SPORCalc: A development of a database analysis that provides putative metabolic enzyme reactions for ligand-based drug design

Understanding both the enzyme reactions that contribute to intermediate metabolism and the biochemical fate of candidate therapeutic and toxic agents are essential for drug design. Traditional metabolic databases indicate whether reactions have been observed but do not provide the likelihoods of reactions occurring, for example those of mixed function oxygenases and oxidases, during phase I metabolism. The desire for more quantitative predictions motivated the development of the recently introduced Substrate Product Occurrence Ratio Calculator (SPORCalc) that identifies metabolically labile atom positions in candidate compounds. This paper describes a further development and provides a clearer explanation of SPORCalc for the computational pharmacology, medicinal chemistry and drug design communities interested in metabolic prediction of xenobiotics using chemical databases of biotransformations.Examples of reaction centre detection in Metabolite? are described followed by a demonstration of almokalant, an anti-arrhythmic agent, undergoing phase I metabolism. In general, occurrence ratio (OR) values are calculated throughout a compound and its transformed metabolites to give propensity (p) values at each atom position. The OR values from substrates and products in the database are essential for addition and elimination reactions. For almokalant, the resulting p values ranged from 10^?1 to 10^?5 and their order of magnitude reflected the known and experimentally observed metabolites.SPORCalc depends entirely on the level of detail from isoform- or species-specific reaction classes in Metabolite?. Labile atom positions (sites of metabolism) are identified in both the candidate compound and its metabolites. In general, the likelihood of one enzyme isoform-dependent reaction occurring relative to another and the putative metabolic routes from different isoforms can be investigated. SPORCalc can be developed further to include suitable three-dimensional, structure–activity and physiochemical information.     

A model validation and consensus building environment

Over half of the failures in drug development are due to problems with the absorption, distribution, metabolism, excretion, and toxicity, or ADME/Tox properties of a candidate compound. The utilization of in silico tools to predict ADME/Tox and physicochemical properties holds great potential for reducing the attrition rate in drug research and development, as this technology can prioritize candidate compounds in the pharmaceutical R&D pipeline. However, a major concern surrounding the use of in silico ADME/Tox technology is the reliability of the property predictions. Bio-Rad Laboratories, Inc. has created a computational environment that addresses these concerns. This environment is referred to as KnowItAll®. Within this platform are encoded a number of ADME/Tox predictors, the ability to validate these predictors with/without in-house data and models, as well as build a ‘consensus’ model that may be a much better model than any of the individual predictive model. The KnowItAll® system can handle two types of predictions: real number and categorical classification.     

Investigation of novel compounds via in silico approaches of EGFR inhibitors as anticancer agents

Before the experimental studies of a compound to be synthesized from in vitro to in vivo, it is possible to save both time and money with in silico approaches only with Computer Aided Drug Design (CADD) methods. In other words, compounds that can be new drug candidates can be suggested by drug design using computational drug discovery strategies. In this study, all molecules in the ChEMBL Database were virtual screened based on drugs with inhibitory properties on the Epidermal Growth Factor Receptor (EGFR), one of the receptor tyrosine kinases, which is effective in cancer cells. During this High-Throughput Screening (HTS), the number of compounds was minimized according to the parameters of the reference drugs, physicochemical properties such as logP, M.W., HBA, HDB, RosLip. As a result of in silico approaches and molecular docking analysis, ten compounds with the highest docking scores were determined and a model compound that could be a new drug candidate was proposed.     

