How Do Metabolites Differ from Their Parent Molecules and How Are They Excreted?

Expanding the Role of Chiral Drugs and Chiral Nanomaterials as a Potential Therapeutic Tool

Chirality, the property of molecules having mirror-image forms, plays a crucial role in pharmaceutical and biomedical research. This review highlights its growing importance, emphasizing how chiral drugs and nanomaterials impact drug effectiveness, safety, and diagnostics. Chiral molecules serve as precise diagnostic tools, aiding in accurate disease detection through unique biomolecule interactions. The article extensively covers chiral drug applications in treating cardiovascular diseases, CNS disorders, local anesthesia, anti-inflammatories, antimicrobials, and anticancer drugs. Additionally, it explores the emerging field of chiral nanomaterials, highlighting their suitability for biomedical applications in diagnostics and therapeutics, enhancing medical treatments.

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.

The Therapeutic Potential of 2-{[4-(2-methoxyphenyl)piperazin-1-yl]alkyl}-1H-benzo[d]imidazoles as Ligands for Alpha1-Adrenergic Receptor - Comparative In Silico and In Vitro Study

Adrenergic receptors are among the most studied G protein-coupled receptors. Activation or blockade of these receptors is a major therapeutic approach for the treatment of numerous disorders such as cardiac hypertrophy, congestive heart failure, hypertension, angina pectoris, cardiac arrhythmias, depression, benign prostate hyperplasia, anaphylaxis, asthma, and hyperthyroidism. Among all nine cloned adrenoceptor subtypes and the subsequent development of animal models, a significant target for various neurological conditions treatment is alpha1-adrenergic receptors. 2-{[4-(2-Methoxyphenyl)piperazin-1-yl]alkyl}-1H-benzo[d]imidazoles, their 5 substituted derivatives, and structurally similar, arylpiperazine based alpha1-adrenergic receptors antagonists (trazodone, naftopidil, and urapidil) have been subjects of comparative analysis. Most of the novel compounds showed alpha1-adrenergic affinity in the range from 22 nM to 250 nM. The in silico docking and molecular dynamics simulations, binding data together with absorption, distribution, metabolism, and excretion (ADME) calculations identified the promising lead compounds. The results brought out the conclusions which allowed us to propose a rationale for the activity of these molecules and to highlight six compounds (2-5, 8, and 12) that exhibited an acceptable pharmacokinetic profile to the advanced investigation as the potential alpha1-adrenergic receptor antagonists.

A comprehensive review on the analytical method, occurrence, transformation and toxicity of a reactive pollutant: BADGE

Bisphenol A diglycidyl ether (BADGE)-based epoxy resin is one of the most widely used epoxy resins with an annual production amount of several million tons. Compared with all other legacy or emerging organic compounds, BADGE is special due to its toxicity and high reactivity in the environment. More and more studies are available on its analytical methods, occurrence, transformation and toxicity. Here, we provided a comprehensive review of the current BADGE-related studies, with focus on its production, application, available analytical methods, occurrences in the environment and human specimen, abiotic and biotic transformation, as well as the in vitro and in vivo toxicities. The available data show that BADGE and its derivatives are ubiquitous environmental chemicals and often well detected in human specimens. For their analysis, a water-free sample pretreatment should be considered to avoid hydrolysis. Additionally, their complex reactions with endogenous metabolites are areas of great interest. To date, the monitoring and further understanding of their transport and fate in the environment are still quite lacking, comparing with its analogues bisphenol A (BPA) and bisphenol S (BPS). In terms of toxicity, the summary of its current studies and Environmental Protection Agency (EPA) ToxCast toxicity database suggests BADGE might be an endocrine disruptor, though more detailed evidence is still needed to confirm this hypothesis in in vivo animal models. Future study of BADGE should focus on its metabolic transformation, reaction with protein and validation of its role as an endocrine disruptor. We believe that the elucidation of BADGEs can greatly enhance our understandings of those reactive compounds in the environment and human.

