DrugCentral 2023 extends human clinical data and integrates veterinary drugs.

DrugCentral monitors new drug approvals and standardizes drug information. The current update contains 285 drugs (131 for human use). New additions include: (i) the integration of veterinary drugs (154 for animal use only), (ii) the addition of 66 documented off-label uses and iii) the identification of adverse drug events from pharmacovigilance data for pediatric and geriatric patients. Additional enhancements include chemical substructure searching using SMILES and 'Target Cards' based on UniProt accession codes. Statistics of interests include the following: (i) 60% of the covered drugs are on-market drugs with expired patent and exclusivity coverage, 17% are off-market, and 23% are on-market drugs with active patents and exclusivity coverage; (ii) 59% of the drugs are oral, 33% are parenteral and 18% topical, at the level of the active ingredients; (iii) only 3% of all drugs are for animal use only; however, 61% of the veterinary drugs are also approved for human use; (iv) dogs, cats and horses are by far the most represented target species for veterinary drugs; (v) the physicochemical property profile of animal drugs is very similar to that of human drugs. Use cases include azaperone, the only sedative approved for swine, and ruxolitinib, a Janus kinase inhibitor.

Exploring DrugCentral: from molecular structures to clinical effects.

DrugCentral, accessible at https://drugcentral.org , is an open-access online drug information repository. It covers over 4950 drugs, incorporating structural, physicochemical, and pharmacological details to support drug discovery, development, and repositioning. With around 20,000 bioactivity data points, manual curation enhances information from several major digital sources. Approximately 724 mechanism-of-action (MoA) targets offer updated drug target insights. The platform captures clinical data: over 14,300 on- and off-label uses, 27,000 contraindications, and around 340,000 adverse drug events from pharmacovigilance reports. DrugCentral encompasses information from molecular structures to marketed formulations, providing a comprehensive pharmaceutical reference. Users can easily navigate basic drug information and key features, making DrugCentral a versatile, unique resource. Furthermore, we present a use-case example where we utilize experimentally determined data from DrugCentral to support drug repurposing. A minimum activity threshold t should be considered against novel targets to repurpose a drug. Analyzing 1156 bioactivities for human MoA targets suggests a general threshold of 1 µM: t = 6 when expressed as - log[Activity(M)]). This applies to 87% of the drugs. Moreover, t can be refined empirically based on water solubility (S): t = 3 - logS, for logS < - 3. Alongside the drug repurposing classification scheme, which considers intellectual property rights, market exclusivity protections, and market accessibility, DrugCentral provides valuable data to prioritize candidates for drug repurposing programs efficiently.

DrugCentral 2021 supports drug discovery and repositioning.

DrugCentral is a public resource (http://drugcentral.org) that serves the scientific community by providing up-to-date drug information, as described in previous papers. The current release includes 109 newly approved (October 2018 through March 2020) active pharmaceutical ingredients in the US, Europe, Japan and other countries; and two molecular entities (e.g. mefuparib) of interest for COVID19. New additions include a set of pharmacokinetic properties for ∼1000 drugs, and a sex-based separation of side effects, processed from FAERS (FDA Adverse Event Reporting System); as well as a drug repositioning prioritization scheme based on the market availability and intellectual property rights forFDA approved drugs. In the context of the COVID19 pandemic, we also incorporated REDIAL-2020, a machine learning platform that estimates anti-SARS-CoV-2 activities, as well as the 'drugs in news' feature offers a brief enumeration of the most interesting drugs at the present moment. The full database dump and data files are available for download from the DrugCentral web portal.

Bioprospecting Fluorescent Plant Growth Regulators from Arabidopsis to Vegetable Crops.

The phytohormone auxin is involved in almost every process of a plant's life, from germination to plant development. Nowadays, auxin research connects synthetic chemistry, plant biology and computational chemistry in order to develop innovative and safe compounds to be used in sustainable agricultural practice. In this framework, we developed new fluorescent compounds, ethanolammonium p-aminobenzoate (HEA-pABA) and p-nitrobenzoate (HEA-pNBA), and investigated their auxin-like behavior on two main commercial vegetables cultivated in Europe, cucumber (Cucumis sativus) and tomato (Solanumlycopersicum), in comparison to the model plant Arabidopsis (Arabidopsis thaliana). Moreover, the binding modes and affinities of two organic salts in relation to the natural auxin indole-3-acetic acid (IAA) into TIR1 auxin receptor were investigated by computational approaches (homology modeling and molecular docking). Both experimental and theoretical results highlight HEA-pABA as a fluorescent compound with auxin-like activity both in Arabidopsis and the commercial cucumber and tomato. Therefore, alkanolammonium benzoates have a great potential as promising sustainable plant growth stimulators to be efficiently used in vegetable crops.

