• Environmental Analysis Health and Toxicology
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• 제30권sup호
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• pp.7.1-7.10
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• 2015

Objectives For successful adoption of legislation controlling registration and assessment of chemical substances, it is important to obtain sufficient toxicological experimental evidence and other related information. It is also essential to obtain a sufficient number of predicted risk and toxicity results. Particularly, methods used in predicting toxicities of chemical substances during acquisition of required data, ultimately become an economic method for future dealings with new substances. Although the need for such methods is gradually increasing, the-required information about reliability and applicability range has not been systematically provided. Methods There are various representative environmental and human toxicity models based on quantitative structure-activity relationships (QSAR). Here, we secured the 10 representative QSAR-based prediction models and its information that can make predictions about substances that are expected to be regulated. We used models that predict and confirm usability of the information expected to be collected and submitted according to the legislation. After collecting and evaluating each predictive model and relevant data, we prepared methods quantifying the scientific validity and reliability, which are essential conditions for using predictive models. Results We calculated predicted values for the models. Furthermore, we deduced and compared adequacies of the models using the Alternative non-testing method assessed for Registration, Evaluation, Authorization, and Restriction of Chemicals Substances scoring system, and deduced the applicability domains for each model. Additionally, we calculated and compared inclusion rates of substances expected to be regulated, to confirm the applicability. Conclusions We evaluated and compared the data, adequacy, and applicability of our selected QSAR-based toxicity prediction models, and included them in a database. Based on this data, we aimed to construct a system that can be used with predicted toxicity results. Furthermore, by presenting the suitability of individual predicted results, we aimed to provide a foundation that could be used in actual assessments and regulations.

• Tuberculosis and Respiratory Diseases
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• 제75권5호
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• pp.181-187
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• 2013

The emergence of new therapeutic agents for non-small cell lung cancer (NSCLC) implies that histologic subtyping and molecular predictive testing are now essential for therapeutic decisions. Histologic subtype predicts the efficacy and toxicity of some treatment agents, as do genetic alterations, which can be important predictive factors in treatment selection. Molecular markers, such as epidermal growth factor receptor (EGFR) mutation and anaplastic lymphoma kinase (ALK) rearrangement, are the best predictors of response to specific tyrosine kinase inhibitor treatment agents. As the majority of patients with NSCLC present with unresectable disease, it is therefore crucial to optimize the use of tissue samples for diagnostic and predictive examinations, particularly for small biopsy and cytology specimens. Therefore, each institution needs to develop a diagnostic approach requiring close communication between the pulmonologist, radiologist, pathologist, and oncologist in order to preserve sufficient biopsy materials for molecular analysis as well as to ensure rapid diagnosis. Currently, personalized medicine in NSCLC is based on the histologic subtype and molecular status. This review summarizes strategies for tissue acquisition, histologic subtyping and molecular analysis for predictive testing in NSCLC.

• Asian Pacific Journal of Cancer Prevention
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• 제13권12호
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• pp.6121-6128
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• 2012

Radiation-induced side effects on normal tissue are determined largely by the capacity of cells to repair radiation-induced DNA damage. X-ray repair cross-complementing group 1 (XRCC1) plays an important role in the repair of DNA single-strand breaks. Studies have shown conflicting results regarding the association between XRCC1 gene polymorphisms (Arg399Gln, Arg194Trp, -77T>C and Arg280His) and radiation-induced side effects in patients undergoing whole breast radiotherapy. Therefore, we conducted a meta-analysis to determine the predictive value of XRCC1 gene polymorphisms in this regard. Analysis of the 11 eligible studies comprising 2,199 cases showed that carriers of the XRCC1 399 Gln allele had a higher risk of radiation-induced toxicity than those with the 399 ArgArg genotype in studies based on high-quality genotyping methods [Gln vs. ArgArg: OR, 1.85; 95% CI, 1.20-2.86] or in studies with mixed treatment regimens of radiotherapy alone and in combination with chemotherapy [Gln vs. ArgArg: OR, 1.60; 95% CI, 1.09-2.23]. The XRCC1 Arg399Gln variant allele was associated with mixed acute and late adverse reactions when studies on late toxicity only were excluded [Gln allele vs. Arg allele: OR, 1.22; 95% CI, 1.00-1.49]. In contrast, the XRCC1 Arg280His variant allele was protective against radiation-induced toxicity in studies including patients treated by radiotherapy alone [His allele vs. Arg allele: OR, 0.58; 95% CI, 0.35-0.96]. Our results suggest that XRCC1 399Gln and XRCC1 280Arg may be independent predictors of radiation-induced toxicity in post-surgical breast cancer patients, and the selection of genotyping method is an important factor in determining risk factors. No evidence for any predictive value of XRCC1 Arg194Trp and XRCC1 -77T>C was found. So, larger and well-designed studies might be required to further evaluate the predictive value of XRCC1 gene variation on radiation-induced side effects in patients undergoing whole breast radiotherapy.

