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.

Molecular interactions between a diphenyl scaffold and PED/PEA15: Implications for type II diabetes therapeutics targeting PED/PEA15 - Phospholipase D1 interaction.

In a recent study, we have identified BPH03 as a promising scaffold for the development of compounds aimed at modulating the interaction between PED/PEA15 (Phosphoprotein Enriched in Diabetes/Phosphoprotein Enriched in Astrocytes 15) and PLD1 (phospholipase D1), with potential applications in type II diabetes therapy. PED/PEA15 is known to be overexpressed in certain forms of diabetes, where it binds to PLD1, thereby reducing insulin-stimulated glucose transport. The inhibition of this interaction reestablishes basal glucose transport, indicating PED as a potential target of ligands capable to recover glucose tolerance and insulin sensitivity. In this study, we employ computational methods to provide a detailed description of BPH03 interaction with PED, evidencing the presence of a hidden druggable pocket within its PLD1 binding surface. We also elucidate the conformational changes that occur during PED interaction with BPH03. Moreover, we report new NMR data supporting the in-silico findings and indicating that BPH03 disrupts the PED/PLD1 interface displacing PLD1 from its interaction with PED. Our study represents a significant advancement toward the development of potential therapeutics for the treatment of type II diabetes.

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.

DeLA-DrugSelf: Empowering multi-objective de novo design through SELFIES molecular representation.

In this paper, we introduce DeLA-DrugSelf, an upgraded version of DeLA-Drug [J. Chem. Inf. Model. 62 (2022) 1411-1424], which incorporates essential advancements for automated multi-objective de novo design. Unlike its predecessor, which relies on SMILES notation for molecular representation, DeLA-DrugSelf employs a novel and robust molecular representation string named SELFIES (SELF-referencing Embedded String). The generation process in DeLA-DrugSelf not only involves substitutions to the initial string representing the starting query molecule but also incorporates insertions and deletions. This enhancement makes DeLA-DrugSelf significantly more adept at executing data-driven scaffold decoration and lead optimization strategies. Remarkably, DeLA-DrugSelf explicitly addresses the SELFIES-related collapse issue, considering only collapse-free compounds during generation. These compounds undergo a rigorous quality metrics evaluation, highlighting substantial advancements in terms of drug-likeness, uniqueness, and novelty compared to the molecules generated by the previous version of the algorithm. To evaluate the potential of DeLA-DrugSelf as a mutational operator within a genetic algorithm framework for multi-objective optimization, we employed a fitness function based on Pareto dominance. Our objectives focused on target-oriented properties aimed at optimizing known cannabinoid receptor 2 (CB2R) ligands. The results obtained indicate that DeLA-DrugSelf, available as a user-friendly web platform (https://www.ba.ic.cnr.it/softwareic/delaself/), can effectively contribute to the data-driven optimization of starting bioactive molecules based on user-defined parameters.

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.

A conformational rearrangement of the SARS-CoV-2 host protein sigma-1 is required for antiviral activity: insights from a combined in-silico/in-vitro approach.

The development of effective drugs to treat coronavirus infections remains a significant challenge for the scientific community. Recent evidence reports on the sigma-1 receptor (S1R) as a key druggable host protein in the SARS-CoV-1 and SARS-CoV-2 interactomes and shows a potent antiviral activity against SARS-CoV-2 for the S1R antagonist PB28. To improve PB28 activity, we designed and tested a series of its analogues and identified a compound that is fourfold more potent against SARS-CoV-2 than PB28 itself. Interestingly, we found no direct correlation between S1R affinity and SARS-CoV-2 antiviral activity. Building on this, we employed comparative induced fit docking and molecular dynamics simulations to gain insights into the possible mechanism that occurs when specific ligand-protein interactions take place and that may be responsible for the observed antiviral activity. Our findings offer a possible explanation for the experimental observations, provide insights into the S1R conformational changes upon ligand binding and lay the foundation for the rational design of new S1R ligands with potent antiviral activity against SARS-CoV-2 and likely other viruses.

