Association of Increased Homocysteine Levels with Impaired Folate Metabolism and Vitamin B Deficiency in Early-Onset Multiple Sclerosis.

The contents of homocysteine (HCy), cyanocobalamin (vitamin B12), folic acid (vitamin B9), and pyridoxine (vitamin B6) were analyzed and the genotypes of the main gene polymorphisms associated with folate metabolism (C677T and A1298C of the MTHFR gene, A2756G of the MTR gene and A66G of the MTRR gene) were determined in children at the onset of multiple sclerosis (MS) (with disease duration of no more than six months), healthy children under 18 years (control group), healthy adults without neurological pathology, adult patients with MS at the onset of disease, and adult patients with long-term MS. A significant increase in the HCy levels was found in children at the MS onset compared to healthy children of the corresponding age. It was established that the content of HCy in children has a high predictive value. At the same time, an increase in the HCy levels was not accompanied by the deficiency of vitamins B6, B9, and B12 in the blood. The lack of correlation between the laboratory signs of vitamin deficiency and HCy levels may be due to the polymorphic variants of folate cycle genes. An increased HCy level should be considered as a marker of functional disorders of folate metabolism accompanying the development of pathological process in pediatric MS. Our finding can be used to develop new approaches to the prevention of demyelination in children and treatment of pediatric MS.

MetaTox 2.0: Estimating the Biological Activity Spectra of Drug-like Compounds Taking into Account Probable Biotransformations.

After the biotransformation of xenobiotics in the human body, the biological activity of the metabolites may differ from the activity of parent compounds. Therefore, to assess the overall biological activity of a drug-like compound, it is important to take into account its metabolites and their biological activity. We developed MetaTox 2.0-an updated version of the MetaTox web application that was able to predict the metabolites of xenobiotics. Innovations include estimating the biological activity profile of a compound and taking into account its metabolites. The estimation is based on the PASS (prediction of activity spectra for substances) algorithm and on the latest version of the training set covering over 1900 biological activities predicted with an average accuracy exceeding 0.97. Also, MetaTox 2.0 allows the search for similar substances among more than 2000 drugs with known metabolic networks, which were extracted from the ChEMBL, MetXBIODB, and DrugBank databases. MetaTox 2.0 is freely available on the web at https://www.way2drug.com/metatox.

Prediction of Adverse Effects of Drug-Drug Interactions on Cardiovascular System Based on the Analysis of Structure-Activity Relationships.

Co-administration of drugs often leads to drug-drug interactions, which could be accompanied by various adverse drug reactions that pose a threat to life and health of the patient. The effect caused by adverse drug reactions on cardiovascular system is one of the most significant manifestations of drug-drug interaction. Clinical assessment of adverse drug reactions resulting from drug-drug interaction between all drug pairs used in therapeutic practice is not possible. The purpose of this work was to build models using structure-activity analysis to predict adverse effects of drugs on cardiovascular system, mediated by pairwise interactions between the drug pairs when they are taken together. Data on the adverse effects resulting from drug-drug interaction were obtained from the DrugBank database. The data on drug pairs that do not cause such effects, which are necessary for building accurate structure-activity models, were obtained from the TwoSides database, which contains the results of analysis of the spontaneous reports. Two types of descriptors were used to describe a pair of drug structures: PoSMNA descriptors and probabilistic estimates of the prediction of biological activities obtained using the PASS program. Structure-activity relationships were established using the Random Forest method. Prediction accuracy was calculated by means of five-fold cross-validation. The highest accuracy values were obtained using PASS probabilistic estimates as descriptors. The area under the ROC curve was 0.94 for bradycardia, 0.96 for tachycardia, 0.90 for arrhythmia, 0.90 for ECG QT prolongation, 0.91 for hypertension, 0.89 for hypotension.

A Genome of Temperate Enterococcus Bacteriophage Placed in a Space of Pooled Viral Dark Matter Sequences.

