VenomPred 2.0: A Novel In Silico Platform for an Extended and Human Interpretable Toxicological Profiling of Small Molecules.

The application of artificial intelligence and machine learning (ML) methods is becoming increasingly popular in computational toxicology and drug design; it is considered as a promising solution for assessing the safety profile of compounds, particularly in lead optimization and ADMET studies, and to meet the principles of the 3Rs, which calls for the replacement, reduction, and refinement of animal testing. In this context, we herein present the development of VenomPred 2.0 (http://www.mmvsl.it/wp/venompred2/), the new and improved version of our free of charge web tool for toxicological predictions, which now represents a powerful web-based platform for multifaceted and human-interpretable in silico toxicity profiling of chemicals. VenomPred 2.0 presents an extended set of toxicity endpoints (androgenicity, skin irritation, eye irritation, and acute oral toxicity, in addition to the already available carcinogenicity, mutagenicity, hepatotoxicity, and estrogenicity) that can be evaluated through an exhaustive consensus prediction strategy based on multiple ML models. Moreover, we also implemented a new utility based on the Shapley Additive exPlanations (SHAP) method that allows human interpretable toxicological profiling of small molecules, highlighting the features that strongly contribute to the toxicological predictions in order to derive structural toxicophores.

Watermelon: setup and validation of an in silico fragment-based approach.

We present a new computational approach, named Watermelon, designed for the development of pharmacophore models based on receptor structures. The methodology involves the sampling of potential hotspots for ligand interactions within a protein target's binding site, utilising molecular fragments as probes. By employing docking and molecular dynamics (MD) simulations, the most significant interactions formed by these probes within distinct regions of the binding site are identified. These interactions are subsequently transformed into pharmacophore features that delineates key anchoring sites for potential ligands. The reliability of the approach was experimentally validated using the monoacylglycerol lipase (MAGL) enzyme. The generated pharmacophore model captured features representing ligand-MAGL interactions observed in various X-ray co-crystal structures and was employed to screen a database of commercially available compounds, in combination with consensus docking and MD simulations. The screening successfully identified two new MAGL inhibitors with micromolar potency, thus confirming the reliability of the Watermelon approach.

MolBook UNIPI─Create, Manage, Analyze, and Share Your Chemical Data for Free.

Here, we present MolBook UNIPI, freely available and user-friendly software specifically designed for medicinal chemists as a powerful tool for the easy management of virtual libraries of chemical compounds. With MolBook UNIPI, it is possible to create, store, handle, and share molecular databases in a very simple and intuitive way. The software allows users to rapidly generate libraries of bioactive ligands, building blocks, or commercial compounds by either manually creating single molecules or automatically importing compounds from public databases and pre-existing libraries. MolBook UNIPI databases can be enriched with all kinds of data and can be filtered based on molecular structures or properties, allowing the desired molecules, along with their structures and features, to be easily accessible in just a few clicks. Moreover, new molecular properties and potential toxicological effects of compounds can be rapidly and reliably predicted. Notably, all of these functions can be easily mastered even by inexperienced users, with no prior cheminformatics knowledge or programming skills, which makes MolBook UNIPI an invaluable tool for medicinal chemists. MolBook UNIPI can be downloaded free of charge from the project web page https://molbook.farm.unipi.it/.

An in silico toolbox for the prediction of the potential pathogenic effects of missense mutations in the dimeric region of hRPE65.

hRPE65 is a fundamental enzyme of the retinoid visual cycle, and many missense mutations affecting its expression or function are associated with a wide range of diseases. Many hRPE65 missense mutations lack a clear pathogenicity classification or are labelled as VUS. In this context, we recently developed a protocol based on µs-long molecular dynamics simulations to study the potential pathogenic effect of hRPE65 missense mutations. In the present work, the structure-based protocol was integrated with a hRPE65-tailored consensus bioinformatics strategy, named ConPath, that showed high performance in predicting known pathogenic/benign hRPE65 missense mutations. The combined strategy was used to perform a multi-level evaluation of the potential pathogenicity of 13 different hRPE65 VUS, which were classified based on their likelihood of pathogenic effect. The obtained results provide information that may support the reclassification of these VUS and help clinicians evaluate the eligibility for gene therapy of patients diagnosed with such variants.

