HGMMX: Host Gut Microbiota Metabolism Xenobiotics Database.

The metagenome of bacteria colonizing the human intestine is a set of genes that is almost 150 times greater than the set of host genes. Some of these genes encode enzymes whose functioning significantly expands the number of potential pathways for xenobiotic metabolism. The resulting metabolites can exhibit activity different from that of the parent compound. This can decrease the efficacy of pharmacotherapy as well as induce undesirable and potentially life-threatening side effects. Thus, analysis of the biotransformation of small drug-like compounds mediated by the gut microbiota is an important step in the development of new pharmaceutical agents and repurposing of the approved drugs. In vitro research, the interaction of drug-like compounds with the gut microbiota is a multistep and time-consuming process. Systematic testing of large sets of chemical structures is associated with a number of challenges, including the lack of standardized techniques and significant financial costs to identify the structure of the final metabolites. Estimation of the compounds' ability to be biotransformed by the gut microbiota and prediction of the structures of their metabolites are possible in silico. However, the development of computational approaches is limited by the lack of information about chemical structures metabolized by microbiota enzymes. The aim of this study is to create a database containing information on the metabolism of drug-like compounds by the gut microbiota. We created the data set containing information about 368 structures metabolized and 310 structures not metabolized by the human gut microbiota. The HGMMX database is freely available at https://www.way2drug.com/hgmmx. The information presented will be useful in the development of computational approaches for analyzing the impact of the human microbiota on metabolism of drug-like molecules.

Identification of Molecular Mechanisms Involved in Viral Infection Progression Based on Text Mining: Case Study for HIV Infection.

Viruses cause various infections that may affect human lifestyle for durations ranging from several days to for many years. Although preventative and therapeutic remedies are available for many viruses, they may still have a profound impact on human life. The human immunodeficiency virus type 1 is the most common cause of HIV infection, which represents one of the most dangerous and complex diseases since it affects the immune system and causes its disruption, leading to secondary complications and negatively influencing health-related quality of life. While highly active antiretroviral therapy may decrease the viral load and the velocity of HIV infection progression, some individual peculiarities may affect viral load control or the progression of T-cell malfunction induced by HIV. Our study is aimed at the text-based identification of molecular mechanisms that may be involved in viral infection progression, using HIV as a case study. Specifically, we identified human proteins and genes which commonly occurred, overexpressed or underexpressed, in the collections of publications relevant to (i) HIV infection progression and (ii) acute and chronic stages of HIV infection. Then, we considered biological processes that are controlled by the identified protein and genes. We verified the impact of the identified molecules in the associated clinical study.

CLC-Pred 2.0: A Freely Available Web Application for In Silico Prediction of Human Cell Line Cytotoxicity and Molecular Mechanisms of Action for Druglike Compounds.

In vitro cell-line cytotoxicity is widely used in the experimental studies of potential antineoplastic agents and evaluation of safety in drug discovery. In silico estimation of cytotoxicity against hundreds of tumor cell lines and dozens of normal cell lines considerably reduces the time and costs of drug development and the assessment of new pharmaceutical agent perspectives. In 2018, we developed the first freely available web application (CLC-Pred) for the qualitative prediction of cytotoxicity against 278 tumor and 27 normal cell lines based on structural formulas of 59,882 compounds. Here, we present a new version of this web application: CLC-Pred 2.0. It also employs the PASS (Prediction of Activity Spectra for Substance) approach based on substructural atom centric MNA descriptors and a Bayesian algorithm. CLC-Pred 2.0 provides three types of qualitative prediction: (1) cytotoxicity against 391 tumor and 47 normal human cell lines based on ChEMBL and PubChem data (128,545 structures) with a mean accuracy of prediction (AUC), calculated by the leave-one-out (LOO CV) and the 20-fold cross-validation (20F CV) procedures, of 0.925 and 0.923, respectively; (2) cytotoxicity against an NCI60 tumor cell-line panel based on the Developmental Therapeutics Program's NCI60 data (22,726 structures) with different thresholds of IG50 data (100, 10 and 1 nM) and a mean accuracy of prediction from 0.870 to 0.945 (LOO CV) and from 0.869 to 0.942 (20F CV), respectively; (3) 2170 molecular mechanisms of actions based on ChEMBL and PubChem data (656,011 structures) with a mean accuracy of prediction 0.979 (LOO CV) and 0.978 (20F CV). Therefore, CLC-Pred 2.0 is a significant extension of the capabilities of the initial web application.