An electron transfer competent structural ensemble of membrane-bound cytochrome P450 1A1 and cytochrome P450 oxidoreductase

Cytochrome P450 (CYP) heme monooxygenases require two electrons for their catalytic cycle. For mammalian microsomal CYPs, key enzymes for xenobiotic metabolism and steroidogenesis and important drug targets and biocatalysts, the electrons are transferred by NADPH-cytochrome P450 oxidoreductase (CPR). No structure of a mammalian CYP-CPR complex has been solved experimentally, hindering understanding of the determinants of electron transfer (ET), which is often rate-limiting for CYP reactions. Here, we investigated the interactions between membrane-bound CYP 1A1, an antitumor drug target, and CPR by a multiresolution computational approach. We find that upon binding to CPR, the CYP 1A1 catalytic domain becomes less embedded in the membrane and reorients, indicating that CPR may affect ligand passage to the CYP active site. Despite the constraints imposed by membrane binding, we identify several arrangements of CPR around CYP 1A1 that are compatible with ET. In the complexes, the interactions of the CPR FMN domain with the proximal side of CYP 1A1 are supplemented by more transient interactions of the CPR NADP domain with the distal side of CYP 1A1. Computed ET rates and pathways agree well with available experimental data and suggest why the CYP-CPR ET rates are low compared to those of soluble bacterial CYPs.

GLORYx: Prediction of the Metabolites Resulting from Phase 1 and Phase 2 Biotransformations of Xenobiotics

Predicting the structures of metabolites formed in humans can provide advantageous insights for the development of drugs and other compounds. Here we present GLORYx, which integrates machine learning-based site of metabolism (SoM) prediction with reaction rule sets to predict and rank the structures of metabolites that could potentially be formed by phase 1 and/or phase 2 metabolism. GLORYx extends the approach from our previously developed tool GLORY, which predicted metabolite structures for cytochrome P450-mediated metabolism only. A robust approach to ranking the predicted metabolites is attained by using the SoM probabilities predicted by the FAME 3 machine learning models to score the predicted metabolites. On a manually curated test data set containing both phase 1 and phase 2 metabolites, GLORYx achieves a recall of 77% and an area under the receiver operating characteristic curve (AUC) of 0.79. Separate analysis of performance on a large amount of freely available phase 1 and phase 2 metabolite data indicates that achieving a meaningful ranking of predicted metabolites is more difficult for phase 2 than for phase 1 metabolites. GLORYx is freely available as a web server at https://nerdd.zbh.uni-hamburg.de/ and is also provided as a software package upon request. The data sets as well as all the reaction rules from this work are also made freely available.

Deep Learning Based Drug Metabolites Prediction

Drug metabolism research plays a key role in the discovery and development of drugs. Based on the discovery of drug metabolites, new chemical entities can be identified and potential safety hazards caused by reactive or toxic metabolites can be minimized. Nowadays, computational methods are usually complementary tools for experiments. However, current metabolites prediction methods tend to have high false positive rates with low accuracy and are usually only used for specific enzyme systems. In order to overcome this difficulty, a method was developed in this paper by first establishing a database with broad coverage of SMARTS-coded metabolic reaction rule, and then extracting the molecular fingerprints of compounds to construct a classification model based on deep learning algorithms. The metabolic reaction rule database we built can supplement chemically reasonable negative reaction examples. Based on deep learning algorithms, the model could determine which reaction types are more likely to occur than the others. In the test set, our method can achieve the accuracy of 70% (Top-10), which is significantly higher than that of random guess and the rule-based method SyGMa. The results demonstrated that our method has a certain predictive ability and application value.

Establishment of rat liver microsome-hydrogel system for in vitro phase II metabolism and its application to study pharmacological effects of UGT substrates

Studies on the efficacy evaluation of UDP-glucuronosyltransferases (UGTs) substrates often ignore the existence of active metabolites. However, the present study aims to establish an in-vitro Phase II metabolism system to predict their pharmacological effects after metabolism. Rat liver microsomes (RLMs) encapsulated in a F127'-Acr-Bis (FAB) hydrogel were placed in the incubation system. Baicalein (BA) was chosen as a model drug and the metabolic activity was investigated by quantitating the metabolite Baicalin (BG). The 3-(4, 5-dimethylthiazol-2-yl)-2, 5-diphenyltetrazolium bromide (MTT) assay was used to measure the cell viability in Traditional cell culture system (TCCS) and Microsome-hydrogel added to cell culture system for Phase II metabolism (MHCCS-II). Finally, MHCCS-II was applied to predict the metabolic effects of Oroxylin A (OA) and Wogonin (W). Compared to TCCS group, for HepG2 and MCF-7 cells, BA in MHCCS-II led to lower survival ratios of cells (P < 0.05), while for PC12 cells it led to higher survival ratios of cells (P < 0.01). For HepG2 cells, OA and W showed obviously enhanced tumor inhibition after metabolism with the IC₅₀ of 32.7 ± 2.9 μM and 76.1 ± 5.1 μM, respectively (P < 0.01). In conclusion, the MHCCS-II could be a useful tool for studying the pharmacokinetics and pharmacodynamics of UGTs substrates.

Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.

