• 제목/요약/키워드: toxicity prediction

검색결과 86건 처리시간 0.024초

Identification of Urinary Biomarkers Related to Cisplatin-Induced Acute Renal Toxicity Using NMR-Based Metabolomics

  • Wen, He;Yang, Hye-Ji;Choi, Myung-Joo;Kwon, Hyuk-Nam;Kim, Min-Ah;Hong, Soon-Sun;Park, Sung-Hyouk
    • Biomolecules & Therapeutics
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    • 제19권1호
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    • pp.38-44
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    • 2011
  • Cisplatin is widely used for various types of cancers. However, its side effects, most notably, renal toxicity often limit its clinical utility. Although previous metabolomic studies reported possible toxicity markers, they used small number of animals and statistical approaches that may not perform best in the presence of intra-group variation. Here, we identified urinary biomarkers associated with renal toxicity induced by cisplatin using NMR-based metabolomics combined with Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA). Male Sprague-Dawley rats (n=22) were treated with cisplatin (10 mg/kg single dose), and the urines obtained before and after treatment were analyzed by NMR. Multivariable analysis of NMR data presented clear separation between non-treated and treated groups. The OPLS-DA statistical results revealed that 1,3-dimethylurate, taurine, glucose, glycine and branched-chain amino acid (isoleucine, leucine and valine) were significantly elevated in the treated group and that phenylacetylglycine and sarcosine levels were decreased in the treated group. To test the robustness of the approach, we built a prediction model for the toxicity and were able to predict all the unknown samples (n=14) correctly. We believe the proposed NMR-based metabolomics with OPLS-DA approach and the resulting urine markers can be used to augment the currently available blood markers.

Natural radioprotectors and their impact on cancer drug discovery

  • Kuruba, Vinutha;Gollapalli, Pavan
    • Radiation Oncology Journal
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    • 제36권4호
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    • pp.265-275
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    • 2018
  • Cancer is a complex multifaceted illness that affects different patients in discrete ways. For a number of cancers the use of chemotherapy has become standard practice. Chemotherapy is a use of cytostatic drugs to cure cancer. Cytostatic agents not only affect cancer cells but also affect the growth of normal cells; leading to side effects. Because of this, radiotherapy gained importance in treating cancer. Slaughtering of cancerous cells by radiotherapy depends on the radiosensitivity of the tumor cells. Efforts to improve the therapeutic ratio have resulted in the development of compounds that increase the radiosensitivity of tumor cells or protect the normal cells from the effects of radiation. Amifostine is the only chemical radioprotector approved by the US Food and Drug Administration (FDA), but due to its side effect and toxicity, use of this compound was also failed. Hence the use of herbal radioprotectors bearing pharmacological properties is concentrated due to their low toxicity and efficacy. Notably, in silico methods can expedite drug discovery process, to lessen the compounds with unfavorable pharmacological properties at an early stage of drug development. Hence a detailed perspective of these properties, in accordance with their prediction and measurement, are pivotal for a successful identification of radioprotectors by drug discovery process.

독성 감지를 위한 생물 조기 경보 시스템 (Biological Early Warning System for Toxicity Detection)

  • 김성용;권기용;이원돈
    • 한국정보통신학회논문지
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    • 제14권9호
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    • pp.1979-1986
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    • 2010
  • 생물 조기 경보 시스템은 물속 생명체의 행동을 관찰하여 독성을 감지한다. 이 시스템은 분류기를 물의 독성의 유무와 정도를 판단하기 위해 사용한다. 이 분류기의 성능을 높이기 위해 적용할 수 있는 방법 중에 부스팅 알고리즘이 있다. 부스팅은 기본 분류기로는 예측 정확도가 낮았던 분류하기 어려운 사건에 집중할 수 있도록 다음 번 데이터에 해당 훈련 사건(event)들이 뽑힐 확률을 높여준다. 횟수가 진행될수록 분류기가 어려운 사건들을 집중적으로 고려하게 된다. 그 결과 분류하기 어려웠던 사건에 대한 예측 성능은 좋아지지만, 비교적 쉬운 훈련 사건들의 정보는 버려지는 단점이 있다. 본 논문에서는 이 같은 단점을 보완하기 위해 분류기에 확장된 데이터 표현을 위한 점진적 학습법의 적용을 제안한다. 확장된 데이터 표현의 가중치 변수를 사용하면 약하게 분류되는 사건 뿐 아니라 쉽게 분류되는 사건의 정보까지도 사용하여 분류기의 예측 정확도를 높일 수 있게 된다. 새로 적용된 알고리즘과 기존의 중요도 변수를 사용하지 않는 learn++를 비교하여 성능이 향상됨을 검증하였다.

