OpenTox predictive toxicology framework: toxicological ontology and semantic media wiki-based OpenToxipedia

<p>Abstract</p> <p>Background</p> <p>The OpenTox Framework, developed by the partners in the OpenTox project (<url>http://www.opentox.org</url>), aims at providing a unified access to toxicity data, predictive models and validation procedures. Interoperability of resources is achieved using a common information model, based on the OpenTox ontologies, describing predictive algorithms, models and toxicity data. As toxicological data may come from different, heterogeneous sources, a deployed ontology, unifying the terminology and the resources, is critical for the rational and reliable organization of the data, and its automatic processing.</p> <p>Results</p> <p>The following related ontologies have been developed for OpenTox: a) Toxicological ontology – listing the toxicological endpoints; b) Organs system and Effects ontology – addressing organs, targets/examinations and effects observed in <it>in vivo</it> studies; c) ToxML ontology – representing semi-automatic conversion of the ToxML schema; d) OpenTox ontology– representation of OpenTox framework components: chemical compounds, datasets, types of algorithms, models and validation web services; e) ToxLink–ToxCast assays ontology and f) OpenToxipedia community knowledge resource on toxicology terminology.</p> <p>OpenTox components are made available through standardized REST web services, where every compound, data set, and predictive method has a unique resolvable address (URI), used to retrieve its Resource Description Framework (RDF) representation, or to initiate the associated calculations and generate new RDF-based resources.</p> <p>The services support the integration of toxicity and chemical data from various sources, the generation and validation of computer models for toxic effects, seamless integration of new algorithms and scientifically sound validation routines and provide a flexible framework, which allows building arbitrary number of applications, tailored to solving different problems by end users (e.g. toxicologists).</p> <p>Availability</p> <p>The OpenTox toxicological ontology projects may be accessed via the OpenTox ontology development page <url>http://www.opentox.org/dev/ontology</url>; the OpenTox ontology is available as OWL at <url>http://opentox.org/api/1 1/opentox.owl</url>, the ToxML - OWL conversion utility is an open source resource available at <url>http://ambit.svn.sourceforge.net/viewvc/ambit/branches/toxml-utils/</url></p

Threshold of Toxicological Concern - an update for non-genotoxic carcinogens

The Threshold of Toxicological Concern (TTC) concept can be applied to organic compounds with known chemical structure to derive a threshold for exposure below which a toxic effect on human health by the compound is not expected. The TTC concept distinguishes between carcinogens that may act as genotoxic and non-genotoxic compounds. A positive prediction of a genotoxic mode of action, either by structural alerts or experimental data, leads to the application of the threshold value for genotoxic compounds. Non-genotoxic substances are assigned to the TTC value of their respective Cramer class even though it is recognized that they could test positive in a rodent cancer bioassay. This study investigated the applicability of the Cramer classes specifically to provide adequate protection for non-genotoxic carcinogens. For this purpose, benchmark dose levels based on tumour incidence were compared with no observed effect levels (NOEL) derived from non-, pre- or neoplastic lesions. One key aspect was the categorization of compounds as non-genotoxic carcinogens. The recently finished CEFIC LRI project B18 classified the carcinogens of the CPDB as either non- or genotoxic compounds based on experimental or in silico data. A detailed consistency check resulted in a data set of 137 non-genotoxic organic compounds. For these 137 compounds, NOEL values were derived from high quality animal studies with oral exposure and chronic duration using well known repositories including RepDose, ToxRef and COSMOS DB. Further, an effective tumour dose (ETD10) was calculated and compared to the lower confidence limit on benchmark dose levels (BMDL10) derived by model averaging. Comparative analysis of NOEL/EDT10/BMDL10 values showed that potentially bioaccumulative compounds in humans, as well as steroids, which both belong to the exclusion categories, occur predominantly in region of the 5th percentiles of the distributions. Excluding these 25 compounds resulted in significantly higher, but comparable 5th percentile chronic NOEL and BMDL10 values, while the 5th percentile EDT10 value was slightly higher, but not statistically significant. The comparison of the obtained distributions of NOELs with the existing Cramer classes and their derived TTC values supports the application of Cramer class thresholds to all non genotoxic compounds, including non_genotoxic carcinogens