Identification of New GSK3ß Inhibitors through a Consensus Machine Learning-Based Virtual Screening.

Glycogen synthase kinase-3 beta (GSK3ß) is a serine/threonine kinase that plays key roles in glycogen metabolism, Wnt/ß-catenin signaling cascade, synaptic modulation, and multiple autophagy-related signaling pathways. GSK3ß is an attractive target for drug discovery since its aberrant activity is involved in the development of neurodegenerative diseases such as Alzheimer's and Parkinson's disease. In the present study, multiple machine learning models aimed at identifying novel GSK3ß inhibitors were developed and evaluated for their predictive reliability. The most powerful models were combined in a consensus approach, which was used to screen about 2 million commercial compounds. Our consensus machine learning-based virtual screening led to the identification of compounds G1 and G4, which showed inhibitory activity against GSK3ß in the low-micromolar and sub-micromolar range, respectively. These results demonstrated the reliability of our virtual screening approach. Moreover, docking and molecular dynamics simulation studies were employed for predicting reliable binding modes for G1 and G4, which represent two valuable starting points for future hit-to-lead and lead optimization studies.

Insights into the Antioxidant/Antiradical Effects and In Vitro Intestinal Permeation of Oleocanthal and Its Metabolites Tyrosol and Oleocanthalic Acid.

(1) Background: In recent years, numerous studies have highlighted the beneficial effects of extra virgin olive oil (EVOO) as an active ingredient against chronic diseases. The properties of EVOO are due to its peculiar composition, mainly to its rich content of polyphenols. In fact, polyphenols may contribute to counteract oxidative stress, which often accompanies chronic diseases. In this work, the antioxidant effects of high-value polyphenol oleocanthal (OC) and its main metabolites, tyrosol (Tyr) and oleocanthalic acid (OA), respectively, have been investigated along with their impact on cell viability. (2) Methods: OC, Tyr, and OA have been evaluated regarding antiradical properties in term of scavenging capacity towards biologically relevant reactive species, including O2●-, HOCl, and ROO●, as well as their antioxidant/antiradical capacity (FRAP, DPPH●, ABTS●+). Moreover, the ability to permeate the intestinal membrane was assessed by an intestinal co-culture model composed by Caco-2 and HT29-MTX cell lines. (3) Results: The capacity of OC and Tyr as radical oxygen species (ROS) scavengers, particularly regarding HOCl and O2●-, was clearly demonstrated. Furthermore, the ability to permeate the intestinal co-culture model was plainly proved by the good permeations (>50%) achieved by all compounds. (4) Conclusions: OC, OA, and Tyr revealed promising properties against oxidative diseases.

Predicting potentially pathogenic effects of hRPE65 missense mutations: a computational strategy based on molecular dynamics simulations.

The human retinal pigment epithelium-specific 65-kDa protein (hRPE65) plays a crucial role within the retinoid visual cycle and several mutations affecting either its expression level or its enzymatic function are associated with inherited retinal diseases such as Retinitis Pigmentosa. The gene therapy product voretigene neparvovec (Luxturna) has been recently approved for treating hereditary retinal dystrophies; however, the treatment is currently accessible only to patients presenting confirmed biallelic mutations that severely impair hRPE65 function, and many reported hRPE65 missense mutations lack sufficient evidences for proving their pathogenicity. In this context, we developed a computational approach aimed at evaluating the potential pathogenic effect of hRPE65 missense variants located on the dimerisation domain of the protein. The protocol evaluates how mutations may affect folding and conformation stability of this protein region, potentially helping clinicians to evaluate the eligibility for gene therapy of patients diagnosed with this type of hRPE65 variant of uncertain significance.

New PIN1 inhibitors identified through a pharmacophore-driven, hierarchical consensus docking strategy.