Blood Plasma Proteome: A Meta-Analysis of the Results of Protein Quantification in Human Blood by Targeted Mass Spectrometry.

A meta-analysis of the results of targeted quantitative screening of human blood plasma was performed to generate a reference standard kit that can be used for health analytics. The panel included 53 of the 296 proteins that form a "stable" part of the proteome of a healthy individual; these proteins were found in at least 70% of samples and were characterized by an interindividual coefficient of variation <40%. The concentration range of the selected proteins was 10−10−10−3 M and enrichment analysis revealed their association with rare familial diseases. The concentration of ceruloplasmin was reduced by approximately three orders of magnitude in patients with neurological disorders compared to healthy volunteers, and those of gelsolin isoform 1 and complement factor H were abruptly reduced in patients with lung adenocarcinoma. Absolute quantitative data of the individual proteome of a healthy and diseased individual can be used as the basis for personalized medicine and health monitoring. Storage over time allows us to identify individual biomarkers in the molecular landscape and prevent pathological conditions.

Towards Novel Potential Molecular Targets for Antidepressant and Antipsychotic Pharmacotherapies.

Depression and schizophrenia are two highly prevalent and severely debilitating neuropsychiatric disorders. Both conventional antidepressant and antipsychotic pharmacotherapies are often inefficient clinically, causing multiple side effects and serious patient compliance problems. Collectively, this calls for the development of novel drug targets for treating depressed and schizophrenic patients. Here, we discuss recent translational advances, research tools and approaches, aiming to facilitate innovative drug discovery in this field. Providing a comprehensive overview of current antidepressants and antipsychotic drugs, we also outline potential novel molecular targets for treating depression and schizophrenia. We also critically evaluate multiple translational challenges and summarize various open questions, in order to foster further integrative cross-discipline research into antidepressant and antipsychotic drug development.

Relationships between the Structure and Severe Drug-Induced Liver Injury for Low, Medium, and High Doses of Drugs.

Assessment of structure-activity relationships (SARs) for predicting severe drug-induced liver injury (DILI) is essential since in vivo and in vitro preclinical methods cannot detect many druglike compounds disrupting liver functions. To date, plenty of SAR models for the prediction of DILI have been developed; however, none of them considered the route of drug administration and daily dose, which may introduce significant bias into prediction results. We have created a dataset of 617 drugs with parenteral and oral administration routes and consistent information on DILI severity. We have found a clear relationship between route, dose, and DILI severity. According to SAR, nearly 40% of moderate- and non-DILI-causing drugs would cause severe DILI if they were administered at high oral doses. We have proposed the following approach to predict severe DILI. New compounds recommended to be used at low oral doses (<∼10 mg daily), or parenterally, can be considered not causing severe DILI. DILI for compounds administered at medium oral doses (∼10-100 mg daily; 22.2% of drugs under consideration) can be considered unpredictable because reasonable SAR models were not obtained due to the small size and heterogeneity of the corresponding dataset. The DILI potential of the compounds recommended to be used at high oral doses (more than ∼100 mg daily) can be estimated using SAR modeling. The balanced accuracy of the approach calculated by a 10-fold cross-validation procedure is 0.803. The developed approach can be used to estimate severe DILI for druglike compounds proposed to use at low and high oral doses or parenterally at the early stages of drug development.

Inhibitory Effects of 7-Methylguanine and Its Metabolite 8-Hydroxy-7-Methylguanine on Human Poly(ADP-Ribose) Polymerase 1.

