• 제목/요약/키워드: Enzyme Interaction Metabolite

검색결과 6건 처리시간 0.02초

데이터 마이닝을 활용한 효소 대사물의 분석 (Enzyme Metabolite Analysis Using Data Mining)

  • 정희택;박춘구
    • 한국전자통신학회논문지
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    • 제11권10호
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    • pp.969-982
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    • 2016
  • 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 대부분이 아주 낮은 지지도임을 확인할 수 있었다. 연구결과는 순수 기질대사물은 효과적인 대사변환 예측 모델 개발에 활용될 수 있다.

Vitamin $B_6$와 Pyrazinamide의 약물상호작용 (II) (Drug Interaction of Vitamin $B_6$ and Pyrazinamide (II))

  • 허근;이상일;박종민;김석환
    • 약학회지
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    • 제31권4호
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    • pp.197-203
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    • 1987
  • When pyrazinamide is used in the treatment of tuberculosis, the measurement of pyrazinoic acid which is an intermediate metabolite of pyrazinamide in body is required in order to prevent its associated side-effects, especially that of hyperuricemia. Effects of vitamin B$_6$ on pyrazinoic acid metabolism were studied in this experiment. The activity of hepatic pyrazinoic acid oxidizing enzyme in the presence of pyridoxal was powerfully inhibited, and the pattern was competitive inhibition type. Whereas, its enzyme activity was significantly increased by the treatment of pyridoxal, and the characteristics of the increase may include induction of enzyme proteins. As mice received pyrazinoic acid(300mg/kg) after pyridoxal-pretreatment(40mg/kg) once daily for 4 days, the blood level of pyrazinoic acid and uric acid was decreased significantly.

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Effect of Cimetidine and Phenobarbital on Metabolite Kinetics of Omeprazole in Rats

  • Park Eun-Ja;Cho Hea-Young;Lee Yong-Bok
    • Archives of Pharmacal Research
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    • 제28권10호
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    • pp.1196-1202
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    • 2005
  • Omeprazole (OMP) is a proton pump inhibitor used as an oral treatment for acid-related gastrointestinal disorders. In the liver, it is primarily metabolized by cytochrome P-450 (CYP450) isoenzymes such as CYP2C19 and CYP3A4. 5-Hyroxyomeprazole (5-OHOMP) and omeprazole sulfone (OMP-SFN) are the two major metabolites of OMP in human. Cimetidine (CMT) inhibits the breakdown of drugs metabolized by CYP450 and reduces, the clearance of coad-ministered drug resulted from both the CMT binding to CYP450 and the decreased hepatic blood flow due to CMT. Phenobarbital (PB) induces drug metabolism in laboratory animals and human. PB induction mainly involves mammalian CYP forms in gene families 2B and 3A. PB has been widely used as a prototype inducer for biochemical investigations of drug metabolism and the enzymes catalyzing this metabolism, as well as for genetic, pharmacological, and toxicological investigations. In order to investigate the influence of CMT and PB on the metabolite kinetics of OMP, we intravenously administered OMP (30 mg/kg) to rats intraperitoneally pretreated with normal saline (5 mL/kg), CMT (100 mg/kg) or PB (75 mg/kg) once a day for four days, and compared the pharmacokinetic parameters of OMP. The systemic clearance ($CL_{t}$) of OMP was significantly (p<0.05) decreased in CMT-pretreated rats and significantly (p<0.05) increased in PB-pretreated rats. These results indicate that CMT inhibits the OMP metabolism due to both decreased hepatic blood flow and inhibited enzyme activity of CYP2C19 and 3A4 and that PB increases the OMP metabolism due to stimulation of the liver blood flow and/or bile flow, due not to induction of the enzyme activity of CYP3A4.

Interaction of 2-Hydroxyquinoxaline (2-HQ) on Soil Enzymes and Its Degradation: A Review

  • Gangireddygari, Venkata Subba Reddy;Bontha, Rajasekhar Reddy;Yoon, Ju-Yeon
    • 인간식물환경학회지
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    • 제23권4호
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    • pp.399-410
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    • 2020
  • The United Nations project the world population to reach 10 billion by the year 2057. To increase the food of the ever-increasing world population, agrochemicals are indispensable tools to the boon in agriculture production. These agrochemicals are a serious threat to the health of humans, plants, and animals. Agrochemicals are ultimately reached to the main reservoir/sink such as soil and contaminating the groundwater, disturb the soil health and in turn a serious threat to biogeochemical cycling and the entire biosphere. Among agrochemicals, quinalphosis one of the most repeatedly and widely used insecticides in the control of a wide range of pests that attack various crops. Quinalphos is shown to be primarily toxic in organisms by acetylcholinesterase enzyme action. Hydrolysis of quinalphos produces amajor metabolite 2-hydroxyquinoxaline (2-HQ), which has shown secondary toxicity in organisms. 2-HQ is reported to be mutagenic, carcinogenic, growth inhibition and induce oxidative stress in organisms. Quinoline is a heterocyclic compound and structural resemblance of 2-HQ with minor changes, but its degradation studies are enormous compared to the 2-HQ compound. Biotic factors in fate and behavior of 2-HQ in the environment are least studied. 2-HQ interactions with soil enzymes are vary from soil to soil. Based on the toxicity of 2-HQ in our stockpile we need to isolate a handful of microorganisms to treat this persistent metabolite and also other metabolites/compounds.This brief review will be significant from the point of biological and environmental safety.

