Assessing relative bioactivity of chemical substances using quantitative molecular network topology analysis

Structurally different chemical substances may cause similar systemic effects in mammalian cells. It is therefore necessary to go beyond structural comparisons to quantify similarity in terms of their bioactivities. In this work, we introduce a generic methodology to achieve this on the basis of Network Biology principles and using publicly available molecular network topology information. An implementation of this method, denoted QuantMap, is outlined and applied to antidiabetic drugs, NSAIDs, 17β-estradiol, and 12 substances known to disrupt estrogenic pathways. The similarity of any pair of compounds is derived from topological comparison of intracellular protein networks, directly and indirectly associated with the respective query chemicals, via a straightforward pairwise comparison of ranked proteins. Although output derived from straightforward chemical/structural similarity analysis provided some guidance on bioactivity, QuantMap produced substance interrelationships that align well with reports on their respective perturbation properties. We believe that QuantMap has potential to provide substantial assistance to drug repositioning, pharmacology evaluation, and toxicology risk assessment.     

Assessing toxicological data quality: basic principles,existing schemes and current limitations

Existing toxicological data may be used for a variety of purposes such as hazard and risk assessment or toxicity prediction. The potential use of such data is, in part, dependent upon their quality. Consideration of data quality is of key importance with respect to the application of chemicals legislation such as REACH. Whether data are being used to make regulatory decisions or build computational models, the quality of the output is reflected by the quality of the data employed. Therefore, the need to assess data quality is an important requirement for making a decision or prediction with an appropriate level of confidence. This study considers the biological and chemical factors that may impact upon toxicological data quality and discusses the assessment of data quality. Four general quality criteria are introduced and existing data quality assessment schemes are discussed. Two case study datasets of skin sensitization data are assessed for quality providing a comparison of existing assessment methods. This study also discusses the limitations and difficulties encountered during quality assessment, including the use of differing quality schemes and the global versus chemical-specific assessments of quality. Finally, a number of recommendations are made to aid future data quality assessments.     

Assessing toxicological data quality: basic principles, existing schemes and current limitations

Existing toxicological data may be used for a variety of purposes such as hazard and risk assessment or toxicity prediction. The potential use of such data is, in part, dependent upon their quality. Consideration of data quality is of key importance with respect to the application of chemicals legislation such as REACH. Whether data are being used to make regulatory decisions or build computational models, the quality of the output is reflected by the quality of the data employed. Therefore, the need to assess data quality is an important requirement for making a decision or prediction with an appropriate level of confidence. This study considers the biological and chemical factors that may impact upon toxicological data quality and discusses the assessment of data quality. Four general quality criteria are introduced and existing data quality assessment schemes are discussed. Two case study datasets of skin sensitization data are assessed for quality providing a comparison of existing assessment methods. This study also discusses the limitations and difficulties encountered during quality assessment, including the use of differing quality schemes and the global versus chemical-specific assessments of quality. Finally, a number of recommendations are made to aid future data quality assessments.     

Evaluation of SARs for the prediction of eye irritation/corrosion potential: structural inclusion rules in the BfR decision support system

The proposed REACH regulation within the European Union (EU) aims to minimise the number of laboratory animals used for human hazard and risk assessment while ensuring adequate protection of human health and the environment. One way to achieve this goal is to develop non-testing methods, such as (quantitative) structure-activity relationships ([Q]SARs), suitable for identifying toxicological hazard from chemical structure and physicochemical properties alone. A database containing data submitted within the EU New Chemicals Notification procedure was compiled by the German Bundesinstitut für Risikobewertung (BfR). On the basis of these data, the BfR built a decision support system (DSS) for the prediction of several toxicological endpoints. For the prediction of eye irritation and corrosion potential, the DSS contains 31 physicochemical exclusion rules evaluated previously by the European Chemicals Bureau (ECB), and 27 inclusion rules that define structural alerts potentially responsible for eye irritation and/or corrosion. This work summarises the results of a study carried out by the ECB to assess the performance of the BfR structural rulebase. The assessment included: (a) evaluation of the structural alerts by using the training set of 1341 substances with experimental data for eye irritation and corrosion; and (b) external validation by using an independent test set of 199 chemicals. Recommendations are made for the further development of the structural rules in order to increase the overall predictivity of the DSS.     

