The SMARTCyp cytochrome P450 metabolism prediction server

Structure-guided optimization of small molecules targeting the yeast casein kinase, Yck2, as a therapeutic strategy to combat Candida albicans

Candida albicans is the most common cause of life-threatening fungal infection in the developed world but remains a therapeutic challenge. Protein kinases have been rewarding drug targets across diverse indications but remain untapped for antifungal development. Previously, screening kinase inhibitors against C. albicans revealed a 2,3-aryl-pyrazolopyridine, GW461484A (GW), which targets casein kinase 1 (CK1) family member Yck2. Here, we report optimization of GW via two complementary approaches, synthesis of bioisosteres possessing an imidazo[1,2-a]pyridine core, and R-group substitution of GW's pyrazolo[1,5-a]pyridine core. Characterization of compounds synthesized revealed two 6-cyano derivatives with improved pharmacological properties that retained whole-cell bioactivity and selectivity for fungal Yck2 compared to human CK1α. Efficacy studies in mice indicated both analogs possess single-agent activity against C. albicans resistant to first-line echinocandin antifungals and potentiate non-curative echinocandin treatment. Results validate Yck2 as an antifungal target and encourage further development of inhibitors acting by this previously unexploited mode of action.

The Use of a Penta-Deuterophenyl Substituent to Improve the Metabolic Stability of a Tyrosine Kinase Inhibitor

In cases in which a rapid metabolism is the cause of an unfavorable pharmacokinetic profile, it is important to determine the Sites of Metabolism (SoMs) of a molecule to introduce the necessary modifications to improve the stability of the compound. The substitution of hydrogen atoms by deuterium atoms has been proposed to ameliorate such properties due to the greater stability of the C-D bonds. IQS016, bearing a 2-phenylamino substituent, is a compound previously described by our group with good biological activity as a discoidin domain receptor (DDR2) inhibitor but suffers from low metabolic stability determined in a test with rat-liver microsomes (less than 50% of the initial compound after 60 min). We have obtained the corresponding 2-(penta-deuterophenyl) analog (IQS016-d5) from aniline-2,3,4,5,6-d5 showing that it has a better metabolic stability than IQS016 and a higher inhibitory effect on isolated tyrosine kinase receptors but not a better 2D in vitro effect.

Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.

Impact of plastic-related compounds on the gene expression signature of HepG2 cells transfected with CYP3A4

The presence of plastic and microplastic within the oceans as well as in marine flora and fauna have caused a multitude of problems that have been the topic of numerous investigations for many years. However, their impact on human health remains largely unknown. Such plastic and microplastic particles have been detected in blood and placenta, underlining their ability to enter the human body. Plastics also contain other compounds, such as plasticizers, antioxidants, or dyes, whose impact on human health is currently being studied. Critical enzymes within the metabolism of endogenous molecules, especially of xenobiotics, are the cytochrome P450 monooxygenases (CYPs). Although their importance in maintaining cellular balance has been confirmed, their interactions with plastics and related products are poorly understood. In this study, the possible relationship between different plastic-related compounds and CYP3A4 as one of the most important CYPs was analyzed using hepatic cells overexpressing this enzyme. Beginning with virtual compound screening and molecular docking of more than 1000 plastic-related compounds, several candidates were identified to interact with CYP3A4. In a second step, RNA-sequencing was used to study in detail the transcriptome-wide gene expression levels affected by the selected compounds. Three candidate molecules ((2,2'-methylenebis(6-tert-butyl-4-methylphenol), 1,1-bis(3,5-di-tert-butyl-2-hydroxyphenyl)ethane, and 2,2'-methylenebis(6-cyclohexyl-4-methylphenol)) had an excellent binding affinity to CYP3A4 in-silico as well as cytotoxic effects and interactions with several metabolic pathways in-vitro. We identified common pathways influenced by all three selected plastic-related compounds. In particular, the suppression of pathways related to mitosis and 'DNA-templated DNA replication' which were confirmed by cell cycle analysis and single-cell gel electrophoresis. Furthermore, several mis-regulated metabolic and inflammation-related pathways were identified, suggesting the induction of hepatotoxicity at different levels. These findings imply that these compounds may cause liver problems subsequently affecting the entire organism.

Chemical Catalysis Guides Structural Identification for the Major In Vivo Metabolite of the BET Inhibitor JQ1

The bromodomain inhibitor (+)-JQ1 is a highly validated chemical probe; however, it exhibits poor in vivo pharmacokinetics. To guide efforts toward improving its pharmacological properties, we identified the (+)-JQ1 primary metabolite using chemical catalysis methods. Treatment of (+)-JQ1 with tetrabutylammonium decatungstate under photochemical conditions resulted in selective formation of an aldehyde at the 2-position of the thiophene ring [(+)-JQ1-CHO], which was further reduced to the 2-hydroxymethyl analog [(+)-JQ1-OH]. Comparative LC/MS analysis of (+)-JQ1-OH to the product obtained from liver microsomes suggested (+)-JQ1-OH as the major metabolite of (+)-JQ1. The 2-thienyl position was then substituted to generate a trideuterated (-CD3, (+)-JQ1-D) analog having half-lives that were 1.8- and 2.8-fold longer in mouse and human liver microsomes, respectively. This result unambiguously confirmed (+)-JQ1-OH as the major metabolite of (+)-JQ1. These studies demonstrate an efficient process for studying drug metabolism and identifying the metabolic soft spots of bioactive compounds.

Unlocking The Mysteries of DNA Adducts with Artificial Intelligence

Cellular genome is considered a dynamic blueprint of a cell since it encodes genetic information that gets temporally altered due to various endogenous and exogenous insults. Largely, the extent of genomic dynamicity is controlled by the trade-off between DNA repair processes and the genotoxic potential of the causative agent (genotoxins or potential carcinogens). A subset of genotoxins form DNA adducts by covalently binding to the cellular DNA, triggering structural or functional changes that lead to significant alterations in cellular processes via genetic (e. g., mutations) or non-genetic (e. g., epigenome) routes. Identification, quantification, and characterization of DNA adducts are indispensable for their comprehensive understanding and could expedite the ongoing efforts in predicting carcinogenicity and their mode of action. In this review, we elaborate on using Artificial Intelligence (AI)-based modeling in adducts biology and present multiple computational strategies to gain advancements in decoding DNA adducts. The proposed AI-based strategies encompass predictive modeling for adduct formation via metabolic activation, novel adducts' identification, prediction of biochemical routes for adduct formation, adducts' half-life predictions within biological ecosystems, and, establishing methods to predict the link between adducts chemistry and its location within the genomic DNA. In summary, we discuss some futuristic AI-based approaches in DNA adduct biology.

Optimization of 3-Cyano-7-cyclopropylamino-pyrazolo[1,5-a]pyrimidines toward the Development of an In Vivo Chemical Probe for CSNK2A

3-Cyano-7-cyclopropylamino-pyrazolo[1,5-a]pyrimidines, including the chemical probe SGC-CK2-1, are potent and selective inhibitors of CSNK2A in cells but have limited utility in animal models due to their poor pharmacokinetic properties. While developing analogues with reduced intrinsic clearance and the potential for sustained exposure in mice, we discovered that phase II conjugation by GST enzymes was a major metabolic transformation in hepatocytes. A protocol for codosing with ethacrynic acid, a covalent reversible GST inhibitor, was developed to improve the exposure of analogue 2h in mice. A double codosing protocol, using a combination of ethacrynic acid and irreversible P450 inhibitor 1-aminobenzotriazole, increased the blood level of 2h by 40-fold at a 5 h time point.

