top-performing ones (AUC in validation ≥0.96) and were made freely accessible through a user-friendly web platform developed ad hoc and called ALPACA (https://www.ba.ic.cnr.it/softwareic/alpaca/). Due to its user-friendly interface and robust predictive power, ALPACA can be a valuable tool in saving both time and resources involved in the design of selective CB2R modulators.

Bioisosteric replacement based on 1,2,4-oxadiazoles in the discovery of 1H-indazole-bearing neuroprotective MAO B inhibitors.

Following a hybridization strategy, a series of 5-substituted-1H-indazoles were designed and evaluated in vitro as inhibitors of human monoamine oxidase (hMAO) A and B. Among structural modifications, the bioisostere-based introduction of 1,2,4-oxadiazole ring returned the most potent and selective human MAO B inhibitor (compound 20, IC50 = 52 nM, SI > 192). The most promising inhibitors were studied in cell-based neuroprotection models of SH-SY5Y and astrocytes line against H2O2. Moreover, preliminary drug-like features (aqueous solubility at pH 7.4; hydrolytic stability at acidic and neutral pH) were assessed for selected 1,2,4-oxadiazoles and compared to amide analogues through RP-HPLC methods. Molecular docking simulations highlighted the crucial role of molecular flexibility in providing a better shape complementarity for compound 20 within MAO B enzymatic cleft than rigid analogue 18. Enzymatic kinetics analysis along with thermal stability curves (Tm shift = +2.9 °C) provided clues of a tight-binding mechanism for hMAO B inhibition by 20.

Development of Fluorescent 4-[4-(3H-Spiro[isobenzofuran-1,4'-piperidin]-1'-yl)butyl]indolyl Derivatives as High-Affinity Probes to Enable the Study of σ Receptors via Fluorescence-Based Techniques.

Sigma (σ) receptor subtypes, σ1 and σ2, are targets of wide pharmaceutical interest. The σ2 receptor holds promise for the development of diagnostics and therapeutics against cancer and Alzheimer's disease. Nevertheless, little is known about the mechanisms activated by the σ2 receptor. To contribute to the exploitation of its therapeutic potential, we developed novel specific fluorescent ligands. Indole derivatives bearing the N-butyl-3H-spiro[isobenzofuran-1,4'-piperidine] portion were functionalized with fluorescent tags. Nanomolar-affinity fluorescent σ ligands, spanning from green to red to near-infrared emission, were obtained. Compounds 19 (σ pan affinity) and 29 (σ2 selective), which displayed the best compromise between pharmacodynamic and photophysical properties, were investigated in flow cytometry, confocal, and live cell microscopy, demonstrating their specificity for the σ2 receptor. To the best of our knowledge, these are the first red-emitting fluorescent σ2 ligands, validated as powerful tools for the study of σ2 receptors via fluorescence-based techniques.

Targeting Siderophore-Mediated Iron Uptake in M. abscessus: A New Strategy to Limit the Virulence of Non-Tuberculous Mycobacteria.

Targeting pathogenic mechanisms, rather than essential processes, represents a very attractive approach for the development of new antimycobacterial drugs. In this context, iron acquisition routes have recently emerged as potentially druggable pathways. However, the importance of siderophore biosynthesis in the virulence and pathogenicity of M. abscessus (Mab) is still poorly understood. In this study, we investigated the Salicylate Synthase (SaS) of Mab as an innovative molecular target for the development of inhibitors of siderophore production. Notably, Mab-SaS does not have any counterpart in human cells, making it an interesting candidate for drug discovery. Starting from the analysis of the binding of a series of furan-based derivatives, previously identified by our group as inhibitors of MbtI from M. tuberculosis (Mtb), we successfully selected the lead compound 1, exhibiting a strong activity against Mab-SaS (IC50 ≈ 5 µM). Computational studies characterized the key interactions between 1 and the enzyme, highlighting the important roles of Y387, G421, and K207, the latter being one of the residues involved in the first step of the catalytic reaction. These results support the hypothesis that 5-phenylfuran-2-carboxylic acids are also a promising class of Mab-SaS inhibitors, paving the way for the optimization and rational design of more potent derivatives.

N-adamantyl-anthranil amide derivatives: New selective ligands for the cannabinoid receptor subtype 2 (CB2R).