Experimental and computational modeling of the biological activity of benzaldehyde sulphur trioxide as a potential drug for the treatment of Alzheimer disease

Alzheimer's disease is a major public brain infection that has resulted in many deaths as revealed by the world health organization (WHO). Conventional Alzheimer treatments such as chemotherapy, surgery, and radiotherapy are not very effective and are usually associated with several adverse effects. Therefore, it is necessary to find new therapeutic approach that completely treat Alzheimer disease without much side effects. In this research work, we report the experimental and in silico molecular modeling of the biological activity of a novel azo-based compound as potential candidate for Alzheimer's disease. The synthesized compound was obtained by coupling reactions with 4-amino-3-nitrobenzaldehyde. Suitable purification and characterization techniques were employed and density functional theory (DFT) computational approach as well as in-silico molecular modeling has been employed to assess the electronic properties and anti-Alzheimer's potency. Suitable protein targets often regarded as target sites for most therapeutic vaccines for the said disease (4EY7, 1QTN, 4EY7, and 6EUE) have been selected for molecular docking investigation. For proper valuation of the drug candidacy, molecular docking studies were compared with conventional Alzheimer drug (donepezil). Also, the spectroscopic properties of the studied compound were investigated and compared with experimental data. Our findings show that the studied structure is relatively stable and expresses greater binding affinities of ?6.10, ?9.01, ?5.90, and ?11.20 kcal/mol than donepezil which had binding affinities of: 5.30, ?6.30, 5.90, and ?10.70 kcal/mol for each protein target. Thus, demonstrating the efficacy of the studied compound as potential candidate for Alzheimer's disease.     

Rapid structural characterization of in vivo and in vitro metabolites of tinoridine using UHPLC–QTOF–MS/MS and in silico toxicological screening of its metabolites

Tinoridine is a nonsteroidal anti‐inflammatory drug and also has potent radical scavenger and antiperoxidative activity. However, metabolism of tinoridine has not been thoroughly investigated. To identify in vivo metabolites, the drug was administered to Sprague–Dawley rats (n = 5) at a dose of 20 mg kg^?1, and blood, urine and feces were collected at different time points up to 24 h. In vitro metabolism was delved by incubating the drug with rat liver microsomes and human liver microsomes. The metabolites were enriched by optimized sample preparation involving protein precipitation using acetonitrile, followed by solid‐phase extraction. Data processes were carried out using multiple mass defects filters to eliminate false‐positive ions. A total of 11 metabolites have been identified in urine samples including hydroxyl, dealkylated, acetylated and glucuronide metabolites; among them, some were also observed in plasma and feces samples. Only two major metabolites were formed using liver microsomal incubations. These metabolites were also observed in vivo. All the 11 metabolites, which are hitherto unknown and novel, were characterized by using ultrahigh‐performance liquid chromatography–quadrupole time‐of‐flight tandem mass spectrometry in combination with accurate mass measurements. Finally, in silico toxicological screening of all metabolites was evaluated, and two metabolites were proposed to show a certain degree of lung or liver toxicity. Copyright © 2015 John Wiley & Sons, Ltd.     

From Crystal Structures and Their Analysis to the in silico Prediction of Toxic Phenomena

While the development of potential drug molecules based on the known three‐dimensional structure of the macromolecular target is doubtless one of the more‐potent approaches to rational drug design, the estimation of associated changes in the free energy of ligand binding is all but trivial. Major obstacles include the treatment of long‐range electrostatic effects and charge transfer, the calculation of solvation energies, the treatment of entropic effects, and the quantification of induced fit. In the last decade, a number of computational concepts have nonetheless matured into powerful tools for the development of drug‐candidate molecules. These concepts have mainly focussed on the binding of the small molecule to a bioregulator. More recently, need has arisen to develop tools for a safe prediction of more‐complex phenomena such as metabolism, toxicity, and bioavailability. We describe the ongoing development of a virtual laboratory on the Internet to allow for a reliable in silico estimation of harmful effects triggered by drugs, chemicals, and their metabolites. For this, we used our recently developed underlying technology (5D‐QSAR, based on five‐dimensional quantitative structure‐activity relationships) and compiled a pilot project, including the models of five receptor systems known to mediate adverse effects (the aryl hydrocarbon (Ah), 5 HT_2A, cannabinoid, GABA_A, and estrogen receptor, resp.) which were already validated against 280 compounds (drugs, chemicals, toxins). Within this setup, we could demonstrate that our virtual laboratory is able both to recognize toxic compounds substantially different from those used in the training set as well as to classify harmless compounds as being nontoxic. The results suggest that our approach can be used for the prediction of adverse effects of drug molecules and chemicals and, thus, bears a significant potential to recognize hazardous compounds early in the development process hence improving resource and waste management and reducing animal testing. It is the aim to provide free access to this technology – particularly to universities, hospitals, and regulatory bodies.     

