Drug repositioning for enzyme modulator based on human metabolite-likeness

Lee, Yoon Hyeok,Choi, Hojae,Park, Seongyong,Lee, Boah,Yi, Gwan-Su BioMed Central 2017 BMC bioinformatics Vol.18 No.suppl7

<P><B>Background</B></P><P>Recently, the metabolite-likeness of the drug space has emerged and has opened a new possibility for exploring human metabolite-like candidates in drug discovery. However, the applicability of metabolite-likeness in drug discovery has been largely unexplored. Moreover, there are no reports on its applications for the repositioning of drugs to possible enzyme modulators, although enzyme-drug relations could be directly inferred from the similarity relationships between enzyme’s metabolites and drugs.</P><P><B>Methods</B></P><P>We constructed a drug-metabolite structural similarity matrix, which contains 1,861 FDA-approved drugs and 1,110 human intermediary metabolites scored with the Tanimoto similarity. To verify the metabolite-likeness measure for drug repositioning, we analyzed 17 known antimetabolite drugs that resemble the innate metabolites of their eleven target enzymes as the gold standard positives. Highly scored drugs were selected as possible modulators of enzymes for their corresponding metabolites. Then, we assessed the performance of metabolite-likeness with a receiver operating characteristic analysis and compared it with other drug-target prediction methods. We set the similarity threshold for drug repositioning candidates of new enzyme modulators based on maximization of the Youden’s index. We also carried out literature surveys for supporting the drug repositioning results based on the metabolite-likeness.</P><P><B>Results</B></P><P>In this paper, we applied metabolite-likeness to repurpose FDA-approved drugs to disease-associated enzyme modulators that resemble human innate metabolites. All antimetabolite drugs were mapped with their known 11 target enzymes with statistically significant similarity values to the corresponding metabolites. The comparison with other drug-target prediction methods showed the higher performance of metabolite-likeness for predicting enzyme modulators. After that, the drugs scored higher than similarity score of 0.654 were selected as possible modulators of enzymes for their corresponding metabolites. In addition, we showed that drug repositioning results of 10 enzymes were concordant with the literature evidence.</P><P><B>Conclusions</B></P><P>This study introduced a method to predict the repositioning of known drugs to possible modulators of disease associated enzymes using human metabolite-likeness. We demonstrated that this approach works correctly with known antimetabolite drugs and showed that the proposed method has better performance compared to other drug target prediction methods in terms of enzyme modulators prediction. This study as a proof-of-concept showed how to apply metabolite-likeness to drug repositioning as well as potential in further expansion as we acquire more disease associated metabolite-target protein relations.</P><P><B>Electronic supplementary material</B></P><P>The online version of this article (doi:10.1186/s12859-017-1637-5) contains supplementary material, which is available to authorized users.</P>