Cannabinoid type 2 receptor (CB2R) is a G-protein-coupled receptor that, together with Cannabinoid type 1 receptor (CB1R), endogenous cannabinoids and enzymes responsible for their synthesis and degradation, forms the EndoCannabinoid System (ECS). In the last decade, several studies have shown that CB2R is overexpressed in activated central nervous system (CNS) microglia cells, in disorders based on an inflammatory state, such as neurodegenerative diseases, neuropathic pain, and cancer. For this reason, the anti-inflammatory and immune-modulatory potentials of CB2R ligands are emerging as a novel therapeutic approach. The design of selective ligands is however hampered by the high sequence homology of transmembrane domains of CB1R and CB2R. Based on a recent three-arm pharmacophore hypothesis and latest CB2R crystal structures, we designed, synthesized, and evaluated a series of new N-adamantyl-anthranil amide derivatives as CB2R selective ligands. Interestingly, this new class of compounds displayed a high affinity for human CB2R along with an excellent selectivity respect to CB1R. In this respect, compounds exhibiting the best pharmacodynamic profile in terms of CB2R affinity were also evaluated for the functional behavior and molecular docking simulations provided a sound rationale by highlighting the relevance of the arm 1 substitution to prompt CB2R action. Moreover, the modulation of the pro- and anti-inflammatory cytokines production was also investigated to exert the ability of the best compounds to modulate the inflammatory cascade.

A Comparative Molecular Dynamics Study of Selected Point Mutations in the Shwachman-Bodian-Diamond Syndrome Protein SBDS.

The Shwachman-Diamond Syndrome (SDS) is an autosomal recessive disease whose majority of patients display mutations in a ribosome assembly protein named Shwachman-Bodian-Diamond Syndrome protein (SBDS). A specific therapy for treating this rare disease is missing, due to the lack of knowledge of the molecular mechanisms responsible for its pathogenesis. Starting from the observation that SBDS single-point mutations, localized in different domains of the proteins, are responsible for an SDS phenotype, we carried out the first comparative Molecular Dynamics simulations on three SBDS mutants, namely R19Q, R126T and I212T. The obtained 450-ns long trajectories were compared with those returned by both the open and closed forms of wild type SBDS and strongly indicated that two distinct conformations (open and closed) are both necessary for the proper SBDS function, in full agreement with recent experimental observations. Our study supports the hypothesis that the SBDS function is governed by an allosteric mechanism involving domains I and III and provides new insights into SDS pathogenesis, thus offering a possible starting point for a specific therapeutic option.

DeLA-Drug: A Deep Learning Algorithm for Automated Design of Druglike Analogues.

In this paper, we present a deep learning algorithm for automated design of druglike analogues (DeLA-Drug), a recurrent neural network (RNN) model composed of two long short-term memory (LSTM) layers and conceived for data-driven generation of similar-to-bioactive compounds. DeLA-Drug captures the syntax of SMILES strings of more than 1 million compounds belonging to the ChEMBL28 database and, by employing a new strategy called sampling with substitutions (SWS), generates molecules starting from a single user-defined query compound. Remarkably, the algorithm preserves druglikeness and synthetic accessibility of the known bioactive compounds present in the ChEMBL28 repository. The absence of any time-demanding fine-tuning procedure enables DeLA-Drug to perform a fast generation of focused libraries for further high-throughput screening and makes it a suitable tool for performing de novo design even in low-data regimes. To provide a concrete idea of its applicability, DeLA-Drug was applied to the cannabinoid receptor subtype 2 (CB2R), a known target involved in different pathological conditions such as cancer and neurodegeneration. DeLA-Drug, available as a free web platform (http://www.ba.ic.cnr.it/softwareic/deladrugportal/), can help medicinal chemists interested in generating analogues of compounds already available in their laboratories and, for this reason, good candidates for an easy and low-cost synthesis.

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study.