In Silico Identification of Potential Sites for a Plastic-Degrading Enzyme by a Reverse Screening through the Protein Sequence Space and Molecular Dynamics Simulations

The accumulation of polyethylene terephthalate (PET) seriously harms the environment because of its high resistance to degradation. The recent discovery of the bacteria-secreted biodegradation enzyme, PETase, sheds light on PET recycling; however, the degradation efficiency is far from practical use. Here, in silico alanine scanning mutagenesis (ASM) and site-saturation mutagenesis (SSM) were employed to construct the protein sequence space from binding energy of the PETase–PET interaction to identify the number and position of mutation sites and their appropriate side-chain properties that could improve the PETase–PET interaction. The binding mechanisms of the potential PETase variant were investigated through atomistic molecular dynamics simulations. The results show that up to two mutation sites of PETase are preferable for use in protein engineering to enhance the PETase activity, and the proper side chain property depends on the mutation sites. The predicted variants agree well with prior experimental studies. Particularly, the PETase variants with S238C or Q119F could be a potential candidate for improving PETase. Our combination of in silico ASM and SSM could serve as an alternative protocol for protein engineering because of its simplicity and reliability. In addition, our findings could lead to PETase improvement, offering an important contribution towards a sustainable future.     

Identification of potential inhibitor and enzyme-inhibitor complex on trypanothione reductase to control Chagas disease

Chagas is a parasitic disease with major threat to public health due to its resistance against commonly available drugs. Trypanothione reductase (TryR) is the key enzyme to develop this disease. Though this enzyme is well thought-out as potential drug target, the accurate structure of enzyme-inhibitor complex is required to design a potential inhibitor which is less available for TryR. In this research, we aimed to investigate the advanced drug over the available existing drugs by designing inhibitors as well as to identify a new enzyme-inhibitor complex that may act as a template for drug design. A set of analogues were designed from a known inhibitor Quinacrine Mustard (QUM) to identify the effective inhibitor against this enzyme. Further, the pharmacoinformatics elucidation and structural properties of designed inhibitor proposed effective drug candidates against Chagas disease. Molecular docking study suggests that a designed inhibitor has higher binding affinity in both crystal and modeled TryR and also poses similar interacting residues as of crystal TryR-QUM complex structure. The comparative studies based on in silico prediction proposed an enzyme-inhibitor complex which could be effective to control the disease activity. So our in silico analysis based on TryR built model, Pharmacophore and docking analysis might play an important role for the development of novel therapy for Chagas disease. But both animal model experiments and clinical trials must be done to confirm the efficacy of the therapy.     

In silico drug-like properties prediction and in vivo antifungal potentials of Citrullus lanatus seed oil against Candida albicans

Citrullus lanatus seed is an important but neglected seed rich in essential fatty acids. The study sought to investigate in silico and in vivo antifungal activity of some bioactive compounds of Citrullus lanatus (watermelon) seed oil (CLSO) on oral candidiasis induced by Candida albicans in immunosuppressed female albino rats and to predict the Absorption, Distribution, Metabolism and Excretion (ADME) properties of isolated natural compound. Docking studies was performed using standard procedure, standard microbiological and histopathological techniques was employed for study of in vivo antifungal activity of the oil; as well as renal function tests at days 7 and 14 post-infection treatments. Treated groups were compared with that of the control groups. In vitro studies showed varied zone of inhibitions at different concentrations. Ligand-protein interaction showed better binding activity between Palmitic acid and SAP-5 as well as CYP51 when compared with fluconazole (reference drug). Treatment with CLSO showed that there was a significant reduction in the kidney fungal burden (cfu/ml/g) of rats treated with CLSO after 14 days post-infection treatment, compared to group 2 (untreated control) rats. Histomorphology of group 2 showed multifocal aggregation and widespread distribution of fungal blastospores when compared with CLSO-treated groups, which had minimal fungal blastospores in the renal tissues. Thus, in silico and histological data agreed with the findings in microbiology. Furthermore, within the CLSO treated group, a significant increase in the serum concentrations of creatinine was observed, while no significant difference in blood urea values was recorded after day 14 post-infection study. Linoleic and palmitic acid could be considered as a potential antifungal drug candidate with palmitic acid playing a significant role.     