데이터 마이닝을 활용한 효소 대사물의 분석

정희택(Hyi Thaek Ceong),박춘구(Chun Goo Park) 한국전자통신학회 2016 한국전자통신학회 논문지 Vol.11 No.10

최근 천연물로부터 신약 후보물질을 개발하려는 연구가 활발히 이루어지고 있다. 인체 내에서 천연물은 주로 효소에 의해 대사된다. 본 연구는 화합물의 인체내 대사반응과 주로 관련된 효소에 의한 대사반응의 특징을 연관규칙마이닝을 활용하여 분석한다. 화합물이 인체 내에서 효소 대사반응과 관련된 데이터를 BRENDA(: BRaunschweig ENzyme DAtabase)로부터 수집하였다. 수집된 데이터를 효소대사반응의 기본 틀에 근거하여, 대사물들을 기질대사물, 생성대사물, 억제대사물, 그리고 활성대사물들로 구분한다. 이러한 대사물들로 이루어진 기질대사물 트랜잭션, 생성대사물 트랜잭션, 그리고 모든 대사물들을 포함한 효소반응트랜잭션들을 구성하였다. 또한 종 정보를 반영한 6개의 트랜잭션들로 구성하였다. 연관규칙 마이닝을 활용하여 6개의 트랜잭션에서 빈발대사물 및 패턴을 분석하였다. 또한 대사물들 사이의 관련성을 분석하였다. 그 결과 효소대사반응에 참여하는 대사물들의 분포와 패턴을 식별할 수 있었다. 더욱이 기질에만 속하는 순수 기질대사물들을 식별하였고 이들 대부분 이 아주 낮은 지지도임을 확인할 수 있었다. 연구결과는 순수 기질대사물은 효과적인 대사변환 예측 모델 개발에 활용될 수 있다. Recently, the researches to discovery drug candidates from natural herbs have received considerable attention. In human body, enzyme mostly metabolize the compounds of natural herbs. In this study, we analysis the enzyme interactions using assoication mining. We get this data from BRENDA(: BRaunschweig ENzyme DAtabase) system. Based on enzyme interaction model, we divide the metabolites into substrate metabolites, product metabolites, inhibitor metabolites, and activating metabolites. We then compose substrate metabolite transaction, product metabolite transaction with each metabolites and enzyme interaction transaction with all metabolites. Also we take account of organism for each transactions. We mine frequent metabolites and patterns from six transactions using association rule mining. And we analysis the relationship among metabolites. As a result, we identify the distributions and patterns of metabolites consist in enzyme interactions. We found that metabolites include in only substrate are identified and have very low supports. This results can be useful to develop the effective metabolism prediction model for compounds of natural herbs.

Metabolite identification of AZD8055 in Sprague-Dawley rats after a single oral administration using ultra-performance liquid chromatography and mass spectrometry

Rashid, Md Mamunur,Oh, Hyun-A.,Lee, Hyunbeom,Jung, Byung Hwa Pergamon Press 2017 Journal of pharmaceutical and biomedical analysis Vol.145 No.-

<P><B>Abstract</B></P> <P>AZD8055 is an ATP-competitive specific dual mTOR inhibitor and exhibited potent antitumor activity on several types of solid tumors. However, the metabolism of AZD8055 in the body still remains unknown. In this study, metabolite identification of AZD8055 was performed using ultra high-performance liquid chromatography-ion trap mass spectrometry (UHPLC-IT-MS) through both <I>in vitro</I> and <I>in vivo</I> approaches using rat liver microsomes (RLMs) and rat plasma, urine and feces, respectively. A total of eight putative metabolites (five phase I and three phase II) were identified, and a tentative metabolic pathway was suggested for the first time. Considering the accurate mass and mass fragmentations of the detected metabolites, their plausible structures were suggested. Demethylation, hydroxylation, oxidation and morpholine ring opening were the major biotransformation processes for the phase-I metabolism, while phase-II metabolites were merely generated by the glucuronide conjugation reaction. The cumulative excretion of AZD8055 in urine and feces was 0.13% and 1.11% of the dose, respectively. When the semi-quantitative analysis of the metabolites was performed using UHPLC–MS/MS (ultra-performance liquid chromatography tandem mass spectrometry) to evaluate the overall trend of metabolites formation and excretion, AZD8055 was excreted more in the form of the metabolites than itself and their formation was very fast. Therefore it was presumed that biotransformation was playing a crucial role in its elimination. Ultimately, this study provides novel insights regarding the <I>in vitro</I> and <I>in vivo</I> biotransformations of AZD8055. Further investigations of metabolites of this potent anti-cancer compound could be beneficial for the antitumor drug design and development process.</P> <P><B>Highlights</B></P> <P> <UL> <LI> Metabolites of AZD8055 have been identified and a metabolic pathway is suggested. </LI> <LI> AZD8055 absorbs rapidly and the formation of metabolites is very fast. </LI> <LI> Demethylated and glucuronide conjugated metabolites are the major metabolites. </LI> <LI> AZD8055 shows significant inhibitory properties toward CYP3A4, CYP2C9 and CYP2E1. </LI> </UL> </P> <P><B>Graphical abstract</B></P> <P>[DISPLAY OMISSION]</P>