Previously, we have found that a nucleic acid metabolite, 7-methylguanine (7mGua), produced in the body can have an inhibitory effect on the poly(ADP-ribose) polymerase 1 (PARP1) enzyme, an important pharmacological target in anticancer therapy. In this work, using an original method of analysis of PARP1 activity based on monitoring fluorescence anisotropy, we studied inhibitory properties of 7mGua and its metabolite, 8-hydroxy-7-methylguanine (8h7mGua). Both compounds inhibited PARP1 enzymatic activity in a dose-dependent manner, however, 8h7mGua was shown to be a stronger inhibitor. The IC50 values for 8h7mGua at different concentrations of the NAD+ substrate were found to be 4 times lower, on average, than those for 7mGua. The more efficient binding of 8h7mGua in the PARP1 active site is explained by the presence of an additional hydrogen bond with the Glu988 catalytic residue. Experimental and computational studies did not reveal the effect of 7mGua and 8h7mGua on the activity of other DNA repair enzymes, indicating selectivity of their inhibitory action.

New Positive TRPC6 Modulator Penetrates Blood-Brain Barrier, Eliminates Synaptic Deficiency and Restores Memory Deficit in 5xFAD Mice.

Synapse loss in the brain of Alzheimer's disease patients correlates with cognitive dysfunctions. Drugs that limit synaptic loss are promising pharmacological agents. The transient receptor potential cation channel, subfamily C, member 6 (TRPC6) regulates the formation of an excitatory synapse. Positive regulation of TRPC6 results in increased synapse formation and enhances learning and memory in animal models. The novel selective TRPC6 agonist, 3-(3-,4-Dihydro-6,7-dimethoxy-3,3-dimethyl-1-isoquinolinyl)-2H-1-benzopyran-2-one, has recently been identified. Here we present in silico, in vitro, ex vivo, pharmacokinetic and in vivo studies of this compound. We demonstrate that it binds to the extracellular agonist binding site of the human TRPC6, protects hippocampal mushroom spines from amyloid toxicity in vitro, efficiently recovers synaptic plasticity in 5xFAD brain slices, penetrates the blood-brain barrier and recovers cognitive deficits in 5xFAD mice. We suggest that C20 might be recognized as the novel TRPC6-selective drug suitable to treat synaptic deficiency in Alzheimer's disease-affected hippocampal neurons.

Extraction of Data on Parent Compounds and Their Metabolites from Texts of Scientific Abstracts.

The growing amount of experimental data on chemical objects includes properties of small molecules, results of studies of their interaction with human and animal proteins, and methods of synthesis of organic compounds (OCs). The data obtained can be used to identify the names of OCs automatically, including all possible synonyms and relevant data on the molecular properties and biological activity. Utilization of different synonymic names of chemical compounds allows researchers to increase the completeness of data on their properties available from publications. Enrichment of the data on the names of chemical compounds by information about their possible metabolites can help estimate the biological effects of parent compounds and their metabolites more thoroughly. Therefore, an attempt at automated extraction of the names of parent compounds and their metabolites from the texts is a rather important task. In our study, we aimed at developing a method that provides the extraction of the named entities (NEs) of parent compounds and their metabolites from abstracts of scientific publications. Based on the application of the conditional random fields' algorithm, we extracted the NEs of chemical compounds. We developed a set of rules allowing identification of parent compound NEs and their metabolites in the texts. We evaluated the possibility of extracting the names of potential metabolites based on cosine similarity between strings representing names of parent compounds and all other chemical NEs found in the text. Additionally, we used conditional random fields to fetch the names of parent compounds and their metabolites from the texts based on the corpus of texts labeled manually. Our computational experiments showed that usage of rules in combination with cosine similarity could increase the accuracy of recognition of the names of metabolites compared to the rule-based algorithm and application of a machine-learning algorithm (conditional random fields).

Antitumor Profile of Carbon-Bridged Steroids (CBS) and Triterpenoids.

