Drug Metabolism for the Perplexed Medicinal Chemist

Exploring Metabolic Pathways of Anamorelin, a Selective Agonist of the Growth Hormone Secretagogue Receptor, via Molecular Networking

In this study, we delineated the poorly characterized metabolism of anamorelin, a growth hormone secretagogue receptor agonist, in vitro using human liver microsomes (HLM), based on classical molecular networking (MN) and feature-based molecular networking (FBMN) from the Global Natural Products Social Molecular Networking platform. Following the in vitro HLM reaction, the MN analysis showed 11 neighboring nodes whose information propagated from the node corresponding to anamorelin. The FBMN analysis described the separation of six nodes that the MN analysis could not achieve. In addition, the similarity among neighboring nodes could be discerned via their respective metabolic pathways. Collectively, 18 metabolites (M1-M12) were successfully identified, suggesting that the metabolic pathways involved were demethylation, hydroxylation, dealkylation, desaturation, and N-oxidation, whereas 6 metabolites (M13a*-b*, M14a*-b*, and M15a*-b*) remained unidentified. Furthermore, the major metabolites detected in HLM, M1 and M7, were dissimilar from those observed in the CYP3A4 isozyme assay, which is recognized to be markedly inhibited by anamorelin. Specifically, M7, M8, and M9 were identified as the major metabolites in the CYP3A4 isozyme assay. Therefore, a thorough investigation of metabolism is imperative for future in vivo studies. These findings may offer prospective therapeutic opportunities for anamorelin.

Antimicrobial analysis of honey against Staphylococcus aureus isolates from wound, ADMET properties of its bioactive compounds and in-silico evaluation against dihydropteroate synthase

BACKGROUND

One of the main challenges of wound healing is infection with multi-drug resistant (MDR) bacteria such as Staphylococcus aureus. The spectrum of antibiotics used to treat them is declining; thus, there is a need for alternatives. Our study was designed to evaluate the antimicrobial properties of honey, its pharmacokinetics (ADMET) properties and in-silico analysis of its bioactive compounds against dihydropteroate synthase of S. aureus using trimethoprim as control.

METHODS

Standard protocols were employed in collection and preparation of samples, generation of canonical strings, and conduction of microbiological analyses. Bioactive compounds' ADMET properties were evaluated using the SWISSADME and the MCULE toxicity checker tools. The MCULE one-click docking tool was used in carrying out the dockings.

RESULTS

The gas chromatography-mass spectrophotometry revealed twenty (20) bioactive compounds and was dominated by sugars (> 60%). We isolated a total of 47 S. aureus isolates from the wound samples. At lower concentrations, resistance to trimethoprim (95.74 to 100.00%) was higher than honey (70.21 to 96.36%). Only seven (7) isolates meet Lipinski's rule of five and ADMET properties. The docking scores of the bioactive compounds ranged from -3.3 to -4.6 while that of trimethoprim was -6.1, indicating better binding or interaction with the dihydropteroate synthase. The bioactive compounds were not substrates to P450 cytochrome enzymes (CYP1A2, CYP2CI9 and CYP2D6) and p-glycoprotein, indicating better gastrointestinal tract (GIT) absorption.

CONCLUSION

The favourable docking properties shown by the bioactive compounds suggest they could be lead compounds for newer antimetabolites for management of MDR S. aureus.

In silico prediction of UGT-mediated metabolism in drug-like molecules via graph neural network

UDP-glucuronosyltransferases (UGTs) have gained increasing attention as they play important roles in the phase II metabolism of drugs. Due to the time-consuming process and high cost of experimental approaches to identify the metabolic fate of UGT enzymes, in silico methods have been developed to predict the UGT-mediated metabolism of drug-like molecules. We developed consensus models with the combination of machine learning (ML) and graph neural network (GNN) methods to predict if a drug-like molecule is a potential UGT substrate, and then we applied the Weisfeiler-Lehman Network (WLN) model to identify the sites of metabolism (SOMs) of UGT-catalyzed substrates. For the substrate model, the accuracy of the single substrate prediction model on the test set could reach to 0.835. Compared with the single estimators, the consensus models are more stable and have better generalization ability, and the accuracy on the test set reached to 0.851. For the SOM model, the top-1 accuracy of the SOM model on the test set reached to 0.898, outperforming existing works. Thus, in this study, we proposed a computational framework, named Meta-UGT, which would provide a useful tool for the prediction and optimization of metabolic profiles and drug design.