Comparison and summary of in silico prediction tools for CYP450-mediated drug metabolism

The cytochrome P450 (CYP450) enzyme system is responsible for the metabolism of more than two-thirds of xenobiotics. This review summarizes reports of a series of in silico tools for CYP450 enzyme-drug interaction predictions, including the prediction of sites of metabolism (SOM) of a drug and the identification of inhibitor/substrates for CYP subtypes. We also evaluated four prediction tools to identify CYP inhibitors utilizing 52 of the most frequently prescribed drugs. ADMET Predictor and CYPlebrity demonstrated the best performance. We hope that this review provides guidance for choosing appropriate enzyme prediction tools from a variety of in silico platforms to meet individual needs. Such predictions are useful for medicinal chemists to prioritize their designed compounds for further drug discovery.

The Effect of Plastic-Related Compounds on Transcriptome-Wide Gene Expression on CYP2C19-Overexpressing HepG2 Cells

In recent years, plastic and especially microplastic in the oceans have caused huge problems to marine flora and fauna. Recently, such particles have also been detected in blood, breast milk, and placenta, underlining their ability to enter the human body, presumably via the food chain and other yet-unknown mechanisms. In addition, plastic contains plasticizers, antioxidants, or lubricants, whose impact on human health is also under investigation. At the cellular level, the most important enzymes involved in the metabolism of xenobiotic compounds are the cytochrome P450 monooxygenases (CYPs). Despite their extensive characterization in the maintenance of cellular balance, their interactions with plastic and related products are unexplored. In this study, the possible interactions between several plastic-related compounds and one of the most important cytochromes, CYP2C19, were analyzed. By applying virtual compound screening and molecular docking to more than 1000 commercially available plastic-related compounds, we identified candidates that are likely to interact with this protein. A growth inhibition assay confirmed their cytotoxic activity on a CYP2C19-transfected hepatic cell line. Subsequently, we studied the effect of the selected compounds on the transcriptome-wide gene expression level by conducting RNA sequencing. Three candidate molecules were identified, i.e., 2,2'-methylene bis(6-tert-butyl-4-methylphenol), 1,1-bis(3,5-di-tert-butyl-2-hydroxyphenyl) ethane, and 2,2'-methylene bis(6-cyclohexyl-4-methylphenol)), which bound with a high affinity to CYP2C19 in silico. They exerted a profound cytotoxicity in vitro and interacted with several metabolic pathways, of which the 'cholesterol biosynthesis process' was the most affected. In addition, other affected pathways involved mitosis, DNA replication, and inflammation, suggesting an increase in hepatotoxicity. These results indicate that plastic-related compounds could damage the liver by affecting several molecular pathways.

Optimization of 3-Cyano-7-cyclopropylamino-pyrazolo[1,5-a]pyrimidines Toward the Development of an In Vivo Chemical Probe for CSNK2A

3-cyano-7-cyclopropylamino-pyrazolo[1,5-a]pyrimidines, including the chemical probe SGC-CK2-1, are potent and selective inhibitors of CSNK2A in cells but have limited utility in animal models due to their poor pharmacokinetic properties. While developing analogs with reduced intrinsic clearance and the potential for sustained exposure in mice, we discovered that Phase II conjugation by GST enzymes was a major metabolic transformation in hepatocytes. A protocol for co-dosing with ethacrynic acid, a covalent reversible GST inhibitor, was developed to improve the exposure of analog 2h in mice. A double co-dosing protocol, using a combination of ethacrynic acid and irreversible P450 inhibitor 1-aminobenzotriazole increased the blood level of 2h by 40-fold at a 5 h time point.

Advancing chemical carcinogenicity prediction modeling: opportunities and challenges

Carcinogenicity assessment of any compound is a laborious and expensive exercise with several associated ethical and practical concerns. While artificial intelligence (AI) offers promising solutions, unfortunately, it is contingent on several challenges concerning the inadequacy of available experimentally validated (non)carcinogen datasets and variabilities within bioassays, which contribute to the compromised model training. Existing AI solutions that leverage classical chemistry-driven descriptors do not provide adequate biological interpretability involved in imparting carcinogenicity. This highlights the urgency to devise alternative AI strategies. We propose multiple strategies, including implementing data-driven (integrated databases) and known carcinogen-characteristic-derived features to overcome these apparent shortcomings. In summary, these next-generation approaches will continue facilitating robust chemical carcinogenicity prediction, concomitant with deeper mechanistic insights.

Predicting Regioselectivity of Cytosolic Sulfotransferase Metabolism for Drugs

Cytosolic sulfotransferases (SULTs) are a family of enzymes responsible for the sulfation of small endogenous and exogenous compounds. SULTs contribute to the conjugation phase of metabolism and share substrates with the uridine 5'-diphospho-glucuronosyltransferase (UGT) family of enzymes. UGTs are considered to be the most important enzymes in the conjugation phase, and SULTs are an auxiliary enzyme system to them. Understanding how the regioselectivity of SULTs differs from that of UGTs is essential from the perspective of developing novel drug candidates. We present a general ligand-based SULT model trained and tested using high-quality experimental regioselectivity data. The current study suggests that, unlike other metabolic enzymes in the modification and conjugation phases, the SULT regioselectivity is not strongly influenced by the activation energy of the rate-limiting step of the catalysis. Instead, the prominent role is played by the substrate binding site of SULT. Thus, the model is trained only on steric and orientation descriptors, which mimic the binding pocket of SULT. The resulting classification model, which predicts whether a site is metabolized, achieved a Cohen's kappa of 0.71.

Metabolite Identification of Isopropoxy Benzene Guanidine in Rat Liver Microsomes by Using UHPLC-Q-TOF-MS/MS

Isopropoxy benzene guanidine (IBG) is a guanidine derivative with antibacterial activity against multidrug-resistant bacteria. A few studies have revealed the metabolism of IBG in animals. The aim of the current study was to identify potential metabolic pathways and metabolites of IBG. The detection and characterization of metabolites were performed with high-performance liquid chromatography tandem mass spectrometry (UHPLC-Q-TOF-MS/MS). Seven metabolites were identified from the microsomal incubated samples by using the UHPLC-Q-TOF-MS/MS system. The metabolic pathways of IBG in the rat liver microsomes involved O-dealkylation, oxygenation, cyclization, and hydrolysis. Hydroxylation was the main metabolic pathway of IBG in the liver microsomes. This research investigated the in vitro metabolism of IBG to provide a basis for the further pharmacology and toxicology of this compound.

Carbazole-based semicarbazones and hydrazones as multifunctional anti-Alzheimer agents

With the aim to combat a multi-faceted neurodegenerative Alzheimer's disease (AD), a series of carbazole-based semicarbazide and hydrazide derivatives were designed, synthesized and assessed for their cholinesterase (ChE) inhibitory, antioxidant and biometal chelating activity. Among them, (E)-2-((9-ethyl-9H-carbazol-3-yl)methylene)-N-(pyridin-2-yl)hydrazinecarbothioamide (62) and (E)-2-((9-ethyl-9H-carbazol-3-yl)methylene)-N-(5-chloropyridin-2-yl)hydrazinecarbothioamide (63) emerged as the premier candidates with good ChE inhibitory activities (IC₅₀ values of 1.37 µM and 1.18 µM for hAChE, IC₅₀ values of 2.69 µM and 3.31 µM for EqBuChE, respectively). All the test compounds displayed excellent antioxidant activity (reduction percentage of DPPH values for compounds (62) and (63) were 85.67% and 84.49%, respectively at 100 µM concentration). Compounds (62) and (63) conferred specific copper ion chelating property in metal chelation study. Molecular docking studies of compounds (62) and (63) indicate strong interactions within the active sites of both the ChE enzymes. Besides that, these compounds also exhibited significant in silico drug-like pharmacokinetic properties. Thus, taken together, they can serve as a starting point in the designing of multifunctional ligands in pursuit of potential anti-AD agents that might further prevent the progression of ADs.Communicated by Ramaswamy H. Sarma.