PIN1 is considered as a therapeutic target for a wide variety of tumours. However, most of known inhibitors are devoid of cellular activity despite their good enzyme inhibitory profile. Hence, the lack of effective compounds for the clinic makes the identification of novel PIN1 inhibitors a hot topic in the medicinal chemistry field. In this work, we reported a virtual screening study for the identification of new promising PIN1 inhibitors. A receptor-based procedure was applied to screen different chemical databases of commercial compounds. Based on the whole workflow, two compounds were selected and biologically evaluated. Both ligands, compounds VS1 and VS2, showed a good enzyme inhibitory activity and VS2 also demonstrated a promising antitumoral activity in ovarian cancer cells. These results confirmed the reliability of our in silico protocol and provided a structurally novel ligand as a valuable starting point for the development of new PIN1 inhibitors.

VenomPred: A Machine Learning Based Platform for Molecular Toxicity Predictions.

The use of in silico toxicity prediction methods plays an important role in the selection of lead compounds and in ADMET studies since in vitro and in vivo methods are often limited by ethics, time, budget and other resources. In this context, we present our new web tool VenomPred, a user-friendly platform for evaluating the potential mutagenic, hepatotoxic, carcinogenic and estrogenic effects of small molecules. VenomPred platform employs several in-house Machine Learning (ML) models developed with datasets derived from VEGA QSAR, a software that includes a comprehensive collection of different toxicity models and has been used as a reference for building and evaluating our ML models. The results showed that our models achieved equal or better performance than those obtained with the reference models included in VEGA QSAR. In order to improve the predictive performance of our platform, we adopted a consensus approach combining the results of different ML models, which was able to predict chemical toxicity better than the single models. This improved method was thus implemented in the VenomPred platform, a freely accessible webserver that takes the SMILES (Simplified Molecular-Input Line-Entry System) strings of the compounds as input and sends the prediction results providing a probability score about their potential toxicity.

Machine Learning-Based Virtual Screening for the Identification of Cdk5 Inhibitors.

Cyclin-dependent kinase 5 (Cdk5) is an atypical proline-directed serine/threonine protein kinase well-characterized for its role in the central nervous system rather than in the cell cycle. Indeed, its dysregulation has been strongly implicated in the progression of synaptic dysfunction and neurodegenerative diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), and also in the development and progression of a variety of cancers. For this reason, Cdk5 is considered as a promising target for drug design, and the discovery of novel small-molecule Cdk5 inhibitors is of great interest in the medicinal chemistry field. In this context, we employed a machine learning-based virtual screening protocol with subsequent molecular docking, molecular dynamics simulations and binding free energy evaluations. Our virtual screening studies resulted in the identification of two novel Cdk5 inhibitors, highlighting an experimental hit rate of 50% and thus validating the reliability of the in silico workflow. Both identified ligands, compounds CPD1 and CPD4, showed a promising enzyme inhibitory activity and CPD1 also demonstrated a remarkable antiproliferative activity in ovarian and colon cancer cells. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent Cdk5 inhibitors.

Electrospun Poly(3-Hydroxybutyrate-Co-3-Hydroxyvalerate)/Olive Leaf Extract Fiber Mesh as Prospective Bio-Based Scaffold for Wound Healing.

Polyhydroxyalkanoates (PHAs) are a family of biopolyesters synthesized by various microorganisms. Due to their biocompatibility and biodegradation, PHAs have been proposed for biomedical applications, including tissue engineering scaffolds. Olive leaf extract (OLE) can be obtained from agri-food biowaste and is a source of polyphenols with remarkable antioxidant properties. This study aimed at incorporating OLE inside poly(hydroxybutyrate-co-hydroxyvalerate) (PHBHV) fibers via electrospinning to obtain bioactive bio-based blends that are useful in wound healing. PHBHV/OLE electrospun fibers with a size of 1.29 ± 0.34 µm were obtained. Fourier transform infrared chemical analysis showed a uniform surface distribution of hydrophilic -OH groups, confirming the presence of OLE in the electrospun fibers. The main OLE phenols were released from the fibers within 6 days. The biodegradation of the scaffolds in phosphate buffered saline was investigated, demonstrating an adequate stability in the presence of metalloproteinase 9 (MMP-9), an enzyme produced in chronic wounds. The scaffolds were preliminarily tested in vitro with HFFF2 fibroblasts and HaCaT keratinocytes, suggesting adequate cytocompatibility. PHBHV/OLE fiber meshes hold promising features for wound healing, including the treatment of ulcers, due to the long period of durability in an inflamed tissue environment and adequate cytocompatibility.