나린제닌과 딜티아젬과의 약물동태학적 상호작용 (Pharmacokinetic Interaction Between Diltiazem and Naringenin in Rabbits)

  • 설효찬;최준식
    • 한국임상약학회지
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    • 제16권1호
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    • pp.57-62
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    • 2006
  • The purpose of this study was to investigate the effect of naringenin, one of flavonoids, on the pharmacokinetics and bioavailability of diltiazem (15 mg/kg) after oral administration of diltiazem with or without naringenin (2.0, 10 and 20 mg/kg) in rabbits. Coadministration of naringenin increased the absorption rate constant $(K_a)$, the area under the plasma concentration-time curve (AUC) and peak concentration $(C_{max})$ of diltiazem compared to the control group, but only significantly (p<0.05) by 10mg/kg of naringenin coadministration. The absolute bioavailability (AB%) of diltiazem by coadministration ranges from 7.8% to 10.3%, increased more than control (7.2%), and relative bioavailability (RB%) of diltiazem is increased from 1.08- to 1.43-fold. Coadministration caused on significant changes in the terminal half-lives $(t_{1/2})$ and the time to reach the peak concentration $(T_{max})$ of diltiazem. On the other hand, coadministration of naringenin increased the AUC desacetyldiltiazem, significantly at the dose of 10mg/kg. But the metabolite ratio (MR) was decreased, significantly at 10mg/kg of naringenin. Based on these results, we can make a conclusion that the increased bioavailability and the significant changes of these pharmacokinetic parameters might be due to naringenin, which possess the potency to inhibit the metabolizing enzyme (CYP3A4) in the liver and intestinal mucosa, and also inhibit the P-glycoprotein efflux pump in the intestinal mucosa.

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BIOLOGICALLY-BASED DOSE-RESPONSE MODEL FOR NEUROTOXICITY RISK ASSESSMENT

  • Slikker, William Jr.;Gaylor, David W.
    • Toxicological Research
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    • 제6권2호
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    • pp.205-213
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    • 1990
  • The regulation of neurotoxicants has usually been based upon setting reference doses by dividing a no observed adverse effect level (NOAEL) by uncertainty factors that theoretically account for interspecies and intraspecies extraploation of experimental results in animals to humans. Recently, we have proposed a four-step alternative procedure which provides quantitative estimates of risk as a function of dose. The first step is to establish a mathematical relationship between a biological effect or biomarker and the dose of chemical administered. The second step is to determine the distribution (variability) of individual measurements of biological effects or their biomarkers about the dose response curve. The third step is to define an adverse or abnormal level of a biological effect or biomarker in an untreated population. The fourth and final step is to combine the information from the first three steps to estimate the risk (proportion of individuals exceeding on adverse or abnormal level of a biological effect or biomarker) as a function of dose. The primary purpose of this report is to enhance the certainty of the first step of this procedure by improving our understanding of the relationship between a biomarker and dose of administered chemical. Several factors which need to be considered include: 1) the pharmacokinetics of the parent chemical, 2) the target tissue concentrations of the parent chemical or its bioactivated proximate toxicant, 3) the uptake kinetics of the parent chemical or metabolite into the target cell(s) and/or membrane interactions, and 4) the interaction of the chemical or metabolite with presumed receptor site(s). Because these theoretical factors each contain a saturable step due to definitive amounts of required enzyme, reuptake or receptor site(s), a nonlinear, saturable dose-response curve would be predicted. In order to exemplify this process, effects of the neurotoxicant, methlenedioxymethamphetamine (MDMA), were reviewed and analyzed. Our results and those of others indicate that: 1) peak concentrations of MDMA and metabolites are ochieved in rat brain by 30 min and are negligible by 24 hr, 2) a metabolite of MDMA is probably responsible for its neurotoxic effects, and 3) pretreatment with monoamine uptake blockers prevents MDMA neurotoxicity. When data generated from rats administerde MDMA were plotted as bilolgical effect (decreases in hippocampal serotonin concentrations) versus dose, a saturation curve best described the observed relationship. These results support the hypothesis that at least one saturable step is involved in MDMA neurotoxicity. We conclude that the mathematical relationship between biological effect and dose of MDMA, the first step of our quantitative neurotoxicity risk assessment procedure, should reflect this biological model information generated from the whole of the dose-response curve.

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