한국인의 항결핵제에 의한 간독성 위험인자 예측 (Prediction of the Hepatotoxicity Risk Factor Induced by Antituberculosis Agents in Koreans)

  • 이지선;김현아;조은;이옥상;임성실
    • 약학회지
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    • 제55권4호
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    • pp.352-360
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    • 2011
  • Standard combination chemotherapy including isoniazid, rifampin, pyrazinamide, and ethambutol is very effective against tuberculosis. But, these medicines can cause hepatotoxicity which is the main reason for treatment interruption or change in drug regimen. In order to identify risk factors associated with hepatotoxcity in Koreans and assess elevated baseline LFTs' contributions to hepatotoxicity, a retrospective case control study was performed. The medical records of 277 patients who diagnosed with tuberculosis at a community hospital from January 1st, 2007 to June 30th, 2010 were reviewed. Patients were categorized into 3 groups (non toxic group, patients without increase in LFT levels; mild to moderate hepatotoxic group and severe hepatotoxic group). And the correlation between risk factors and hepatotoxicity was analyzed by using SPSS program. The overall incidence of hepatotoxicity was 18% and 8.7% of patients developed severe toxicity. Patients in the severe toxic group had the longest treatment period among the three groups. In 75% of severe toxic group, hepatotoxicity occurred within 18.3 days after starting medication. Hypoalbuminemia (serum albumin <3 g/dl) was a significant risk factor for development of severe toxicity. Elevated baseline transaminase (except ALT), total bilirubin, and preexisting hepatitis were also risk factors which were more than twice as likely to increase risk of severe hepatotoxicity (p>0.05). In conclusion, hypoalbuminemia (serum albumin level <3 g/dl) was a significant risk factor for anti-tuberculosis druginduced severe toxicity. Therefore, before starting antituberculosis chemotherapy, serum albumin level should be assessed at baseline. In high-risk patients (hypoalbuminemia, elevated LFTs) for hepatotoxicty, liver function should be closely monitored up to at least 21 days after taking medication.

유해화학물질의 종합위해등급 알고리즘 개발에 관한 연구 (A Study on Total Hazard Level Algorithm Development for Hazardous Chemical Substances)

  • 고재선;김광일;정상태
    • 한국화재소방학회논문지
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    • 제14권4호
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    • pp.7-16
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    • 2000
  • 본 연구에서는 대상물질을 선점한 후 그에 따른 세 가지 기준 즉 독성, 화재폭발, 환경기준과 각각의 피해예측기법을 설정하고 이 기준들을 알고리즘을 통한 통합한 종합위해등급으로서 선정된 대상물질에 적용하였다. 특히, 환경기준은 포괄적인 개념으로서 USCG 및 MSDS의 환경기준 분류와 NFPA의 건강위해성(Nh) 중 환경관련 부분을 조합하여 환경지수 모델화를 하였다. 또한 각 기준에 따른 피해예측 기법을 선택하여 지역별 인의에 위치한 화학물질 관련업체에 사용 또는 저장 중인 유해화학물질에 대해 적용하여 사용물질에 대한 종합위해등급 설정(단일물질에 대한 가연성, 독성, 반응성, 환경성에 대한 Hazard level 및 표시 모델화) 및 그에 따른 사고시 피해예측 강도산정 (CPQRA, IAEA, VZ eq), Risk contour를 구할 수 있었다. 이 결과 모든 화학공정 및 저장 등에서 발생할 수 있는 독성 누출, 화재폭발의 잠재적 위험성산정을 통한 안전성 평가의 Tool로 활용이 가능하다.

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A Study on integrated water management system based on Web maps

  • Choi, Ho Sung;Jung, Jin Young;Park, Koo Rack
    • 한국컴퓨터정보학회논문지
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    • 제21권8호
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    • pp.57-64
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    • 2016
  • Initial prevention activities and rapid propagation conditions is the most important to prevent diffusion of water pollution. If water pollutants flow into streams river or main stresm located in environmental conservation area or water intake facilities, we must predict immediately arrival time and the diffusion concentration to the proactive. National Institute of Environmental Research developed water pollution incident response prediction system linking dam and movable weir. the system is mathematical model which is updated daily. Therefore it can quickly predict the arrival time and the diffusion concentration when there are accident of oil spills and hazardous chemicals. Also we equipped with mathematical model and toxicity model of EFDC(Environmental Fluid Dynamics Code) to calculate the arrival time and the diffusion concentration. However these systems offer the services of an offline manner than real-time control services. we have ensured the reliability of data collection and have developed a real-time water quality measurement data transmission device by using the data linkage utilizing a mode bus communication and a commercial SCADA system, in particular, we implemented to be able to do real-time water quality prediction through information infrastructure of the water quality integrated management business created by utilizing the construction of the real-time prediction system that utilizes the data collected, the Open map, the visual representation using charts API and development of integrated management system development based on web maps.

