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Proteolysis-targeting chimeras (PROTACs) are novel therapeutics for the treatment of human disease. They exploit the enormous potential of the E3 ligases, a class of proteins that mark a target protein for degradation via the ubiquitin-proteasome system. Despite the existence of several E3 ligase-related databases, the choice of the functioning ligase is limited to only 1.6% of those available, probably due to the fragmentary understanding of their structures and their known ligands; in fact, none of the existing databases report detailed studies covering their 3D structure or their pockets. Here, we report ELIOT (E3 LIgase pocketOme navigaTor), an accurate and complete platform containing the E3 ligase pocketome to enable navigation and selection of new E3 ligases and new ligands for the design of new PROTACs. All E3 ligase pockets were characterized with innovative 3D descriptors including their PROTAC-ability score, and similarity analyses between E3 pockets are presented. Tissue specificity and their degree of involvement in patients with specific cancer types are also annotated for each E3 ligase, enabling appropriate selection for the design of a PROTAC with improved specificity. All data are available at https://eliot.moldiscovery.com.

Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a technology that utilizes the high sensitivity and specificity of mass spectrometry, combined with a high spatial resolution to characterize the molecular species present in skin tissue. In this article, we use MALDI IMS to map specific lipids characteristic of two important skin appendages in minipig skin: the sebaceous glands and hair follicles. A set of specific lipid markers linked to the synthesis of sebum, stages of sebum production, and the secretion of sebum for two different sebaceous gland subzones, the peripheral and central necrotic, were identified. Furthermore, biochemical pathway analysis of the identified markers provides potential drug-targeting strategies to reduce sebum overproduction in pathological conditions. In addition, specific lipid markers characteristic of the different layers in the hair follicle bulge area, including the outer root sheath, the inner root sheath, and the medulla that are associated with the growth cycles of the hair, were determined. This research highlights the ability of MALDI IMS to link a molecular distribution not only to the morphological features in skin tissue but to the physiological state as well. Thus, this platform can provide a basis for the investigation of biochemical pathways as well as the mechanisms of disease and pharmacology in the skin, which will ultimately be critical for drug discovery and the development of dermatology-related illnesses.

We have recently developed a new synthetic methodology that provided both N-aryl-5-hydroxytriazoles and N-pyridine-4-alkyl triazoles. A selection of these products was carried through virtual screening towards targets that are contemporary and validated for drug discovery and development. This study determined a number of potential structure target dyads of which N-pyridinium-4-carboxylic-5-alkyl triazole displayed the highest score specificity towards KAT2A. Binding affinity tests of abovementioned triazole and related analogs towards KAT2A confirmed the predictions of the in-silico assay. Finally, we have run in vitro inhibition assays of selected triazoles towards KAT2A; the ensemble of binding and inhibition assays delivered pyridyl-triazoles carboxylates as the prototype of a new class of inhibitors of KAT2A.

In the past decades, rising safety concerns about drug-induced phospholipidosis (DIPL) have induced scientists to develop novel strategies to detect the risk of DIPL early in drug discovery and drug development. An early detection of phospholipidosis (PL)-inducing potential of new chemical entities will contribute to the release of safer drugs, also reducing associated investments and time. This chapter focuses on concisely summarizing the current knowledge on DIPL, from the chemical features that are compatible with warnings to the proposed mechanisms behind phospholipid accumulation. In addition, strategies to mitigate the risk of DIPL are discussed.

Molecular interaction fields (MIFs), describing molecules in terms of their ability to interact with any chemical entity, are one of the most established and versatile concepts in drug discovery. Improvement of this molecular description is highly desirable for in silico drug discovery and medicinal chemistry applications. In this work, we revised a well-established molecular mechanics' force field and applied a hybrid quantum mechanics and machine learning approach to parametrize the hydrogen-bonding (HB) potentials of small molecules, improving this aspect of the molecular description. Approximately 66,000 molecules were chosen from available drug databases and subjected to density functional theory calculations (DFT). For each atom, the molecular electrostatic potential (EP) was extracted and used to derive new HB energy contributions; this was subsequently combined with a fingerprint-based description of the structural environment via partial least squares modeling, enabling the new potentials to be used for molecules outside of the training set. We demonstrate that parameter prediction for molecules outside of the training set correlates with their DFT-derived EP, and that there is correlation of the new potentials with hydrogen-bond acidity and basicity scales. We show the newly derived MIFs vary in strength for various ring substitution in accordance with chemical intuition. Finally, we report that this derived parameter, when extended to non-HB atoms, can also be used to estimate sites of reaction.

Molecules online 26(6), 1613 (2021). doi:10.3390/molecules26061613 special issue: "Special Issue "Computational Methods in Drug Design and Food Chemistry" / Special Issue Editor: Prof. Dr. Giosuè Costa, Guest Editor", Published by Springer, Berlin ; Heidelberg

The importance of adsorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis is expected to grow substantially due to recent failures in detecting severe toxicity issues of new chemical entities during preclinical/clinical development. Traditionally, safety risk assessment studies for humans have been conducted in animals during advanced preclinical or clinical phase of drug development. However, potential drug toxicity in humans now needs to be detected in the drug discovery process as soon as possible without reliance on animal studies. The "omics", such as genomics, proteomics, and metabolomics, have recently entered pharmaceutical research in both drug discovery and drug development, but to the best of our knowledge, no applications in high-throughput safety risk assessment have been attempted so far. This paper reports an innovative method to anticipate adverse drug effects in an early discovery phase based on lipid fingerprints using human three-dimensional microtissues. The risk of clinical hepatotoxicity potential was evaluated for a data set of 22 drugs belonging to five different therapeutic chemical classes and with various drug-induced liver injury effect. The treatment of microtissues with repeated doses of each drug allowed collecting lipid fingerprints for five time points (2, 4, 7, 9, and 11 days), and multivariate statistical analysis was applied to search for correlations with the hepatotoxic effect. The method allowed clustering of the drugs based on their hepatotoxic effect, and the observed lipid impairments for a number of drugs was confirmed by literature sources. Compared to traditional screening methods, here multiple interconnected variables (lipids) are measured simultaneously, providing a snapshot of the cellular status from the lipid perspective at a molecular level. Applied here to hepatotoxicity, the proposed workflow can be applied to several tissues, being tridimensional microtissues from various origins.