Identification of potential inhibitors against nuclear Dam1 complex subunit Ask1 of Candida albicans using virtual screening and MD simulations

Identification of hit compounds against specific target form the starting point for a drug discovery program. A consistent decline of new chemical entities (NCEs) in recent years prompted a challenge to explore newer approaches to discover potential hit compounds that in turn can be converted into leads, and ultimately drug with desired therapeutic efficacy. The vast amount of omics and activity data available in public databases offers an opportunity to identify novel targets and their potential inhibitors. State of the art in silico methods viz., clustering of compounds, virtual screening, molecular docking, MD simulations and MMPBSA calculations were employed in a pipeline to identify potential ‘hits’ against those targets as well whose structures, as of now, could only predict through threading approaches. In the present work, we have started from scratch, amino acid sequence of target and compounds retrieved from PubChem compound database, modeled it in such a way that led to the identification of possible inhibitors of Dam1 complex subunit Ask1 of Candida albicans. We also propose a ligand based binding site determination approach. We have identified potential inhibitors of Ask1 subunit of a Dam1 complex of C. albicans, which is required to prevent precocious spindle elongation in pre-mitotic phases. The proposed scheme may aid to find virtually potential inhibitors of other unique targets against candida.     

Computational algorithmic and molecular dynamics study of functional and structural impacts of non-synonymous single nucleotide polymorphisms in human DHFR gene

Human dihydrofolate reductase (DHFR) is a conserved enzyme that is central to folate metabolism and is widely targeted in pathogenic diseases as well as cancers. Although studies have reported the fact that genetic mutations in DHFR leads to a rare autosomal recessive inborn error of folate metabolism and drug resistance, there is a lack of an extensive study on how the deleterious non-synonymous SNPs (nsSNPs) disrupt its phenotypic effects. In this study, we aim at discovering the structural and functional consequences of nsSNPs in DHFR by employing a combined computational approach consisting of ten recently developed in silico tools for identification of damaging nsSNPs and molecular dynamics (MD) simulation for getting deeper insights into the magnitudes of damaging effects. Our study revealed the presence of 12 most deleterious nsSNPs affecting the native phenotypic effects, with three (R71T, G118D, Y122D) identified in the co-factor and ligand binding active sites. MD simulations also suggested that these three SNPs particularly Y122D, alter the overall structural flexibility and dynamics of the native DHFR protein which can provide more understandings into the crucial roles of these mutants in influencing the loss of DHFR function.     

Elucidating organ-specific metabolic toxicity chemistry from electrochemiluminescent enzyme/DNA arrays and bioreactor bead-LC-MS/MS

Human toxic responses are very often related to metabolism. Liver metabolism is traditionally studied, but other organs also convert chemicals and drugs to reactive metabolites leading to toxicity. When DNA damage is found, the effects are termed genotoxic. Here we describe a comprehensive new approach to evaluate chemical genotoxicity pathways from metabolites formed in situ by a broad spectrum of liver, lung, kidney and intestinal enzymes. DNA damage rates are measured with a microfluidic array featuring a 64-nanowell chip to facilitate fabrication of films of DNA, electrochemiluminescent (ECL) detection polymer [Ru(bpy)₂(PVP)₁₀]²⁺ {(PVP = poly(4-vinylpyridine))} and metabolic enzymes. First, multiple enzyme reactions are run on test compounds using the array, then ECL light related to the resulting DNA damage is measured. A companion method next facilitates reaction of target compounds with DNA/enzyme-coated magnetic beads in 96 well plates, after which DNA is hydrolyzed and nucleobase-metabolite adducts are detected by LC-MS/MS. The same organ enzymes are used as in the arrays. Outcomes revealed nucleobase adducts from DNA damage, enzymes responsible for reactive metabolites (e.g. cyt P450s), influence of bioconjugation, relative dynamics of enzymes suites from different organs, and pathways of possible genotoxic chemistry. Correlations between DNA damage rates from the cell-free array and organ-specific cell-based DNA damage were found. Results illustrate the power of the combined DNA/enzyme microarray/LC-MS/MS approach to efficiently explore a broad spectrum of organ-specific metabolic genotoxic pathways for drugs and environmental chemicals.     