GENERA: A Combined Genetic/Deep-Learning Algorithm for Multiobjective Target-Oriented De Novo Design.

This study introduces a new de novo design algorithm called GENERA that combines the capabilities of a deep-learning algorithm for automated drug-like analogue design, called DeLA-Drug, with a genetic algorithm for generating molecules with desired target-oriented properties. Specifically, GENERA was applied to the angiotensin-converting enzyme 2 (ACE2) target, which is implicated in many pathological conditions, including COVID-19. The ability of GENERA to de novo design promising candidates for a specific target was assessed using two docking programs, PLANTS and GLIDE. A fitness function based on the Pareto dominance resulting from computed PLANTS and GLIDE scores was applied to demonstrate the algorithm's ability to perform multiobjective optimizations effectively. GENERA can quickly generate focused libraries that produce better scores compared to a starting set of known ACE-2 binders. This study is the first to utilize a DL-based algorithm designed for analogue generation as a mutational operator within a GA framework, representing an innovative approach to target-oriented de novo design.

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.

Development of Riluzole Analogs with Improved Use-Dependent Inhibition of Skeletal Muscle Sodium Channels.

Several commercially available and newly synthesized riluzole analogs were evaluated in vitro as voltage-gated skeletal muscle sodium-channel blockers. Data obtained from the patch-clamp technique demonstrated that potency is well correlated with lipophilicity and the introduction of a protonatable amino function in the benzothiazole 2-position enhances the use-dependent behavior. The most interesting compound, the 2-piperazine analog of riluzole (14), although slightly less potent than the parent compound in the patch-clamp assay as well as in an in vitro model of myotonia, showed greater use-dependent Nav1.4 blocking activity. Docking studies allowed the identification of the key interactions that 14 makes with the amino acids of the local anesthetic binding site within the pore of the channel. The reported results pave the way for the identification of novel compounds useful in the treatment of cell excitability disorders.

hERG stereoselective modulation by mexiletine-derived ureas: Molecular docking study, synthesis, and biological evaluation.

Long QT syndrome (LQTS) is a disorder of cardiac electrophysiology resulting in life-threatening arrhythmias; nowadays, only a few drugs are available for the management of LQTS. Focusing our attention on LQT2, one of the most common subtypes of LQTS caused by mutations in the human ether-à-go-go-related gene (hERG), in the present work, the stereoselectivity of the recently discovered mexiletine-derived urea 8 was investigated on the hERG potassium channel. According to preliminary in silico predictions, in vitro studies revealed a stereoselective behavior, with the meso form showing the greatest hERG opening activity. In addition, functional studies on guinea pig isolated left atria, aorta, and ileum demonstrated that 8 does not present any cardiac or intestinal liability in our ex vivo studies. Due to its overall profile, (R,S)-8 paves the way for the design and development of a new series of compounds potentially useful in the treatment of both congenital and drug-induced forms of LQTS.

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.

Endocannabinoid Degradation Enzyme Inhibitors as Potential Antipsychotics: A Medicinal Chemistry Perspective.

The endocannabinoid system (ECS) plays a very important role in numerous physiological and pharmacological processes, such as those related to the central nervous system (CNS), including learning, memory, emotional processing, as well pain control, inflammatory and immune response, and as a biomarker in certain psychiatric disorders. Unfortunately, the half-life of the natural ligands responsible for these effects is very short. This perspective describes the potential role of the inhibitors of the enzymes fatty acid amide hydrolase (FAAH) and monoacylglycerol lipase (MGL), which are mainly responsible for the degradation of endogenous ligands in psychic disorders and related pathologies. The examination was carried out considering both the impact that the classical exogenous ligands such as Δ9-tetrahydrocannabinol (THC) and (-)-trans-cannabidiol (CBD) have on the ECS and through an analysis focused on the possibility of predicting the potential toxicity of the inhibitors before they are subjected to clinical studies. In particular, cardiotoxicity (hERG liability), probably the worst early adverse reaction studied during clinical studies focused on acute toxicity, was predicted, and some of the most used and robust metrics available were considered to select which of the analyzed compounds could be repositioned as possible oral antipsychotics.