Evaluation of SARs for the prediction of eye irritation/corrosion potential–structural inclusion rules in the BfR decision support system†

The proposed REACH regulation within the European Union (EU) aims to minimise the number of laboratory animals used for human hazard and risk assessment while ensuring adequate protection of human health and the environment. One way to achieve this goal is to develop non-testing methods, such as (quantitative) structure–activity relationships ([Q]SARs), suitable for identifying toxicological hazard from chemical structure and physicochemical properties alone. A database containing data submitted within the EU New Chemicals Notification procedure was compiled by the German Bundesinstitut für Risikobewertung (BfR). On the basis of these data, the BfR built a decision support system (DSS) for the prediction of several toxicological endpoints. For the prediction of eye irritation and corrosion potential, the DSS contains 31 physicochemical exclusion rules evaluated previously by the European Chemicals Bureau (ECB), and 27 inclusion rules that define structural alerts potentially responsible for eye irritation and/or corrosion. This work summarises the results of a study carried out by the ECB to assess the performance of the BfR structural rulebase. The assessment included: (a) evaluation of the structural alerts by using the training set of 1341 substances with experimental data for eye irritation and corrosion; and (b) external validation by using an independent test set of 199 chemicals. Recommendations are made for the further development of the structural rules in order to increase the overall predictivity of the DSS.     

Defining the Human-Biota Thresholds of Toxicological Concern for Organic Chemicals in Freshwater: The Proposed Strategy of the LIFE VERMEER Project Using VEGA Tools

Several tons of chemicals are released every year into the environment and it is essential to assess the risk of adverse effects on human health and ecosystems. Risk assessment is expensive and time-consuming and only partial information is available for many compounds. A consolidated approach to overcome this limitation is the Threshold of Toxicological Concern (TTC) for assessment of the potential health impact and, more recently, eco-TTCs for the ecological aspect. The aim is to allow a safe assessment of substances with poor toxicological characterization. Only limited attempts have been made to integrate the human and ecological risk assessment procedures in a “One Health” perspective. We are proposing a strategy to define the Human-Biota TTCs (HB-TTCs) as concentrations of organic chemicals in freshwater preserving both humans and ecological receptors at the same time. Two sets of thresholds were derived: general HB-TTCs as preliminary screening levels for compounds with no eco- and toxicological information, and compound-specific HB-TTCs for chemicals with known hazard assessment, in terms of Predicted No effect Concentration (PNEC) values for freshwater ecosystems and acceptable doses for human health. The proposed strategy is based on freely available public data and tools to characterize and group chemicals according to their toxicological profiles. Five generic HB-TTCs were defined, based on the ecotoxicological profiles reflected by the Verhaar classes, and compound-specific thresholds for more than 400 organic chemicals with complete eco- and toxicological profiles. To complete the strategy, the use of in silico models is proposed to predict the required toxicological properties and suitable models already available on the VEGAHUB platform are listed.     

Improving confidence in (Q)SAR predictions under Canada’s Chemicals Management Plan – a chemical space approach$

Abstract

One of the key challenges of Canada’s Chemicals Management Plan (CMP) is assessing chemicals with limited/no empirical hazard data for their risk to human health. In some instances, these chemicals have not been tested broadly for their toxicological potency; as such, limited information exists on their potential to induce human health effects following exposure. Although (quantitative) structure activity relationship ((Q)SAR) models are able to generate predictions to address data gaps for certain toxicological endpoints, the confidence in predictions also needs to be addressed. One way to address this issue is to apply a chemical space approach. This approach uses international toxicological databases, for example, those available in the Organisation for Economic Co-operation and Development (OECD) QSAR Toolbox. The approach,assesses a model’s ability to predict the potential hazards of chemicals that have limited hazard data that require assessment under the CMP when compared to a larger, data-rich chemical space that is structurally similar to chemicals of interest. This evaluation of a model’s predictive ability makes (Q)SAR analysis more transparent and increases confidence in the application of these predictions in a risk-assessment context. Using this approach, predictions for such chemicals obtained from four (Q)SAR models were successfully classified into high, medium and low confidence levels to better inform their use in decision-making.     

Using chemical categories to fill data gaps in hazard assessment

Hazard assessments of chemicals have been limited by the availability of test data and the time needed to evaluate the test data. While available data may be inadequate for the majority of industrial chemicals, the body of existing knowledge for most hazards is large enough to permit reliable estimates to be made for untested chemicals without additional animal testing. We provide a summary of the growing use by regulatory agencies of the chemical categories approach, which groups chemicals based on their similar toxicological behaviour and fills in the data gaps in animal test data such as genotoxicity and aquatic toxicity. Although the categories approach may be distinguished from the use of quantitative structure–activity relationships (QSARs) for specific hazard endpoints, robust chemical categories are founded on quantifying the chemical structure with parameters that control chemical behaviour in conventional hazard assessment. The dissemination of the QSAR Application Toolbox by the Organisation for Economic Cooperation and Development (OECD) is an effort to facilitate the use of the categories approach and reduce the need for additional animal testing.     