Exploring the Effect of Halogenation in a Series of Potent and Selective A2B Adenosine Receptor Antagonists

The modulation of the A_2B adenosine receptor is a promising strategy in cancer (immuno) therapy, with A_2BAR antagonists emerging as immune checkpoint inhibitors. Herein, we report a systematic assessment of the impact of (di- and mono-)halogenation at positions 7 and/or 8 on both A_2BAR affinity and pharmacokinetic properties of a collection of A_2BAR antagonists and its study with structure-based free energy perturbation simulations. Monohalogenation at position 8 produced potent A_2BAR ligands irrespective of the nature of the halogen. In contrast, halogenation at position 7 and dihalogenation produced a halogen-size-dependent decay in affinity. Eight novel A_2BAR ligands exhibited remarkable affinity (K_i < 10 nM), exquisite subtype selectivity, and enantioselective recognition, with some eutomers eliciting sub-nanomolar affinity. The pharmacokinetic profile of representative derivatives showed enhanced solubility and microsomal stability. Finally, two compounds showed the capacity of reversing the antiproliferative effect of adenosine in activated primary human peripheral blood mononuclear cells.

Induction of Ferroptosis in Glioblastoma and Ovarian Cancers by a New Pyrrole Tubulin Assembly Inhibitor

We synthesized new aroyl diheterocyclic pyrrole (ARDHEP) 15 that exhibited the hallmarks of ferroptosis. Compound 15 strongly inhibited U-87 MG, OVCAR-3, and MCF-7 cancer cells, induced an increase of cleaved PARP, but was not toxic for normal human primary T lymphocytes at 0.1 μM. Analysis of the levels of lactoperoxidase, malondialdehyde, lactic acid, total glutathione, and ATP suggested that the in vivo inhibition of cancer cell proliferation by 15 went through stimulation of oxidative stress injury and Fe²⁺ accumulation. Quantitative polymerase chain reaction analysis of the mRNA expression in U-87 MG and SKOV-3 tumor tissues from 15-treated mice showed the presence of Ptgs2/Nfe2l2/Sat1/Akr1c1/Gpx4 genes correlated with ferroptosis in both groups. Immunofluorescence staining revealed significantly lower expressions of proteins Ki67, CD31, and ferroptosis negative regulation proteins glutathione peroxidase 4 (GPX4) and FTH1. Compound 15 was found to be metabolically stable when incubated with human liver microsomes.

Predicting Regioselectivity of AO, CYP, FMO, and UGT Metabolism Using Quantum Mechanical Simulations and Machine Learning

Unexpected metabolism in modification and conjugation phases can lead to the failure of many late-stage drug candidates or even withdrawal of approved drugs. Thus, it is critical to predict the sites of metabolism (SoM) for enzymes, which interact with drug-like molecules, in the early stages of the research. This study presents methods for predicting the isoform-specific metabolism for human AOs, FMOs, and UGTs and general CYP metabolism for preclinical species. The models use semi-empirical quantum mechanical simulations, validated using experimentally obtained data and DFT calculations, to estimate the reactivity of each SoM in the context of the whole molecule. Ligand-based models, trained and tested using high-quality regioselectivity data, combine the reactivity of the potential SoM with the orientation and steric effects of the binding pockets of the different enzyme isoforms. The resulting models achieve κ values of up to 0.94 and AUC of up to 0.92.

A Cheminformatics Study Regarding the Human Health Risks Assessment of the Stereoisomers of Difenoconazole

Difenoconazole is a chemical entity containing two chiral centers and having four stereoisomers: (2R,4R)-, (2R,4S)-, (2S,4R)- and (2S,4S)-difenoconazole, the marketed product containing a mixture of these isomers. Residues of difenoconazole have been identified in many agricultural products and drinking water. A computational approach has been used to evaluate the toxicological effects of the difenoconazole stereoisomers on humans. It integrates predictions of absorption, distribution, metabolism, excretion and toxicity (ADMET) profiles, prediction of metabolism sites, and assessment of the interactions of the difenoconazole stereoisomers with human cytochromes, nuclear receptors and plasma proteins by molecular docking. Several toxicological effects have been identified for all the difenoconazole stereoisomers: high plasma protein binding, inhibition of cytochromes, possible hepatotoxicity, neurotoxicity, mutagenicity, skin sensitization potential, moderate potential to produce endocrine disrupting effects. There were small differences in the predicted probabilities of producing various biological effects between the distinct stereoisomers of difenoconazole. Furthermore, there were significant differences between the interacting energies of the difenoconazole stereoisomers with plasma proteins and human cytochromes, the spectra of the hydrogen bonds and aromatic donor-acceptor interactions being quite distinct. Some distinguishing results have been obtained for the (2S,4S)-difenoconazole: it registered the highest value for clearance, exposed reasonable probabilities to produce cardiotoxicity and carcinogenicity and negatively affected numerous nuclear receptors.

In Silico Approaches for Addressing Challenges in CNS Radiopharmaceutical Design

Positron emission tomography (PET) is a highly sensitive and versatile molecular imaging modality that leverages radiolabeled molecules, known as radiotracers, to interrogate biochemical processes such as metabolism, enzymatic activity, and receptor expression. The ability to probe specific molecular and cellular events longitudinally in a noninvasive manner makes PET imaging a particularly powerful technique for studying the central nervous system (CNS) in both health and disease. Unfortunately, developing and translating a single CNS PET tracer for clinical use is typically an extremely resource-intensive endeavor, often requiring synthesis and evaluation of numerous candidate molecules. While existing in vitro methods are beginning to address the challenge of derisking molecules prior to costly in vivo PET studies, most require a significant investment of resources and possess substantial limitations. In the context of CNS drug development, significant time and resources have been invested into the development and optimization of computational methods, particularly involving machine learning, to streamline the design of better CNS therapeutics. However, analogous efforts developed and validated for CNS radiotracer design are conspicuously limited. In this Perspective, we overview the requirements and challenges of CNS PET tracer design, survey the most promising computational methods for in silico CNS drug design, and bridge these two areas by discussing the potential applications and impact of computational design tools in CNS radiotracer design.

Novel deuterated Mnk1/2 protein degrader VNLG-152R analogs: Synthesis, In vitro Anti-TNBC activities and pharmacokinetics in mice

A new and improved synthesis of lead Mnk1/2 protein degrader, VNLG-152R, 4-(±)-(1H-imidazole-1-yl)-N-(4-fluorophenyl)-(E)-retinamide (1) has been developed from commercially available 4-oxo-ATRA (8). This procedure was also utilized to synthesize the seven possible deuterated analogs of compound 1 (11-17). The deuterated analogs were either better or equipotent to 1 in in vitro antiproliferative activities against MDA-MB-231 and MDA-MB-468 human TNBC cells. The Mnk1/2 degraders were equally effective as a standard TNBC therapy (paclitaxel). Importantly, the expression of Mnk1, peIF4E and their associated downstream targets, including cyclin D1 and Bcl2, were strongly decreased in compound 1/analogs (11-17)-treated TNBC cells signifying inhibition of Mnk1-eIF4E signaling. More importantly, we showed that deuterated analogs, 12, 16 and 17 possess improved pharmacokinetics parameters following oral administration to CD-1 female mice compared to the parent non-deuterated compound 1, thus addressing the rapid clearance (short half-life and short residence time) pharmacokinetic inadequacy of compound 1.