New Insights into the Behavior of NHC-Gold Complexes in Cancer Cells.

Among the non-platinum antitumor agents, gold complexes have received increased attention owing to their strong antiproliferative effects, which generally occur through non-cisplatin-like mechanisms of action. Several studies have revealed that many cytotoxic gold compounds, such as N-heterocyclic carbene (NHC)-gold(I) complexes, are potent thioredoxin reductase (TrxR) inhibitors. Many other pathways have been supposed to be altered by gold coordination to protein targets. Within this frame, we have selected two gold(I) complexes based on aromatic ligands to be tested on cancer cells. Differently from bis [1,3-diethyl-4,5-bis(4-methoxyphenyl)imidazol-2-ylidene]gold(I) bromide (Au4BC), bis [1-methyl-3-acridineimidazolin-2-ylidene]gold(I) tetrafluoroborate (Au3BC) inhibited TrxR1 activity in vitro. Treatment of Huh7 hepatocellular carcinoma (HCC) cells, and MDA-MB-231 triple-negative breast cancer (TNBC) cells, with Au4BC inhibited cell viability, increased reactive oxygen species (ROS) levels, caused DNA damage, and induced autophagy and apoptosis. Notably, we found that, although Au3BC inhibited TrxR1 activity, no effect on the cell viabilities of HCC and BC cells was observed. At the molecular level, Au3BC induced a protective response mechanism in Huh7 and MDA-MB-231 cells, by inducing up-regulation of RAD51 and p62 protein expression, two proteins involved in DNA damage repair and autophagy, respectively. RAD51 gene knock-down in HCC cells increased cell sensitivity to Au3BC by significant reduction of cell viability, induction of DNA damage, and induction of apoptosis and autophagy. All together, these results suggest that the tested NHC-Gold complexes, Au3BC and Au4BC, showed different mechanisms of action, either dependent or independent of TrxR1 inhibition. As a result, Au3BC and Au4BC were found to be promising candidates as anticancer drugs for the treatment of HCC and BC.

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.

Development of N-(1-Adamantyl)benzamides as Novel Anti-Inflammatory Multitarget Agents Acting as Dual Modulators of the Cannabinoid CB2 Receptor and Fatty Acid Amide Hydrolase.

Cannabinoid type 2 receptor (CB2R), belonging to the endocannabinoid system, is overexpressed in pathologies characterized by inflammation, and its activation counteracts inflammatory states. Fatty acid amide hydrolase (FAAH) is an enzyme responsible for the degradation of the main endocannabinoid anandamide; thus, the simultaneous CB2R activation and FAAH inhibition may be a synergistic anti-inflammatory strategy. Encouraged by principal component analysis (PCA) data identifying a wide chemical space shared by CB2R and FAAH ligands, we designed a small library of adamantyl-benzamides, as potential dual agents, CB2R agonists, and FAAH inhibitors. The new compounds were tested for their CB2R affinity/selectivity and CB2R and FAAH activity. Derivatives 13, 26, and 27, displaying the best pharmacodynamic profile as CB2R full agonists and FAAH inhibitors, decreased pro-inflammatory and increased anti-inflammatory cytokines production. Molecular docking simulations complemented the experimental findings by providing a molecular rationale behind the observed activities. These multitarget ligands constitute promising anti-inflammatory agents.

Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques.