• Interdisciplinary Bio Central
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• 제1권3호
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• pp.10.1-10.5
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• 2009

Toxicity is usually investigated using a set of standardized animal-based studies which, unfortunately, fail to detect all compounds that induce human adverse events and do not provide detailed mechanistic information of observed toxicity. As an alternative to conventional toxicology, toxicogenomics takes advantage of currently advanced technologies in genomics, proteomics, metabolomics, and bioinformatics to gain a molecular level understanding of toxicity and to enhance the predictive power of toxicity testing in drug development and risk/safety assessment. In addition, there has been a renewed interest, particularly in various government agencies, to prioritize and/or supplement animal testing with a battery of mechanistically informative in vitro assays. This article provides a brief summary of the issues, challenges and lessons learned in these fields and discuss the ways forward to further advance toxicology using these technologies.

• 한국환경보건학회지
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• 제47권5호
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• pp.462-471
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• 2021

Background: The number of synthesized chemicals has rapidly increased over the past decade. For many chemicals, there is a lack of information on toxicity. With the current movement toward reducing animal testing, the use of toxicity big data and deep learning could be a promising tool to screen potential toxicants. Objectives: This study identified potential chemicals related to reproductive and estrogen receptor (ER)-mediated toxicities for 1135 cleaning products and 886 laundry products. Methods: We listed chemicals contained in cleaning and laundry products from a publicly available database. Then, chemicals that potentially exhibited reproductive and ER-mediated toxicities were identified using the European Union Classification, Labeling and Packaging classification and ToxCast database, respectively. For chemicals absent from the ToxCast database, ER activity was predicted using deep learning models. Results: Among the 783 listed chemicals, there were 53 with potential reproductive toxicity and 310 with potential ER-mediated toxicity. Among the 473 chemicals not tested with ToxCast assays, deep learning models indicated that 42 chemicals exhibited ER-mediated toxicity. A total of 13 chemicals were identified as causing reproductive toxicity by reacting with the ER. Conclusions: We demonstrated a screening method to identify potential chemicals related to reproductive and ER-mediated toxicities utilizing chemical toxicity big data and deep learning. Integrating toxicity data from in vivo, in vitro, and deep learning models may contribute to screening chemicals in consumer products.

• 한국환경보건학회지
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• 제48권3호
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• pp.159-166
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• 2022

Background: A quantitative structure-activity relationship (QSAR) model was adopted in the Registration, Evaluation, Authorization, and Restriction of Chemicals (REACH, EU) regulations as well as the Act on Registration, Evaluation, etc. of Chemicals (AREC, Republic of Korea). It has been previously used in the registration of chemicals. Objectives: In this study, we investigated the correlation between the predicted data provided by three prediction programs using a QSAR model and actual experimental results (acute fish, daphnia magna toxicity). Through this approach, we aimed to effectively conjecture on the performance and determine the most applicable programs when designating toxic substances through the AREC. Methods: Chemicals that had been registered and evaluated in the Toxic Chemicals Control Act (TCCA, Republic of Korea) were selected for this study. Two prediction programs developed and operated by the U.S. EPA - the Ecological Structure-Activity Relationship (ECOSAR) and Toxicity Estimation Software Tool (T.E.S.T.) models - were utilized along with the TOPKAT (Toxicity Prediction by Komputer Assisted Technology) commercial program. The applicability of these three programs was evaluated according to three parameters: accuracy, sensitivity, and specificity. Results: The prediction analysis on fish and daphnia magna in the three programs showed that the TOPKAT program had better sensitivity than the others. Conclusions: Although the predictive performance of the TOPKAT program when using a single predictive program was found to perform well in toxic substance designation, using a single program involves many restrictions. It is necessary to validate the reliability of predictions by utilizing multiple methods when applying the prediction program to the regulation of chemicals.