Hetero-bifunctional PROteolysis TArgeting Chimeras (PROTACs) represent a new emerging class of small molecules designed to induce polyubiquitylation and proteasomal-dependent degradation of a target protein. Despite the increasing number of publications about the synthesis, biological evaluation, and mechanism of action of PROTACs, the characterization of the pharmacokinetic properties of this class of compounds is still minimal. Here, we report a study on the metabolism of a series of 40 PROTACs in cryopreserved human hepatocytes at multiple time points. Our results indicated that the metabolism of PROTACs could not be predicted from that of their constituent ligands. Their linkers' chemical nature and length resulted in playing a major role in the PROTACs' liability. A subset of compounds was also tested for metabolism by human cytochrome P450 3A4 (CYP3A4) and human aldehyde oxidase (hAOX) for more in-depth data interpretation, and both enzymes resulted in active PROTAC metabolism.

Indomethacin (INM), a well-known non-steroidal anti-inflammatory drug, has recently gained attention for its antiviral activity demonstrated in drug repurposing studies against severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). Although the mechanism of action of INM is not yet fully understood, recent studies have indicated that it acts at an early stage of the coronaviruses (CoVs) replication cycle. In addition, a proteomic study reported that the anti-SARS-CoV-2 activity of INM could be also ascribed to its ability to inhibit human prostaglandin E synthase type 2 (PGES-2), a host protein which interacts with the SARS-CoV-2 NSP7 protein. Although INM does not potently inhibit SARS-CoV-2 replication in infected Vero E6 cells, here we have explored for the first time the application of the Proteolysis Targeting Chimeras (PROTACs) technology in order to develop more potent INM-derived PROTACs with anti-CoV activity. In this study, we report the design, synthesis, and biological evaluation of a series of INM-based PROTACs endowed with antiviral activity against a panel of human CoVs, including different SARS-CoV-2 strains. Two PROTACs showed a strong improvement in antiviral potency compared to INM. Molecular modelling studies support human PGES-2 as a potential target of INM-based antiviral PROTACs, thus paving the way toward the development of host-directed anti-CoVs strategies. To the best of our knowledge, these PROTACs represent the first-in-class INM-based PROTACs with antiviral activity and also the first example of the application of PROTACs to develop pan-coronavirus agents., Graphical abstract Image 1

[Image: see text] The value of including protein flexibility in structure-based drug design (SBDD) is widely documented, and currently, molecular dynamics (MD) simulations represent a powerful tool to investigate protein dynamics. Yet, the inclusion of MD-derived information in pre-existing SBDD workflows is still far from trivial. We recently published an integrated MD-FLAP (Fingerprints for Ligands and Proteins) approach combining MD, clustering and Linear Discriminant Analysis (LDA) for enhancing accuracy, efficacy, and for protein conformational selection in virtual screening (VS) campaigns. Here we prospectively applied the MD-FLAP workflow to discover novel chemotypes inhibiting the Casein Kinase 1 delta (CSNK1D) enzyme. We first obtained a VS model able to separate active from inactive compounds, with a global AUC of 0.9 and a partial ROC enrichment at 0.5% of 0.18, and use it to mine the internal Pfizer screening database. Seven active molecules sharing a phenyl-indazole scaffold, not yet reported among CSNK1D inhibitors, were found. The most potent inhibitor showed an IC(50) of 134 nM.

This editorial discusses the foundation of aspects of computational chemistry and is a tribute to Peter Goodford, one of those founders, who recently passed away. Several colleagues describe Professor Goodford's work and the person himself.

St. John’s Wort (Hypericum perforatum L.) flowers are commonly used in ethnomedical preparations with promising outcomes to treat inflammation both per os and by topical application. However, the underlying molecular mechanisms need to be described toward a rational, evidence-based, and reproducible use. For this purpose, the aptitude of the prominent Hypericum metabolite hypericin was assessed, along with that of its main congeners, to behave as an inhibitor of janus kinase 1, a relevant enzyme in inflammatory response. It was used a molecular modeling approach relying on docking simulations, pharmacophoric modeling, and molecular dynamics to estimate the capability of molecules to interact and persist within the enzyme pocket. Our results highlighted the capability of hypericin, and some of its analogues and metabolites, to behave as ATP-competitive inhibitor providing: (i) a likely mechanistic elucidation of anti-inflammatory activity of H. perforatum extracts containing hypericin and related compounds, and (ii) a rational-based prioritization of H. perforatum components to further characterize their actual effectiveness as anti-inflammatory agents.

After more than 90 years from its discovery and thousands of papers published, the physiological roles of vitamin E (tocopherols and tocotrienols) are still not fully clarified. In the last few decades, the enzymatic metabolism of this vitamin has represented a stimulating subject of research. Its elucidation has opened up new horizons to the interpretation of the biological function of that class of molecules. The identification of specific properties for some of the physiological metabolites and the definition of advanced analytical techniques to assess the human metabolome of this vitamin in vivo, have paved the way to a series of hypotheses on the functional implications that this metabolism may have far beyond its catabolic role. The present review collects the available information on the most relevant analytical strategies employed to assess the status and metabolism of vitamin E in humans as well as in other model systems. A particular focus is dedicated to the analytical methods used to measure vitamin E metabolites, and particularly long-chain metabolites, in biological fluids and tissues. Preliminary information on a new LC-APCI-MS/MS method to measure these metabolites in human serum is reported.