LC-MS/MS Profiling-Based Secondary Metabolite Screening of Myxococcus xanthus

( Ji Young Kim ),( Jung Nam Choi ),( Pil Kim ),( Dai Eun Sok ),( Soo Wan Nam ),( Choong Hwan Lee ) 한국미생물 · 생명공학회 2009 Journal of microbiology and biotechnology Vol.19 No.1

Myxobacteria, Gram-negative soil bacteria, are a well-known producer of bioactive secondary metabolites. Therefore, this study presents a methodological approach for the high-throughput screening of secondary metabolites from 4 wild-type Myxococcus xanthus strains. First, electrospray ionization mass spectrometry (ESI-MS) was performed using extracellular crude extracts. As a result, 22 metabolite peaks were detected, and the metabolite profiling was then conducted using the m/z value, retention time, and MS/MS fragmentation pattern analyses. Among the peaks, one unknown compound peak was identified as analogous to the myxalamid A, B, and C series. An analysis of the tandem mass spectrometric fragmentation patterns and HR-MS identified myxalamid K as a new compound derived from M. xanthus. In conclusion, LC-MS/MS-based chemical screening of diverse secondary metabolites would appear to be an effective approach for discovering unknown microbial secondary metabolites.

Comparison of Traditional and Commercial Vinegars Based on Metabolite Profiling and Antioxidant Activity

( Yu Kyung Jang ),( Mee Youn Lee ),( Hyang Yeon Kim ),( Sarah Lee ),( Soo Hwan Yeo ),( Seong Yeol Baek ),( Choong Hwan Lee ) 한국미생물 · 생명공학회 2015 Journal of microbiology and biotechnology Vol.25 No.2

Metabolite profiles of seven commercial vinegars and two traditional vinegars were performed by gas chromatography time-of-flight mass spectrometry with multivariate statistical analysis. During alcohol fermentation, yeast, nuruk, and koji were used as sugars for nutrients and as fermentation substrates. Commercial and traditional vinegars were significantly separated in the principal component analysis and orthogonal partial least square discriminant analysis. Six sugars and sugar alcohols, three organic acids, and two other components were selected as different metabolites. Target analysis by ultra-performance liquid chromatography quadruple-time-of-flight mass spectrometry and liquid chromatographyion trap-mass spectrometry/mass spectrometry were used to detect several metabolites having antioxidant activity, such as cyanidin-3-xylosylrutinoside, cyanidin-3-rutinoside, and quercetin, which were mainly detected in Rural Korean Black raspberry vinegar (RKB). These metabolites contributed to the highest antioxidant activity measured in RKB among the nine vinegars. This study revealed that MS-based metabolite profiling was useful in helping to understand the metabolite differences between commercial and traditional vinegars and to evaluate the association between active compounds of vinegar and antioxidant activity.

A systematic approach to identify therapeutic effects of natural products based on human metabolite information

Noh, Kyungrin,Yoo, Sunyong,Lee, Doheon BioMed Central 2018 BMC bioinformatics Vol.19 No.-