Identification of the absorbed components and metabolites of modified Huo Luo Xiao Ling Dan in rat plasma by UHPLC-Q-TOF/MS/MS

To reveal the material basis of Huo Luo Xiao Ling Dan (HLXLD), a sensitive and selective ultra-high performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) method was developed to identify the absorbed components and metabolites in rat plasma after oral administration of HLXLD. The plasma samples were pretreated by liquid-liquid extraction and separated on a Shim-pack XR-ODS C₁₈ column (75 × 3.0 mm, 2.2 μm) using a gradient elution program. With the optimized conditions and single sample injection of each positive or negative ion mode, a total of 109 compounds, including 78 prototype compounds and 31 metabolites, were identified or tentatively characterized. The fragmentation patterns of representative compounds were illustrated as well. The results indicated that aromatization and hydration were the main metabolic pathways of lactones and tanshinone-related metabolites; demethylation and oxidation were the major metabolic pathways of alkaloid-related compounds; methylation and sulfation were the main metabolic pathways of phenolic acid-related metabolites. It is concluded the developed UHPLC-Q-TOF/MS method with high sensitivity and resolution is suitable for identifying and characterizing the absorbed components and metabolites of HLXLD, and the results will provide essential data for further studying the relationship between the chemical components and pharmacological activity of HLXLD.

In silico pharmacology: Drug membrane partitioning and crossing

Over the past decade, molecular dynamics (MD) simulations have become particularly powerful to rationalize drug insertion and partitioning in lipid bilayers. MD simulations efficiently support experimental evidences, with a comprehensive understanding of molecular interactions driving insertion and crossing. Prediction of drug partitioning is discussed with respect to drug families (anesthetics; β-blockers; non-steroidal anti-inflammatory drugs; antioxidants; antiviral drugs; antimicrobial peptides). To accurately evaluate passive permeation coefficients turned out to be a complex theoretical challenge; however the recent methodological developments based on biased MD simulations are particularly promising. Particular attention is paid to membrane composition (e.g., presence of cholesterol), which influences drug partitioning and permeation. Recent studies concerning in silico models of membrane proteins involved in drug transport (influx and efflux) are also reported here. These studies have allowed gaining insight in drug efflux by, e.g., ABC transporters at an atomic resolution, explicitly accounting for the mandatory forces induced by the surrounded lipid bilayer. Large-scale conformational changes were thoroughly analyzed.

A Redox-Active, Compact Molecule for Cross-Linking Amyloidogenic Peptides into Nontoxic, Off-Pathway Aggregates: In Vitro and In Vivo Efficacy and Molecular Mechanisms

Chemical reagents targeting and controlling amyloidogenic peptides have received much attention for helping identify their roles in the pathogenesis of protein-misfolding disorders. Herein, we report a novel strategy for redirecting amyloidogenic peptides into nontoxic, off-pathway aggregates, which utilizes redox properties of a small molecule (DMPD, N,N-dimethyl-p-phenylenediamine) to trigger covalent adduct formation with the peptide. In addition, for the first time, biochemical, biophysical, and molecular dynamics simulation studies have been performed to demonstrate a mechanistic understanding for such an interaction between a small molecule (DMPD) and amyloid-β (Aβ) and its subsequent anti-amyloidogenic activity, which, upon its transformation, generates ligand-peptide adducts via primary amine-dependent intramolecular cross-linking correlated with structural compaction. Furthermore, in vivo efficacy of DMPD toward amyloid pathology and cognitive impairment was evaluated employing 5xFAD mice of Alzheimer's disease (AD). Such a small molecule (DMPD) is indicated to noticeably reduce the overall cerebral amyloid load of soluble Aβ forms and amyloid deposits as well as significantly improve cognitive defects in the AD mouse model. Overall, our in vitro and in vivo studies of DMPD toward Aβ with the first molecular-level mechanistic investigations present the feasibility of developing new, innovative approaches that employ redox-active compounds without the structural complexity as next-generation chemical tools for amyloid management.

Cheminformatics Research at the Unilever Centre for Molecular Science Informatics Cambridge

The Centre for Molecular Informatics, formerly Unilever Centre for Molecular Science Informatics (UCMSI), at the University of Cambridge is a world-leading driving force in the field of cheminformatics. Since its opening in 2000 more than 300 scientific articles have fundamentally changed the field of molecular informatics. The Centre has been a key player in promoting open chemical data and semantic access. Though mainly focussing on basic research, close collaborations with industrial partners ensured real world feedback and access to high quality molecular data. A variety of tools and standard protocols have been developed and are ubiquitous in the daily practice of cheminformatics. Here, we present a retrospective of cheminformatics research performed at the UCMSI, thereby highlighting historical and recent trends in the field as well as indicating future directions.