Phloridzin Acts as an Inhibitor of Protein-Tyrosine Phosphatase MEG2 Relevant to Insulin Resistance

Inhibition of the megakaryocyte protein tyrosine phosphatase 2 (PTP-MEG2, also named PTPN9) activity has been shown to be a potential therapeutic strategy for the treatment of type 2 diabetes. Previously, we reported that PTP-MEG2 knockdown enhances adenosine monophosphate activated protein kinase (AMPK) phosphorylation, suggesting that PTP-MEG2 may be a potential antidiabetic target. In this study, we found that phloridzin, isolated from Ulmus davidiana var. japonica, inhibits the catalytic activity of PTP-MEG2 (half-inhibitory concentration, IC₅₀ = 32 ± 1.06 μM) in vitro, indicating that it could be a potential antidiabetic drug candidate. Importantly, phloridzin stimulated glucose uptake by differentiated 3T3-L1 adipocytes and C2C12 muscle cells compared to that by the control cells. Moreover, phloridzin led to the enhanced phosphorylation of AMPK and Akt relevant to increased insulin sensitivity. Importantly, phloridzin attenuated palmitate-induced insulin resistance in C2C12 muscle cells. We also found that phloridzin did not accelerate adipocyte differentiation, suggesting that phloridzin improves insulin sensitivity without significant lipid accumulation. Taken together, our results demonstrate that phloridzin, an inhibitor of PTP-MEG2, stimulates glucose uptake through the activation of both AMPK and Akt signaling pathways. These results strongly suggest that phloridzin could be used as a potential therapeutic candidate for the treatment of type 2 diabetes.     

Prediction of drug metabolites using neural machine translation

Metabolic processes in the human body can alter the structure of a drug affecting its efficacy and safety. As a result, the investigation of the metabolic fate of a candidate drug is an essential part of drug design studies. Computational approaches have been developed for the prediction of possible drug metabolites in an effort to assist the traditional and resource-demanding experimental route. Current methodologies are based upon metabolic transformation rules, which are tied to specific enzyme families and therefore lack generalization, and additionally may involve manual work from experts limiting scalability. We present a rule-free, end-to-end learning-based method for predicting possible human metabolites of small molecules including drugs. The metabolite prediction task is approached as a sequence translation problem with chemical compounds represented using the SMILES notation. We perform transfer learning on a deep learning transformer model for sequence translation, originally trained on chemical reaction data, to predict the outcome of human metabolic reactions. We further build an ensemble model to account for multiple and diverse metabolites. Extensive evaluation reveals that the proposed method generalizes well to different enzyme families, as it can correctly predict metabolites through phase I and phase II drug metabolism as well as other enzymes. Compared to existing rule-based approaches, our method has equivalent performance on the major enzyme families while it additionally finds metabolites through less common enzymes. Our results indicate that the proposed approach can provide a comprehensive study of drug metabolism that does not restrict to the major enzyme families and does not require the extraction of transformation rules.

The structure of the drug, represented with a SMILES sequence, is being translated into the structures of possible metabolites that can be formed in the human body.     