Antibiotic resistance represents a worldwide concern, especially regarding the outbreak of methicillin-resistant Staphylococcus aureus, a common cause for serious skin and soft tissues infections. A major contributor to Staphylococcus aureus antibiotic resistance is the NorA efflux pump, which is able to extrude selected antibacterial drugs and biocides from the membrane, lowering their effective concentrations. Thus, the inhibition of NorA represents a promising and challenging strategy that would allow recycling of substrate antimicrobial agents. Among NorA inhibitors, the indole scaffold proved particularly effective and suitable for further optimization. In this study, some unexplored modifications on the indole scaffold are proposed. In particular, for the first time, substitutions at the C5 and N1 positions have been designed to give 48 compounds, which were synthesized and tested against norA-overexpressing S. aureus. Among them, 4 compounds have NorA IC50 values lower than 5.0 μM proving to be good efflux pump inhibitor (EPI) candidates. In addition, preliminary data on their ADME (absorption, distribution, metabolism, and excretion) profile is reported.

Aldehyde oxidase (AOX) is a molibdo-flavoenzyme that has raised great interest in recent years, since its contribution in xenobiotic metabolism has not always been identified before clinical trials, with consequent negative effects on the fate of new potential drugs. The fundamental role of AOX in metabolizing xenobiotics is also due to the attempt of medicinal chemists to stabilize candidates toward cytochrome P450 activity, which increases the risk for new compounds to be susceptible to AOX nucleophile attack. Therefore, novel strategies to predict the potential liability of new entities toward the AOX enzyme are urgently needed to increase effectiveness, reduce costs, and prioritize experimental studies. In the present work, we present the most up-to-date computational method to predict liability toward human AOX (hAOX), for applications in drug design and pharmacokinetic optimization. The method was developed using a large data set of homogeneous experimental data, which is also disclosed as Supporting Information .

Combinatorially generated molecular repertoires have been largely used to identify novel bioactive compounds. Ever more sophisticated technological solutions have been proposed to simplify and speed up such process, expanding the chemical diversity space and increasing the prospect to select new molecular entities with specific and potent activities against targets of therapeutic relevance. In this context, random mixtures of oligomeric peptides were originally used and since 25 years they represent a continuous source of bioactive molecules with potencies ranging from the sub-nM to microM concentration. Synthetic peptide libraries are still employed as starting “synthetic broths” of structurally and chemically diversified molecular fragments from which lead compounds can be extracted and further modified. Thousands of studies have been reported describing the application of combinatorial mixtures of synthetic peptides with different complexity and engrafted on diverse structural scaffolds for the identification of new compounds which have been further developed and also tested in in vivo models of relevant diseases. We briefly review some of the most used methodologies for library preparation and screening and the most recent case studies appeared in the literature where compounds have reached at least in vivo testing in animal or similar models. Recent technological advancements in biotechnology, engineering and computer science have suggested new options to facilitate the discovery of new bioactive peptides. In this instance, we anticipate here a new approach for the design of simple but focused tripeptide libraries against druggable cavities of therapeutic targets and its complementation with existing approaches.

Fomitopsis pinicola (Sw.) P. Karst. (Fomitopsidaceae) is a medicinal mushroom with a variety of healthy properties. In this study we tested the radical scavenging activity and antimicrobial and anticancer potential of methanol extracts of F pinicola from central Italy. Molecular identification confirmed that the samples were F. pinicola; a Basic Local Alignment Search Tool search showed a close match (99% sequence identity) with European isolates of this species. The free radical scavenging capacities, measured by DPPH assay, showed that the extract activity was 3.5% that of Trolox. The MTT test, evaluated after 72 hours of treatment with increasing doses of extract (5-500 mu g.mL(-1)), considerably inhibited proliferation in a dose-dependent manner in 2 human tumor cell lines. This reduction was coupled with a relevant induction of apoptosis in the human leukemia THP-1 cell line after 24 hours of treatment, but a relevant toxic effect occurred in the human colon adenocarcinoma HT29 cell line. The genotoxic potential of the methanol extracts was studied by single-cell gel electrophoresis of normal human leukocytes exposed to 20 mu g extract at 37 degrees C for 30 minutes; no DNA damage was observed. The F. pinicola methanol extract was found to have varying degrees of antifungal effects against the pathogenic fungi tested (minimum inhibitory concentration from 23.63 to 66.81 mu g.mL(-1)). The results show that the tested F. pinicola extract has strong antimicrobial and chemo-preventive activities, but is a poor antioxidant.

We introduce a new chemical space for drugs and drug-like molecules, exclusively based on their in silico ADME behaviour. This ADME-Space is based on self-organizing map (SOM) applied to 26,000 molecules. Twenty accurate QSPR models, describing important ADME properties, were developed and, successively, used as new molecular descriptors not related to molecular structure. Applications include permeability, active transport, metabolism and bioavailability studies, but the method can be even used to discuss drug-drug interactions (DDIs) or it can be extended to additional ADME properties. Thus, the ADME-Space opens a new framework for the multi-parametric data analysis in drug discovery where all ADME behaviours of molecules are condensed in one map: it allows medicinal chemists to simultaneously monitor several ADME properties, to rapidly select optimal ADME profiles, retrieve warning on potential ADME problems and DDIs or select proper in vitro experiments.

The NorA efflux pump is a potential drug target for reversal of resistance to selected antibacterial agents, and recently we described indole-based inhibitor candidates. Herein we report a second class of inhibitors derived from them but with significant differences in shape and size. In particular, compounds 13 and 14 are very potent inhibitors in that they demonstrated the lowest IC50 values (2 μM) ever observed among all indole-based compounds we have evaluated.

A series of stigmasterol and ergosterol derivatives, characterized by the presence of oxygenated functions at C-22 and/or C-23 positions, were designed as potential liver X receptor (LXR) agonists. The absolute configuration of the newly created chiral centers was definitively assigned for all the corresponding compounds. Among the 16 synthesized compounds, 21, 27, and 28 were found to be selective LXRα agonists, whereas 20, 22, and 25 showed good selectivity for the LXRβ isoform. In particular, 25 showed the same degree of potency as 22R-HC (3) at LXRβ, while it was virtually inactive at LXRα (EC50 = 14.51 μM). Interestingly, 13, 19, 20, and 25 showed to be LXR target gene-selective modulators, by strongly inducing the expression of ABCA1, while poorly or not activating the lipogenic genes SREBP1 and SCD1 or FASN, respectively.