In the human gut, temperate bacteriophages interact with bacteria through predation and horizontal gene transfer. Relying on taxonomic data, metagenomic studies have associated shifts in phage abundance with a number of human diseases. The temperate bacteriophage VEsP-1 with siphovirus morphology was isolated from a sample of river water using Enterococcus faecalis as a host. Starting from the whole genome sequence of VEsP-1, we retrieved related phage genomes in blastp searches of the tail protein and large terminase sequences, and blastn searches of the whole genome sequences, with matches compiled from several different databases, and visualized a part of viral dark matter sequence space. The genome network and phylogenomic analyses resulted in the proposal of a novel genus "Vespunovirus", consisting of temperate, mainly metagenomic phages infecting Enterococcus spp.

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.

Combined Use of Antimicrobial Peptides with Antiseptics against Multidrug-Resistant Bacteria: Pros and Cons.

Antimicrobial peptides (AMPs) are acknowledged as a promising template for designing new antimicrobials. At the same time, existing toxicity issues and limitations in their pharmacokinetics make topical application one of the less complicated routes to put AMPs-based therapeutics into actual medical practice. Antiseptics are one of the common components for topical treatment potent against antibiotic-resistant pathogens but often with toxicity limitations of their own. Thus, the interaction of AMPs and antiseptics is an interesting topic that is also less explored than combined action of AMPs and antibiotics. Herein, we analyzed antibacterial, antibiofilm, and cytotoxic activity of combinations of both membranolytic and non-membranolytic AMPs with a number of antiseptic agents. Fractional concentration indices were used as a measure of possible effective concentration reduction achievable due to combined application. Cases of both synergistic and antagonistic interaction with certain antiseptics and surfactants were identified, and trends in the occurrence of these types of interaction were discussed. The data may be of use for AMP-based drug development and suggest that the topic requires further attention for successfully integrating AMPs-based products in the context of complex treatment. AMP/antiseptic combinations show promise for creating topical formulations with improved activity, lowered toxicity, and, presumably, decreased chances of inducing bacterial resistance. However, careful assessment is required to avoid AMP neutralization by certain antiseptic classes in either complex drug design or AMP application alongside other therapeutics/care products.

Two Novel Lytic Bacteriophages Infecting Enterococcus spp. Are Promising Candidates for Targeted Antibacterial Therapy.

The rapid emergence of antibiotic resistance is of major concern globally. Among the most worrying pathogenic bacteria are vancomycin-resistant enterococci. Phage therapy is a highly promising method for controlling enterococcal infections. In this study, we described two virulent tailed bacteriophages possessing lytic activity against Enterococcus faecalis and E. faecium isolates. The SSsP-1 bacteriophage belonged to the Saphexavirus genus of the Siphoviridae family, and the GVEsP-1 bacteriophage belonged to the Schiekvirus genus of Herelleviridae. The genomes of both viruses carried putative components of anti-CRISPR systems and did not contain known genes coding for antibiotic-resistance determinants and virulence factors. The conservative arrangement of protein-coding sequences in Saphexavirus and Schiekvirus genomes taken together with positive results of treating enterococcal peritonitis in an animal infection model imply the potential suitability of GVEsP-1 and SSsP-1 bacteriophages for clinical applications.

In Silico Prediction of Drug-Drug Interactions Mediated by Cytochrome P450 Isoforms.

Drug-drug interactions (DDIs) can cause drug toxicities, reduced pharmacological effects, and adverse drug reactions. Studies aiming to determine the possible DDIs for an investigational drug are part of the drug discovery and development process and include an assessment of the DDIs potential mediated by inhibition or induction of the most important drug-metabolizing cytochrome P450 isoforms. Our study was dedicated to creating a computer model for prediction of the DDIs mediated by the seven most important P450 cytochromes: CYP1A2, CYP2B6, CYP2C19, CYP2C8, CYP2C9, CYP2D6, and CYP3A4. For the creation of structure-activity relationship (SAR) models that predict metabolism-mediated DDIs for pairs of molecules, we applied the Prediction of Activity Spectra for Substances (PASS) software and Pairs of Substances Multilevel Neighborhoods of Atoms (PoSMNA) descriptors calculated based on structural formulas. About 2500 records on DDIs mediated by these cytochromes were used as a training set. Prediction can be carried out both for known drugs and for new, not-yet-synthesized substances. The average accuracy of the prediction of DDIs mediated by various isoforms of cytochrome P450 estimated by leave-one-out cross-validation (LOO CV) procedures was about 0.92. The SAR models created are publicly available as a web resource and provide predictions of DDIs mediated by the most important cytochromes P450.