Medicinal Chemistry approach, pharmacology and neuroprotective benefits of CB2R modulators in neurodegenerative diseases.

In the last decades, cannabinoid receptor 2 (CB2R) has continued to receive attention as a key therapeutic target in neuroprotection. Indeed, several findings highlight the neuroprotective effects of CB2R through suppression of both neuronal excitability and reactive microglia. Additionally, CB2R seems to be a more promising target than cannabinoid receptor 1 (CB1R) thanks to the lack of central side effects, its lower expression levels in the central nervous system (CNS), and its inducibility, since its expression enhances quickly in the brain following pathological conditions. This review aims to provide a thorough overview of the main natural and synthetic selective CB2R modulators, their chemical classification and their potential therapeutic usefulness in neuroprotection, a crucial aspect for the treatment of neurodegenerative diseases.

Modification on the 1,2-dihydro-2-oxo-pyridine-3-carboxamide core to obtain multi-target modulators of endocannabinoid system.

Several preclinical evidence indicate that the modulation of the endocannabinoid system (ECS) represents a promising therapeutic approach for different diseases. However, only few modulators of this system have reached so far an advanced stage of clinical development, mainly due to limited efficacy and CB1 receptor-dependent side effects. Those limitations might be overcome by multi-target compounds that exert pro-cannabinoid activities through the modulation of two or more targets in the ECS. This approach can offer a safer and more effective pharmacological strategy as compared to the modulation of a single target. In this work, we report the synthesis and biological characterization of new 6-aryl-1,2-dihydro-2-oxo-pyridine-3-carboxamide derivatives. Our results identified several compounds exhibiting interesting multi-target profiles within the ECS. In particular, compound B1 showed moderate-to-high affinity for cannabinoid receptors (Ki CB1R = 304 nM, partial agonist, Ki CB2R = 3.1 nM, inverse agonist) and a potent inhibition of AEA uptake (IC50 = 62 nM) with moderate inhibition of FAAH (IC50 = 2.9 µM). The corresponding 2-alkoxypyridine analogue B14 exhibited significant inhibitor activity on both FAAH (IC50 = 69 nM) and AEA uptake (IC50 = 76 nM) without significantly binding to both cannabinoid receptor subtypes. Molecular docking analysis was carried out on the three-dimensional structures of CB1R and CB2R and of FAAH to rationalize the structure-activity relationships of this series of compounds.

Discovery of Monoacylglycerol Lipase (MAGL) Inhibitors Based on a Pharmacophore-Guided Virtual Screening Study.

Monoacylglycerol lipase (MAGL) is an important enzyme of the endocannabinoid system that catalyzes the degradation of the major endocannabinoid 2-arachidonoylglycerol (2-AG). MAGL is associated with pathological conditions such as pain, inflammation and neurodegenerative diseases like Parkinson's and Alzheimer's disease. Furthermore, elevated levels of MAGL have been found in aggressive breast, ovarian and melanoma cancer cells. Due to its different potential therapeutic implications, MAGL is considered as a promising target for drug design and the discovery of novel small-molecule MAGL inhibitors is of great interest in the medicinal chemistry field. In this context, we developed a pharmacophore-based virtual screening protocol combined with molecular docking and molecular dynamics simulations, which showed a final hit rate of 50% validating the reliability of the in silico workflow and led to the identification of two promising and structurally different reversible MAGL inhibitors, VS1 and VS2. These ligands represent a valuable starting point for structure-based hit-optimization studies aimed at identifying new potent MAGL inhibitors.

