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.

Synthesis and pharmacological evaluation of novel N-aryl-cinnamoyl-hydrazone hybrids designed as neuroprotective agents for the treatment of Parkinson's disease.

Molecular hybridization between structural fragments from the structures of curcumin (1) and resveratrol (2) was used as a designing tool to generate a new N-acyl-cinnamoyl-hydrazone hybrid molecular architecture. Twenty-eight new compounds were synthesized and evaluated for multifunctional activities related to Parkinson's disease (PD), including neuroprotection, antioxidant, metal chelating ability, and Keap1/Nrf2 pathway activation. Compounds 3b (PQM-161) and 3e (PQM-164) were highlighted for their significant antioxidant profile, acting directly as induced free radical stabilizers by DPPH and indirectly by modulating intracellular inhibition of t-BOOH-induced ROS formation in neuronal cells. The mechanism of action was determined as a result of Keap1/Nrf2 pathway activation by both compounds and confirmed by different experiments. Furthermore, compound 3e (PQM-164) exhibited a significant effect on the accumulation of α-synuclein and anti-inflammatory activity, leading to an expressive decrease in gene expression of iNOS, IL-1ß, and TNF-α. Overall, these results highlighted compound 3e as a promising and innovative multifunctional drug prototype candidate for PD treatment.

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.

CAG RNAs induce DNA damage and apoptosis by silencing NUDT16 expression in polyglutamine degeneration.

DNA damage plays a central role in the cellular pathogenesis of polyglutamine (polyQ) diseases, including Huntington's disease (HD). In this study, we showed that the expression of untranslatable expanded CAG RNA per se induced the cellular DNA damage response pathway. By means of RNA sequencing (RNA-seq), we found that expression of the Nudix hydrolase 16 (NUDT16) gene was down-regulated in mutant CAG RNA-expressing cells. The loss of NUDT16 function results in a misincorporation of damaging nucleotides into DNAs and leads to DNA damage. We showed that small CAG (sCAG) RNAs, species generated from expanded CAG transcripts, hybridize with CUG-containing NUDT16 mRNA and form a CAG-CUG RNA heteroduplex, resulting in gene silencing of NUDT16 and leading to the DNA damage and cellular apoptosis. These results were further validated using expanded CAG RNA-expressing mouse primary neurons and in vivo R6/2 HD transgenic mice. Moreover, we identified a bisamidinium compound, DB213, that interacts specifically with the major groove of the CAG RNA homoduplex and disfavors the CAG-CUG heteroduplex formation. This action subsequently mitigated RNA-induced silencing complex (RISC)-dependent NUDT16 silencing in both in vitro cell and in vivo mouse disease models. After DB213 treatment, DNA damage, apoptosis, and locomotor defects were rescued in HD mice. This work establishes NUDT16 deficiency by CAG repeat RNAs as a pathogenic mechanism of polyQ diseases and as a potential therapeutic direction for HD and other polyQ diseases.

The Benzoylpiperidine Fragment as a Privileged Structure in Medicinal Chemistry: A Comprehensive Review.

The phenyl(piperidin-4-yl)methanone fragment (here referred to as the benzoylpiperidine fragment) is a privileged structure in the development of new drugs considering its presence in many bioactive small molecules with both therapeutic (such as anti-cancer, anti-psychotic, anti-thrombotic, anti-arrhythmic, anti-tubercular, anti-parasitic, anti-diabetic, and neuroprotective agents) and diagnostic properties. The benzoylpiperidine fragment is metabolically stable, and it is also considered a potential bioisostere of the piperazine ring, thus making it a feasible and reliable chemical frame to be exploited in drug design. Herein, we discuss the main therapeutic and diagnostic agents presenting the benzoylpiperidine motif in their structure, covering articles reported in the literature since 2000. A specific section is focused on the synthetic strategies adopted to obtain this versatile chemical portion.

Historical perspective of tumor glycolysis: A century with Otto Warburg.

Tumors have long been known to rewire their metabolism to endorse their proliferation, growth, survival, and invasiveness. One of the common characteristics of these alterations is the enhanced glucose uptake and its subsequent transformation into lactic acid by means of glycolysis, regardless the availability of oxygen or the mitochondria effectiveness. This phenomenon is called the "Warburg effect", which has turned into a century of age now, since its first disclosure by German physiologist Otto Heinrich Warburg. Since then, this peculiar metabolic switch in tumors has been addressed by extensive studies covering several areas of research. In this historical perspective, we aim at illustrating the evolution of these studies over time and their implication in various fields of science.

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.

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.

The dichotomous role of the glycolytic metabolism pathway in cancer metastasis: Interplay with the complex tumor microenvironment and novel therapeutic strategies.

Cancer metastasis to distant organs is initiated by tumor cells that disseminate from primary heterogeneous tumors. The subsequent growth and survival of tumor metastases depend on different metabolic changes, which constitute one of the enigmatic properties of tumor cells. Aerobic glycolysis, 'the Warburg effect', contributes to tumor energy supply, by oxidizing glucose in a faster manner compared to oxidative phosphorylation, leading to an increased lactate production by lactate dehydrogenase A (LDH-A), which in turn affects the immune response. Surrounding stromal cells contribute to feedback mechanisms further prompting the acquisition of pro-invasive metabolic features. Hence, therapeutic strategies targeting the glycolytic pathway are intensively investigated, with a special interest on their anti-metastatic properties. Various small molecules, such as LDH-A inhibitors, have shown pre-clinical activity against different cancer types, and blocking LDH-A could also help in designing future complimentary therapies. Modulation of specific targets in cells with an altered glycolytic metabolism should indeed result in a milder and distinct toxicity profile, compared to conventional cytotoxic therapy, while a combination treatment with vitamin C leading to increasing reactive oxygen species levels, should further inhibit cancer cell survival and invasion. In this review we describe the impact of metabolic reprogramming in cancer metastasis, the contribution of lactate in this aberrant process and its effect on oncogenic processes. Furthermore, we discuss experimental compounds that target glycolytic metabolism, such as LDH-A inhibitors, and their potential to improve current and experimental therapeutics against metastatic tumors.

Development of a fluorogenic ADAMTS-7 substrate.

The extracellular protease ADAMTS-7 has been identified as a potential therapeutic target in atherosclerosis and associated diseases such as coronary artery disease (CAD). However, ADAMTS-7 inhibitors have not been reported so far. Screening of inhibitors has been hindered by the lack of a suitable peptide substrate and, consequently, a convenient activity assay. Here we describe the first fluorescence resonance energy transfer (FRET) substrate for ADAMTS-7, ATS7FP7. ATS7FP7 was used to measure inhibition constants for the endogenous ADAMTS-7 inhibitor, TIMP-4, as well as two hydroxamate-based zinc chelating inhibitors. These inhibition constants match well with IC50 values obtained with our SDS-PAGE assay that uses the N-terminal fragment of latent TGF-ß-binding protein 4 (LTBP4S-A) as a substrate. Our novel fluorogenic substrate ATS7FP7 is suitable for high throughput screening of ADAMTS-7 inhibitors, thus accelerating translational studies aiming at inhibition of ADAMTS-7 as a novel treatment for cardiovascular diseases such as atherosclerosis and CAD.

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.