Computational Investigation of the Bisphenolic Drug Metabolism by Cytochrome P450: What Factors Favor Intramolecular Phenol Coupling

Intramolecular phenol coupling reactions of alkaloids can lead to active metabolites catalyzed by the mammalian cytochrome P450 enzyme (P450); however, the mechanistic knowledge of such an "unusual" process is lacking. This work performs density functional theory computations to reveal the P450-mediated metabolic pathway leading from R-reticuline to the morphine precursor salutaridine by exploring possible intramolecular phenol coupling mechanisms involving diradical coupling, radical addition, and electron transfer. The computed results show that the outer-sphere electron transfer with a high barrier (>20.0 kcal/mol) is unlikely to happen. However, for inter-sphere intramolecular phenol coupling, it reveals that intramolecular phenol coupling of R-reticuline proceeds via the diradical mechanism consecutively by compound I and protonated compound II of P450 rather than the radical addition mechanism. The existence of a much higher radical rebound barrier than that of H-abstraction in the quartet high-spin state can endow the R-reticuline phenoxy radical with a sufficient lifetime to enable intramolecular phenol coupling, while the H-abstraction/radical rebound mode with a negligible rebound barrier leading to phenol hydroxylation can only happen in the doublet low-spin state. Therefore, the ratio [coupling]/[hydroxylation] can be approximately reflected by the relative yield of the high-spin and low-spin H-abstraction by P450, which thus can provide a theoretical ratio of 16:1 for R-reticuline, which is in accordance with previous experimental results. Especially, the high rebound barrier of the phenoxy radical derived from the weak electron-donating ability of the phenoxy radical is revealed as an intrinsic nature. Therefore, the revealed intramolecular phenol coupling mechanism can be potentially extended to several other bisphenolic drugs to infer groups of unexpected metabolites in organisms.

MetaPASS: A Web Application for Analyzing the Biological Activity Spectrum of Organic Compounds Taking into Account their Biotransformation

Most drug-like compounds can interact with several pharmacological targets and exhibit complex biological activity spectra. Analysis of these spectra helps find and optimize new pharmaceutical agents or identify new uses for approved and investigational drugs (drug repurposing). Since most pharmaceuticals usually undergo biotransformation in the human body, it is reasonable during drug discovery to take into account biological activity spectra of metabolites. A new freely available MetaPASS web application (http://www.way2drug.com/metapass) has been developed for analyzing the probable biological activity spectra of drug-like organic compounds taking into account their metabolites - integrated activity profile. To obtain an integrated biological activity profile, one can create a biotransformation network for any compound or analyze known networks for more than 950 compounds from ChEBML and DrugBank. Biological activity profile prediction is based on the PASS Refined software that predicts 1,333 biological activities with an average accuracy (IAP, calculated by leave-one-out cross-validation procedure) exceeded 0.97.

Metabolites profiling and pharmacokinetics of troxipide and its pharmacodynamics in rats with gastric ulcer