Computationally driven discovery of phenyl(piperazin-1-yl)methanone derivatives as reversible monoacylglycerol lipase (MAGL) inhibitors.

Monoacylglycerol lipase (MAGL) is an attractive therapeutic target for many pathologies, including neurodegenerative diseases, cancer as well as chronic pain and inflammatory pathologies. The identification of reversible MAGL inhibitors, devoid of the side effects associated to prolonged MAGL inactivation, is a hot topic in medicinal chemistry. In this study, a novel phenyl(piperazin-1-yl)methanone inhibitor of MAGL was identified through a virtual screening protocol based on a fingerprint-driven consensus docking (CD) approach. Molecular modeling and preliminary structure-based hit optimization studies allowed the discovery of derivative 4, which showed an efficient reversible MAGL inhibition (IC50 = 6.1 µM) and a promising antiproliferative activity on breast and ovarian cancer cell lines (IC50 of 31-72 µM), thus representing a lead for the development of new and more potent reversible MAGL inhibitors. Moreover, the obtained results confirmed the reliability of the fingerprint-driven CD approach herein developed.

Extensive Reliability Evaluation of Docking-Based Target-Fishing Strategies.

The development of target-fishing approaches, aimed at identifying the possible protein targets of a small molecule, represents a hot topic in medicinal chemistry. A successful target-fishing approach would allow for the elucidation of the mechanism of action of all therapeutically interesting compounds for which the actual target is still unknown. Moreover, target-fishing would be essential for preventing adverse effects of drug candidates, by predicting their potential off-targets, and it would speed up drug repurposing campaigns. However, due to the huge number of possible protein targets that a small-molecule might interact with, experimental target-fishing approaches are out of reach. In silico target-fishing represents a valuable alternative, and examples of receptor-based approaches, exploiting the large number of crystallographic protein structures determined to date, have been reported in the literature. To the best of our knowledge, no proper evaluation of such approaches is, however, reported yet. In the present work, we extensively assessed the reliability of docking-based target-fishing strategies. For this purpose, a set of X-ray structures belonging to different targets was selected, and a dataset of compounds, including 10 experimentally active ligands for each target, was created. A target-fishing benchmark database was then obtained, and used to assess the performance of 13 different docking procedures, in identifying the correct target of the dataset ligands. Moreover, a consensus docking-based target-fishing strategy was developed and evaluated. The analysis highlighted that specific features of the target proteins could affect the reliability of the protocol, which however, proved to represent a valuable tool in the proper applicability domain. Our study represents the first extensive performance assessment of docking-based target-fishing approaches, paving the way for the development of novel efficient receptor-based target fishing strategies.

Waste Autochthonous Tuscan Olive Leaves (Olea europaea var. Olivastra seggianese) as Antioxidant Source for Biomedicine.

Olive leaf extract (OLE) can be obtained as biowaste and is extensively used a food supplement and an over-the-counter drug for its beneficial effects. New studies have investigated OLE concerning the role of oxidative stress in the pathogenesis of vascular disease. This in vitro study aims to evaluate if OLE extracted from the Tuscan Olea europaea protects endothelial cells against oxidative stress generated by reactive oxygen species (ROS). METHODS: OLE total polyphenols (TPs) were characterized by the Folin-Ciocalteu method. Endothelial cells were grown in conventional cultures (i.e., two-dimensional, 2D) and on a biomaterial scaffold (i.e., three-dimensional, 3D) fabricated via electrospinning. Cell viability and ROS measurement after H2O2 insults were performed. RESULTS: OLE TP content was 23.29 mg GAE/g, and oleuropein was the principal compound. The dose-dependent viability curve highlighted the absence of significant cytotoxic effects at OLE concentrations below 250 µg/mL TPs. By using OLE preconditioning at 100 µg/mL, cell viability decrease was observed, being in 3D lower than in the 2D model. OLE was protective against ROS in both models. CONCLUSIONS: OLE represents a high-value antioxidant source obtained by biowaste that is interesting for biomedical products. Using a 3D scaffold could be the best predictive model to mimic the physiological conditions of vascular tissue reaction.