Off-Patent Drug Repositioning.

Drug repositioning aims to reuse "old" drugs to treat diseases outside their approved indication(s). Composition-of-matter patents and FDA exclusivities can hinder the immediate availability of some drugs to be repositioned (repurposed). Here, we analyze data from the FDA Orange Book and use current on-market patent validity and exclusivities to classify drugs into on-patent (ONP), off-patent (OFP), and off-market (OFM) sets. In the absence of an unanimously accepted definition for small molecules, these sets include organic molecules and peptides with molecular weight between 100 and 1250, which resulted in 237 ONP drugs, 320 OFM, and 996 OFP drugs, respectively. We discuss the differences between the three categories in terms of primary molecular properties, chemical diversity, mechanism-of-action target classes, and therapeutic areas and comment on the enrichment of OFP drugs in the near future. Given the intellectual property landscape, and in the absence of specific property rights, we suggest that drugs should be prioritized as follows, to improve the repositioning strategy: (i) OFP, (ii) OFM, and (iii) ONP, respectively.

Enhancing Molecular Promiscuity Evaluation Through Assay Profiles.

PURPOSE: The growing amount of heterogeneous bioactivity data requires effective strategies to assess the promiscuity/selectivity of small-molecules and aid drug discovery. In the current study, we aim to evaluate the potential of assay profiles (APs, i.e., unique combinations of assay-related features describing how activity determinations were performed and reported) in molecular promiscuity analysis. METHODS: Using PubChem bioactivity data, we computed for all Molecular Libraries Small Molecule Repository (MLSMR library) compounds the frequency of hits score (FoH, i.e., the ratio between the number of times the compound was found active and the number of times it was tested), which were subsequently fit into 32 theoretical APs. The promiscuity of drugs and non-drugs was compared at different levels of test results. RESULTS: We found 8 dominant APs, indicating that compounds tested in more than ten assays (or against ten targets) and found active at least once tend to reach near to maximum hit rates in scientific literature and confirmatory assays (e.g., 95% of the drugs show FoH scores >0.93). Primary and high-throughput screening testing results in very low hit rates (e.g., 95% of the compounds show FoH scores <0.11), promoting a different perspective of promiscuity. In general, drugs exert higher promiscuity compared to non-drugs. Targets and classes of drugs are also discussed within the main APs. CONCLUSION: APs contain relevant features and are suited for big data promiscuity analysis. The activity data of the main APs are freely available on www.chembioinf.ro .

Modeling Kinase Inhibition Using Highly Confident Data Sets.

Protein kinases form a consistent class of promising drug targets, and several efforts have been made to predict the activities of small molecules against a representative part of the kinome. This study continues our previous work ( Bora , A. ; Avram , S. ; Ciucanu , I. ; Raica , M. ; Avram , S. Predictive Models for Fast and Effective Profiling of Kinase Inhibitors . J. Chem. Inf. MODEL: 2016 , 56 , 895 - 905 ; www.chembioinf.ro ) aiming to build and measure the performance of ligand-based kinase inhibitor prediction models. Here we analyzed kinase-inhibitor pairs with multiple activity points extracted from the ChEMBL database and identified the main sources of inconsistency. Our results indicate that lower IC50 values are usually less affected by errors and reflect more accurately the structure-activity relationship of the molecules against the target, ideally for quantitative structure-activity relationship studies. Further, we modeled the activities of 104 kinases using unbiased target-specific activity points. The performance of predictors built on extended connectivity fingerprints (ECFP4) and two-dimensional pharmacophore fingerprints (PFPs) are compared by means of tolerance intervals (TIs) (95%/95%) in virtual screening (VS) and classification tasks using external random ( RandSets) and diversity-based ( DivSets) test sets. We found that the two encodings perform superior to each other on different kinases in VS and that PFP models perform consistently better in classifying actives (higher sensitivity). Next, we combined the two encodings into a single one (PFPECFP) and demonstrated that especially in VS (as indicated by the exponential receiver operating curve enrichment metric (eROCE)), for the vast majority of kinases the model performance increased compared with the individual fingerprint models. These findings are highlighted in the more challenging DivSets compared with RandSets. The current paper explores the boundaries of inhibitor predictors for individual kinases to enhance VS and ultimately aid the discovery of novel compounds with desirable polypharmacology.