Characterization of Phase I Hepatic Metabolites of Anti-Premature Ejaculation Drug Dapoxetine by UHPLC-ESI-Q-TOF

Determination of the metabolism pathway of xenobiotics undergoing the hepatic pass is a crucial aspect in drug development since the presence of toxic biotransformation products may result in significant side effects during the therapy. In this study, the complete hepatic metabolism pathway of dapoxetine established according to the human liver microsome assay with the use of a high-resolution LC–MS system was described. Eleven biotransformation products of dapoxetine, including eight metabolites not reported in the literature so far, were detected and identified. N-dealkylation, hydroxylation, N-oxidation and dearylation were found to be the main metabolic reactions for the investigated xenobiotic. In silico analysis of toxicity revealed that the reaction of didesmethylation may contribute to the increased carcinogenic potential of dapoxetine metabolites. On the other hand, N-oxidation and aromatic hydroxylation biotransformation reactions possibly lead to the formation of mutagenic compounds.     

A fast-screening approach for the tentative identification of drug-related metabolites from three non-steroidal anti-inflammatory drugs in hydroponically grown edible plants by HPLC-drift-tube-ion-mobility quadrupole time-of-flight mass spectrometry

The (tentative) identification of unknown drug-related phase II metabolites in plants upon drug uptake remains a challenging task despite improved analytical instrument performance. To broaden the knowledge of possible drug metabolization, a fast-screening approach for the tentative identification of drug-related phase II metabolites is presented in this work. Therefore, an in silico database for the three non-steroidal anti-inflammatory drugs (ketoprofen, mefenamic acid, and naproxen) and a sub-group of their theoretical phase II metabolites (based on combinations with glucose, glucuronic acid, and malonic acid) was created. Next, the theoretical exact masses (protonated species and ammonia adducts) were calculated and used as precursor ions in an autoMS/MS measurement method. The applicability of this workflow was tested on the example of eleven edible plants, which were hydroponically grown in solutions containing the respective drug at a concentration level of 20 mg/L. For the three drugs investigated this led to the tentative identification of 41 metabolites (some of them so far not described in this context), such as combinations of hydroxylated mefenamic acid with up to four glucose units or hydroxylated mefenamic acid with two glucose and three malonic acid units.     

Drug metabolite cluster centers-based strategy for comprehensive profiling of Neomangiferin metabolites in vivo and in vitro and network pharmacology study on anti-inflammatory mechanism

Neomangiferin (NMF) is an extremely special xanthone that could be simultaneously attributed to C-glycoside and O-glycoside with a variety of biological activities, such as anti-inflammatory, antitumor, antipyretic, and so on. So far as we know, the metabolism profiling has been insufficient until now. Herein, Drug Metabolite Cluster Centers (DMCCs)-based Strategy has been developed to profile the NMF metabolites in vivo and in vitro. Firstly, the DMCCs was proposed depending on literature-related and preliminary analysis results. Secondly, the specific metabolic rule was implemented to screen the metabolites of candidate DMCCs from the acquired Ultra High Performance Liquid Chromatography Quadrupole Exactive Orbitrap Mass Spectrometry (UHPLC-Q-Exactive Orbitrap MS) data by extracted ion chromatography (EIC) method. Thirdly, candidate metabolites were accurately and tentatively identified according to the pyrolysis law of mass spectrometry, literature reports, comparison of reference substances, and especially the diagnostic product ions (DPIs) deduced preliminarily. Finally, network pharmacology was adopted to elucidate the anti-inflammatory action mechanism of NMF on the basis of DMCCs. As a result, 3 critical metabolites including NMF, Mangiferin (MF) and Norathyriol (NA) were proposed as DMCCs, and a total of 61 NMF metabolites (NMF included) were finally screened and characterized coupled with 3 different biological sample preparation methods including solid phase extraction (SPE), acetonitrile precipitation and methanol precipitation. Among them, 32 metabolites were discovered in rat urine, 30 in rat plasma, 12 in rat liver, 9 metabolites in liver microsomes and 8 in rat faeces, respectively. Our results also illustrated that NMF primarily underwent deglucosylation, glucuronidation, methylation, sulfation, dihydroxylation and their composite reactions in vivo and in vitro. Additionally, network pharmacology analysis based on DMCCs revealed 85 common targets of disease-metabolites, and the key targets were TNF, EGFR, ESR1, PTGS2, HIF1A, IL-2, PRKCA and PRKCB. They exerted anti-inflammatory effects mainly through the pathways of inflammatory response, calcium-dependent protein kinase C activity, nitrogen metabolism, pathways in cancer and so on. In general, our study constructed a novel strategy to comprehensive elucidate the biotransformation pathways of NMF in vivo and in vitro, and provided vital reference for further understanding its anti-inflammatory action mechanism. Moreover, the established strategy could be generalized to the metabolism and action mechanism study of other natural products.     