<P><B>Background</B></P><P>Natural products have been widely investigated in the drug development field. Their traditional use cases as medicinal agents and their resemblance of our endogenous compounds show the possibility of new drug development. Many researchers have focused on identifying therapeutic effects of natural products, yet the resemblance of natural products and human metabolites has been rarely touched.</P><P><B>Methods</B></P><P>We propose a novel method which predicts therapeutic effects of natural products based on their similarity with human metabolites. In this study, we compare the structure, target and phenotype similarities between natural products and human metabolites to capture molecular and phenotypic properties of both compounds. With the generated similarity features, we train support vector machine model to identify similar natural product and human metabolite pairs. The known functions of human metabolites are then mapped to the paired natural products to predict their therapeutic effects.</P><P><B>Results</B></P><P>With our selected three feature sets, structure, target and phenotype similarities, our trained model successfully paired similar natural products and human metabolites. When applied to the natural product derived drugs, we could successfully identify their indications with high specificity and sensitivity. We further validated the found therapeutic effects of natural products with the literature evidence.</P><P><B>Conclusions</B></P><P>These results suggest that our model can match natural products to similar human metabolites and provide possible therapeutic effects of natural products. By utilizing the similar human metabolite information, we expect to find new indications of natural products which could not be covered by previous in silico methods.</P>

Effects of Age, Environments and Sex on Plasma Metabolite Levels in Young Holstein Calves

Sasaki, O.,Yamamoto, N.,Togashi, K.,Minezawa, M. Asian Australasian Association of Animal Productio 2002 Animal Bioscience Vol.15 No.5

Thirty Holstein calves were used to determine effects of age, environment and sex on blood metabolite concentrations during 1 to 90 d of age. Calves were weaned at 75 d of age. Environmental effects are grouped by the difference in month at birth and site of feeding. Blood samples were obtained every 2 or 3 d. The mean metabolite concentration every 3 d was used for the statistical analysis. Dairy bodyweight gain was not affected by environmental group and sex effect. Concentrations of plasma glucose, nonesterified fatty acids (NEFA), triglyceride, total cholesterol and total ketone changed with growth. These developmental changes in metabolite levels would be caused by ruminal maturation with increment of grain intake. Levels of plasma urea nitrogen, glucose, NEFA, triglyceride and total cholesterol drastically changed during a few weeks after birth, indicating that the physiological state in calves greatly changed during that time. Effects of the environmental group and sex were significant in almost all metabolites. Temperature influenced plasma metabolite concentrations. The plasma metabolite concentrations were affected more intensely by heat stress in the infant period than in the neonatal period.

Interplay between the Gut Microbiome and Metabolism in Ulcerative Colitis Mice Treated with the Dietary Ingredient Phloretin

( Jie Ren ),( Puze Li ),( Dong Yan ),( Min Li ),( Jinsong Qi ),( Mingyong Wang ),( Genshen Zhong ),( Minna Wu ) 한국미생물 · 생명공학회 2021 Journal of microbiology and biotechnology Vol.31 No.10

A growing number of healthy dietary ingredients in fruits and vegetables have been shown to exhibit diverse biological activities. Phloretin, a dihydrochalcone flavonoid that is abundant in apples and pears, has anti-inflammatory effects on ulcerative colitis (UC) mice. The gut microbiota and metabolism are closely related to each other due to the existence of the food-gut axis in the human colon. To investigate the interplay of faecal metabolites and the microbiota in UC mice after phloretin treatment, phloretin (60 mg/kg) was administered by gavage to ameliorate dextran sulfate sodium (DSS)-induced UC in mice. Gut microbes and faecal metabolite profiles were detected by high-throughput sequencing and liquid chromatography mass spectrometry (LC-MS) analysis, respectively. The correlations between gut microbes and their metabolites were evaluated by Spearman correlation coefficients. The results indicated that phloretin reshaped the disturbed faecal metabolite profile in UC mice and improved the metabolic pathways by balancing the composition of faecal metabolites such as norepinephrine, mesalazine, tyrosine, 5-acetyl-2,4- dimethyloxazole, and 6-acetyl-2,3-dihydro-2-(hydroxymethyl)-4(1H)-pyridinone. Correlation analysis identified the relations between the gut microbes and their metabolites. Proteus was negatively related to many faecal metabolites, such as norepinephrine, L-tyrosine, laccarin, dopamine glucuronide, and 5-acetyl-2,4-dimethyloxazole. The abundance of unidentified Bacteriodales_S24-7_group was positively related to ecgonine, 15-KETE and 6-acetyl-2,3-dihydro-2- (hydroxymethyl)-4(1H)-pyridinone. The abundance of Christensenellaceae_R-7_group was negatively related to the levels of 15-KETE and netilmicin. Stenotrophomonas and 15-KETE were negatively related, while Intestinimonas and alanyl-serine were positively related. In conclusion, phloretin treatment had positive impacts on faecal metabolites in UC mice, and the changes in faecal metabolites were closely related to the gut microbiota.