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).

Silver Nanoparticles Functionalized With Antimicrobial Polypeptides: Benefits and Possible Pitfalls of a Novel Anti-infective Tool.

Silver nanoparticles (AgNPs) and antimicrobial peptides or proteins (AMPs/APs) are both considered as promising platforms for the development of novel therapeutic agents effective against the growing number of drug-resistant pathogens. The observed synergy of their antibacterial activity suggested the prospect of introducing antimicrobial peptides or small antimicrobial proteins into the gelatinized coating of AgNPs. Conjugates with protegrin-1, indolicidin, protamine, histones, and lysozyme were comparatively tested for their antibacterial properties and compared with unconjugated nanoparticles and antimicrobial polypeptides alone. Their toxic effects were similarly tested against both normal eukaryotic cells (human erythrocytes, peripheral blood mononuclear cells, neutrophils, and dermal fibroblasts) and tumor cells (human erythromyeloid leukemia K562 and human histiocytic lymphoma U937 cell lines). The AMPs/APs retained their ability to enhance the antibacterial activity of AgNPs against both Gram-positive and Gram-negative bacteria, including drug-resistant strains, when conjugated to the AgNP surface. The small, membranolytic protegrin-1 was the most efficient, suggesting that a short, rigid structure is not a limiting factor despite the constraints imposed by binding to the nanoparticle. Some of the conjugated AMPs/APs clearly affected the ability of nanoparticle to permeabilize the outer membrane of Escherichia coli, but none of the conjugated AgNPs acquired the capacity to permeabilize its cytoplasmic membrane, regardless of the membranolytic potency of the bound polypeptide. Low hemolytic activity was also found for all AgNP-AMP/AP conjugates, regardless of the hemolytic activity of the free polypeptides, making conjugation a promising strategy not only to enhance their antimicrobial potential but also to effectively reduce the toxicity of membranolytic AMPs. The observation that metabolic processes and O2 consumption in bacteria were efficiently inhibited by all forms of AgNPs is the most likely explanation for their rapid and bactericidal action. AMP-dependent properties in the activity pattern of various conjugates toward eukaryotic cells suggest that immunomodulatory, wound-healing, and other effects of the polypeptides are at least partially transferred to the nanoparticles, so that functionalization of AgNPs may have effects beyond just modulation of direct antibacterial activity. In addition, some conjugated nanoparticles are selectively toxic to tumor cells. However, caution is required as not all modulatory effects are necessarily beneficial to normal host cells.

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.

In vitro interactions of abiraterone, erythromycin, and CYP3A4: implications for drug-drug interactions.

Potential drug-drug interactions of the antitumor drug abiraterone and the macrolide antibiotic erythromycin were studied at the stage of cytochrome P450 3A4 (CYP3A4) biotransformation. Using differential spectroscopy, we have shown that abiraterone is a type II ligand of CYP3A4. The dependence of CYP3A4 spectral changes on the concentration of abiraterone is sigmoidal, which indicates cooperative interactions of CYP3A4 with abiraterone; these interactions were confirmed by molecular docking. The dissociation constant (Kd ) and Hill coefficient (h) values for the CYP3A4-abiraterone complex were calculated as 3.8 ± 0.1 µM and 2.3 ± 0.2, respectively. An electrochemical enzymatic system based on CYP3A4 immobilized on a screen-printed electrode was used to show that abiraterone acts as a competitive inhibitor toward erythromycin N-demethylase activity of CYP3A4 (apparent Ki  = 8.1 ± 1.2 µM), while erythromycin and its products of enzymatic metabolism do not affect abiraterone N-oxidation by CYP3A4. In conclusion, the inhibition properties of abiraterone toward CYP3A4-dependent N-demethylation of erythromycin and the biologically inert behavior of erythromycin toward abiraterone hydroxylation were demonstrated.

Prediction of Drug-Drug Interactions Related to Inhibition or Induction of Drug-Metabolizing Enzymes.