Drug-induced cardiotoxicity is a common side effect of drugs in clinical use or under postmarket surveillance and is commonly due to off-target interactions with the cardiac human-ether-a-go-go-related (hERG) potassium channel. Therefore, prioritizing drug candidates based on their hERG blocking potential is a mandatory step in the early preclinical stage of a drug discovery program. Herein, we trained and properly validated 30 ligand-based classifiers of hERG-related cardiotoxicity based on 7,963 curated compounds extracted by the freely accessible repository ChEMBL (version 25). Different machine learning algorithms were tested, namely, random forest, K-nearest neighbors, gradient boosting, extreme gradient boosting, multilayer perceptron, and support vector machine. The application of 1) the best practices for data curation, 2) the feature selection method VSURF, and 3) the synthetic minority oversampling technique (SMOTE) to properly handle the unbalanced data, allowed for the development of highly predictive models (BAMAX = 0.91, AUCMAX = 0.95). Remarkably, the undertaken temporal validation approach not only supported the predictivity of the herein presented classifiers but also suggested their ability to outperform those models commonly used in the literature. From a more methodological point of view, the study put forward a new computational workflow, freely available in the GitHub repository (https://github.com/PDelre93/hERG-QSAR), as valuable for building highly predictive models of hERG-mediated cardiotoxicity.

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.

Why a Diffusing Single-Molecule can be Detected in Few Minutes by a Large Capturing Bioelectronic Interface.

Single-molecule detection at a nanometric interface in a femtomolar solution, can take weeks as the encounter rate between the diffusing molecule to be detected and the transducing nanodevice is negligibly small. On the other hand, several experiments prove that macroscopic label-free sensors based on field-effect-transistors, engaging micrometric or millimetric detecting interfaces are capable to assay a single-molecule in a large volume within few minutes. The present work demonstrates why at least a single molecule out of a few diffusing in a 100 µL volume has a high probability to hit a large capturing and detecting electronic interface. To this end, sensing data, measured with an electrolyte-gated FET whose gate is functionalized with 1012 capturing anti-immunoglobulin G, are here provided along with a Brownian diffusion-based modeling. The EG-FET assays solutions down to some tens of zM in concentrations with volumes ranging from 25 µL to 1 mL in which the functionalized gates are incubated for times ranging from 30 s to 20 min. The high level of accordance between the experimental data and a model based on the Einstein's diffusion-theory proves how the single-molecule detection process at large-capturing interfaces is controlled by Brownian diffusion and yet is highly probable and fast.

Structural Characterization of the Full-Length Anti-CD20 Antibody Rituximab.

Rituximab, a murine-human chimera, is the first monoclonal antibody (mAb) developed as a therapeutic agent to target CD20 protein. Its Fab domain and its interaction with CD20 have been extensively studied and high-resolution atomic models obtained by X-ray diffraction or cryo-electron microscopy are available. However, the structure of the full-length antibody is still missing as the inherent protein flexibility hampers the formation of well-diffracting crystals and the reconstruction of 3D microscope images. The global structure of rituximab from its dilute solution is here elucidated by small-angle X-ray scattering (SAXS). The limited data resolution achievable by this technique has been compensated by intensive computational modelling that led to develop a new and effective procedure to characterize the average mAb conformation as well as that of the single domains. SAXS data indicated that rituximab adopts an asymmetric average conformation in solution, with a radius of gyration and a maximum linear dimension of 52 Å and 197 Å, respectively. The asymmetry is mainly due to an uneven arrangement of the two Fab units with respect to the central stem (the Fc domain) and reflects in a different conformation of the individual units. As a result, the Fab elbow angle, which is a crucial determinant for antigen recognition and binding, was found to be larger (169°) in the more distant Fab unit than that in the less distant one (143°). The whole flexibility of the antibody has been found to strongly depend on the relative inter-domain orientations, with one of the Fab arms playing a major role. The average structure and the amount of flexibility has been studied in the presence of different buffers and additives, and monitored at increasing temperature, up to the complete unfolding of the antibody. Overall, the structural characterization of rituximab can help in designing next-generation anti-CD20 antibodies and finding more efficient routes for rituximab production at industrial level.