In vitro safety assessment in early drug discovery represents an important step to detect potential safety-related liabilities. It reduces late stage attrition and allows candidate optimization. In this study, we report on the use of the LogDBPL assay (a recently published assay for the determination of drug distribution coefficients between an aqueous phase and porcine brain polar lipids extract) for phospholipidosis (PLD) risk evaluation. The LogDBPL parameter was first compared to the effective permeability in the parallel artificial membrane permeability assay (PAMPA), previously reported as correlating with PLD risk. Subsequently, the LogDBPL for a set of 234 drugs with known PLD effect was measured, representing the largest data set of LogDBPL data published so far, and the correlation with phospholipid accumulation was further investigated. In addition, a comparison with other in silico methods based on physicochemical parameters is reported. Results showed that LogDBPL is an efficient descriptor t...

The limited number of drug classes licensed for treatment of influenza virus (Flu), together with the continuous emergence of viral variants and drug resistant mutants, highlights the urgent need to find antivirals with novel mechanisms of action. In this context, the viral RNA-dependent RNA polymerase (RdRP) subunits assembly has emerged as an attractive target. Starting from a cycloheptathiophene-3-carboxamide derivative recently identified by us for its ability to disrupt the interaction between the PA and PB1 subunits of RdRP, we have designed and synthesized a series of analogues. Their biological evaluation led to the identification of more potent protein-protein interaction inhibitors, endowed with antiviral activity that also encompassed a number of clinical isolates of FluA, including an oseltamivir-resistant strain, and FluB, without showing appreciable toxicity. From this study, the cycloheptathiophene-3-carboxamide scaffold emerged as being particularly suitable to impart anti-Flu activity.

Drug discovery is a highly complex and costly process, and in recent years, the pharmaceutical industry has shifted from traditional to genomics- and proteomics-based drug research strategies. The identification of druggable target sites, promising hits, and high quality leads are crucial steps in the early stages of drug discovery projects. Pharmacokinetic (PK) and drug metabolism profiling to optimize bioavailability, clearance, and toxicity are increasingly important areas to prevent costly failures in preclinical and clinical studies. The integration of a wide variety of technologies and expertise in multidisciplinary research teams combining synergistic effects between experimental and computational approaches on the selection and optimization of bioactive compounds to pass these hurdles is now commonplace, although there remain challenging areas. Molecular interaction fields (MIFs) are widely used in a range of applications to support the discovery teams, characterizing molecules according to their favorable interaction sites and therefore enabling predictions to be made about how molecules might interact. The utility of MIF-based in silico approaches in drug design is extremely broad, including approaches to support experimental design in hit-finding, lead-optimization, physicochemical property prediction and PK modeling, drug metabolism prediction, and toxicity. C � 2013 John Wiley & Sons, Ltd.

Repurposing and repositioning drugs has become a frequently pursued and successful strategy in the current era, as new chemical entities are increasingly difficult to find and get approved. Herein we report an integrated BioGPS/FLAPdock pipeline for rapid and effective off-target identification and drug repurposing. Our method is based on the structural and chemical properties of protein binding sites, that is, the ligand image, encoded in the GRID molecular interaction fields (MIFs). Protein similarity is disclosed through the BioGPS algorithm by measuring the pockets' overlap according to which pockets are clustered. Co-crystallized and known ligands can be cross-docked among similar targets, selected for subsequent in vitro binding experiments, and possibly improved for inhibitory potency. We used human thymidylate synthase (TS) as a test case and searched the entire RCSB Protein Data Bank (PDB) for similar target pockets. We chose casein kinase IIα as a control and tested a series of its inhibitors against the TS template. Ellagic acid and apigenin were identified as TS inhibitors, and various flavonoids were selected and synthesized in a second-round selection. The compounds were demonstrated to be active in the low-micromolar range.

Bosentan, the first-in-class drug used in treatment of pulmonary arterial hypertension, is principally metabolized by the cytochromes P450, and it is responsible for cytochromes induction and drug-drug interaction events with moderate to severe consequences. A strategy to reduce drug-drug interactions consists of increasing the metabolic stability of the perpetrator, and fluorinated analogues are often designed to block the major sites of metabolism. In this paper bosentan analogues were synthesized, and their metabolism and biological activity were evaluated. All synthesized compounds showed an improved metabolic stability towards CYP2C9, with one maintaining a moderate antagonist effect towards the ETA receptor.

Nowadays clinical therapy witnesses a challenging bacterial resistance limiting the available armament of antibiotics. Over the decades strains resistant to all antibiotics have been selected while medicinal chemists were not able to develop agents capable of destroying them or to prevent their extension. In particular, carbapenem-resistant Enterobacteriaceae (CRE), representing one of the most common human pathogens, have been reported with increased frequency since their first identification twenty years ago. The enterobacterial carbapenemases differ from the extended spectrum β-lactamases (ESBL) in their ability to hydrolyze β-lactams, cephalosporins and most importantly monobactams and carbapenems. They are progressively spreading throughout the world, therefore leaving no effective β-lactam to cure bacterial infections. Several BLs-carbapenemase Xray structures have been determined making these enzymes attractive targets for structure-based drug design studies. However, very little has been done so far to powerfully address the inhibitor design issues for this emerging type of BLs. Here, we focus on the structural basis for molecular recognition and for broad spectrum activity of class A carbapenemases: based on available 3-dimensional structural information we identify a theoretical pharmacophoric model as a starting point for the development of needed carbapenemases inhibitors.

