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.

A comprehensive review of synthetic strategies and SAR studies for the discovery of PfDHODH inhibitors as antimalarial agents. Part 1: triazolopyrimidine, isoxazolopyrimidine and pyrrole-based (DSM) compounds.

One of the deadliest infectious diseases, malaria, still has a significant impact on global morbidity and mortality. Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) catalyzes the fourth step in de novo pyrimidine nucleotide biosynthesis and has been clinically validated as an innovative and promising target for the development of novel targeted antimalarial drugs. PfDHODH inhibitors have the potential to significantly slow down parasite growth at the blood and liver stages. Several PfDHODH inhibitors based on various scaffolds have been explored over the past two decades. Among them, triazolopyrimidines, isoxazolopyrimidines, and pyrrole-based derivatives known as DSM compounds showed tremendous potential as novel antimalarial agents, and one of the triazolopyrimidine-based compounds (DSM265) was able to reach phase IIa clinical trials. DSM compounds were synthesized as PfDHODH inhibitors with various substitutions based on structure-guided medicinal chemistry approaches and further optimised as well. For the first time, this review provides an overview of all the synthetic approaches used for the synthesis, alternative synthetic routes, and novel strategies involving various catalysts and chemical reagents that have been used to synthesize DSM compounds. We have also summarized SAR study of all these PfDHODH inhibitors. In an attempt to assist readers, scientists, and researchers involved in the development of new PfDHODH inhibitors as antimalarials, this review provides accessibility of all synthetic techniques and SAR studies of the most promising triazolopyrimidines, isoxazolopyrimidines, and pyrrole-based PfDHODH inhibitors.

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.

Identification of Novel Non-Nucleoside Inhibitors of Zika Virus NS5 Protein Targeting MTase Activity.

Zika virus (ZIKV) is a positive-sense single-stranded virus member of the Flaviviridae family. Among other arboviruses, ZIKV can cause neurological disorders such as Guillain Barré syndrome, and it can have congenital neurological manifestations and affect fertility. ZIKV nonstructural protein 5 (NS5) is essential for viral replication and limiting host immune detection. Herein, we performed virtual screening to identify novel small-molecule inhibitors of the ZIKV NS5 methyltransferase (MTase) domain. Compounds were tested against the MTases of both ZIKV and DENV, demonstrating good inhibitory activities against ZIKV MTase. Extensive molecular dynamic studies conducted on the series led us to identify other derivatives with improved activity against the MTase and limiting ZIKV infection with an increased selectivity index. Preliminary pharmacokinetic parameters have been determined, revealing excellent stability over time. Preliminary in vivo toxicity studies demonstrated that the hit compound 17 is well tolerated after acute administration. Our results provide the basis for further optimization studies on novel non-nucleoside MTase inhibitors.

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.

New Insights into Bitopic Orthosteric/Allosteric Ligands of Cannabinoid Receptor Type 2.

Very recently, we have developed a new generation of ligands targeting the cannabinoid receptor type 2 (CB2R), namely JR compounds, which combine the pharmacophoric portion of the CB2R positive allosteric modulator (PAM), EC21a, with that of the CB2R selective orthosteric agonist LV62, both synthesized in our laboratories. The functional examination enabled us to identify JR14a, JR22a, and JR64a as the most promising compounds of the series. In the current study, we focused on the assessment of the bitopic (dualsteric) nature of these three compounds. Experiments in cAMP assays highlighted that only JR22a behaves as a CB2R bitopic (dualsteric) ligand. In parallel, computational studies helped us to clarify the binding mode of these three compounds at CB2R, confirming the bitopic (dualsteric) nature of JR22a. Finally, the potential of JR22a to prevent neuroinflammation was investigated on a human microglial cell inflammatory model.

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.

Identification of Human Dihydroorotate Dehydrogenase Inhibitor by a Pharmacophore-Based Virtual Screening Study.

Human dihydroorotate dehydrogenase (hDHODH) is an enzyme belonging to a flavin mononucleotide (FMN)-dependent family involved in de novo pyrimidine biosynthesis, a key biological pathway for highly proliferating cancer cells and pathogens. In fact, hDHODH proved to be a promising therapeutic target for the treatment of acute myelogenous leukemia, multiple myeloma, and viral and bacterial infections; therefore, the identification of novel hDHODH ligands represents a hot topic in medicinal chemistry. In this work, we reported a virtual screening study for the identification of new promising hDHODH inhibitors. A pharmacophore-based approach combined with a consensus docking analysis and molecular dynamics simulations was applied to screen a large database of commercial compounds. The whole virtual screening protocol allowed for the identification of a novel compound that is endowed with promising inhibitory activity against hDHODH and is structurally different from known ligands. These results validated the reliability of the in silico workflow and provided a valuable starting point for hit-to-lead and future lead optimization studies aimed at the development of new potent hDHODH inhibitors.