Re-evaluation of neohesperidine dihydrochalcone (E 959) as a food additive

The present opinion deals with the re-evaluation of neohesperidine dihydrochalcone (E 959) when used as a food additive. It is obtained by catalytic hydrogenation of a flavanone - neohesperidine - which is naturally occurring and thus isolated by alcohol extraction in bitter oranges (Citrus aurantium). Based on in vivo data in rat, neohesperidine dihydrochalcone is likely to be absorbed, also in humans, and to become systemically available. It does not raise a concern regarding genotoxicity. The toxicity data set consisted of studies on subchronic and prenatal developmental toxicity. No human studies were available. The data set was considered sufficient to derive a new acceptable daily intake (ADI). Based on the weight of evidence (WoE) analysis, the Panel considered unlikely that neohesperidine dihydrochalcone would lead to adverse effects on health in animals in the dose ranges tested. The Panel also considered that a carcinogenicity study was not warranted and that the lack of human data did not affect the overall confidence in the body of evidence. The Panel derived an ADI of 20 mg/kg bodyweight (bw) per day based on a no observed adverse effect level (NOAEL) of 4,000 mg/kg bw per day from a 13-week study in rat, applying the standard default factors of 100 for inter- and intraspecies differences and of 2 for extrapolation from subchronic to chronic exposure. For the refined brand-loyal exposure assessment scenario, considered to be the most appropriate for the risk assessment, the exposure estimates at the mean ranged from < 0.01 to 0.09 mg/kg bw per day and at the 95th percentile (P95) from 0.01 to 0.24 mg/kg bw per day. Considering the derived ADI of 20 mg/kg bw per day, the exposure estimates were below the reference value in all age groups. Therefore, the Panel concluded that dietary exposure to the food additive neohesperidine dihydrochalcone (E 959) at the reported uses and use levels would not raise a safety concern

FeDTex database - a versatile tool condensing data from developmental and reproductive toxicity studies

Studies on developmental and reproductive toxicity are the major cost factor and the major consumer of animals in toxicity testing. Thus, these studies are in contrast to the 3R-principle (Reduction, Refinement and Replacement of animal testing) and there is great necessity to develop new alternative in vitro and in silico methods. Existing animal data can be integrated in this process by identifying critical targets and by validation of alternative methods covering diverse adverse outcome pathways. The Fertility and Developmental Toxicity in experimental animals database (FeDTex DB) developed by Fraunhofer ITEM focusses on developmental and reproductive toxicity studies. Currently, 269 chemicals are covered with 535 studies conducted in rodents and rabbits during the last three decades. FeDTex DB is structured into three major parts: reference, study design and toxicological data. Unique tick-sheets and pick-lists were implemented for a multitude of subcategories to ensure consistent and precise database entries. The study design covers the general study data including test substance, study type, species used including strain, sex and number of animals per dose group, exposure including dosage, route of application and duration and sacrifice. Notably, a documentation of the scope of examination is also integrated in the study design to allow a matching with the toxicological effects. This is important as FeDTex DB contains studies following international guidelines as well as non-guideline studies with varying examinations. (Adverse) effects are assigned to the corresponding generation (i.e. F0, F1, F2 or F3), developmental stage (i.e. prenatal, postnatal or adult), and target/organ. Every single effect is finally characterised by a specific LOEL for the affected sex, examinations without effect result in a related NOEL. In order to determine the added value of the F2 generation in reproductive toxicity studies, NOELs of the F0, F1 and F2 generation have been compared. Further, a considerable chemical overlap with the Fraunhofer Repeated Dose Toxicity Database (RepDose DB) allows combined analysis of reproductive toxicity and repeated dose toxicity data

Evaluation of the environmental risk assessment procedure according to Directive 2001/18/EC for Gene Modified Organism used as medicinal products

The deliberate release of genetically modified organisms (GMOs) including GMOs used as medicinal products, e.g. gene therapies, into the environment is regulated by directive 2001/18/EC of the European Parliament and of the Council of 12 March2001. An integral part of the directive regulates the provision of information on the GMO and, based on this, the risk management with regard to the environmental effects of such releases. As regulated by this directive, a publicly accessible database is the "GMO Register" of the JOINT RESEARCH CENTER of the EC (http://gmoinfo.jrc.ec.europa.eu/Default.aspx), which contains information about all releases under the guideline. As of 07.11.2016, there were 238 entries of medicinal GMOs” in the "Summary Notification Information Format (SNIF).SNIFs are prepared as a summary document of the confidential environmental risk assessments (ERA) by the respective Sponsors of clinical trials in the EU and evaluated during the clinical trial application by the national competent authorities. They comprise, inter alia, information regarding the GMOs and the parental organism’s nature, release, environmental interactions, monitoring, waste treatment and emergency response plans. We strived to assess information concerning the environmental risk, derived measures and the overall standard of SNIFs concerning compliance with the regulatory requirements. To do so, we picked a homogeneous group of GMOs, namely gene modified Adenovirus, the most frequently used vector in gene therapy trials worldwide. Relevant information were entered into a database and categorized, applying unified vocabulary. Different challenges regarding the information available within the SNIFs were identified by analyzing the database: in several cases mandatory information was not available, e.g. monitoring plans, and in other cases the SNIF documents were misinterpreted, e.g. the connection between replication, dissemination and survivability was interpreted heterogeneously. Although this analysis has been performed using only Adenovirus data, information gaps and inconsistencies are transferable to other species as well. Consequently, it is proposed to specify some parts of the SNIFs in order to make more reliable information transparently available