Computer Program을 이용한 화학물질의 환경동태 예측 (Prediction of Environmental Fate of Certain Chemicals Using Computer Simulation Programs)

  • 김균;김용화
    • 한국환경농학회지
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    • 제12권1호
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    • pp.69-80
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    • 1993
  • Environmental hazards of a chemical could be assessed by two different approaches : toxicity test and assessment of exposure potentials to human and environmental organisms. For the prediction of environmental fate of chemicals three available computer programs were compared each other and were verified. The results obtained by using these computer programs, PCHEM, EXAMS, and E4CHEM were summarized as follows. The estimated octanol/water partition coefficients by PCHEM were similar to the experimental values in the literature. But the other factors, water solubility and vapor pressure were different from the data in the literature. The simulation results of selected compounds by EXAMS showed similar tendency to the literature results of model field environment. Therefore, this computer program could be utilized to predict the environmental fate of chemicals. E4CHEM program is very simple and this program could predict the ultimate environmental fate of stable chemicals by input of two or three parameters. However, the validity should further be verified in the future field study using more compounds. It is suggested that these approaches could be fully utilized by understanding their limitations to predict the environmental fate of new chemicals under development, to screen the potential environmental pollutants among chemicals already-in use, and to devise measures to minimize the hazards to the environment.

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Comparing In Vitro and In Vivo Genomic Profiles Specific to Liver Toxicity Induced by Thioacetamide

  • Kang, Jin-Seok;Jeong, Youn-Kyoung;Shin, Ji-He;Suh, Soo-Kyung;Kim, Joo-Hwan;Lee, Eun-Mi;Kim, Seung-Hee;Park, Sue-Nie
    • Biomolecules & Therapeutics
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    • 제15권4호
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    • pp.252-260
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    • 2007
  • As it is needed to assay possible feasibility of extrapolation between in vivo and in vitro systems and to develop a new in vitro method for toxicity testing, we investigated global gene expression from both animal and cell line treated with thioacetamide (TAA) and compared between in vivo and in vitro genomic profiles. For in vivo study, mice were orally treated with TAA and sacrificed at 6 and 24 h. For in vitro study, TAA was administered to a mouse hepatic cell line, BNL CL.2 and sampling was carried out at 6 and 24 h. Hepatotoxicity was assessed by analyzing hepatic enzymes and histopathological examination (in vivo) or lactate dehydrogenase (LDH) assay and morphological examination (in vitro). Global gene expression was assessed using microarray. In high dose TAA-treated group, there was centrilobular necrosis (in vivo) and cellular toxicity with an elevation of LDH (in vitro) at 24 h. Statistical analysis of global gene expression identified that there were similar numbers of altered genes found between in vivo and in vitro at each time points. Pathway analysis identified several common pathways existed between in vivo and in vitro system such as glutathione metabolism, bile acid biosynthesis, nitrogen metabolism, butanoate metabolism for hepatotoxicty caused by TAA. Our results suggest it may be feasible to develop toxicogenomics biomarkers by comparing in vivo and in vitro genomic profiles specific to TAA for application to prediction of liver toxicity.

독성발현경로(Adverse Outcome Pathway)를 활용한 In Silico 예측기술 연구동향 분석 (Trend of In Silico Prediction Research Using Adverse Outcome Pathway)

  • 이수진;박종서;김선미;서명원
    • 한국환경보건학회지
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    • 제50권2호
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    • pp.113-124
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    • 2024
  • Background: The increasing need to minimize animal testing has sparked interest in alternative methods with more humane, cost-effective, and time-saving attributes. In particular, in silico-based computational toxicology is gaining prominence. Adverse outcome pathway (AOP) is a biological map depicting toxicological mechanisms, composed of molecular initiating events (MIEs), key events (KEs), and adverse outcomes (AOs). To understand toxicological mechanisms, predictive models are essential for AOP components in computational toxicology, including molecular structures. Objectives: This study reviewed the literature and investigated previous research cases related to AOP and in silico methodologies. We describe the results obtained from the analysis, including predictive techniques and approaches that can be used for future in silico-based alternative methods to animal testing using AOP. Methods: We analyzed in silico methods and databases used in the literature to identify trends in research on in silico prediction models. Results: We reviewed 26 studies related to AOP and in silico methodologies. The ToxCast/Tox21 database was commonly used for toxicity studies, and MIE was the most frequently used predictive factor among the AOP components. Machine learning was most widely used among prediction techniques, and various in silico methods, such as deep learning, molecular docking, and molecular dynamics, were also utilized. Conclusions: We analyzed the current research trends regarding in silico-based alternative methods for animal testing using AOPs. Developing predictive techniques that reflect toxicological mechanisms will be essential to replace animal testing with in silico methods. In the future, since the applicability of various predictive techniques is increasing, it will be necessary to continue monitoring the trend of predictive techniques and in silico-based approaches.

Human Tumor Xenograft Models for Preclinical Assessment of Anticancer Drug Development

  • Jung, Joohee
    • Toxicological Research
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    • 제30권1호
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    • pp.1-5
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    • 2014
  • Xenograft models of human cancer play an important role in the screening and evaluation of candidates for new anticancer agents. The models, which are derived from human tumor cell lines and are classified according to the transplant site, such as ectopic xenograft and orthotopic xenograft, are still utilized to evaluate therapeutic efficacy and toxicity. The metastasis model is modified for the evaluation and prediction of cancer progression. Recently, animal models are made from patient-derived tumor tissue. The patient-derived tumor xenograft models with physiological characters similar to those of patients have been established for personalized medicine. In the discovery of anticancer drugs, standard animal models save time and money and provide evidence to support clinical trials. The current strategy for using xenograft models as an informative tool is introduced.