Computational Toxicology最新文献

{"title":"Toxicity prediction using locality-sensitive deep learner","authors":"Xiu Huan Yap ,&nbsp;Michael Raymer","doi":"10.1016/j.comtox.2021.100210","DOIUrl":"10.1016/j.comtox.2021.100210","url":null,"abstract":"<div><p>Toxicity prediction using linear QSAR models typically show good predictivity when trained on a small-scale, local level of similar chemicals, but not on a global level spanning a chemical library. We hypothesize that large chemical toxicity datasets generally have a <em>locally-linear data</em> structure, and propose the <em>locality-sensitive deep learner</em> (LSDL), a deep neural network with attention mechanism <span>[1]</span> and an optional instance-based feature weighting component, to tackle the challenges of heterogeneous classification space with locally-varying noise features. On carefully-constructed synthetic data with extremely unbalanced classes (10% positive), the locality-sensitive deep learner with learned feature weights retained high test performance (AUC &gt; 0.9) in the presence of 60% cluster-specific feature noise, while feed-forward neural network appeared to over-fit the data (AUC &lt; 0.6). For the Tox21 dataset <span>[2]</span>, locality-sensitive deep learner out-performed feed-forward neural network in 9 out of 12 labels. For acetylcholinesterase inhibition (AChEi) <span>[3]</span>, Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) <span>[4]</span>, and Acute Oral Toxicity (AOT) <span>[5]</span> datasets, we observed that the combination of locality-sensitive deep learner with feed-forward neural network showed improved test performance than individual models in almost all cases. Generalizing machine learning models to fit locally-linear data may potentially improve predictivity of chemical toxicity models. The proposed modeling approach could potentially complement and add diversity to the current suite of predictive toxicity algorithms for use in ensemble and/or consensus models.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100210"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47254022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of mammalian maximal rates of metabolism and Michaelis constants for industrial and environmental compounds: Revisiting four quantitative structure activity relationship (QSAR) publications","authors":"Lisa M. Sweeney ,&nbsp;Teresa R. Sterner","doi":"10.1016/j.comtox.2022.100214","DOIUrl":"10.1016/j.comtox.2022.100214","url":null,"abstract":"<div><p>Traditional in vivo strategies for investigating toxicokinetics can be time consuming, expensive, and often do not directly address species of interest, e.g., humans. As such, conventional approaches for addressing emerging human health risk assessment concerns that rely on toxicokinetic information have been slow and suboptimal. Alternatives to rodent in vivo toxicokinetic studies include in vitro and in silico approaches for estimating toxicokinetic parameters. This paper focuses on quantitative structure-activity relationships (QSARs) that predict both maximal capacity for metabolism (Vmax) and KM (Michaelis constant, or half-maximal concentration for metabolism). The QSARs, identified from four publications, were evaluated using a previously published 10-step work flow. None of the evaluated QSARs in their published forms could be fully validated. Literature review, finding alternative sources of descriptors and identifiers, substitution of correlated descriptors, and use of graphical information allowed the deficiencies to be addressed for QSARs describing alkylbenzenes, volatile organic compounds (VOCs), and substrates of alcohol dehydrogenase (ADH), aldehyde dehydrogenase (ALDH), cytochrome P450 (CYP), and flavin containing monooxygenases (FMO). Ultimately, reliable, well-documented, updated expressions for Vmax and KM (or Vmax/KM) were derived for each source/data set. The smaller data sets tended to have better predictivity, and Vmax was generally more accurately predicted than KM. Comparisons of the QSARs’ source chemicals found limited overlap in source chemicals, but substantial overlap in descriptor domains. In a feasibility case study, applicability of these QSARs to jet fuel components with limited toxicokinetic parameterization was assessed to determine the potential utility for investigation of mixture toxicokinetics. The VOC QSARs and alkylbenzene QSARs were identified as having greater potential to accurately predict in vivo toxicokinetics of the selected jet fuel components than the CYP QSARs, due to the physicochemical characteristics of the chemicals used in their development.