BioTransformer 3.0-a web server for accurately predicting metabolic transformation products

BioTransformer 3.0 (https://biotransformer.ca) is a freely available web server that supports accurate, rapid and comprehensive in silico metabolism prediction. It combines machine learning approaches with a rule-based system to predict small-molecule metabolism in human tissues, the human gut as well as the external environment (soil and water microbiota). Simply stated, BioTransformer takes a molecular structure as input (SMILES or SDF) and outputs an interactively sortable table of the predicted metabolites or transformation products (SMILES, PNG images) along with the enzymes that are predicted to be responsible for those reactions and richly annotated downloadable files (CSV and JSON). The entire process typically takes less than a minute. Previous versions of BioTransformer focused exclusively on predicting the metabolism of xenobiotics (such as plant natural products, drugs, cosmetics and other synthetic compounds) using a limited number of pre-defined steps and somewhat limited rule-based methods. BioTransformer 3.0 uses much more sophisticated methods and incorporates new databases, new constraints and new prediction modules to not only more accurately predict the metabolic transformation products of exogenous xenobiotics but also the transformation products of endogenous metabolites, such as amino acids, peptides, carbohydrates, organic acids, and lipids. BioTransformer 3.0 can also support customized sequential combinations of these transformations along with multiple iterations to simulate multi-step human biotransformation events. Performance tests indicate that BioTransformer 3.0 is 40–50% more accurate, far less prone to combinatorial ‘explosions’ and much more comprehensive in terms of metabolite coverage/capabilities than previous versions of BioTransformer.

Development of new HPLC method for identification of metabolic degradation of N-pyrrolylhydrazide hydrazones with determined MAO- B activity in cellular cultures

In this research, a new rapid PR- HPLC method was developed for the determination of metabolites in isolated rat hapatocytes. The chromatographic parameters, including the stationary and mobile phases, outlet pressure, temperature and flow rate, were optimized. The method identified two initial from the synthesis molecules in higher concentration and one new unidentified structure as products of the hepatocytic processing of the evaluated analyte. The results identified as first step of metabolism the hydrolysis of the hydrazone group. Further investigations should be aimed into determining the next metabolic transformations, predicted by the in silico application of the web server SMARTCyp.

MODOMICS: a database of RNA modification pathways. 2021 update

The MODOMICS database has been, since 2006, a manually curated and centralized resource, storing and distributing comprehensive information about modified ribonucleosides. Originally, it only contained data on the chemical structures of modified ribonucleosides, their biosynthetic pathways, the location of modified residues in RNA sequences, and RNA-modifying enzymes. Over the years, prompted by the accumulation of new knowledge and new types of data, it has been updated with new information and functionalities. In this new release, we have created a catalog of RNA modifications linked to human diseases, e.g., due to mutations in genes encoding modification enzymes. MODOMICS has been linked extensively to RCSB Protein Data Bank, and sequences of experimentally determined RNA structures with modified residues have been added. This expansion was accompanied by including nucleotide 5′-monophosphate residues. We redesigned the web interface and upgraded the database backend. In addition, a search engine for chemically similar modified residues has been included that can be queried by SMILES codes or by drawing chemical molecules. Finally, previously available datasets of modified residues, biosynthetic pathways, and RNA-modifying enzymes have been updated. Overall, we provide users with a new, enhanced, and restyled tool for research on RNA modification. MODOMICS is available at https://iimcb.genesilico.pl/modomics/.

Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases

The discovery and development of drugs is a long and expensive process with a high attrition rate. Computational drug discovery contributes to ligand discovery and optimization, by using models that describe the properties of ligands and their interactions with biological targets. In recent years, artificial intelligence (AI) has made remarkable modeling progress, driven by new algorithms and by the increase in computing power and storage capacities, which allow the processing of large amounts of data in a short time. This review provides the current state of the art of AI methods applied to drug discovery, with a focus on structure- and ligand-based virtual screening, library design and high-throughput analysis, drug repurposing and drug sensitivity, de novo design, chemical reactions and synthetic accessibility, ADMET, and quantum mechanics.

In Silico Approaches In Carcinogenicity Hazard Assessment: Current Status and Future Needs

Historically, identifying carcinogens has relied primarily on tumor studies in rodents, which require enormous resources in both money and time. In silico models have been developed for predicting rodent carcinogens but have not yet found general regulatory acceptance, in part due to the lack of a generally accepted protocol for performing such an assessment as well as limitations in predictive performance and scope. There remains a need for additional, improved in silico carcinogenicity models, especially ones that are more human-relevant, for use in research and regulatory decision-making. As part of an international effort to develop in silico toxicological protocols, a consortium of toxicologists, computational scientists, and regulatory scientists across several industries and governmental agencies evaluated the extent to which in silico models exist for each of the recently defined 10 key characteristics (KCs) of carcinogens. This position paper summarizes the current status of in silico tools for the assessment of each KC and identifies the data gaps that need to be addressed before a comprehensive in silico carcinogenicity protocol can be developed for regulatory use.

Constructing xenobiotic maps of metabolism to predict enzymes catalyzing metabolites capable of binding to DNA

Background

The liver plays a major role in the metabolic activation of xenobiotics (drugs, chemicals such as pollutants, pesticides, food additives...). Among environmental contaminants of concern, heterocyclic aromatic amines (HAA) are xenobiotics classified by IARC as possible or probable carcinogens (2A or 2B). There exist little information about the effect of these HAA in humans. While HAA is a family of more than thirty identified chemicals, the metabolic activation and possible DNA adduct formation have been fully characterized in human liver for only a few of them (MeIQx, PhIP, A\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}αC).

Results

We have developed a modeling approach in order to predict all the possible metabolites of a xenobiotic and enzymatic profiles that are linked to the production of metabolites able to bind DNA. Our prediction of metabolites approach relies on the construction of an enriched and annotated map of metabolites from an input metabolite.The pipeline assembles reaction prediction tools (SyGMa), sites of metabolism prediction tools (Way2Drug, SOMP and Fame 3), a tool to estimate the ability of a xenobotics to form DNA adducts (XenoSite Reactivity V1), and a filtering procedure based on Bayesian framework. This prediction pipeline was evaluated using caffeine and then applied to HAA. The method was applied to determine enzymes profiles associated with the maximization of metabolites derived from each HAA which are able to bind to DNA. The classification of HAA according to enzymatic profiles was consistent with their chemical structures.

Conclusions

Overall, a predictive toxicological model based on an in silico systems biology approach opens perspectives to estimate the genotoxicity of various chemical classes of environmental contaminants. Moreover, our approach based on enzymes profile determination opens the possibility of predicting various xenobiotics metabolites susceptible to bind to DNA in both normal and physiopathological situations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04363-6.

2,6-Di-tert-butylphenol and its quinone metabolite trigger aberrant transcriptional responses in C57BL/6 mice liver

2,6-Di-tert-butylphenol (2,6-DTBP) is used as an antioxidant with wide commercial applications and its residues have been detected in various environmental matrices. 2,6-DTBP may enter human body via ingestion, inhalation or other exposure pathways. However, its susceptibility to biotransformation and potential of the metabolic products to trigger aberrant transcriptional responses remain unclear. Here, we investigated in vitro and in vivo biotransformation of 2,6-DTBP and characterized the RNA-Seq based transcriptional profiling of C57BL/6 mice liver after the exposure to 2,6-DTBP and its metabolites. 2,6-DTBP was metabolized into hydroxylated (2,6-DTBH) and para-quinone (2,6-DTBQ) products with residues detected in serum and liver of C57BL/6 mice. 2,6-DTBP and 2,6-DTBQ induced the aberrant transcription in C57BL/6 mice liver featured with 373-2861 differentially expressed genes (DEGs). They also up-regulated 1.09-2.92 fold mRNA expression of carcinogenesis-related genes such as Ccnd1, TGFβ1 and FOS in C57BL/6 mice liver. Our study indicated potential carcinogenic risk of 2,6-DTBP and its metabolites, beneficial to further evaluation of health risk of TBPs-related contaminants.