Tentative identification of 20(S)-protopanaxadiol metabolites in human plasma and urine using ultra-performance liquid chromatography coupled with triple quadrupole time-of-flight mass spectrometry

Ling, Jin,Yu, Yingjia,Long, Jiakun,Li, Yan,Jiang, Jiebing,Wang, Liping,Xu, Changjiang,Duan, Gengli The Korean Society of Ginseng 2019 Journal of Ginseng Research Vol.43 No.4

Background: 20(S)-Protopanaxadiol (PPD), the aglycone part of 20(S)-protopanaxadiol ginsenosides, possesses antidepressant activity among many other pharmacological activities. It is currently undergoing clinical trial in China as an antidepressant. Methods: In this study, an ultra-performance liquid chromatography coupled with triple quadrupole time-of-flight mass tandem mass spectrometry method was established to identify the metabolites of PPD in human plasma and urine following oral administration in phase IIa clinical trial. Results: A total of 40 metabolites in human plasma and urine were identified using this method. Four metabolites identified were isolated from rat feces, and two of them were analyzed by NMR to elucidate the exact structures. The structures of isolated compounds were confirmed as (20S,24S)-epoxydammarane-12,23,25-triol-3-one and (20S,24S)-epoxydammarane-3,12,23,25-tetrol. Both compounds were found as metabolites in human for the first time. Upon comparing our findings with the findings of the in vitro study of PPD metabolism in human liver microsomes and human hepatocytes, metabolites with m/z 475.3783 and phase II metabolites were not found in our study whereas metabolites with m/z 505.3530, 523.3641, and 525.3788 were exclusively detected in our experiments. Conclusion: The metabolites identified using ultra-performance liquid chromatography coupled with triple quadrupole time-of-flight mass spectrometry in our study were mostly hydroxylated metabolites. This indicated that PPD was metabolized in human body mainly through phase I hepatic metabolism. The main metabolites are in 20,24-oxide form with multiple hydroxylation sites. Finally, the metabolic pathways of PPD in vivo (human) were proposed based on structural analysis.

Extraction of Individual Metabolite Spectrum in Proton Magnetic Resonance Spectroscopy of Mouse Brain Using Deep Learning

Yoon Ho Hwang,Woo-Seung Kim,Chang-Soo Yun,Jae-Hyung Yeon,Hyeon-Man Baek,Bong Soo Han,Dong Youn Kim 한국자기학회 2021 Journal of Magnetics Vol.26 No.3

The present study aims to develop a deep learning (DL) model to quantify metabolites. To apply DL to metabolite quantification using ¹H-MRS data, Convolutional autoencoder (CAE) were designed to extract line‐narrowed, baseline‐removed, and noise-free metabolite spectra for each metabolite. Fifty thousand simulation data were generated by varying the SNR (4-12), linewidth (6-22 Hz), phase shift (± 5°), and frequency shift (± 5 Hz) on phantom spectra. The data were divided into 45,000 simulation data for training and 5,000 test data, and the mean absolute percent errors (MAPEs) were used to evaluate the performance of the CAE. The average MAPE of the metabolites was 13.64 ± 11.38 %. Fourteen metabolites were within the reported concentration ranges. These findings showed that the proposed method had similar or improved performance than conventional methods. The proposed method using DL was the recent and up-to-date quantification one and has clinically potential applicability.