This review focuses on the rare group of carbon-bridged steroids (CBS) and triterpenoids found in various natural sources such as green, yellow-green, and red algae, marine sponges, soft corals, ascidians, starfish, and other marine invertebrates. In addition, this group of rare lipids is found in amoebas, fungi, fungal endophytes, and plants. For convenience, the presented CBS and triterpenoids are divided into four groups, which include: (a) CBS and triterpenoids containing a cyclopropane group; (b) CBS and triterpenoids with cyclopropane ring in the side chain; (c) CBS and triterpenoids containing a cyclobutane group; (d) CBS and triterpenoids containing cyclopentane, cyclohexane or cycloheptane moieties. For the comparative characterization of the antitumor profile, we have added several semi- and synthetic CBS and triterpenoids, with various additional rings, to identify possible promising sources for pharmacologists and the pharmaceutical industry. About 300 CBS and triterpenoids are presented in this review, which demonstrate a wide range of biological activities, but the most pronounced antitumor profile. The review summarizes biological activities both determined experimentally and estimated using the well-known PASS software. According to the data obtained, two-thirds of CBS and triterpenoids show moderate activity levels with a confidence level of 70 to 90%; however, one third of these lipids demonstrate strong antitumor activity with a confidence level exceeding 90%. Several CBS and triterpenoids, from different lipid groups, demonstrate selective action on different types of tumor cells such as renal cancer, sarcoma, pancreatic cancer, prostate cancer, lymphocytic leukemia, myeloid leukemia, liver cancer, and genitourinary cancer with varying degrees of confidence. In addition, the review presents graphical images of the antitumor profile of both individual CBS and triterpenoids groups and individual compounds.

Antiprotozoal and Antitumor Activity of Natural Polycyclic Endoperoxides: Origin, Structures and Biological Activity.

Polycyclic endoperoxides are rare natural metabolites found and isolated in plants, fungi, and marine invertebrates. The purpose of this review is a comparative analysis of the pharmacological potential of these natural products. According to PASS (Prediction of Activity Spectra for Substances) estimates, they are more likely to exhibit antiprotozoal and antitumor properties. Some of them are now widely used in clinical medicine. All polycyclic endoperoxides presented in this article demonstrate antiprotozoal activity and can be divided into three groups. The third group includes endoperoxides, which show weak antiprotozoal activity with a reliability of up to 70%, and this group includes only 1.1% of metabolites. The second group includes the largest number of endoperoxides, which are 65% and show average antiprotozoal activity with a confidence level of 70 to 90%. Lastly, the third group includes endoperoxides, which are 33.9% and show strong antiprotozoal activity with a confidence level of 90 to 99.6%. Interestingly, artemisinin and its analogs show strong antiprotozoal activity with 79 to 99.6% confidence against obligate intracellular parasites which belong to the genera Plasmodium, Toxoplasma, Leishmania, and Coccidia. In addition to antiprotozoal activities, polycyclic endoperoxides show antitumor activity in the proportion: 4.6% show weak activity with a reliability of up to 70%, 65.6% show an average activity with a reliability of 70 to 90%, and 29.8% show strong activity with a reliability of 90 to 98.3%. It should also be noted that some polycyclic endoperoxides, in addition to antiprotozoal and antitumor properties, show other strong activities with a confidence level of 90 to 97%. These include antifungal activity against the genera Aspergillus, Candida, and Cryptococcus, as well as anti-inflammatory activity. This review provides insights on further utilization of polycyclic endoperoxides by medicinal chemists, pharmacologists, and the pharmaceutical industry.

Pharmacological profile of natural and synthetic compounds with rigid adamantane-based scaffolds as potential agents for the treatment of neurodegenerative diseases.

This review is dedicated to the comparative analysis of structure-activity relationships for more than 75 natural and synthetic derivatives of adamantane. Some of these compounds, such as amantadine and memantine, are currently used to treat dementia, Alzheimer's and Parkinson's diseases and other neurodegenerative diseases. The data presented show that the pharmacological potential of 1-fluoro- and 1-phosphonic acid adamantane derivatives against Alzheimer's and Parkinson's diseases and other neurodegenerative diseases exceeds those of well-known amantadine and memantine. The information presented in this review highlights the promising directions of studies for biochemists, pharmacologists, medicinal chemists, physiologists, and neurologists, as well as to the pharmaceutical industry.

Chemical Diversity of Soft Coral Steroids and Their Pharmacological Activities.