CHEMICAL AND BIOLOGICAL DESCRIPTOR INTEGRATION IMPROVES COMPUTATIONAL MODELING OF IN VIVO RAT TOXICITY.

Computational toxicology is a new discipline in the area of computational molecular sciences, which is rapidly developing as a result of the public interest stirred by several European and US initiatives. Here, we report the use of primary high throughput screening (HTS) data as biological descriptors to complement the chemical descriptors for the modelling of the acute toxicity. The combination of biological and chemical descriptors was performed on the median lethal dose following oral administration in rats (rat LD50). The hybrid model developed based on chemical and biological descriptors is superior to models based on the chemical or biological description alone. Using this model, besides the accurately prediction of a compound's toxicity we also identified molecular fragments whose presence may contribute to increase or decrease of the toxicity.

A crowdsourcing evaluation of the NIH chemical probes.

Between 2004 and 2008, the US National Institutes of Health Molecular Libraries and Imaging initiative pilot phase funded 10 high-throughput screening centers, resulting in the deposition of 691 assays into PubChem and the nomination of 64 chemical probes. We crowdsourced the Molecular Libraries and Imaging initiative output to 11 experts, who expressed medium or high levels of confidence in 48 of these 64 probes.

Synthesis, structure and toxicity evaluation of ethanolamine nitro/chloronitrobenzoates: a combined experimental and theoretical study.

BACKGROUND: Nitroaromatic and chloronitroaromatic compounds have been a subject of great interest in industry and recently in medical-pharmaceutic field. 2-Chloro-4-nitro/2-chloro-5-nitrobenzoic acids and 4-nitrobenzoic acid are promising new agents for the treatment of main infectious killing diseases in the world: immunodeficiency diseases and tuberculosis. RESULTS: New ethanolamine nitro/chloronitrobenzoates were synthesized and characterized by X-ray crystallography, UV-vis, FT-IR and elementary analysis techniques. The toxicity of the compounds prepared and correspondent components was evaluated using Hydractinia echinata as test system. A significant lower toxicity was observed for nitro-derivative compared with chloronitro-derivatives and individual components. Crystallographic studies, together with the chemical reactivity and stability profiles resulted from density functional theory and ab initio molecular orbital calculations, explain the particular behavior of ethanolamine 4-nitrobenzoate in biological test. CONCLUSIONS: The experimental and theoretical data reveal the potential of these compounds to contribute to the design of new active pharmaceutical ingredients with lower toxicity.

FRET detection of lymphocyte function-associated antigen-1 conformational extension.

Lymphocyte function-associated antigen 1 (LFA-1, CD11a/CD18, αLß2-integrin) and its ligands are essential for adhesion between T-cells and antigen-presenting cells, formation of the immunological synapse, and other immune cell interactions. LFA-1 function is regulated through conformational changes that include the modulation of ligand binding affinity and molecular extension. However, the relationship between molecular conformation and function is unclear. Here fluorescence resonance energy transfer (FRET) with new LFA-1-specific fluorescent probes showed that triggering of the pathway used for T-cell activation induced rapid unquenching of the FRET signal consistent with extension of the molecule. Analysis of the FRET quenching at rest revealed an unexpected result that can be interpreted as a previously unknown LFA-1 conformation.

Structural determinants of the alpha2 adrenoceptor subtype selectivity.