Sirtuin 1-Activating Compounds: Discovery of a Class of Thiazole-Based Derivatives.

Sirtuin 1 (SIRT1) is a NAD+-dependent deacetylase implicated in various biological and pathological processes, including cancer, diabetes, and cardiovascular diseases. In recent years, SIRT1-activating compounds have been demonstrated to exert cardioprotective effects. Therefore, this enzyme has become a feasible target to treat cardiovascular diseases, and many SIRT1 activators, of a natural or synthetic origin, have been identified. In the present work, we developed thiazole-based SIRT1 activators, which showed remarkably higher SIRT1 activation potencies compared with those of the reference compound resveratrol when tested in enzymatic assays. Thiazole 8, a representative compound of this series, was also subjected to further pharmacological investigations, where it was proven to reduce myocardial damage induced by an in vivo occlusion/reperfusion event, thus confirming its cardioprotective properties. In addition, the cardioprotective effect of compound 8 was significantly higher than that of resveratrol. Molecular modeling studies suggest the binding mode of these derivatives within SIRT1 in the presence of the p53-AMC peptide. These promising results could pave the way to further expand and optimize this chemical class of new and potent SIRT1 activators as potential cardioprotective agents.

Resveratrol Analogues as Dual Inhibitors of Monoamine Oxidase B and Carbonic Anhydrase VII: A New Multi-Target Combination for Neurodegenerative Diseases?

Neurodegenerative diseases (NDs) are described as multifactorial and progressive syndromes with compromised cognitive and behavioral functions. The multi-target-directed ligand (MTDL) strategy is a promising paradigm in drug discovery, potentially leading to new opportunities to manage such complex diseases. Here, we studied the dual ability of a set of resveratrol (RSV) analogs to inhibit two important targets involved in neurodegeneration. The stilbenols 1−9 were tested as inhibitors of the human monoamine oxidases (MAOs) and carbonic anhydrases (CAs). The studied compounds displayed moderate to excellent in vitro enzyme inhibitory activity against both enzymes at micromolar/nanomolar concentrations. Among them, the best compound 4 displayed potent and selective inhibition against the MAO-B isoform (IC50 MAO-A 0.43 µM vs. IC50 MAO-B 0.01 µM) with respect to the parent compound resveratrol (IC50 MAO-A 13.5 µM vs. IC50 MAO-B > 100 µM). It also demonstrated a selective inhibition activity against hCA VII (KI 0.7 µM vs. KI 4.3 µM for RSV). To evaluate the plausible binding mode of 1−9 within the two enzymes, molecular docking and dynamics studies were performed, revealing specific and significant interactions in the active sites of both targets. The new compounds are of pharmacological interest in view of their considerably reduced toxicity previously observed, their physicochemical and pharmacokinetic profiles, and their dual inhibitory ability. Compound 4 is noteworthy as a promising lead in the development of MAO and CA inhibitors with therapeutic potential in neuroprotection.

Recent Advances in In Silico Target Fishing.

In silico target fishing, whose aim is to identify possible protein targets for a query molecule, is an emerging approach used in drug discovery due its wide variety of applications. This strategy allows the clarification of mechanism of action and biological activities of compounds whose target is still unknown. Moreover, target fishing can be employed for the identification of off targets of drug candidates, thus recognizing and preventing their possible adverse effects. For these reasons, target fishing has increasingly become a key approach for polypharmacology, drug repurposing, and the identification of new drug targets. While experimental target fishing can be lengthy and difficult to implement, due to the plethora of interactions that may occur for a single small-molecule with different protein targets, an in silico approach can be quicker, less expensive, more efficient for specific protein structures, and thus easier to employ. Moreover, the possibility to use it in combination with docking and virtual screening studies, as well as the increasing number of web-based tools that have been recently developed, make target fishing a more appealing method for drug discovery. It is especially worth underlining the increasing implementation of machine learning in this field, both as a main target fishing approach and as a further development of already applied strategies. This review reports on the main in silico target fishing strategies, belonging to both ligand-based and receptor-based approaches, developed and applied in the last years, with a particular attention to the different web tools freely accessible by the scientific community for performing target fishing studies.