The review is devoted to the chemical diversity of steroids produced by soft corals and their determined and potential activities. There are about 200 steroids that belong to different types of steroids such as secosteroids, spirosteroids, epoxy- and peroxy-steroids, steroid glycosides, halogenated steroids, polyoxygenated steroids and steroids containing sulfur or nitrogen heteroatoms. Of greatest interest is the pharmacological activity of these steroids. More than 40 steroids exhibit antitumor and related activity with a confidence level of over 90 percent. A group of 32 steroids shows anti-hypercholesterolemic activity with over 90 percent confidence. Ten steroids exhibit anti-inflammatory activity and 20 steroids can be classified as respiratory analeptic drugs. Several steroids exhibit rather rare and very specific activities. Steroids exhibit anti-osteoporotic properties and can be used to treat osteoporosis, as well as have strong anti-eczemic and anti-psoriatic properties and antispasmodic properties. Thus, this review is probably the first and exclusive to present the known as well as the potential pharmacological activities of 200 marine steroids.

Computer-Aided Estimation of Biological Activity Profiles of Drug-Like Compounds Taking into Account Their Metabolism in Human Body.

Most pharmaceutical substances interact with several or even many molecular targets in the organism, determining the complex profiles of their biological activity. Moreover, due to biotransformation in the human body, they form one or several metabolites with different biological activity profiles. Therefore, the development and rational use of novel drugs requires the analysis of their biological activity profiles, taking into account metabolism in the human body. In silico methods are currently widely used for estimating new drug-like compounds' interactions with pharmacological targets and predicting their metabolic transformations. In this study, we consider the estimation of the biological activity profiles of organic compounds, taking into account the action of both the parent molecule and its metabolites in the human body. We used an external dataset that consists of 864 parent compounds with known metabolites. It is shown that the complex assessment of active pharmaceutical ingredients' interactions with the human organism increases the quality of computer-aided estimates. The toxic and adverse effects showed the most significant difference: reaching 0.16 for recall and 0.14 for precision.

Data Mining Approach for Extraction of Useful Information About Biologically Active Compounds from Publications.

A lot of high quality data on the biological activity of chemical compounds are required throughout the whole drug discovery process: from development of computational models of the structure-activity relationship to experimental testing of lead compounds and their validation in clinics. Currently, a large amount of such data is available from databases, scientific publications, and patents. Biological data are characterized by incompleteness, uncertainty, and low reproducibility. Despite the existence of free and commercially available databases of biological activities of compounds, they usually lack unambiguous information about peculiarities of biological assays. On the other hand, scientific papers are the primary source of new data disclosed to the scientific community for the first time. In this study, we have developed and validated a data-mining approach for extraction of text fragments containing description of bioassays. We have used this approach to evaluate compounds and their biological activity reported in scientific publications. We have found that categorization of papers into relevant and irrelevant may be performed based on the machine-learning analysis of the abstracts. Text fragments extracted from the full texts of publications allow their further partitioning into several classes according to the peculiarities of bioassays. We demonstrate the applicability of our approach to the comparison of the endpoint values of biological activity and cytotoxicity of reference compounds.

AntiBac-Pred: A Web Application for Predicting Antibacterial Activity of Chemical Compounds.

Discovery of new antibacterial agents is a never-ending task of medicinal chemistry. Every new drug brings significant improvement to patients with bacterial infections, but prolonged usage of antibacterials leads to the emergence of resistant strains. Therefore, novel active structures with new modes of action are required. We describe a web application called AntiBac-Pred aimed to help users in the rational selection of the chemical compounds for experimental studies of antibacterial activity. This application is developed using antibacterial activity data available in ChEMBL and PASS software. It allows users to classify chemical structures of interest into growth inhibitors or noninhibitors of 353 different bacteria strains, including both resistant and nonresistant ones.

Naturally occurring of α,ß-diepoxy-containing compounds: origin, structures, and biological activities.