Alpha2-adrenergic receptor (α2-AR) subtypes, acting mainly on the central nervous and cardiovascular systems, represent important targets for drug design, confirmed by the high number of studies published so far. Presently, only a few α2-AR subtype selective compounds are known. Using homology modeling and ligand docking, the present study analyzes the similarities and differences between binding sites, and between extracellular loops of the three subtypes of α2-ARs. Several α2-AR subtype selective ligands were docked into the active sites of the three α2-AR subtypes, key interactions between ligands and receptors were mapped, and the predicted results were compared with the available experimental data. Binding site analysis reveals a strong identity between important amino acid residues in each receptor, the very few differences being the key toward modulating selectivity of α2-AR ligands. The observed differences between binding site residues provide an excellent starting point for virtual screening of chemical databases, in order to identify potentially selective ligands for α2-ARs.

A QSAR study using MTD method and Dragon descriptors for a series of selective ligands of α2C adrenoceptor.

A QSAR (quantitative structure-activity relationship) analysis of the binding affinities for a series of 43 quinoline derivatives active against the alpha2C adrenergic receptor was performed. Multiple linear regressions (MLR) were obtained using the minimum topological difference (MTD) descriptor and various descriptors which were calculated with Dragon3.0. The variable selection was performed either through the forward stepwise method or backward stepwise combined with forward stepwise methods, providing two satisfactory models. The first one, obtained as a result of the forward stepwise method, contains MTD, Mor24v, MATS5m, MATS7m, G3m, L1s, G_N_N descriptors, while the other one obtained through the combination of backward and forward stepwise methods contains the following descriptors: MTD, ZM2V, X5V, IC5, MATS4v, and E2u. Both models highlight the importance of steric interactions and can be used as tools for predicting the binding affinity of related compounds.

Modulation of bitter taste perception by a small molecule hTAS2R antagonist.

Human bitter taste is mediated by the hTAS2R family of G protein-coupled receptors. The discovery of the hTAS2Rs enables the potential to develop specific bitter receptor antagonists that could be beneficial as chemical probes to examine the role of bitter receptor function in gustatory and nongustatory tissues. In addition, they could have widespread utility in food and beverages fortified with vitamins, antioxidants, and other nutraceuticals, because many of these have unwanted bitter aftertastes. We employed a high-throughput screening approach to discover a novel bitter receptor antagonist (GIV3727) that inhibits activation of hTAS2R31 (formerly hTAS2R44) by saccharin and acesulfame K, two common artificial sweeteners. Pharmacological analyses revealed that GIV3727 likely acts as an orthosteric, insurmountable antagonist of hTAS2R31. Surprisingly, we also found that this compound could inhibit five additional hTAS2Rs, including the closely related receptor hTAS2R43. Molecular modeling and site-directed mutagenesis studies suggest that two residues in helix 7 are important for antagonist activity in hTAS2R31 and hTAS2R43. In human sensory trials, GIV3727 significantly reduced the bitterness associated with the two sulfonamide sweeteners, indicating that hTAS2R antagonists are active in vivo. Our results demonstrate that small molecule bitter receptor antagonists can effectively reduce the bitter taste qualities of foods, beverages, and pharmaceuticals.

Real-time analysis of conformation-sensitive antibody binding provides new insights into integrin conformational regulation.

Integrins are heterodimeric adhesion receptors that regulate immune cell adhesion. Integrin-dependent adhesion is controlled by multiple conformational states that include states with different affinity to the ligand, states with various degrees of molecule unbending, and others. Affinity change and molecule unbending play major roles in the regulation of cell adhesion. The relationship between different conformational states of the integrin is unclear. Here we have used conformationally sensitive antibodies and a small LDV-containing ligand to study the role of the inside-out signaling through formyl peptide receptor and CXCR4 in the regulation of alpha(4)beta(1) integrin conformation. We found that in the absence of ligand, activation by formyl peptide or SDF-1 did not result in a significant exposure of HUTS-21 epitope. Occupancy of the ligand binding pocket without cell activation was sufficient to induce epitope exposure. EC(50) for HUTS-21 binding in the presence of LDV was identical to a previously reported ligand equilibrium dissociation constant at rest and after activation. Furthermore, the rate of HUTS-21 binding was also related to the VLA-4 activation state even at saturating ligand concentration. We propose that the unbending of the integrin molecule after guanine nucleotide-binding protein-coupled receptor-induced signaling accounts for the enhanced rate of HUTS-21 binding. Taken together, current results support the existence of multiple conformational states independently regulated by both inside-out signaling and ligand binding. Our data suggest that VLA-4 integrin hybrid domain movement does not depend on the affinity state of the ligand binding pocket.