Identification and characterization of in vitro and in vivo fidarestat metabolites: Toxicity and efficacy evaluation of metabolites

The progression of diabetic complications can be prevented by inhibition of aldose reductase and fidarestat considered to be highly potent. To date, metabolites of the fidarestat, toxicity, and efficacy are unknown. Therefore, the present study on characterization of hitherto unknown in vitro and in vivo metabolites of fidarestat using liquid chromatography–electrospray ionization tandem mass spectrometry (LC/ESI/MS/MS) is undertaken. In vitro and in vivo metabolites of fidarestat have been identified and characterized by using LC/ESI/MS/MS and accurate mass measurements. To identify in vivo metabolites, plasma, urine, and feces samples were collected after oral administration of fidarestat to Sprague–Dawley rats, whereas for in vitro metabolites, fidarestat was incubated in human S9 fraction, human liver microsomes, and rat liver microsomes. Furthermore, in silico toxicity and efficacy of the identified metabolites were evaluated. Eighteen metabolites have been identified. The main in vitro phase I metabolites of fidarestat are oxidative deamination, oxidative deamination and hydroxylation, reductive defluroniation, and trihydroxylation. Phase II metabolites are methylation, acetylation, glycosylation, cysteamination, and glucuronidation. Docking studies suggest that oxidative deaminated metabolite has better docking energy and conformation that keeps consensus with fidarestat whereas the rest of the metabolites do not give satisfactory results. Aldose reductase activity has been determined for oxidative deaminated metabolite (F‐1), and it shows an IC50 value of 0.44 μM. The major metabolite, oxidative deaminated, did not show any cytotoxicity in H9C2, HEK, HEPG2, and Panc1 cell lines. However, in silico toxicity, the predication result showed toxicity in skin irritation and ocular irritancy SEV/MOD versus MLD/NON (v5.1) model for fidarestat and its all metabolites. In drug discovery and development research, it is distinctly the case that the potential for pharmacologically active metabolites must be considered. Thus, the active metabolites of fidarestat may have an advantage as drug candidates as many drugs were initially observed as metabolites.     

N-Dealkylation of Amines

N-dealkylation, the removal of an N-alkyl group from an amine, is an important chemical transformation which provides routes for the synthesis of a wide range of pharmaceuticals, agrochemicals, bulk and fine chemicals. N-dealkylation of amines is also an important in vivo metabolic pathway in the metabolism of xenobiotics. Identification and synthesis of drug metabolites such as N-dealkylated metabolites are necessary throughout all phases of drug development studies. In this review, different approaches for the N-dealkylation of amines including chemical, catalytic, electrochemical, photochemical and enzymatic methods will be discussed.     

Assessing Heterologous Expression of Hyoscyamine 6β-Hydroxylase – A Feasibility Study

The protein sequence of the hyoscyamine 6β-hydroxylase gene from Hyoscyamusniger was analysed in silico for its potential of heterologous expression. Therefore different parameters determining the proteins properties and structure in prokaryotic or eukaryotic protein expression systems were taken into account. In silico prediction of co- and post-translational modifications revealed 25 putative glycosylation sites, one of which reported to be a co-factor stabilizing residue in 2-oxoglutarate dependent dioxygenases. Potential protein solubility and degradation (PEST) motifs were also evaluated. Together with the calculated physico-chemical properties the results indicated reasonable solubility but potential instability of the protein in Escherichia coli and Saccharomyces cerevisiae. Further a synthetic h6h-gene was introduced into the prokaryotic or eukaryotic hostsEscherichia coli and Saccharomyces cerevisiae to determine protein expression. The protein could be expressed in both organisms, though stability was confirmed to be an issue.