Co-Inhibition of P-gp and Hsp90 by an Isatin-Derived Compound Contributes to the Increase of the Chemosensitivity of MCF7/ADR-Resistant Cells to Doxorubicin.

Breast cancer is a complex and multi-drug resistant (MDR) disease, which could result in the failure of many chemotherapeutic clinical agents. Discovering effective molecules from natural products or by derivatization from known compounds is the interest of many research studies. The first objective of the present study is to investigate the cytotoxic combinatorial, chemosensitizing, and apoptotic effects of an isatin derived compound (5,5-diphenylimidazolidine-2,4-dione conjugated with 5-substituted isatin, named HAA2021 in the present study) against breast cancer cells (MCF7) and breast cancer cells resistant to doxorubicin (MCF7/ADR) when combined with doxorubicin. The second objective is to investigate the binding mode of HAA2021 withP-glycoprotein (P-gp) and heat shock protein 90 (Hsp90), and to determine whether their co-inhibition by HAA2021 contribute to the increase of the chemosensitization of MCF7/ADR cells to doxorubicin. The combination of HAA2021, at non-toxic doses, with doxorubicin synergistically inhibited the proliferation while inducing significant apoptosis in MCF7 cells. Moreover, HAA2021 increased the chemosensitization of MCF7/ADR cells to doxorubicin, resulting in increased cytotoxicity/selectivity and apoptosis-inducing efficiency compared with the effect of doxorubicin or HAA2021 alone against MCF7/ADR cells. Molecular modeling showed that two molecules of HAA2021 bind to P-gp at the same time, causing P-gp inhibitory effect of the MDR efflux pump, and accumulation of Rhodamine-123 (Rho123) in MCF7/ADR cells. Furthermore, HAA2021 stably interacted with Hsp90α more efficiently compared with 17-N-allylamino-17-demethoxygeldanamycin (17-AAG), which was confirmed with the surface plasmon resonance (SPR) and molecular modeling studies. Additionally, HAA2021 showed multi-target effects via the inhibition of Hsp90 and nuclear factor kappa B (NF-𝜅B) proteins in MCF7 and MCF7/ADR cells. Results of real time-PCR also confirmed the synergistic co-inhibition of P-gp/Hsp90α genes in MCF7/ADR cells. Further pharmacokinetic and in vivo studies are warranted for HAA2021 to confirm its anticancer capabilities.

Antibacterial and ATP Synthesis Modulating Compounds from Salvia tingitana.

A surface extract of the aerial parts of Salvia tingitana afforded a nor-sesterterpenoid (1) and eight new sesterterpenoids (2-̵9), along with five known sesterterpenoids, five labdane and one abietane diterpenoid, one sesquiterpenoid, and four flavonoids. The structures of the new compounds were established by 1D and 2D NMR spectroscopy, HRESIMS, and VCD data and Mosher's esters analysis. The antimicrobial activity of compounds was evaluated against 30 human pathogens including 27 clinical strains and three isolates of marine origin for their possible implications on human health. The methyl ester of salvileucolide (10), salvileucolide-6,23-lactone (11), sclareol (15), and manool (17) were the most active against Gram-positive bacteria. The compounds were also tested for the inhibition of ATP production in purified mammalian rod outer segments. Terpenoids 10, 11, 15, and 17 inhibited ATP production, while only 17 inhibited also ATP hydrolysis. Molecular modeling studies confirmed the capacity of 17 to interact with mammalian ATP synthase. A significant reduction of ATP production in the presence of 17 was observed in Enterococcus faecalis and E. faecium isolates.

N-aryl-N'-ureido-O-sulfamates as potent and selective inhibitors of hCA VB over hCA VA: Deciphering the binding mode of new potential agents in mitochondrial dysfunctions.

N-aryl-N'-ureido-O-sulfamates (AUSs) were recently reported as new class of Carbonic Anhydrase Inhibitors (CAIs), endowed of high potency and selectivity against hCA VII and XII. In this work, we extended the investigational study on this new class of CAIs profiling them against the mitochondrial CA isoforms hCA VA and VB. The results revealed a very interesting selectivity profile, with dramatic selectivity against hCA VB over the VA isoform observed for all the analyzed compounds 2-22. On derivative 15, selected as one of the most promising among the series, molecular modeling studies were conducted, highlighting the importance of small residue substitution between the two isoforms in substantially changing the tail orientation and interaction with the enzymes.

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.