Five in silico principal properties (PPs) for 218 heterocyclic cations and four PPs for 38 organic and inorganic anionic counterparts of ionic liquids (ILs) were derived by the VolSurf+ approach. VolSurf+ physicochemical descriptors take into account several cationic structural features of ILs such as heterocyclic aromatic and non-aromatic cationic cores, alkyl chain length, presence of oxygen atoms in the substituents as well as the properties of a wide variety of inorganic and organic anions. Combination of these cation and anion PPs can provide descriptors for over 8000 ILs, thus allowing the development of QSPR models for IL cytotoxicity (IPC-81 rat cell line) and enzyme toxicity (acetylcholinesterase inhibition). The adoption of a Partial Least Squares approach, relating PPs and toxicities, provided affordable predictions for ILs in both learning and external validation sets, implying the possibility to extend the predictive model to a set of 520 ILs. This allows us to establish priorities in selecting ILs for experimental hazard assessment as required by the REACH regulation.

Four novel inhibitors of the NorA efflux pump of Staphylococcus aureus, discovered through a virtual screening process, are reported. The four compounds belong to different chemical classes and were tested for their in vitro ability to block the efflux of a well-known NorA substrate, as well as for their ability to potentiate the effect of ciprofloxacin (CPX) on several strains of S. aureus, including a NorA overexpressing strain. Additionally, the MIC values of each of the compounds individually are reported. A structure-activity relationship study was also performed on these novel chemotypes, revealing three new compounds that are also potent NorA inhibitors. The virtual screening procedure employed FLAP, a new methodology based on GRID force field descriptors.

We have recently developed a tool, MoKa, to predict the pK(a) of organic compounds using a large dataset of over 26,500 literature pK(a) values as a training set. However, predicting accurately pK(a) (0.5 pH units) remains challenging for novel series, and this can be a drawback in the optimization of activity and ADME properties of lead compounds. To address this issue it is important to expand our knowledge of pK(a) determinants, therefore we have conducted high-throughput pK(a) measurements by using Spectral Gradient Analysis (SGA) on novel series of compounds selected from vendor databases. Here we report our findings on the effect of specific chemical groups and steric constraints on the pK(a) of common functionalities in medicinal chemistry, such as amines, sulfonamides, and amides. Furthermore, we report the pK(a) of ionizable groups that were not well represented in the database of literature pK(a) of MoKalpha, such as hydrazide derivatives. These findings helped us to enhance MoKalpha, which is here benchmarked on a set of experimental pK(a) values from the Roche in-house library (N = 5581; RMSE = 1.09; R2 = 0.82). The accuracy of the predictions was greatly improved (RMSE = 0.49, R2 = 0.96) after training the software by using the automated tool Kibitzer with 6226 pK(a) values taken from a different set of Roche compounds appropriately selected, and this demonstrates the value of using high-throughput pK(a) measurements to expand the training set of pK(a) values used by the software MoKalpha.

We studied the stereoselective behavior of 1-[(4-chlorophenyl)sulfonyl]-2-(2-thienyl)pyrrolidine, a recently described blocker of cardiovascular L-type calcium channels that binds to the diltiazem site. Given the stereocenter at C-2 of the pyrrolidine ring, the two enantiomers were separated by chiral HPLC and, using VCD in conjunction with DFT calculations of chiroptical properties, the absolute configuration was assigned as R-(+)/S-(-). For both forms, functional, electrophysiological, and binding properties were studied and the three-dimensional superimpositions of the two enantiomers over diltiazem were obtained in silico. The significant differences observed for the two enantiomers well agreed with the experimental data, and molecular regions were hypothesized as responsible for the cardiac stereoselectivity and vascular stereospecificity.

The limited clinical efficacy of many cancer therapeutics has initiated intense research efforts toward the discovery of novel chemical entities in this field. In this study, 31 hit candidates were selected from nearly 800,000 database compounds in a ligand-based virtual screening campaign. In turn, three of these hits were found to have (sub)micromolar potencies in proliferation assays with the Jurkat acute lymphatic leukemic cell line. In this assay, the three hits were found to exhibit higher potency than clinically tested cell-death inducers (GDC-0152, AT-406, and birinapant). Importantly, antiproliferative activity toward non-cancer peripheral blood mononuclear cells (PBMCs) was found to be marginal. Further biological characterization demonstrated the cell-death-inducing properties of these compounds. Biological testing of hit congeners excluded a nonspecific, toxic effect of the novel structures. Altogether, these findings may have profound relevance for the development of clinical candidates in tumor therapy.

In continuing our efforts to identify small molecules able to disrupt the interaction of the polymerase acidic protein–basic protein 1 (PA–PB1) subunits of influenza virus (Flu) RNA-dependent RNA polymerase, this paper is devoted to the optimization of a dihydrotriazolopyrimidine derivative, previously identified through structure-based drug discovery. The structure modifications performed around the bicyclic core led to the identification of compounds endowed with both the ability to disrupt PA–PB1 subunits interaction and anti-Flu activity with no cytotoxicity. Very interesting results were obtained with the hybrid molecules 36 and 37, designed by merging some peculiar structural features known to impart PA–PB1 interaction inhibition, with compound 36 that emerged as the most potent PA–PB1 interaction inhibitor (IC50 = 1.1 μM) among all the small molecules reported so far. Calculations showed a very favored H-bonding between the 2-amidic carbonyl of 36 and Q408, which seems to justify its potent ability to interfere with the interaction of the polymerase subunits.

Serine racemase, the enzyme responsible for D-serine synthesis in the central nervous system, has been identified as a potential therapeutic target to treat N-methyl-D-aspartate receptors-related pathologies. The search for specific inhibitors of the enzyme has revealed that serine racemase is a difficult target, with the best inhibitor currently identified, 2,2-dichloromalonate, showing a K-i of 19 mu M. In order to expand the chemical space of hit compounds, we have performed an in silico structure-based screening campaign on a filtered ZINC library applying the FLAP software. The identified hits were docked with GOLD and re-scored with HINT, and the most promising molecules experimentally evaluated on recombinant human serine racemase. Two inhibitors, with chemical structures totally unrelated to inhibitors described so far showed K-i values of about 1.5 mM.