Troxipide is widely used to treat gastric ulcer (GU) in the clinic. However, a lack of systematic metabolic, pharmacokinetic and pharmacological studies limits its clinical use. This study aimed to firstly explore the metabolic, pharmacokinetic and pharmacological mechanisms of troxipide in rats with GU compared to normal control (NC) rats. First, metabolic study was perormed by a highly selective, high-resolution mass spectrometry method. A total of 45 metabolites, including 9 phase I metabolites and 36 phase II metabolites, were identified based on MS/MS spectra. Subsequently, the pharmacokinetics results suggested that the Cmax, Ka, t1/2, AUC(0-t) and AUC(0-∞) of troxipide were significantly increased in rats with GU compared with NC rats. The Vz, K10 and absolute bioavailability of troxipide were obviously decreased in rats with GU compared with NC rats, and its tissue distribution (in the liver, lung and kidney) was significantly different between the two groups of rats. Additionally, the pharmacodynamic results suggested that the levels of biochemical factors (IL-17, IL-6, TNF-α, IFN-γ, AP-1, MTL, GAS, and PG-II) were significantly increased, the PG-Ӏ level was obviously decreased, and the protein expression levels of HSP-90, C-Cas-3 and C-PARP-1 were markedly increased in rats with GU compared with NC rats. The above results suggested that the therapeutic mechanisms underlying the metabolic, pharmacokinetic and pharmacological properties of troxipide in vivo in rats deserve further attention based on the importance of troxipide in the treatment of GU in this study, and these mechanisms could be targets for future studies.

A Novel Lipidomics-Based Approach to Evaluating the Risk of Clinical Hepatotoxicity Potential of Drugs in 3D Human Microtissues

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.

BioTransformer: a comprehensive computational tool for small molecule metabolism prediction and metabolite identification

Background

A number of computational tools for metabolism prediction have been developed over the last 20 years to predict the structures of small molecules undergoing biological transformation or environmental degradation. These tools were largely developed to facilitate absorption, distribution, metabolism, excretion, and toxicity (ADMET) studies, although there is now a growing interest in using such tools to facilitate metabolomics and exposomics studies. However, their use and widespread adoption is still hampered by several factors, including their limited scope, breath of coverage, availability, and performance.

Results

To address these limitations, we have developed BioTransformer, a freely available software package for accurate, rapid, and comprehensive in silico metabolism prediction and compound identification. BioTransformer combines a machine learning approach with a knowledge-based approach to predict small molecule metabolism in human tissues (e.g. liver tissue), the human gut as well as the environment (soil and water microbiota), via its metabolism prediction tool. A comprehensive evaluation of BioTransformer showed that it was able to outperform two state-of-the-art commercially available tools (Meteor Nexus and ADMET Predictor), with precision and recall values up to 7 times better than those obtained for Meteor Nexus or ADMET Predictor on the same sets of pharmaceuticals, pesticides, phytochemicals or endobiotics under similar or identical constraints. Furthermore BioTransformer was able to reproduce 100% of the transformations and metabolites predicted by the EAWAG pathway prediction system. Using mass spectrometry data obtained from a rat experimental study with epicatechin supplementation, BioTransformer was also able to correctly identify 39 previously reported epicatechin metabolites via its metabolism identification tool, and suggest 28 potential metabolites, 17 of which matched nine monoisotopic masses for which no evidence of a previous report could be found.

Conclusion

BioTransformer can be used as an open access command-line tool, or a software library. It is freely available at https://bitbucket.org/djoumbou/biotransformerjar/. Moreover, it is also freely available as an open access RESTful application at www.biotransformer.ca, which allows users to manually or programmatically submit queries, and retrieve metabolism predictions or compound identification data.

Electronic supplementary material

The online version of this article (10.1186/s13321-018-0324-5) contains supplementary material, which is available to authorized users.

Approaching Pharmacological Space: Events and Components

With a view to introducing the concept of pharmacological space and its potential applications in investigating and predicting the toxic mechanisms of xenobiotics, this opening chapter describes the logical relations between conformational behavior, physicochemical properties and binding spaces, which are seen as the three key elements composing the pharmacological space. While the concept of conformational space is routinely used to encode molecular flexibility, the concepts of property spaces and, particularly, of binding spaces are more innovative. Indeed, their descriptors can find fruitful applications (a) in describing the dynamic adaptability a given ligand experiences when inserted into a specific environment, and (b) in parameterizing the flexibility a ligand retains when bound to a biological target. Overall, these descriptors can conveniently account for the often disregarded entropic factors and as such they prove successful when inserted in ligand- or structure-based predictive models. Notably, and although binding space parameters can clearly be derived from MD simulations, the chapter will illustrate how docking calculations, despite their static nature, are able to evaluate ligand's flexibility by analyzing several poses for each ligand. Such an approach, which represents the founding core of the binding space concept, can find various applications in which the related descriptors show an impressive enhancing effect on the statistical performances of the resulting predictive models.