Diclofenac-Derived Hybrids for Treatment of Actinic Keratosis and Squamous Cell Carcinoma.

In this work, hybrid compounds 1-4 obtained by conjugation of the non-steroidal anti-inflammatory drug diclofenac, with natural molecules endowed with antioxidant and antiproliferative activity were prepared. The antiproliferative activity of these hybrids was evaluated on immortalized human keratinocyte (HaCaT) cells stimulated with epidermal growth factor (EGF), an actinic keratosis (AK) model, and on human squamous cell carcinoma (SCC) cells (A431). Hybrid 1 presented the best activity in both cell models. Self-assembling surfactant nanomicelles have been chosen as the carrier to drive the hybrid 1 into the skin; the in vitro permeation through and penetration into pig ear skin have been evaluated. Among the nanostructured formulations tested, Nano3Hybrid20 showed a higher tendency of the hybrid 1 to be retained in the skin rather than permeating it, with a desirable topical and non-systemic action. On these bases, hybrid 1 may represent an attractive lead scaffold for the development of new treatments for AK and SCC.

A Proteomic Approach to Uncover Neuroprotective Mechanisms of Oleocanthal against Oxidative Stress.

Neurodegenerative diseases represent a heterogeneous group of disorders that share common features like abnormal protein aggregation, perturbed Ca2+ homeostasis, excitotoxicity, impairment of mitochondrial functions, apoptosis, inflammation, and oxidative stress. Despite recent advances in the research of biomarkers, early diagnosis, and pharmacotherapy, there are no treatments that can halt the progression of these age-associated neurodegenerative diseases. Numerous epidemiological studies indicate that long-term intake of a Mediterranean diet, characterized by a high consumption of extra virgin olive oil, correlates with better cognition in aged populations. Olive oil phenolic compounds have been demonstrated to have different biological activities like antioxidant, antithrombotic, and anti-inflammatory activities. Oleocanthal, a phenolic component of extra virgin olive oil, is getting more and more scientific attention due to its interesting biological activities. The aim of this research was to characterize the neuroprotective effects of oleocanthal against H2O2-induced oxidative stress in neuron-like SH-SY5Y cells. Moreover, protein expression profiling, combined with pathways analyses, was used to investigate the molecular events related to the protective effects. Oleocanthal was demonstrated to counteract oxidative stress, increasing cell viability, reducing reactive oxygen species (ROS) production, and increasing reduced glutathione (GSH) intracellular level. Proteomic analysis revealed that oleocanthal significantly modulates 19 proteins in the presence of H2O2. In particular, oleocanthal up-regulated proteins related to the proteasome, the chaperone heat shock protein 90, the glycolytic enzyme pyruvate kinase, and the antioxidant enzyme peroxiredoxin 1. Moreover, oleocanthal protection seems to be mediated by Akt activation. These data offer new insights into the molecular mechanisms behind oleocanthal protection against oxidative stress.

Sulfonamido-derivatives of unsubstituted carbazoles as BACE1 inhibitors.

A novel series of variously substituted N-[3-(9H-carbazol-9-yl)-2-hydroxypropyl]-arylsulfonamides has been synthesized and assayed for ß-Secretase (BACE1) inhibitory activity. BACE1 is a widely recognized drug target for the prevention and treatment of Alzheimer's Disease (AD). The introduction of benzyl substituents on the nitrogen atom of the arylsulfonamide moiety has so far led to the best results, with three derivatives showing IC50 values ranging from 1.6 to 1.9 µM. Therefore, a significant improvement over the previously reported series of N-carboxamides (displaying IC50's ≥ 2.5 µM) has been achieved, thus suggesting an active role of the sulfonamido-portion in the inhibition process. Preliminary molecular modeling studies have been carried out to rationalize the observed structure-activity relationships.