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100214"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45275502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Towards a qAOP framework for predictive toxicology - Linking data to decisions","authors":"Alicia Paini ,&nbsp;Ivana Campia ,&nbsp;Mark T.D. Cronin ,&nbsp;David Asturiol ,&nbsp;Lidia Ceriani ,&nbsp;Thomas E. Exner ,&nbsp;Wang Gao ,&nbsp;Caroline Gomes ,&nbsp;Johannes Kruisselbrink ,&nbsp;Marvin Martens ,&nbsp;M.E. Bette Meek ,&nbsp;David Pamies ,&nbsp;Julia Pletz ,&nbsp;Stefan Scholz ,&nbsp;Andreas Schüttler ,&nbsp;Nicoleta Spînu ,&nbsp;Daniel L. Villeneuve ,&nbsp;Clemens Wittwehr ,&nbsp;Andrew Worth ,&nbsp;Mirjam Luijten","doi":"10.1016/j.comtox.2021.100195","DOIUrl":"10.1016/j.comtox.2021.100195","url":null,"abstract":"<div><p>The adverse outcome pathway (AOP) is a conceptual construct that facilitates organisation and interpretation of mechanistic data representing multiple biological levels and deriving from a range of methodological approaches including <em>in silico</em>, <em>in vitro</em> and <em>in vivo</em> assays. AOPs are playing an increasingly important role in the chemical safety assessment paradigm and quantification of AOPs is an important step towards a more reliable prediction of chemically induced adverse effects. Modelling methodologies require the identification, extraction and use of reliable data and information to support the inclusion of quantitative considerations in AOP development. An extensive and growing range of digital resources are available to support the modelling of quantitative AOPs, providing a wide range of information, but also requiring guidance for their practical application. A framework for qAOP development is proposed based on feedback from a group of experts and three qAOP case studies. The proposed framework provides a harmonised approach for both regulators and scientists working in this area.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100195"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8850654/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39959705","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic modelling of developmental neurotoxicity based on a simplified adverse outcome pathway network","authors":"Nicoleta Spînu ,&nbsp;Mark T.D. Cronin ,&nbsp;Junpeng Lao ,&nbsp;Anna Bal-Price ,&nbsp;Ivana Campia ,&nbsp;Steven J. Enoch ,&nbsp;Judith C. Madden ,&nbsp;Liadys Mora Lagares ,&nbsp;Marjana Novič ,&nbsp;David Pamies ,&nbsp;Stefan Scholz ,&nbsp;Daniel L. Villeneuve ,&nbsp;Andrew P. Worth","doi":"10.1016/j.comtox.2021.100206","DOIUrl":"10.1016/j.comtox.2021.100206","url":null,"abstract":"<div><p>In a century where toxicology and chemical risk assessment are embracing alternative methods to animal testing, there is an opportunity to understand the causal factors of neurodevelopmental disorders such as learning and memory disabilities in children, as a foundation to predict adverse effects. New testing paradigms, along with the advances in probabilistic modelling, can help with the formulation of mechanistically-driven hypotheses on how exposure to environmental chemicals could potentially lead to developmental neurotoxicity (DNT). This investigation aimed to develop a Bayesian hierarchical model of a simplified AOP network for DNT. The model predicted the probability that a compound induces each of three selected common key events (CKEs) of the simplified AOP network and the adverse outcome (AO) of DNT, taking into account correlations and causal relations informed by the key event relationships (KERs). A dataset of 88 compounds representing pharmaceuticals, industrial chemicals and pesticides was compiled including physicochemical properties as well as <em>in silico</em> and <em>in vitro</em> information. The Bayesian model was able to predict DNT potential with an accuracy of 76%, classifying the compounds into low, medium or high probability classes. The modelling workflow achieved three further goals: it dealt with missing values; accommodated unbalanced and correlated data; and followed the structure of a directed acyclic graph (DAG) to simulate the simplified AOP network. Overall, the model demonstrated the utility of Bayesian hierarchical modelling for the development of quantitative AOP (qAOP) models and for informing the use of new approach methodologies (NAMs) in chemical risk assessment.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100206"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8857173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39959706","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Will qAOPs modernise toxicology?","authors":"Mark T.D. Cronin ,&nbsp;Nicoleta Spînu ,&nbsp;Andrew P. Worth","doi":"10.1016/j.comtox.2021.100199","DOIUrl":"10.1016/j.comtox.2021.100199","url":null,"abstract":"<div><p>In this editorial we reflect on the past decade of developments in predictive toxicology, and in particular on the evolution of the Adverse Outcome Pathway (AOP) paradigm. Starting out as a concept, AOPs have become the focal point of a community of scientists, regulators and decision-makers. AOPs provide the mechanistic knowledge underpinning the development of Integrated Approaches to Testing and Assessment (IATA), including computational models now referred to as quantitative AOPs (qAOPs). With reference to recent and related works on qAOPs, we take a brief historical perspective and ask what is the next stage in modernising chemical toxicology beyond animal testing.