Sensitivity of different generations and developmental stagesin studies on reproductive toxicity

Numerous studies on reproductive toxicity are expected to be necessary under the EU program on Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH). Therefore, it is important to analyse existing testing strategies including also the recently implemented extended one-generation reproduction toxicity study (EOGRTS, OECD guideline 443). For this purpose the responsiveness of the different generations and developmental stages in studies on reproductive toxicity is analysed and critical targets of reproductive toxicity are identified by using the Fraunhofer FeDTex database. The F1 generation is identified as most responsive generation in more than 50% of one-generation and multi-generation reproduction studies. Within the F1 generation the adult stage is mostly affected compared to the prenatal or postnatal stage. The target analysis in F1 has revealed alterations in bodyweight as highly sensitive for all developmental stages. Other important targets are the liver, kidney, testes, prostate, sperm parameters as well as developmental landmarks. The findings in the F2 generation have shown a higher responsiveness than F1 only in 3% of the studies. Although in 29 studies new effects are observed in F2 offspring compared to F1 irrespective of dose levels, overall no severe new effects have emerged that would change classification and labelling and justify an F1 mating. The presented data support the importance of F1 for risk assessment and demonstrate that the study design of the EOGRTS is a suitable alternative to two-generation studies. However, compared to a conventional one-generation study the EOGRTS may identify additional effects but will change risk assessment with respect to NOELs only in rare cases

Development of chemical categories by optimized clustering strategies

According to OECD a chemical category is a group of chemicals whose physicochemical and human health and/or ecotoxicological properties are likely to be similar or follow a regular pattern. The building of categories has often been tried on the basis of conventional structure based approaches. In the present project we developed an approach by which toxicological and structural properties likewise contribute to the building of chemical categories for (sub)chronic toxicity. Two databases on repeated-dose toxicity (RepDose and the "ELINCS" data base) served as data basis. The toxicological data are organized into organ toxicity split into subgroups according to phenotypic and mechanistic observations. For the definition of a category, the following characteristics were considered: organ investigated, effects, no effects; potency in terms of no observed adverse effect level (NOAEL), organ specificity. A multi-label clustering by using predictive clustering trees (PCT) was established. Several decisions concerning structural features and chemicals properties as well as the toxicological data had to be considered during development: - the selection of features and their SMARTS description - the non-use of PC parameters - imputation methods for missing values - the level of detail for a consistency of toxicological data versus data density in the matrix. All resulting category clusters were visualized and checked for plausibility. An important decision about a stop criterion for clustering was the use of toxicological variance data in combination with statistical significance. In the process of developing this approach we needed many incremental improvements; the final approach shows a set of useful and representative clusters now. This project was funded by BMBF

Innovative strategies to develop chemical categories using a combination of structural and toxicological properties

Interest is increasing in the development of non-animal methods for toxicological evaluations. These methods are however, particularly challenging for complex toxicological endpoints such as repeated dose toxicity. European Legislation, e.g. the European Union´s Cosmetic Directive and REACH, demands the use of alternative methods. Frameworks, such as the Read-across Assessment Framework or the Adverse Outcome Pathway Knowledge Base, support the development of these methods. The aim of the project presented in this publication was to develop substance categories for a read-across with complex endpoints of toxicity based on existing databases. The basic conceptual approach was to combine structural similarity with shared mechanisms of action. Substances with similar chemical structure and toxicological profile form candidate categories suitable for read-across. We combined two databases on repeated dose toxicity, RepDose database and ELINCS database to form a common database for the identification of categories. The resulting database contained physicochemical, structural and toxicological data, which were refined and curated for cluster analyses. We applied the Predictive Clustering Tree (PCT) approach for clustering chemicals based on structural and on toxicological information to detect groups of chemicals with similar toxic profiles and pathways/mechanisms of toxicity. As many of the experimental toxicity values were not available, this data was imputed by predicting them with a multi-label classification method, prior to clustering. The clustering results were evaluated by assessing chemical and toxicological similarities with the aim of identifying clusters with a concordance between structural information and toxicity profiles/mechanisms. From these chosen clusters, seven were selected for a quantitative read-across, based on a small ratio of NOAEL of the members with the highest and the lowest NOAEL in the cluster (<5). We discuss the limitations of the approach. Based on this analysis we propose improvements for a follow-up approach, such as incorporation of metabolic information and more detailed mechanistic information. The software enables the user to allocate a substance in a cluster and to use this information for a possible read- across. The clustering tool is provided as a free web service, accessible at http://mlc-reach.informatik.uni-mainz.de