Predicting drug metabolism: experiment and/or computation?

Drug metabolism can produce metabolites with physicochemical and pharmacological properties that differ substantially from those of the parent drug, and consequently has important implications for both drug safety and efficacy. To reduce the risk of costly clinical-stage attrition due to the metabolic characteristics of drug candidates, there is a need for efficient and reliable ways to predict drug metabolism in vitro, in silico and in vivo. In this Perspective, we provide an overview of the state of the art of experimental and computational approaches for investigating drug metabolism. We highlight the scope and limitations of these methods, and indicate strategies to harvest the synergies that result from combining measurement and prediction of drug metabolism.

Optimizing terminology for stroke motor rehabilitation: recommendations from the American Congress of Rehabilitation Medicine Stroke Movement Interventions Subcommittee

As knowledge and interest in stroke motor rehabilitation continue to increase, consistent terminologies that are specific to this discipline must be established. Such language is critical to effective rehabilitative team communication, and is important to facilitating communication among the diverse groups interested in the science and practice of stroke motor rehabilitation. The purpose of this article is to provide operational definitions for 3 concepts that are common-and commonly mislabeled-attributes of stroke motor rehabilitation interventions: intensity, duration, and frequency. In developing these guidelines, conceptual frameworks used in the pharmaceutical, exercise, and rehabilitative therapy realms were used. Implications of these definitions for research and clinical practice are also discussed.

Gene expression profiling and pathway network analysis of hepatic metabolic enzymes targeted by baicalein

ETHNOPHARMACOLOGICAL RELEVANCE

Baicalein is a flavone originally isolated from the roots of traditional Chinese medicinal herb, Scutellaria baicalensis, which has been proved as a promising chemopreventive compound for many chronic human diseases.

AIM OF THE STUDY

The present study aimed to clarify the molecular mechanism targeted by baicalein.

MATERIALS AND METHODS

Gene expression profiling of HepG2 cells treated with baicalein was carried out, using the Affymetrix 42K oligonucleotide microarray in the present study. Microarray data analyzed by Ingenuity Pathway Analysis (IPA), further study performed by real time PCR, reporter gene assay, and Western blot.

RESULTS

Among total 42K gene probes, baicalein treatment up-regulated the signals of 440 gene probes (1.04% of total gene probes) and down-regulated signals of 254 gene probes (0.6% of total gene probes) by ≥2-fold. These genes were categorized into 35 groups and hit for biological processes, molecular functions, and signaling pathways. The network and pathway analyses of these data further revealed that an Nrf2 (nuclear factor-erythroid 2 p45-related factor 2)-mediated ARE (antioxidant response element) pathway is involved in baicalein-induced gene expression of hepatic metabolic enzymes. The representative enzymes involved in Nrf2/ARE pathway were further confirmed at mRNA level by real time PCR and at protein level by Western blot analysis. Moreover, the ARE-reporter gene assay demonstrated that baicalein stimulated Nrf2-mediated ARE transactivation.

CONCLUSIONS

Our results provide a comprehensive data for understanding the hepatic metabolism, bioactive role and the molecular mechanisms of baicalein.

Biotransformation pathway maps in WikiPathways enable direct visualization of drug metabolism related expression changes

In recent decades, our knowledge of the genetics and functional genomics of drug-metabolizing enzymes has increased and a wealth of data on drug-related 'omics' has become available. Despite the availability of large amounts of biological information on xenobiotic biotransformation, the number of available biotransformation pathway maps that can easily be used for visualization of multiple omics data is limited. Here, we created integrated biotransformation pathway maps suitable for multiple omics analysis using PathVisio. The ease of visualizing data on these maps was demonstrated by using published microarray data from human hepatocyte-like cell models, exemplifying - where a sufficient capacity for metabolizing chemicals is a prerequisite for a suited model - how the biotransformation pathway maps can be used for model selection.