To date, the most common way of screening new potential sensitizers for dye sensitized solar cells is via the traditional time and money consuming trial and error approach. In this study we explore the possibility of extending drug discovery and cheminformatic approaches to the field of material science with the aim of a quantitative structure-property relationship elucidation that could lead to a fast and inexpensive in silico screening of new ruthenium sensitizers for third generation solar cells. Starting from the building of a database of already tested candidates used to train the predictive models, appropriate descriptors extracted from images of 3D molecular interaction fields (GRID/MIFs), as well as semi-empirical calculated descriptors, were chosen to describe the target structures. Then, structure-performance (Jsc, Voc and PCE) models were built and analysed in order to elucidate structure-property relationships and interesting results were obtained. In particular, we were able to find the molecular descriptors that more contribute to enhance the performance investigated, thus finding directives for the design of potentially high-performing candidates. We also proposed an efficient correction of the experimental Jsc and Voc based on the quantity of the LiI additive for electrolyte used to build the devices. In the early stage of this project, we demonstrated that molecular modelling methods could be successfully extended to the field of material science as alternative to the traditional expensive and time-consuming trial and error approach.

We report here a general method for the prediction of hERG potassium channel blockers using computational models generated from correlation analyses of a large dataset and pharmacophore-based GRIND descriptors. These 3D-QSAR models are compared favorably with other traditional and chemometric based HQSAR methods.

About 20 non-peptide angiotensin II receptor antagonists are in various stages of clinical development. Different modeling approaches were used to predict the pharmacophoric requirements for AT(1) (angiotensin II receptor subtype 1) affinity. However, to our knowledge, none was used to predict both the selectivity toward AT(1) and AT(2) (angiotensin II receptor subtype 2) receptor subtypes. In this paper, partial least squares discriminant analysis is applied to derive the chemical features guiding AT(1) and AT(2) selectivity or mixed AT(1)/AT(2) receptor binding. The method can be used to modulate AT(1) versus AT(2) selectivity. Concerns that unopposed stimulation of the AT(2) receptor might produce adverse effects initiated a search for new balanced antagonists. Moreover, it can serve as a fast filtering procedure in database searches. Finally, some relevant pharmacokinetics and metabolic properties of the database of 53 compounds are calculated using the VolSurf and MetaSite software to allow the simultaneous characterization of pharmacodynamic and pharmacokinetics properties of the chemical space of angiotensin II receptor antagonists.

Trying to understand the complex interactions that substrates and inhibitors have with the efflux transporter P-glycoprotein has been the subject of various publications. In this work, we have confined our study to substrates by picking a diverse set of 129 compounds based on the efflux ratios from Caco-2 permeability measurements. These compounds were then evaluated for P-glycoprotein inhibition using a calcein-AM assay. The subsequent data was used in a 3D-QSAR analysis using GRIND pharmacophore-based and physicochemical descriptors. Pharmacophore-based descriptors produced a much more robust model than the one obtained from physicochemical-based descriptors. This supports the process proposed by Seelig and co-workers previously published whereby the substrate enters the membrane as the first step and is then recognized by P-glycoprotein in a second step. The strong correlation, highlighted by PLS statistical analysis, between pharmacophoric descriptors and inhibition values suggests that substrate interaction, with perhaps the mouth of the protein or another binding site, plays a key role in the efflux process, yielding a model in which diffusion across the membrane is less important than substrate-protein interaction. One pharmacophore emerged from the analysis of the model. We pose that the recognition elements, at least determined by the molecules used in this study, are two hydrophobic groups 16.5 A apart and two hydrogen-bond-acceptor groups 11.5 A apart and that the dimensions of the molecule also plays a role in its recognition as a substrate.

Structural patterns for antitumor drugs, evidenced by means of a molecular interaction field (MIF) approach using grid independent descriptors (GRIND), resembled closely those of a previous independent pharmacological classification based on their antitumor mechanism of action. For topoisomerase II inhibitors, antimitotic agents and DNA antimetabolites, systematic structural patterns were evidenced by MIF and the structural features of "outliers" in these classes corresponded to peculiar pharmacological mechanisms of action supported by literature evidences. Alkylating agents and DNA/RNA antimetabolites, interacting with a large variety of targets by different molecular mechanisms, did not exhibit clustering in the structure-based MIF approach. Moreover MIFS were able to point out similarities between drugs which, in spite of apparent dramatic differences in chemical structure, exhibit the same pharmacological behaviour.

A conformer- and alignment-independent three-dimensional structure-activity relationship (3D-QSAR) model has been derived that is based on flexible molecular interaction fields calculated in GRID and the subsequent description of these fields by use of alignment-independent descriptors derived in ALMOND. The training set consisted of 22 diverse and flexible competitive inhibitors of the drug-metabolizing enzyme CYP2C9 and generated a model with r(2) of 0.81 and q(2) of 0.62. The predicitive capacity of the model was externally evaluated with a test set of 12 competitive inhibitors and 11 out of 12 were predicted within 0.5 log unit. The most relevant points of interaction in the model correlated well to the amino acids involved in CYP2C9-substrate/inhibitor binding in the active site of a CYP2C9 homology model, further validating the mechanistic sense of our model. This approach offers the possibility to derive predicitve 3D-QSAR models without the need for an alignment rule for chemically diverse ligands and in the absence of target protein crystal structure information.

The aim of the present study is to develop a method for predicting the site at which molecules will be metabolized by CYP 2C9 (cytochrome P450 2C9) using a previously reported protein homology model of the enzyme. Such a method would be of great help in designing new compounds with a better pharmacokinetic profile, or in designing prodrugs where the compound needs to be metabolized in order to become active. The methodology is based on a comparison between alignment-independent descriptors derived from GRID Molecular Interaction Fields for the CYP 2C9 active site, and a distance-based representation of the substrate. The predicted site of metabolism is reported as a ranking list of all the hydrogen atoms of each substrate molecule. Eighty-seven CYP 2C9-catalyzed oxidative reactions reported in the literature have been analyzed. In more than 90% of these cases, the hydrogen atom ranked at the first, second, or third position was the experimentally reported site of oxidation.