In silico investigations on curcuminoids from Curcuma longa as positive regulators of the Wnt/β-catenin signaling pathway in wound healing

Background

Curcuma longa (Turmeric) is a traditionally used herb in wound healing. The efficacy of fresh turmeric paste to heal wounds has already been investigated in multiple ethnobotanical studies. Wnt/β-catenin signaling pathway plays a significant role in wound healing and injury repair processes which has been evident in different in vitro studies. This study aims to analyze the potentiality of curcuminoids (curcumin I, curcumin II and curcumin III) from Curcuma longa to bind and enhance the activity of two intracellular signaling proteins- casein kinase-1 (CK1) and glycogen synthase kinase-3β (GSK3B) involved in Wnt/β-catenin signaling pathway. This study is largely based on a computer-based molecular docking program which mimics the in vivo condition and works on a specific algorithm to interpret the binding affinity and poses of a ligand molecule to a receptor. Subsequently, drug likeness property, ADME/Toxicity profile, pharmacological activity, and site of metabolism of the curcuminoids were also analyzed.

Results

Curcumin I showed better affinity of binding with CK1 (− 10.31 Kcal/mol binding energy) and curcumin II showed better binding affinity (− 7.55 Kcal/mol binding energy) for GSK3B. All of the ligand molecules showed quite similar pharmacological properties.

Conclusion

Curcumin has anti-oxidant, anti-carcinogenic, anti-mutagenic, anti-coagulant, and anti-infective properties. Curcumin has also anti-inflammatory and wound healing properties. It hastens wound healing by acting on different stages of the natural wound healing process. In this study, three curcumins from Curcuma longa were utilized in this experiment in a search for a drug to be used in wound healing and injury repair processes. Hopefully, this study will raise research interest among researchers.

Discovery, Optimization, and Evaluation of Quinazolinone Derivatives with Novel Linkers as Orally Efficacious Phosphoinositide-3-Kinase Delta Inhibitors for Treatment of Inflammatory Diseases

Guided by molecular docking, a commonly used open-chain linker was cyclized into a five-membered pyrrolidine to lock the overall conformation of the propeller-shaped molecule. Different substituents were introduced into the pyrrolidine moiety to block oxidative metabolism. Surprisingly, it was found that a small methyl substituent could be used to alleviate the oxidative metabolism of pyrrolidine while maintaining or enhancing potency, which could be described as a "magic methyl". Further optimization around the "3rd blade" of the propeller led to identification of a series of potent and selective PI3Kδ inhibitors. Among them, compound 50 afforded an optimum balance of PK profiles and potency. Oral administration of 50 attenuated the arthritis severity in a dose-dependent manner in a collagen-induced arthritis model without obvious toxicity. Furthermore, 50 demonstrated excellent pharmacokinetic properties with high bioavailability, suggesting that 50 might be an acceptable candidate for treatment of inflammatory diseases.

Multiomic Big Data Analysis Challenges: Increasing Confidence in the Interpretation of Artificial Intelligence Assessments

The need for holistic molecular measurements to better understand disease initiation, development, diagnosis, and therapy has led to an increasing number of multiomic analyses. The wealth of information available from multiomic assessments, however, requires both the evaluation and interpretation of extremely large data sets, limiting analysis throughput and ease of adoption. Computational methods utilizing artificial intelligence (AI) provide the most promising way to address these challenges, yet despite the conceptual benefits of AI and its successful application in singular omic studies, the widespread use of AI in multiomic studies remains limited. Here, we discuss present and future capabilities of AI techniques in multiomic studies while introducing analytical checks and balances to validate the computational conclusions.

CyProduct: A Software Tool for Accurately Predicting the Byproducts of Human Cytochrome P450 Metabolism

In silico metabolism prediction is a cheminformatic task of autonomously predicting the set of metabolic byproducts produced from a specified molecule and a set of enzymes or reactions. Here, we describe a novel machine learned in silico cytochrome P450 (CYP450) metabolism prediction suite, called CyProduct, that accurately predicts metabolic byproducts for a specified molecule and a human CYP450 isoform. It includes three modules: (1) CypReact, a tool that predicts if the query compound reacts with a given CYP450 enzyme, (2) CypBoM, a tool that accurately predicts the "bond site" of the reaction (i.e., which specific bonds within the query molecule react with the CYP isoform), and (3) MetaboGen, a tool that generates the metabolic byproducts based on CypBoM's bond-site prediction. CyProduct predicts metabolic biotransformation products for each of the nine most important human CYP450 enzymes. CypBoM uses an important new concept called "bond of metabolism" (BoM), which extends the traditional "site of metabolism" (SoM) by specifying the information about the set of chemical bonds that is modified or formed in a metabolic reaction (rather than the specific atom). We created a BoM database for 1845 CYP450-mediated Phase I reactions, then used this to train the CypBoM Predictor to predict the reactive bond locations on substrate molecules. CypBoM Predictor's cross-validated Jaccard score for reactive bond prediction ranged from 0.380 to 0.452 over the nine CYP450 enzymes. Over variants of a test set of 68 known CYP450 substrates and 30 nonreactants, CyProduct outperformed the other packages, including ADMET Predictor, BioTransformer, and GLORY, by an average of 200% (with respect to Jaccard score) in terms of predicting metabolites. The CyProduct suite and the data sets are freely available at https://bitbucket.org/wishartlab/cyproduct/src/master/.

Molecular modeling approaches to address drug-metabolizing enzymes (DMEs) mediated chemoresistance: a review

Resistance against clinically approved anticancer drugs is the main roadblock in cancer treatment. Drug metabolizing enzymes (DMEs) that are capable of metabolizing a variety of xenobiotic get overexpressed in malignant cells, therefore, catalyzing drug inactivation. As evident from the literature reports, the levels of DMEs increase in cancer cells that ultimately lead to drug inactivation followed by drug resistance. To puzzle out this issue, several strategies inclusive of analog designing, prodrug designing, and inhibitor designing have been forged. On that front, the implementation of computational tools can be considered a fascinating approach to address the problem of chemoresistance. Various research groups have adopted different molecular modeling tools for the investigation of DMEs mediated toxicity problems. However, the utilization of these in-silico tools in maneuvering the DME mediated chemoresistance is least considered and yet to be explored. These tools can be employed in the designing of such chemotherapeutic agents that are devoid of the resistance problem. The current review canvasses various molecular modeling approaches that can be implemented to address this issue. Special focus was laid on the development of specific inhibitors of DMEs. Additionally, the strategies to bypass the DMEs mediated drug metabolism were also contemplated in this report that includes analogs and pro-drugs designing. Different strategies discussed in the review will be beneficial in designing novel chemotherapeutic agents that depreciate the resistance problem.

Further Studies on Triazinoindoles as Potential Novel Multitarget-Directed Anti-Alzheimer's Agents

The inadequate clinical efficacy of the present anti-Alzheimer's disease (AD) drugs and their low impact on the progression of Alzheimer's disease in patients have revised the research focus from single targets to multitarget-directed ligands. A novel series of substituted triazinoindole derivatives were obtained by introducing various substituents on the indole ring for the development of multitarget-directed ligands as anti-AD agents. The experimental data indicated that some of these compounds exhibited significant anti-AD properties. Among them, 8-(piperidin-1-yl)-N-(6-(pyrrolidin-1-yl)hexyl)-5H-[1,2,4]triazino[5,6-b]indol-3-amine (60), the most potent cholinesterase inhibitor (AChE, IC₅₀ value of 0.32 μM; BuChE, IC₅₀ value of 0.21 μM), was also found to possess significant self-mediated Aβ_1-42 aggregation inhibitory activity (54% at 25 μM concentration). Additionally, compound 60 showed strong antioxidant activity. In the PAMPA assay, compound 60 exhibited blood-brain barrier penetrating ability. An acute toxicity study in rats demonstrated no sign of toxicity at doses up to 2000 mg/kg. Furthermore, compound 60 significantly restored the cognitive deficits in the scopolamine-induced mice model and Aβ_1-42-induced rat model. In the in silico ADMET prediction studies, the compound satisfied all the parameters of CNS acting drugs. These results highlighted the potential of compound 60 to be a promising multitarget-directed ligand for the development of potential anti-AD drugs.