Drug-drug interaction (DDI) is the phenomenon of alteration of the pharmacological activity of a drug(s) when another drug(s) is co-administered in cases of so-called polypharmacy. There are three types of DDIs: pharmacokinetic (PK), pharmacodynamic, and pharmaceutical. PK is the most frequent type of DDI, which often appears as a result of the inhibition or induction of drug-metabolising enzymes (DME). In this review, we summarise in silico methods that may be applied for the prediction of the inhibition or induction of DMEs and describe appropriate computational methods for DDI prediction, showing the current situation and perspectives of these approaches in medicinal and pharmaceutical chemistry. We review sources of information on DDI, which can be used in pharmaceutical investigations and medicinal practice and/or for the creation of computational models. The problem of the inaccuracy and redundancy of these data are discussed. We provide information on the state-of-the-art physiologically- based pharmacokinetic modelling (PBPK) approaches and DME-based in silico methods. In the section on ligand-based methods, we describe pharmacophore models, molecular field analysis, quantitative structure-activity relationships (QSAR), and similarity analysis applied to the prediction of DDI related to the inhibition or induction of DME. In conclusion, we discuss the problems of DDI severity assessment, mention factors that influence severity, and highlight the issues, perspectives and practical using of in silico methods.

MetaTox: Web Application for Predicting Structure and Toxicity of Xenobiotics' Metabolites.

A new freely available web-application MetaTox ( http://www.way2drug.com/mg ) for prediction of xenobiotic's metabolism and calculation toxicity of metabolites based on the structural formula of chemicals has been developed. MetaTox predicts metabolites, which are formed by nine classes of reactions (aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation). The calculation of probability for generated metabolites is based on analyses of "structure-biotransformation reactions" and "structure-modified atoms" relationships using a Bayesian approach. Prediction of LD50 values is performed by GUSAR software for the parent compound and each of the generated metabolites using quantitative structure-activity relationahip (QSAR) models created for acute rat toxicity with the intravenous type of administration.

Prediction of reacting atoms for the major biotransformation reactions of organic xenobiotics.

BACKGROUND: The knowledge of drug metabolite structures is essential at the early stage of drug discovery to understand the potential liabilities and risks connected with biotransformation. The determination of the site of a molecule at which a particular metabolic reaction occurs could be used as a starting point for metabolite identification. The prediction of the site of metabolism does not always correspond to the particular atom that is modified by the enzyme but rather is often associated with a group of atoms. To overcome this problem, we propose to operate with the term "reacting atom", corresponding to a single atom in the substrate that is modified during the biotransformation reaction. The prediction of the reacting atom(s) in a molecule for the major classes of biotransformation reactions is necessary to generate drug metabolites. RESULTS: Substrates of the major human cytochromes P450 and UDP-glucuronosyltransferases from the Biovia Metabolite database were divided into nine groups according to their reaction classes, which are aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. Each training set consists of positive and negative examples of structures with one labelled atom. In the positive examples, the labelled atom is the reacting atom of a particular reaction that changed adjacency. Negative examples represent non-reacting atoms of a particular reaction. We used Labelled Multilevel Neighbourhoods of Atoms descriptors for the designation of reacting atoms. A Bayesian-like algorithm was applied to estimate the structure-activity relationships. The average invariant accuracy of prediction obtained in leave-one-out and 20-fold cross-validation procedures for five human isoforms of cytochrome P450 and all isoforms of UDP-glucuronosyltransferase varies from 0.86 to 0.99 (0.96 on average). CONCLUSIONS: We report that reacting atoms may be predicted with reasonable accuracy for the major classes of metabolic reactions-aliphatic and aromatic hydroxylation, N- and O-glucuronidation, N-, S- and C-oxidation, and N- and O-dealkylation. The proposed method is implemented as a freely available web service at http://www.way2drug.com/RA and may be used for the prediction of the most probable biotransformation reaction(s) and the appropriate reacting atoms in drug-like compounds.Graphical abstract.

QSAR Modeling and Prediction of Drug-Drug Interactions.