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.

The food metabolome: a window over dietary exposure

The food metabolome is defined as the part of the human metabolome directly derived from the digestion and biotransformation of foods and their constituents. With >25,000 compounds known in various foods, the food metabolome is extremely complex, with a composition varying widely according to the diet. By its very nature it represents a considerable and still largely unexploited source of novel dietary biomarkers that could be used to measure dietary exposures with a high level of detail and precision. Most dietary biomarkers currently have been identified on the basis of our knowledge of food compositions by using hypothesis-driven approaches. However, the rapid development of metabolomics resulting from the development of highly sensitive modern analytic instruments, the availability of metabolite databases, and progress in (bio)informatics has made agnostic approaches more attractive as shown by the recent identification of novel biomarkers of intakes for fruit, vegetables, beverages, meats, or complex diets. Moreover, examples also show how the scrutiny of the food metabolome can lead to the discovery of bioactive molecules and dietary factors associated with diseases. However, researchers still face hurdles, which slow progress and need to be resolved to bring this emerging field of research to maturity. These limits were discussed during the First International Workshop on the Food Metabolome held in Glasgow. Key recommendations made during the workshop included more coordination of efforts; development of new databases, software tools, and chemical libraries for the food metabolome; and shared repositories of metabolomic data. Once achieved, major progress can be expected toward a better understanding of the complex interactions between diet and human health.

Investigating and Predicting how Biology Changes Molecules and Their Properties

Most molecules are transformed and transported by specific metabolising enzymes and transporters resulting in changes in their bioactivities, pharmacokinetics and toxicity profiles. This is a key consideration in the design of drugs. Ideally, when medicines have performed their task, they need to fade away gracefully, and not introduce unexpected or untoward biological effects. Some examples of predictive metabolism, transport and interesting design considerations of drugs are described.

FAst MEtabolizer (FAME): A rapid and accurate predictor of sites of metabolism in multiple species by endogenous enzymes

FAst MEtabolizer (FAME) is a fast and accurate predictor of sites of metabolism (SoMs). It is based on a collection of random forest models trained on diverse chemical data sets of more than 20 000 molecules annotated with their experimentally determined SoMs. Using a comprehensive set of available data, FAME aims to assess metabolic processes from a holistic point of view. It is not limited to a specific enzyme family or species. Besides a global model, dedicated models are available for human, rat, and dog metabolism; specific prediction of phase I and II metabolism is also supported. FAME is able to identify at least one known SoM among the top-1, top-2, and top-3 highest ranked atom positions in up to 71%, 81%, and 87% of all cases tested, respectively. These prediction rates are comparable to or better than SoM predictors focused on specific enzyme families (such as cytochrome P450s), despite the fact that FAME uses only seven chemical descriptors. FAME covers a very broad chemical space, which together with its inter- and extrapolation power makes it applicable to a wide range of chemicals. Predictions take less than 2.5 s per molecule in batch mode on an Ultrabook. Results are visualized using Jmol, with the most likely SoMs highlighted.

In silico enzymatic synthesis of a 400,000 compound biochemical database for nontargeted metabolomics

Current methods of structure identification in mass-spectrometry-based nontargeted metabolomics rely on matching experimentally determined features of an unknown compound to those of candidate compounds contained in biochemical databases. A major limitation of this approach is the relatively small number of compounds currently included in these databases. If the correct structure is not present in a database, it cannot be identified, and if it cannot be identified, it cannot be included in a database. Thus, there is an urgent need to augment metabolomics databases with rationally designed biochemical structures using alternative means. Here we present the In Vivo/In Silico Metabolites Database (IIMDB), a database of in silico enzymatically synthesized metabolites, to partially address this problem. The database, which is available at http://metabolomics.pharm.uconn.edu/iimdb/, includes ~23,000 known compounds (mammalian metabolites, drugs, secondary plant metabolites, and glycerophospholipids) collected from existing biochemical databases plus more than 400,000 computationally generated human phase-I and phase-II metabolites of these known compounds. IIMDB features a user-friendly web interface and a programmer-friendly RESTful web service. Ninety-five percent of the computationally generated metabolites in IIMDB were not found in any existing database. However, 21,640 were identical to compounds already listed in PubChem, HMDB, KEGG, or HumanCyc. Furthermore, the vast majority of these in silico metabolites were scored as biological using BioSM, a software program that identifies biochemical structures in chemical structure space. These results suggest that in silico biochemical synthesis represents a viable approach for significantly augmenting biochemical databases for nontargeted metabolomics applications.