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100199"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111321000451/pdfft?md5=0927eedbfc1ef0325eeb1d7b1bf62ec9&pid=1-s2.0-S2468111321000451-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43063019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ALOHA: Aggregated local extrema splines for high-throughput dose–response analysis","authors":"Sarah E. Davidson ,&nbsp;Matthew W. Wheeler ,&nbsp;Scott S. Auerbach ,&nbsp;Siva Sivaganesan ,&nbsp;Mario Medvedovic","doi":"10.1016/j.comtox.2021.100196","DOIUrl":"10.1016/j.comtox.2021.100196","url":null,"abstract":"<div><p><span>Computational methods for genomic dose–response integrate dose–response modeling with bioinformatics tools to evaluate changes in molecular and cellular functions related to pathogenic processes. These methods use parametric models to describe each gene’s dose–response, but such models may not adequately capture expression changes. Additionally, current approaches do not consider gene co-expression networks. When assessing co-expression networks, one typically does not consider the dose–response relationship, resulting in ‘co-regulated’ gene sets containing genes having different dose–response patterns. To avoid these limitations, we develop an analysis pipeline called Aggregated Local Extrema Splines for High-throughput Analysis (ALOHA), which computes individual genomic dose–response functions using a flexible class </span>Bayesian<span> shape constrained splines and clusters gene co-regulation based upon these fits. Using splines, we reduce information loss due to parametric lack-of-fit issues, and because we cluster on dose–response relationships, we better identify co-regulation clusters for genes that have co-expressed dose–response patterns from chemical exposure. The clustered pathways can then be used to estimate a dose associated with a pre-specified biological response, i.e., the benchmark dose (BMD), and approximate a point of departure dose corresponding to minimal adverse response in the whole tissue/organism. We compare our approach to current parametric methods and our biologically enriched gene sets to cluster on normalized expression data. Using this methodology, we can more effectively extract the underlying structure leading to more cohesive estimates of gene set potency.</span></p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100196"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10597137","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development of a QSAR model to predict comedogenic potential of some cosmetic ingredients","authors":"Sebla Oztan Akturk,&nbsp;Gulcin Tugcu,&nbsp;Hande Sipahi","doi":"10.1016/j.comtox.2021.100207","DOIUrl":"10.1016/j.comtox.2021.100207","url":null,"abstract":"<div><p>Comedogenicity is a common adverse reaction to cosmetic ingredients that cause blackheads or pimples by blocking the pores, especially for acne-prone skin. Before animal testing was banned by European Commission in 2013, comedogenic potential of cosmetics were tested on rabbits. However, full replacement of animal tests by alternatives has not been possible yet. Therefore, there is a need for applying new approach methodologies. In this study, we aimed to develop a QSAR model to predict comedogenic potential of cosmetic ingredients by using different machine learning algorithms and types of molecular descriptors.</p><p>The dataset consists of 121 cosmetic ingredients including such as fatty acids, fatty alcohols and their derivatives and pigments tested on rabbit ears was obtained from the literature. 4837 molecular descriptors were calculated via various software. Different machine learning classification algorithms were used in the modelling studies with WEKA software. The model performance was evaluated by using 10-fold cross validation. All models were compared by the means of classification accuracy, area under the ROC curve, area under the precision-recall curve, MCC, F score, kappa statistic, sensitivity, specificity and the best model was chosen accordingly. The QSAR modelling results for two models are promising for comedogenicity prediction. The random forest models by the means of Mold2 and alvaDesc descriptors gave the successful results with 85.87% and 84.87% accuracy for the cross-validated models and 75.86% and 79.31% accuracy for the test sets. In conclusion, this study is the first step in terms of comedogenicity prediction. In the near future, advances in <em>in silico</em> modelling studies will provide us non-animal based alternative models by regarding animal rights and ethical issues for the safety evaluation of cosmetics.