The Respective Roles of CYP3A4 and CYP2D6 in the Metabolism of Pimozide to Established and Novel Metabolites

Pimozide is a dopamine receptor antagonist indicated for the treatment of Tourette syndrome. Prior in vitro studies characterized N-dealkylation of pimozide to 1,3-dihydro-1-(4-piperidinyl)-2H-benzimidazol-2-one (DHPBI) via CYP3A4 and, to a lesser extent, CYP1A2 as the only notable routes of pimozide biotransformation. However, drug-drug interactions between pimozide and CYP2D6 inhibitors and CYP2D6 genotype-dependent effects have since been observed. To reconcile these incongruities between the prior in vitro and in vivo studies, we characterized two novel pimozide metabolites: 5-hydroxypimozide and 6-hydroxypimozide. Notably, 5-hydroxypimozide was the major metabolite produced by recombinant CYP2D6 (Km ∼82 nM, V max ∼0.78 pmol/min per picomoles), and DHPBI was the major metabolite produced by recombinant CYP3A4 (apparent Km ∼1300 nM, V max ∼2.6 pmol/min per picomoles). Kinetics in pooled human liver microsomes (HLMs) for the 5-hydroxylation (Km ∼2200 nM, V max ∼59 pmol/min per milligram) and N-dealkylation (Km ∼3900 nM, V max ∼600 pmol/min per milligram) reactions were also determined. Collectively, formation of DHPBI, 5-hydroxypimozide, and 6-hydroxypimozide accounted for 90% of pimozide depleted in incubations of NADPH-supplemented pooled HLMs. Studies conducted in HLMs isolated from individual donors with specific cytochrome P450 isoform protein abundances determined via mass spectrometry revealed that 5-hydroxypimozide (r 2 = 0.94) and 6-hydroxypimozide (r 2 = 0.86) formation rates were correlated with CYP2D6 abundance, whereas the DHPBI formation rate (r 2 = 0.98) was correlated with CYP3A4 abundance. Furthermore, the HLMs differed with respect to their capacity to form 5-hydroxypimozide relative to DHPBI. Collectively, these data confirm a role for CYP2D6 in pimozide clearance via 5-hydroxylation and provide an explanation for a lack of involvement when only DHPBI formation was monitored in prior in vitro studies. SIGNIFICANCE STATEMENT: Current CYP2D6 genotype-guided dosing information in the pimozide label is discordant with available knowledge regarding the primary biotransformation pathways. Herein, we characterize the CYP2D6-dependent biotransformation of pimozide to previously unidentified metabolites. In human liver microsomes, formation rates for the novel metabolites and a previously identified metabolite were determined to be a function of CYP2D6 and CYP3A4 content, respectively. These findings provide a mechanistic basis for observations of CYP2D6 genotype-dependent pimozide clearance in vivo.

Metabolic Forest: Predicting the Diverse Structures of Drug Metabolites

Adverse drug metabolism often severely impacts patient morbidity and mortality. Unfortunately, drug metabolism experimental assays are costly, inefficient, and slow. Instead, computational modeling could rapidly flag potentially toxic molecules across thousands of candidates in the early stages of drug development. Most metabolism models focus on predicting sites of metabolism (SOMs): the specific substrate atoms targeted by metabolic enzymes. However, SOMs are merely a proxy for metabolic structures: knowledge of an SOM does not explicitly provide the actual metabolite structure. Without an explicit metabolite structure, computational systems cannot evaluate the new molecule's properties. For example, the metabolite's reactivity cannot be automatically predicted, a crucial limitation because reactive drug metabolites are a key driver of adverse drug reactions (ADRs). Additionally, further metabolic events cannot be forecast, even though the metabolic path of the majority of substrates includes two or more sequential steps. To overcome the myopia of the SOM paradigm, this study constructs a well-defined system-termed the metabolic forest-for generating exact metabolite structures. We validate the metabolic forest with the substrate and product structures from a large, chemically diverse, literature-derived dataset of 20 736 records. The metabolic forest finds a pathway linking each substrate and product for 79.42% of these records. By performing a breadth-first search of depth two or three, we improve performance to 88.43 and 88.77%, respectively. The metabolic forest includes a specialized algorithm for producing accurate quinone structures, the most common type of reactive metabolite. To our knowledge, this quinone structure algorithm is the first of its kind, as the diverse mechanisms of quinone formation are difficult to systematically reproduce. We validate the metabolic forest on a previously published dataset of 576 quinone reactions, predicting their structures with a depth three performance of 91.84%. The metabolic forest accurately enumerates metabolite structures, enabling promising new directions such as joint metabolism and reactivity modeling.

New Proluciferin Substrates for Human CYP4 Family Enzymes

We report the synthesis of seven new proluciferins for convenient activity determination of enzymes belonging to the cytochrome P450 (CYP) 4 family. Biotransformation of these probe substrates was monitored using each of the twelve human CYP4 family members, and eight were found to act at least on one of them. For all substrates, activity of CYP4Z1 was always highest, while that of CYP4F8 was always second highest. Site of metabolism (SOM) predictions involving SMARTCyp and docking experiments helped to rationalize the observed activity trends linked to substrate accessibility and reactivity. We further report the first homology model of CYP4F8 including suggested substrate recognition residues in a catalytically competent conformation accessed by replica exchange solute tempering (REST) simulations.

Design, Synthesis, Pharmacological Evaluation, In silico Modeling, Prediction of Toxicity and Metabolism Studies of Novel 1-(substituted)-2-methyl- 3-(4-oxo-2-phenyl quinazolin-3(4H)-yl)isothioureas

BACKGROUND

Although exhaustive efforts to prevent and treat tuberculosis (TB) have been made, the problem still continues due to multi-drug-resistant (MDR) and extensively drugresistant TB (XDR-TB). It clearly highlights the urgent need to develop novel "druggable" molecules for the co-infection treatment and strains of MDR-TB and XDR-TB.

OBJECTIVE

In this approach, a hybrid molecule was created by merging two or more pharmacophores. The active site of targets may be addressed by each of the pharmacophores and proffers the opportunity for selectivity. In addition, it also reduces undesirable side effects and drug-resistance.

METHODS

In this study, a novel quinazolinone analog was designed and synthesized by substituting thiourea nucleus and phenyl ring at N-3 and C-2 position of quinazoline ring, respectively. All title compounds were tested for antitubercular activity by in vitro M. tuberculosis and anti-human immunodeficiency virus (HIV) activity by MT-4 cell assay method. The agar dilution method was used to test the antibacterial potency of entire prepared derivatives against various strains of grampositive and gram-negative microorganisms.

RESULTS

The title compounds, 1-(substituted)-2-methyl-3-(4-oxo-2-phenyl quinazolin-3(4H)-yl) isothioureas (QTS1 - QTS15) were synthesized by the reaction between key intermediate 3-amino- 2-phenylquinazolin-4(3H)-one with various alkyl/aryl isothiocyanates followed by methylation with dimethyl sulphate. Among the series, compound 1-(3-chlorophenyl)-2-methyl-3-(4-oxo-2-phenyl quinazolin- 3(4H)-yl) isothioureas (QTS14) showed the highest potency against B. subtilis, K. pneumonia and S. aureus at 1.6 μg/mL. The compound QTS14 exhibited the most potent antitubercular activity at the MIC of 0.78 μg/mL and anti-HIV activity at 0.97 μg/mL against HIV1 and HIV2.