Diepoxy-containing compounds are widely distributed in nature. These metabolites are found in plants and marine organisms and are also produced by many microorganisms, fungi, or fungal endophytes. Many of these metabolites are antibiotics and exhibit a wide variety of biological activities. More than 80 α,ß-diepoxy-containing compounds are presented in this article, which belong to different classes of chemical compounds including lipids, terpenoids, alkaloids, quinones, hydroquinones, and pyrones. The main activities that characterize α,ß-diepoxy-containing compounds are antineoplastic with confidence up to 99%, antifungal with confidence up to 94%, antiinflammatory with confidence up to 92%, or antibacterial with confidence up to 78%. In addition, these metabolites can be used as a lipid metabolism regulator with a certainty of up to 81%, antiviral (Arbovirus) activity with a certainty of up to 71%, or antiallergic activity with confidence up to 69%. These data on the biological activity of diepoxy-containing compounds are of considerable interest to pharmacologists, chemists, and medical professionals who are involved in phytomedicine and related areas of science and industry.

(Q)SAR Models of HIV-1 Protein Inhibition by Drug-Like Compounds.

Despite the achievements of antiretroviral therapy, discovery of new anti-HIV medicines remains an essential task because the existing drugs do not provide a complete cure for the infected patients, exhibit severe adverse effects, and lead to the appearance of resistant strains. To predict the interaction of drug-like compounds with multiple targets for HIV treatment, ligand-based drug design approach is widely applied. In this study, we evaluated the possibilities and limitations of (Q)SAR analysis aimed at the discovery of novel antiretroviral agents inhibiting the vital HIV enzymes. Local (Q)SAR models are based on the analysis of structure-activity relationships for molecules from the same chemical class, which significantly restrict their applicability domain. In contrast, global (Q)SAR models exploit data from heterogeneous sets of drug-like compounds, which allows their application to databases containing diverse structures. We compared the information for HIV-1 integrase, protease and reverse transcriptase inhibitors available in the EBI ChEMBL, NIAID HIV/OI/TB Therapeutics, and Clarivate Analytics Integrity databases as the sources for (Q)SAR training sets. Using the PASS and GUSAR software, we developed and validated a variety of (Q)SAR models, which can be further used for virtual screening of new antiretrovirals in the SAVI library. The developed models are implemented in the freely available web resource AntiHIV-Pred.

ADVERPred-Web Service for Prediction of Adverse Effects of Drugs.

Application of structure-activity relationships (SARs) for the prediction of adverse effects of drugs (ADEs) has been reported in many published studies. Training sets for the creation of SAR models are usually based on drug label information which allows for the generation of data sets for many hundreds of drugs. Since many ADEs may not be related to drug consumption, one of the main problems in such studies is the quality of data on drug-ADE pairs obtained from labels. The information on ADEs may be included in three sections of the drug labels: "Boxed warning," "Warnings and Precautions," and "Adverse reactions." The first two sections, especially Boxed warning, usually contain the most frequent and severe ADEs that have either known or probable relationships to drug consumption. Using this information, we have created manually curated data sets for the five most frequent and severe ADEs: myocardial infarction, arrhythmia, cardiac failure, severe hepatotoxicity, and nephrotoxicity, with more than 850 drugs on average for each effect. The corresponding SARs were built with PASS (Prediction of Activity Spectra for Substances) software and had balanced accuracy values of 0.74, 0.7, 0.77, 0.67, and 0.75, respectively. They were implemented in a freely available ADVERPred web service ( http://www.way2drug.com/adverpred/ ), which enables a user to predict five ADEs based on the structural formula of compound. This web service can be applied for estimation of the corresponding ADEs for hits and lead compounds at the early stages of drug discovery.

Naturally occurring aromatic steroids and their biological activities.

The present review describes the distribution and biological activities of natural mono-, di-, and triaromatic steroids. It is shown that the producers of aromatic steroids are microorganisms, fungi, and marine invertebrates, and also they were found in plants, animals, marine sediments, and karst deposits. Eighty biologically active aromatic steroids likely have an anti-tumor, anti-inflammatory, and neuroprotection activity with a confidence of 78 to 92%. The structures and predicted biological activities of aromatic steroids are available. This review emphasizes the role of aromatic steroids as an important source and potential leads for drug discovery and they are of great interest to chemists, physicians, biologists, pharmacologists, and the pharmaceutical industry.