Drug-induced blockade of the human ether-à-go-go-related gene (hERG) channel is today considered the main cause of cardiotoxicity in postmarketing surveillance. Hence, several ligand-based approaches were developed in the last years and are currently employed in the early stages of a drug discovery process for in silico cardiac safety assessment of drug candidates. Herein, we present the first structure-based classifiers able to discern hERG binders from nonbinders. LASSO regularized support vector machines were applied to integrate docking scores and protein-ligand interaction fingerprints. A total of 396 models were trained and validated based on: (i) high-quality experimental bioactivity information returned by 8337 curated compounds extracted from ChEMBL (version 25) and (ii) structural predictor data. Molecular docking simulations were performed using GLIDE and GOLD software programs and four different hERG structural models, namely, the recently published structures obtained by cryoelectron microscopy (PDB codes: 5VA1 and 7CN1) and two published homology models selected for comparison. Interestingly, some classifiers return performances comparable to ligand-based models in terms of area under the ROC curve (AUCMAX = 0.86 ± 0.01) and negative predictive values (NPVMAX = 0.81 ± 0.01), thus putting forward the herein proposed computational workflow as a valuable tool for predicting hERG-related cardiotoxicity without the limitations of ligand-based models, typically affected by low interpretability and a limited applicability domain. From a methodological point of view, our study represents the first example of a successful integration of docking scores and protein-ligand interaction fingerprints (IFs) through a support vector machine (SVM) LASSO regularized strategy. Finally, the study highlights the importance of using hERG structural models accounting for ligand-induced fit effects and allowed us to select the best-performing protein conformation (made available in the Supporting Information, SI) to be employed for a reliable structure-based prediction of hERG-related cardiotoxicity.

Repurposing Known Drugs as Covalent and Non-covalent Inhibitors of the SARS-CoV-2 Papain-Like Protease.

In the absence of an approved vaccine, developing effective severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antivirals is essential to tackle the current pandemic health crisis due to the coronavirus disease 2019 (COVID-19) spread. As any traditional drug discovery program is a time-consuming and costly process requiring more than one decade to be completed, in silico repurposing of existing drugs is the preferred way for rapidly selecting promising clinical candidates. We present a virtual screening campaign to identify covalent and non-covalent inhibitors of the SARS-CoV-2 papain-like protease (PLpro) showing potential multitarget activities (i.e., a desirable polypharmacology profile) for the COVID-19 treatment. A dataset including 688 phase III and 1,702 phase IV clinical trial drugs was downloaded from ChEMBL (version 27.1) and docked to the recently released crystal structure of PLpro in complex with a covalently bound peptide inhibitor. The obtained results were analyzed by combining protein-ligand interaction fingerprint similarities, conventional docking scores, and MM-GBSA-binding free energies and allowed the identification of some interesting candidates for further in vitro testing. To the best of our knowledge, this study represents the first attempt to repurpose drugs for a covalent inhibition of PLpro and could pave the way for new therapeutic strategies against COVID-19.

Cannabinoid Receptor Subtype 2 (CB2R) in a Multitarget Approach: Perspective of an Innovative Strategy in Cancer and Neurodegeneration.

The cannabinoid receptor subtype 2 (CB2R) represents an interesting and new therapeutic target for its involvement in the first steps of neurodegeneration as well as in cancer onset and progression. Several studies, focused on different types of tumors, report a promising anticancer activity induced by CB2R agonists due to their ability to reduce inflammation and cell proliferation. Moreover, in neuroinflammation, the stimulation of CB2R, overexpressed in microglial cells, exerts beneficial effects in neurodegenerative disorders. With the aim to overcome current treatment limitations, new drugs can be developed by specifically modulating, together with CB2R, other targets involved in such multifactorial disorders. Building on successful case studies of already developed multitarget strategies involving CB2R, in this Perspective we aim at prompting the scientific community to consider new promising target associations involving HDACs (histone deacetylases) and σ receptors by employing modern approaches based on molecular hybridization, computational polypharmacology, and machine learning algorithms.

Exploring the role of elongation Factor-Like 1 (EFL1) in Shwachman-Diamond syndrome through molecular dynamics.

Shwachman-Diamond Syndrome (SDS) is an autosomal recessive disorder whose patients present mutations in two ribosome assembly proteins, the Shwachman-Bodian-Diamond Syndrome protein (SBDS) and the Elongation Factor-Like 1 (EFL1). Due to the lack of knowledge of the molecular mechanisms responsible for SDS pathogenesis, current therapy is nonspecific and focuses only at alleviating the symptoms. Building on the recent observation that EFL1 single-point mutations clinically manifest as SDS-like phenotype, we carried out comparative Molecular Dynamics (MD) simulations on three mutants, T127A, M882K and R1095Q and wild type EFL1. As supported by small angle X-ray scattering experiments, the obtained data improve the static EFL1 model resulting from the Cryo-electron microscopy and clearly show that all the mutants experience a peculiar rotation, around the hinge region, of domain IV with respect to domains I and II leading to a different conformation respect to that of wild type protein. This study supports the notion that EFL1 function is governed by an allosteric mechanism involving the concerted action of GTPase domain (domain I) and the domain IV and can help point towards new approaches to SDS treatment.Communicated by Ramaswamy H. Sarma.