CONCLUSION

The results obtained from this study confirm that the synthesized and biologically evaluated quinazolines showed promising antimicrobial, antitubercular and anti-HIV activities. The new scaffolds proffer a plausible lead for further development and optimization of novel antitubercular and anti-HIV drugs.

Nitrogen-Walk Approach to Explore Bioisosteric Replacements in a Series of Potent A2B Adenosine Receptor Antagonists

A systematic exploration of bioisosteric replacements for furan and thiophene cores in a series of potent A_2BAR antagonists has been carried out using the nitrogen-walk approach. A collection of 42 novel alkyl 4-substituted-2-methyl-1,4-dihydrobenzo[4,5]imidazo[1,2-a]pyrimidine-3-carboxylates, which contain 18 different pentagonal heterocyclic frameworks at position 4, was synthesized and evaluated. This study enabled the identification of new ligands that combine remarkable affinity (K_i < 30 nM) and exquisite selectivity. The structure-activity relationship (SAR) trends identified were substantiated by a molecular modeling study, based on a receptor-driven docking model and including a systematic free energy perturbation (FEP) study. Preliminary evaluation of the CYP3A4 and CYP2D6 inhibitory activity in optimized ligands evidenced weak and negligible activity, respectively. The stereospecific interaction between hA_2BAR and the eutomer of the most attractive novel antagonist (S)-18g (K_i = 3.66 nM) was validated.

SMARTCyp 3.0: enhanced cytochrome P450 site-of-metabolism prediction server

MOTIVATION

Cytochromes P450 are the most important class of drug metabolizing enzymes. Prediction of drug metabolism is important in development of new drugs, to understand and reduce adverse drug reactions and to reduce animal testing.

RESULTS

SMARTCyp 3.0 is an updated version of our previous web server for prediction of site-of-metabolism for Cytochrome P450-mediated metabolism, now in Python 3 with increased structural coverage and new features. The SMARTCyp program is a first principle-based method using density functional theory determined activation energies for more than 250 molecules to identify the most likely site-of-metabolism. New features include a similarity measure between the query molecule and the model fragment, a new graphical interface and additional parameters expanding the structural coverage of the SMARTCyp program.

AVAILABILITY AND IMPLEMENTATION

The SMARTCyp server is freely available for use on the web at smartcyp.sund.ku.dk.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Cerebrospinal fluid penetration of targeted therapeutics in pediatric brain tumor patients

Treatment with small-molecule inhibitors, guided by precision medicine has improved patient outcomes in multiple cancer types. However, these compounds are often not effective against central nervous system (CNS) tumors. The failure of precision medicine approaches for CNS tumors is frequently attributed to the inability of these compounds to cross the blood-brain barrier (BBB), which impedes intratumoral target engagement. This is complicated by the fact that information on CNS penetration in CNS-tumor patients is still very limited. Herein, we evaluated cerebrospinal fluid (CSF) drug penetration, a well-established surrogate for CNS-penetration, in pediatric brain tumor patients. We analyzed 7 different oral anti-cancer drugs and their metabolites by high performance liquid chromatography mass spectrometry (HPLC-MS) in 42 CSF samples obtained via Ommaya reservoirs of 9 different patients. Moreover, we related the resulting data to commonly applied predictors of BBB-penetration including ABCB1 substrate-character, physicochemical properties and in silico algorithms. First, the measured CSF drug concentrations depicted good intra- and interpatient precision. Interestingly, ribociclib, vorinostat and imatinib showed high (> 10 nM), regorafenib and dasatinib moderate (1-10 nM) penetrance. In contrast, panobinostat und nintedanib were not detected. In addition, we identified active metabolites of imatinib and ribociclib. Comparison to well-established BBB-penetrance predictors confirmed low molecular weight, high proportion of free-drug and low ABCB1-mediated efflux as central factors. However, evaluation of diverse in silico algorithms showed poor correlation within our dataset. In summary, our study proves the feasibility of measuring CSF concentration via Ommaya reservoirs thus setting the ground for utilization of this method in future clinical trials. Moreover, we demonstrate CNS presence of certain small-molecule inhibitors and even active metabolites in CSF of CNS-tumor patients and provide a potential guidance for physicochemical and biological factors favoring CNS-penetration.

A Modified Arrhenius Approach to Thermodynamically Study Regioselectivity in Cytochrome P450-Catalyzed Substrate Conversion

The regio- (and stereo-)selectivity and specific activity of cytochrome P450s are determined by the accessibility of potential sites of metabolism (SOMs) of the bound substrate relative to the heme, and the activation barrier of the regioselective oxidation reaction(s). The accessibility of potential SOMs depends on the relative binding free energy (ΔΔGbind ) of the catalytically active substrate-binding poses, and the probability of the substrate to adopt a transition-state geometry. An established experimental method to measure activation energies of enzymatic reactions is the analysis of reaction rate constants at different temperatures and the construction of Arrhenius plots. This is a challenge for multistep P450-catalyzed processes that involve redox partners. We introduce a modified Arrhenius approach to overcome the limitations in studying P450 selectivity, which can be applied in multiproduct enzyme catalysis. Our approach gives combined information on relative activation energies, ΔΔGbind values, and collision entropies, yielding direct insight into the basis of selectivity in substrate conversion.

Deciphering the enzymatic target of a new family of antischistosomal agents bearing a quinazoline scaffold using complementary computational tools

A previous phenotypic screening campaign led to the identification of a quinazoline derivative with promising in vitro activity against Schistosoma mansoni. Follow-up studies of the antischistosomal potential of this candidate are presented here. The in vivo studies in a S. mansoni mouse model show a significant reduction of total worms and a complete disappearance of immature eggs when administered concomitantly with praziquantel in comparison with the administration of praziquantel alone. This fact is of utmost importance because eggs are responsible for the pathology and transmission of the disease. Subsequently, the chemical optimisation of the structure in order to improve the metabolic stability of the parent compound was carried out leading to derivatives with improved drug-like properties. Additionally, the putative target of this new class of antischistosomal compounds was envisaged by using computational tools and the binding mode to the target enzyme, aldose reductase, was proposed.

Molecular Basis for Metabolic Regioselectivity and Mechanism of Cytochrome P450s toward Carcinogenic 4-(Methylnitrosamino)-(3-pyridyl)-1-butanone

As an abundantly present tobacco component, carcinogen 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) has also been detected in atmospheric particulate matter, suggesting the ineluctable exposure risk of this contaminant. NNK metabolic activation by cytochrome P450 enzymes (CYPs) is a prerequisite to exerting its genotoxicity, but the metabolic regioselectivity and mechanism are still unknown. Here the binding feature and regioselectivity of CYPs 1A1, 1A2, 2A6, 2A13, 2B6, and 3A4 toward NNK are unraveled through molecular docking and molecular dynamics (MD) simulations. Binding mode analyses reveal that 1A2 and 2B6 have definite preferences for NNK α-methyl hydroxylation, while the other four CYPs preferentially catalyze α-methylene hydroxylation. The binding affinities between NNK and CYPs evaluated by the binding free energies follow the order 2A13 > 2B6 > 1A2 > 2A6 > 1A1 > 3A4. Density functional theory (DFT) calculations are further performed to characterize the mechanism of NNK biotransformation. Results show that the α-hydroxyNNK generated from α-hydroxylation may undergo nonenzymatic decomposition to form genotoxic diazohydroxide and aldehyde, and further oxidation by P450 to yield nitrosamide, which mainly contributes to NNK toxification capacity. Meanwhile the pyridine N-oxidation and denitrosation of C_α-radical intermediate play an important role in detoxifying NNK. Overall, the present study provides the molecular basis for CYP-catalyzed regioselectivity and mechanism of NNK biotransformation, which can enable the identification of metabolites for assessing the health risk of individual NNK exposure.