Severe adverse drug reactions (ADRs) are the fourth leading cause of fatality in the U.S. with more than 100,000 deaths per year. As up to 30% of all ADRs are believed to be caused by drug-drug interactions (DDIs), typically mediated by cytochrome P450s, possibilities to predict DDIs from existing knowledge are important. We collected data from public sources on 1485, 2628, 4371, and 27,966 possible DDIs mediated by four cytochrome P450 isoforms 1A2, 2C9, 2D6, and 3A4 for 55, 73, 94, and 237 drugs, respectively. For each of these data sets, we developed and validated QSAR models for the prediction of DDIs. As a unique feature of our approach, the interacting drug pairs were represented as binary chemical mixtures in a 1:1 ratio. We used two types of chemical descriptors: quantitative neighborhoods of atoms (QNA) and simplex descriptors. Radial basis functions with self-consistent regression (RBF-SCR) and random forest (RF) were utilized to build QSAR models predicting the likelihood of DDIs for any pair of drug molecules. Our models showed balanced accuracy of 72-79% for the external test sets with a coverage of 81.36-100% when a conservative threshold for the model's applicability domain was applied. We generated virtually all possible binary combinations of marketed drugs and employed our models to identify drug pairs predicted to be instances of DDI. More than 4500 of these predicted DDIs that were not found in our training sets were confirmed by data from the DrugBank database.

Inactivation of the Rgg2 transcriptional regulator ablates the virulence of Streptococcus pyogenes.

Streptococcus pyogenes adapts to different niches encountered in the human host via the activity of numerous regulatory proteins including the Rgg family of transcriptional regulators. The S. pyogenes chromosome encodes four Rgg paralogues designated Rgg1 (RopB), Rgg2 (MutR), Rgg3, and Rgg4 (ComR). In order to understand the role of the Rgg2 protein in the regulation of metabolic and virulence-associated properties of S. pyogenes, the rgg2 gene was inactivated in the M1 serotype strain SF370. Inactivation of rgg2 increased the growth yield of S. pyogenes in THY broth, increased biofilm formation, and increased production of SIC, which is an important virulence factor that inhibits complement mediated lysis. To identify Rgg2-regulated genes, the transcriptomes of SF370 and the rgg2 mutant strains were compared in the middle-exponential and post-exponential phases of growth. Rgg2 was found to control the expression of dozens of genes primarily in the exponential phase of growth, including genes associated with virulence (sse, scpA, slo, nga, mf-3), DNA transformation, and nucleotide metabolism. Inactivation of rgg2 decreased the ability of S. pyogenes to adhere to epithelial cells. In addition, the mutant strain was more sensitive to killing when incubated with human blood and avirulent in a murine bacteremia model. Finally, inoculation of mice with the avirulent rgg2 mutant of S. pyogenes SF370 conferred complete protection to mice subsequently challenged with the wild-type strain. Restoration of an intact rgg2 gene in mutant strain restored the wild-type phenotypes. Overall, the results demonstrate that Rgg2 is an important regulatory protein in S. pyogenes involved in controlling genes associated with both metabolism and virulence.

Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review.

In silico approaches have been widely recognised to be useful for drug discovery. Here, we consider the significance of available databases of medicinal plants and chemo- and bioinformatics tools for in silico drug discovery beyond the traditional use of folk medicines. This review contains a practical example of the application of combined chemo- and bioinformatics methods to study pleiotropic therapeutic effects (known and novel) of 50 medicinal plants from Traditional Indian Medicine.

Metabolism site prediction based on xenobiotic structural formulas and PASS prediction algorithm.

A new ligand-based method for the prediction of sites of metabolism (SOMs) for xenobiotics has been developed on the basis of the LMNA (labeled multilevel neighborhoods of atom) descriptors and the PASS (prediction of activity spectra for substances) algorithm and applied to predict the SOMs of the 1A2, 2C9, 2C19, 2D6, and 3A4 isoforms of cytochrome P450. An average IAP (invariant accuracy of prediction) of SOMs calculated by the leave-one-out cross-validation procedure was 0.89 for the developed method. The external validation was made with evaluation sets containing data on biotransformations for 57 cardiovascular drugs. An average IAP of regioselectivity for evaluation sets was 0.83. It was shown that the proposed method exceeds accuracy of SOM prediction by RS-Predictor for CYP 1A2, 2D6, 2C9, 2C19, and 3A4 and is comparable to or better than SMARTCyp for CYP 2C9 and 2D6.