• 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.

• International Journal of Stem Cells
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• 제17권2호
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• pp.130-140
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• 2024

Cardiac organoids have emerged as invaluable tools for assessing the impact of diverse substances on heart function. This report introduces guidelines for general requirements for manufacturing cardiac organoids and conducting cardiac organoid-based assays, encompassing protocols, analytical methodologies, and ethical considerations. In the quest to employ recently developed three-dimensional cardiac organoid models as substitutes for animal testing, it becomes imperative to establish robust criteria for evaluating organoid quality and conducting toxicity assessments. This guideline addresses this need, catering to regulatory requirements, and describes common standards for organoid quality and toxicity assessment methodologies, commensurate with current technological capabilities. While acknowledging the dynamic nature of technological progress and the potential for future comparative studies, this guideline serves as a foundational framework. It offers a comprehensive approach to standardized cardiac organoid testing, ensuring scientific rigor, reproducibility, and ethical integrity in investigations of cardiotoxicity, particularly through the utilization of human pluripotent stem cell-derived cardiac organoids.

• International Journal of Stem Cells
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• 제17권2호
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• pp.120-129
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• 2024

Recent amendments to regulatory frameworks have placed a greater emphasis on the utilization of in vitro testing platforms for preclinical drug evaluations and toxicity assessments. This requires advanced tissue models capable of accurately replicating liver functions for drug efficacy and toxicity predictions. Liver organoids, derived from human cell sources, offer promise as a reliable platform for drug evaluation. However, there is a lack of standardized quality evaluation methods, which hinders their regulatory acceptance. This paper proposes comprehensive quality standards tailored for liver organoids, addressing cell source validation, organoid generation, and functional assessment. These guidelines aim to enhance reproducibility and accuracy in toxicity testing, thereby accelerating the adoption of organoids as a reliable alternative or complementary tool to animal testing in drug development. The quality standards include criteria for size, cellular composition, gene expression, and functional assays, thus ensuring a robust hepatotoxicity testing platform.

• 디지털콘텐츠학회 논문지
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• 제11권1호
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• pp.99-104
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• 2010

화학물질은 생체에 들어오면 여러 가지 독성반응을 나타내는데, 독성반응에 따른 유전자 발현을 분석하기 위해 바이오 칩 등을 이용한 신기술이 확산되면서 바이오 디지털 콘텐츠가 다량으로 생성되고 있다. 이 콘텐츠는 그 자체로는 의미가 적고 컴퓨터를 이용한 분석과 보정과정을 거쳐 생물학적으로 의미 있는 값들을 선별하여야 한다. 이런 콘텐츠에는 유전자들의 발현 양상 측정을 목적으로 하는 유전체학(genomics), 유전자의 발현 양상을 측정하는 전사체학(transcriptomics), 단백질의 발현을 측정하는 단백체학(proteomics), 대사체의 발현을 측정하는 대사체학(metabolomics) 등이 있으며, 이를 통칭하여 오믹스(omics)라고 부른다. 오믹스 기술을 독성을 연구하는 분야에 접목한 것이 독성유전체학(toxicogenomics)이며, 이에 대한 콘텐츠를 분석함으로써 독성을 예측하고 독성기전을 규명할 수 있다. 독성분석에 있어서 초기 단계의 분석은 향후 만성독성의 예측에 있어서 중요한 부분을 차지하고 있다. 바이오 디지털 콘텐츠를 이용하여 독성을 예측함에 있어 기존의 방법보다 더 빠르고 정확하게 예측하기 위해서는 많은 정보에 대한 분석기술의 진보가 필요하다. 또, 바이오 디지털 콘텐츠를 이용한 독성예측에 있어서 전체세포보다는 생물학적 현상을 일으키는 특이세포에서 이런 정보를 얻는 것이 중요하다고 생각된다. 또, 향후 바이오 디지털 콘텐츠 분석은 전략적 실험설계에 의한 데이터가 분석되고 축적되어야 하고, 분석알고리즘을 통한 네트워크 분석이 이루어져야 하며, 통합적 데이터 구축을 통해 이루어져야 할 것으로 생각된다.