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100207"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42179593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthesis and characterization of novel thiazole derivatives as potential anticancer agents: Molecular docking and DFT studies","authors":"R. Raveesha ,&nbsp;A.M. Anusuya ,&nbsp;A.V. Raghu ,&nbsp;K. Yogesh Kumar ,&nbsp;M.G. Dileep Kumar ,&nbsp;S.B. Benaka Prasad ,&nbsp;M.K. Prashanth","doi":"10.1016/j.comtox.2021.100202","DOIUrl":"10.1016/j.comtox.2021.100202","url":null,"abstract":"<div><p>New thiazole derivatives (2a-l) were synthesized via the reaction of 2-(3-cyano-4-isobutoxyphenyl)-4-methylthiazole-5-carboxylic acid with substituted phenyl amines. The anticancer activity of the synthesized thiazole derivatives was examined against MCF-7 (human breast), MDA-MB-231 (mammary carcinomas), HeLa (Cervical cancer), HT-29, HCT 116 (Colon cancer), and normal chang liver cancer cell lines, whereas cisplatin was employed as a positive control. The anticancer mechanisms were studied via apoptosis assessments, as well as molecular docking. The molecular docking study of potent compounds was carried out against the human epidermal growth factor receptor (HER2, PDB ID: 3RCD) as a possible target for anticancer activity using Auto Dock vina. ADMET results indicated that tested compounds have significant results within the close agreement of Lipinski’s rule of five. In addition, computational work employing density functional theory (DFT) was also carried out at the B3LYP/6-31G (d,p) level to investigate the electronic properties of the potent compounds. The frontier molecular orbital energy and atomic net charges were discussed.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100202"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43976194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A review of in silico toxicology approaches to support the safety assessment of cosmetics-related materials","authors":"Mark T.D. Cronin ,&nbsp;Steven J. Enoch ,&nbsp;Judith C. Madden ,&nbsp;James F. Rathman ,&nbsp;Andrea-Nicole Richarz ,&nbsp;Chihae Yang","doi":"10.1016/j.comtox.2022.100213","DOIUrl":"10.1016/j.comtox.2022.100213","url":null,"abstract":"<div><p><em>In silico</em> tools and resources are now used commonly in toxicology and to support the “Next Generation Risk Assessment” (NGRA) of cosmetics ingredients or materials. This review provides an overview of the approaches that are applied to assess the exposure and hazard of a cosmetic ingredient. For both hazard and exposure, databases of existing information are used routinely. In addition, for exposure, <em>in silico</em> approaches include the use of rules of thumb for systemic bioavailability as well as physiologically-based kinetics (PBK) and multi-scale models for estimating internal exposure at the organ or tissue level. (Internal) Thresholds of Toxicological Concern are applicable for the safety assessment of ingredients at low concentrations. The use of structural rules, (Quantitative) Structure-Activity Relationships ((Q)SARs) and read-across are the most typically applied modelling approaches to predict hazard. Data from exposure and hazard assessment are increasingly being brought together in NGRA to provide an overall assessment of the safety of a cosmetic ingredient. All <em>in silico</em> approaches are reviewed in terms of their maturity and robustness for use.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100213"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111322000019/pdfft?md5=4400391120a66de64203858cd49f172c&pid=1-s2.0-S2468111322000019-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46760793","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative Structure-Activity Relationship (QSAR) modeling to predict the transfer of environmental chemicals across the placenta","authors":"Laura Lévêque ,&nbsp;Nadia Tahiri ,&nbsp;Michael-Rock Goldsmith ,&nbsp;Marc-André Verner","doi":"10.1016/j.comtox.2021.100211","DOIUrl":"https://doi.org/10.1016/j.comtox.2021.100211","url":null,"abstract":"<div><p>The increasing diversity of environmental chemicals in the environment, some of which may be developmental toxicants, is a public health concern. The aim of this work was to contribute to the development of rapid and effective methods to assess prenatal exposure. Quantitative structure–activity relationships (QSAR) modeling has emerged as a promising method in the development of a predictive model for the placental transfer of contaminants. Cord to maternal plasma or serum concentration ratios for 105 chemicals were extracted from the literature, and 214 molecular descriptors were generated for each of these chemicals. Ten predictive models were built using Molecular Operating Environment (MOE) software, and the Python and R programming languages. Training and test datasets were used, respectively, to build and validate the models. The Applicability Domain Tool v1.0 was used to determine the applicability domain. Models developed with the partial least squares regression method in MOE and SuperLearner in R showed the best precision and predictivity, with internal coefficients of determination (R²) of 0.88 and 0.82, cross-validated R²s of 0.72 and 0.57, and external R²s of 0.73 and 0.74, respectively. All test chemicals were within the domain of applicability. The results obtained in this study suggest that QSAR modeling can help estimate the placental transfer of environmental chemicals.</p></div>","PeriodicalId":37651,"journal":{"name":"Computational Toxicology","volume":"21 ","pages":"Article 100211"},"PeriodicalIF":0.0,"publicationDate":"2022-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2468111321000578/pdfft?md5=4c8c23c2a121692de4fd42f72e6c133d&pid=1-s2.0-S2468111321000578-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138135682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}