Cocaine is one of the most widely abused drugs in the industrial world. Substantial evidence has accumulated that the dopamine transporter (DAT) is a key target for cocaine regarding its reinforcing effects. This work describes the application of chemometric methods to a data set of 54 N(1)-benzhydryl-oxy-alkyl-N(4)-phenyl-alk(en)yl-piperazines (GBR compounds) and chemically related mepyramines as putative candidates in cocaine abuse therapy. The aim of the study is to gain insight into the structural requirements that determine the affinity of the data set molecules to the DAT and the serotonin transporter (SERT) as well as their inhibitory potency on dopamine uptake. The compounds in the dataset are described using the recently developed GRID independent descriptors (GRIND), which allow one to obtain fast three-dimensional quantitative structure-activity relationship models without the need of aligning and superimposing the structures; the results are interpreted in a convenient pharmacophoric-like fashion. In the first part of the work, the selectivity of the database molecules for DAT binding vs dopamine reuptake inhibition is investigated. In the second part, the selectivity of the compounds for DAT binding vs SERT binding is studied. In both cases, significant models are obtained, which define the structural features responsible for the respective selectivity profiles. Moreover, the information has potential interest for the design of new derivatives with improved selectivity.

In the era of systems biology, multi-target pharmacological strategies hold promise for tackling disease-related networks. In this regard, drug promiscuity may be leveraged to interfere with multiple receptors: the so-called polypharmacology of drugs can be anticipated by analyzing the similarity of binding sites across the proteome. Here, we perform a pairwise comparison of 90,000 putative binding pockets detected in 3,700 proteins, and find that 23,000 pairs of proteins have at least one similar cavity that could, in principle, accommodate similar ligands. By inspecting these pairs, we demonstrate how the detection of similar binding sites expands the space of opportunities for the rational design of drug polypharmacology. Finally, we illustrate how to leverage these opportunities in protein-protein interaction networks related to several therapeutic classes and tumor types, and in a genome-scale metabolic model of leukemia., Author summary Traditionally, the fact that most drugs are promiscuous binders has been a major concern in pharmacology, due to the occurrence of undesired off-target clinical events. In the recent years, however, the realization that many diseases are the result of complex biological processes has encouraged rethinking of drug promiscuity as a promising feature, since it is sometimes necessary to interfere with multiple receptors in order to overcome the robustness of disease-related networks. One way to identify groups of proteins that could be targeted simultaneously is to look for similar binding sites. We have massively done so for all human proteins with a known high-resolution three-dimensional structure, unveiling a vast space of ‘polypharmacology’ opportunities. Of these, we know, a great majority is not of therapeutic interest. To pinpoint promising multi-target combinations, we advocate for the use of computational tools that are able to rapidly simulate the effect of drug-target interactions on biological networks.

FMO enzymes (FMOs) play a key role in the processes of detoxification and/or bioactivation of specific pharmaceuticals and xenobiotics bearing nucleophilic centers. The N-oxide and S-oxide metabolites produced by FMOs are often active metabolites. The FMOs are more active than cytochromes in the brain and work in tandem with CYP3A4 in the liver. FMOs might reduce the risk of phospholipidosis of CAD-like drugs, although some FMOs metabolites seem to be neurotoxic and hepatotoxic. However, in silico methods for FMO metabolism prediction are not yet available. This paper reports, for the first time, a substrate-specificity and catalytic-activity model for FMO3, the most relevant isoform of the FMOs in humans. The application of this model to a series of compounds with unknown FMO metabolism is also reported. The model has also been very useful to design compounds with optimal clearance and in finding erroneous literature data, particularly cases in which substances have been reported to be FMO3 substrates when, in reality, the experimentally validated in silico model correctly predicts that they are not.

Influenza virus infections represent a serious concern to public health, being characterized by high morbidity and significant mortality. To date, compounds targeting the viral ion-channel M2 or the viral neuraminidase are the drugs available for treatment of influenza, but the emergence of drug-resistant viral mutants renders the search for novel targets and their possible inhibitors a major priority. Recently, we demonstrated that the viral RNA-dependent RNA polymerase (RdRP) complex can be an optimal target of protein–protein disruption by small molecules, with thiophene-3-carboxamide derivatives emerging as promising candidates for the development of new anti-influenza drugs with broad-spectrum activity. Here, we report a further dissection of the thiophene-3-carboxamide structure. By using a GRID molecular interaction field (MIF)-based scaffold-hopping approach, more potent and nontoxic polyamido derivatives were identified, highlighting a new space in the chemical variability of RdRP inhibitors. Finally, a possible pharmacophoric model highlighting the key features required for RdRP inhibition is proposed.

Identifying cross-relationships among protein binding sites is becoming increasingly important in the chemo- and bioinformatics field; indeed, protein structural similarity might provide the right answer to a number of questions including Is a drug repurposable for another target? What is the molecular mechanism of a drug side-effect? How can we improve the ligand selectivity? The comparison of protein binding sites in terms of their three-dimensional structure molecular interaction fields can be a useful technique to approach all of these problems. Here, we report a semi-automated method for comparing and clustering protein pockets, called BioGPS, that combines the GRID Molecular Interactions Fields (MIFs) with FLAP pharmacophoric fingerprints. BioGPS identifies and compares protein binding sites by aligning them each other and directly comparing their MIFs. The strengths of this approach are that it is MIF-based, and therefore describes molecular interactions from a ligand perspective, and it is independent of protein superposition or sequence alignment. This approach enables protein-protein virtual screening (drug repurposing, polypharmacology, off-target effects), and also clustering to relate sequence-based similarities to structure-based differences among protein binding sites.