Metatox - Web application for generation of metabolic pathways and toxicity estimation

Xenobiotics biotransformation in humans is a process of the chemical modifications, which may lead to the formation of toxic metabolites. The prediction of such metabolites is very important for drug development and ecotoxicology studies. We created the web-application MetaTox ( http://way2drug.com/mg ) for the generation of xenobiotics metabolic pathways in the human organism. For each generated metabolite, the estimations of the acute toxicity (based on GUSAR software prediction), organ-specific carcinogenicity and adverse effects (based on PASS software prediction) are performed. Generation of metabolites by MetaTox is based on the fragments datasets, which describe transformations of substrates structures to a metabolites structure. We added three new classes of biotransformation reactions: Dehydrogenation, Glutathionation, and Hydrolysis, and now metabolite generation for 15 most frequent classes of xenobiotic's biotransformation reactions are available. MetaTox calculates the probability of formation of generated metabolite - it is the integrated assessment of the biotransformation reactions probabilities and their sites using the algorithm of PASS ( http://way2drug.com/passonline ). The prediction accuracy estimated by the leave-one-out cross-validation (LOO-CV) procedure calculated separately for the probabilities of biotransformation reactions and their sites is about 0.9 on the average for all reactions.

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.

Design and in silico modeling of Indoloquinoxaline incorporated keratin nanoparticles for modulation of glucose metabolism in 3T3-L1 adipocytes

The following study was done to assess the glucose utilizing efficiency of Indoloquinoxaline derivative incorporated keratin nanoparticles (NPs) in 3T3-L1 adipocytes. Indoloquinoxaline derivative had wide range of biological activities including antidiabetic activity. In this view, Indoloquinoxaline moiety containing N, N-dimethyl (3-fluoro-6H-indolo [3,2-b] quinoxalin-6-yl) methanamine compound was designed and synthesized, and further it is incorporated into keratin nanoparticles. The formulated NPs, drug entrapment efficiency, releasing capacity, stability, and physicochemical properties were characterized by various spectral analyzer and obtained results of characterizations were confirmed the properties of NPs. The analysis of mechanism underlying the glucose utilization of NPs was examined through molecular docking with identified target, and observed in silico study reports shown strong interaction of NPs in the binding pockets of AMPK and PTP1B. Based on the in silico screening, the formulated NPs was performed for in vitro cellular viability and glucose uptake studies on 3T3-L1 adipocytes. Interestingly, 40 μg of NPs displayed 78.2 ± 2.76% cellular viability, and no cell death was observed at lower concentrations. Further, the concentration dependent glucose utilization was observed at different concentrations of NPs in 3T3-L1 adipocytes. The results of NPs (40 μg) on glucose utilization have revealed eminent result 58.56 ± 4.54% compared to that of Metformin (10 μM) and Insulin (10 μM). The identified results clearly indicated that Indoloquinoxaline derivative incorporated keratin NPs significantly increased glucose utilization efficiency and protect the cells against the insulin resistance.

In silico approach to study the metabolism and biological activities of oligomeric proanthocyanidin complexes

OBJECTIVES:

Over the past three decades, numerous studies have focused on the biological activities of oligomeric proanthocyanidins (OPCs) in the prevention of many diseases such as neurodegeneration, atherosclerosis, tumorigenesis, and microbial infections. OPC has redox-active metabolites which could modulate the intracellular redox equilibrium to maintain the antioxidant homeostasis. This redox-modulating efficiency of OPC could provide new insights into therapeutic approaches that could reduce the burden of cardiovascular diseases. The main objective of this study was to explore the biological and metabolic activities of OPC using in silico approaches.

METHODS:

To validate the above objective, chemoinformatic tools were used to predict the metabolism of OPC after ingestion, based on both the ligand and structure of the constituent compounds.

RESULTS:

OPC showed possible sites for Phase I metabolism by cytochrome P450, and the metabolites obtained thereafter may be responsible for its biological activities. Absorption, distribution, metabolism, elimination, and toxicity properties showed efficient absorption, distribution, and metabolism of OPC, without toxicity.

CONCLUSION:

Thus, from the results obtained, OPC could be strongly recommended as a cardioprotective drug.

Exploration of Chemical Compound, Conformer, and Reaction Space with Meta-Dynamics Simulations Based on Tight-Binding Quantum Chemical Calculations

The semiempirical tight-binding based quantum chemistry method GFN2-xTB is used in the framework of meta-dynamics (MTD) to globally explore chemical compound, conformer, and reaction space. The biasing potential given as a sum of Gaussian functions is expressed with the root-mean-square-deviation (RMSD) in Cartesian space as a metric for the collective variables. This choice makes the approach robust and generally applicable to three common problems (i.e., conformer search, chemical reaction space exploration in a virtual nanoreactor, and for guessing reaction paths). Because of the inherent locality of the atomic RMSD, functional group or fragment selective treatments are possible facilitating the investigation of catalytic processes where, for example, only the substrate is thermally activated. Due to the approximate character of the GFN2-xTB method, the resulting structure ensembles require further refinement with more sophisticated, for example, density functional or wave function theory methods. However, the approach is extremely efficient running routinely on common laptop computers in minutes to hours of computation time even for realistically sized molecules with a few hundred atoms. Furthermore, the underlying potential energy surface for molecules containing almost all elements ( Z = 1-86) is globally consistent including the covalent dissociation process and electronically complicated situations in, for example, transition metal systems. As examples, thermal decomposition, ethyne oligomerization, the oxidation of hydrocarbons (by oxygen and a P450 enzyme model), a Miller-Urey model system, a thermally forbidden dimerization, and a multistep intramolecular cyclization reaction are shown. For typical conformational search problems of organic drug molecules, the new MTD(RMSD) algorithm yields lower energy structures and more complete conformer ensembles at reduced computational effort compared with its already well performing predecessor.

Fast Methods for Prediction of Aldehyde Oxidase-Mediated Site-of-Metabolism

Aldehyde Oxidase (AO) is an enzyme involved in the metabolism of aldehydes and N-containing heterocyclic compounds. Many drug compounds contain heterocyclic moieties, and AO metabolism has lead to failure of several late-stage drug candidates. Therefore, it is important to take AO-mediated metabolism into account early in the drug discovery process, and thus, to have fast and reliable models to predict the site of metabolism (SOM). We have collected a dataset of 78 substrates of human AO with a total of 89 SOMs and 347 non-SOMs and determined atomic descriptors for each compound. The descriptors comprise NMR shielding and ESP charges from density functional theory (DFT), NMR chemical shift from ChemBioDraw, and Gasteiger charges from RDKit. Additionally, atomic accessibility was considered using 2D-SASA and relative span descriptors from SMARTCyp. Finally, stability of the product, the metabolite, was determined with DFT and also used as a descriptor. All descriptors have AUC larger than 0.75. In particular, descriptors related to the chemical shielding and chemical shift (AUC = 0.96) and ESP charges (AUC = 0.96) proved to be good descriptors. We recommend two simple methods to identify the SOM for a given molecule: 1) use ChemBioDraw to calculate the chemical shift or 2) calculate ESP charges or chemical shift using DFT. The first approach is fast but somewhat difficult to automate, while the second is more time-consuming, but can easily be automated. The two methods predict correctly 93% and 91%, respectively, of the 89 experimentally observed SOMs.