Cystalysin from Treponema denticola is a pyridoxal 5'-phosphate dependent lyase that catalyzes the formation of pyruvate, ammonia, and sulfide from cysteine. It is a virulence factor in adult periodontitis because its reaction contributes to hemolysis, which sustains the pathogen. Therefore, it was proposed as a potential antimicrobial target. To identify specific inhibitors by structure-based in silico methods, we first validated the crystal structure of cystalysin as a reliable starting point for the design of ligands. By using single-crystal absorption microspectrophotometry, we found that the enzyme in the crystalline state, with respect to that in solution, exhibits: 1) the same absorption spectra for the catalytic intermediates, 2) a close pKa value for the residue controlling the keto enamine ionization, and 3) similar reactivity with glycine, L-serine, L-methionine, and the nonspecific irreversible inhibitor aminoethoxyvinylglycine. Next, we screened in silico a library of 9357 compounds with the Fingerprints for Ligands and Proteins (FLAP) software, by using the three-dimensional structure of cystalysin as a template. From the library, 17 compounds were selected and experimentally evaluated by enzyme assays and spectroscopic methods. Two compounds were found to competitively inhibit recombinant T. denticola cystalysin, with inhibition constant (Ki ) values of 25 and 37 μM. One of them exhibited a minimum inhibitory concentration (MIC) value of 64 μg mL(-1) on Moraxella catarrhalis ATCC 23246, which proves its ability to cross bacterial membranes.

Fifty-nine xanthones (= 9H-xanthen-9-ones) of natural or synthetic origin were investigated for their inhibitory activity toward monoamine-oxidase A (MAO-A) and MAO-B. The compounds demonstrated reversible. time-independent activities, with selectivity toward MAO-A. The most active inhibitor had an IC50 of 40 nM. Electron absorption spectroscopy revealed the formation of a 1:1 charge-transfer complex between lumiflavine and xanthones. 3D-QSAR Studies according to the CoMFA/GOLPE procedure provided information on the relationship between steric and electrostatic fields and MAO-A inhibition. The ALMOND procedure yielded additional topographical information on structural factors favoring activity

Antiviral quinolones are promising compounds in the search for new therapeutically effective agents for the treatment of AIDS. To rationalize the SAR for this new interesting class of anti-HIV derivatives, we performed a 3D-QSAR study on a library of 101 6-fluoro and 6-desfluoroquinolones, taken either from the literature or synthesized by us. The chemometric procedure involved a fully semiempirical minimization of the molecular structures by the AMSOL program, which takes into account the solvatation effect, and their 3D characterization by the VolSurf/GRID program. The QSAR analysis, based on PCA and PLS methods, shows the key structural features responsible for the antiviral activity.

Traditional methods for performing 3D-QSAR rely upon an alignment step that is often time-consuming and can introduce user bias, the resultant model being dependent upon and sensitive to the alignment used. There are several methods which overcome this problem, but in general the necessary transformations prevent a simple interpretation of the resultant models in the original descriptor space (i.e. 3D molecular coordinates). Here we present a novel class of molecular descriptors which we have termed GRid-INdependent Descriptors (GRIND). They are derived in such a way as to be highly relevant for describing biological properties of compounds while being alignment-independent, chemically interpretable, and easy to compute. GRIND are obtained starting from a set of molecular interaction fields, computed by the program GRID or by other programs. The procedure for computing the descriptors involves a first step, in which the fields are simplified, and a second step, in which the results are encoded into alignment-independent variables using a particular type of autocorrelation transform. The molecular descriptors so obtained can be used to obtain graphical diagrams called "correlograms" and can be used in different chemometric analyses, such as principal component analysis or partial least-squares. An important feature of GRIND is that, with the use of appropriate software, the original descriptors (molecular interaction fields) can be regenerated from the autocorrelation transform and, thus, the results of the analysis represented graphically, together with the original molecular structures, in 3D plots. In this respect, the article introduces the program ALMOND, a software package developed in our group for the computation, analysis, and interpretation of GRIND. The use of the methodology is illustrated using some examples from the field of 3D-QSAR. Highly predictive and interpretable models are obtained showing the promising potential of the novel descriptors in drug design.

Understanding the development of a scientific approach is a valuable exercise in gauging the potential directions the process could take in the future. The relatively short history of applying computational methods to absorption, distribution, metabolism and excretion (ADME) can be split into defined periods. The first began in the 1960s and continued through the 1970s with the work of Corwin Hansch et al. Their models utilized small sets of in vivo ADME data. The second era from the 1980s through 1990s witnessed the widespread incorporation of in vitro approaches as surrogates of in vivo ADME studies. These approaches fostered the initiation and increase in interpretable computational ADME models available in the literature. The third era is the present were there are many literature data sets derived from in vitro data for absorption, drug–drug interactions (DDI), drug transporters and efflux pumps [P-glycoprotein (P-gp), MRP], intrinsic clearance and brain penetration, which can theoretically be used to predict the situation in vivo in humans. Combinatorial synthesis, high throughput screening and computational approaches have emerged as a result of continual pressure on pharmaceutical companies to accelerate drug discovery while decreasing drug development costs. The goal has become to reduce the drop-out rate of drug candidates in the latter, most expensive stages of drug development. This is accomplished by increasing the failure rate of candidate compounds in the preclinical stages and increasing the speed of nomination of likely clinical candidates. The industry now understands the reasons for clinical failure other than efficacy are mainly related to pharmacokinetics and toxicity. The late 1990s saw significant company investment in ADME and drug safety departments to assess properties such as metabolic stability, cytochrome P-450 inhibition, absorption and genotoxicity earlier in the drug discovery paradigm. The next logical step in this process is the evaluation of higher throughput data to determine if computational (in silico) models can be constructed and validated from it. Such models would allow an exponential increase in the number of compounds screened virtually for ADME parameters. A number of researchers have started to utilize in silico, in vitro and in vivo approaches in parallel to address intestinal permeability and cytochrome P-450-mediated DDI. This review will assess how computational approaches for ADME parameters have evolved and how they are likely to progress.