Hazard Evaluation Support System (HESS) for predicting repeated dose toxicity using toxicological categories

Repeated dose toxicity (RDT) is one of the most important hazard endpoints in the risk assessment of chemicals. However, due to the complexity of the endpoints associated with whole body assessment, it is difficult to build up a mechanistically transparent structure–activity model. The category approach, based on mechanism information, is considered to be an effective approach for data gap filling for RDT by read-across. Therefore, a library of toxicological categories was developed using experimental RDT data for 500 chemicals and mechanistic knowledge of the effects of these chemicals on different organs. As a result, 33 categories were defined for 14 types of toxicity, such as hepatotoxicity, hemolytic anemia, etc. This category library was then incorporated in the Hazard Evaluation Support System (HESS) integrated computational platform to provide mechanistically reasonable predictions of RDT values for untested chemicals. This article describes the establishment of a category library and the associated HESS functions used to facilitate the mechanistically reasonable grouping of chemicals and their subsequent read-across.     

多氯联苯的羟基化代谢产物及其内分泌干扰机制

羟基多氯联苯是典型持久性有机污染物(POPs)--多氯联苯在生物体内的主要活性代谢产物,已在许多野生动物和人体内被检出.由于其化学结构与天然雌激素、甲状腺激素十分类似,因此羟基多氯联苯的内分泌干扰效应近年来已开始受到国际上的高度关注.本文对多氯联苯的代谢途径、羟基多氯联苯在生物体内的浓度水平、内分泌干扰及其多种毒性作用机制进行了综述.有关对多氯联苯的活性代谢产物--羟基多氯联苯的深入研究,将有助于进一步揭示多氯联苯的毒性机制,为我国早日建立有效的POPs污染危害评价与早期预警系统提供科学依据.     

Exploring the effectiveness of the TSR-based protein 3-D structural comparison method for protein clustering,and structural motif identification and discovery of protein kinases,hydrolases, and SARS-CoV-2’s protein via the application of amino acid grouping

Development of protein 3-D structural comparison methods is essential for understanding protein functions. Some amino acids share structural similarities while others vary considerably. These structures determine the chemical and physical properties of amino acids. Grouping amino acids with similar structures potentially improves the ability to identify structurally conserved regions and increases the global structural similarity between proteins. We systematically studied the effects of amino acid grouping on the numbers of Specific/specific, Common/common, and statistically different keys to achieve a better understanding of protein structure relations. Common keys represent substructures found in all types of proteins and Specific keys represent substructures exclusively belonging to a certain type of proteins in a data set. Our results show that applying amino acid grouping to the Triangular Spatial Relationship (TSR)-based method, while computing structural similarity among proteins, improves the accuracy of protein clustering in certain cases. In addition, applying amino acid grouping facilitates the process of identification or discovery of conserved structural motifs. The results from the principal component analysis (PCA) demonstrate that applying amino acid grouping captures slightly more structural variation than when amino acid grouping is not used, indicating that amino acid grouping reduces structure diversity as predicted. The TSR-based method uniquely identifies and discovers binding sites for drugs or interacting proteins. The binding sites of nsp16 of SARS-CoV-2, SARS-CoV and MERS-CoV that we have defined will aid future antiviral drug design for improving therapeutic outcome. This approach for incorporating the amino acid grouping feature into our structural comparison method is promising and provides a deeper insight into understanding of structural relations of proteins.     

Topological Space of the Chemical Elements and its Properties

We carried out a mathematical study of 72 chemical elements taking advantage of the chemotopological method. We selected 128 properties to define the elements (physico-chemical, geochemical and chemical properties). Then, we looked for correlated properties and we reduced the number of them to 90. In this way we defined each element as a 90-tuple. Afterwards, we applied principal component analysis and cluster analysis (4 similarity functions and 5 grouping methodologies). Then, we calculated a consensus tree for the 20 dendrograms generated by the CA. Afterwards, we extracted the similarity relationships from the consensus tree and built up a basis for a topology on the set of chemical elements. Finally, we calculated some topological properties (closures, derived sets, boundaries, interiors and exteriors) of several subsets of chemical elements. We found that alkali metals, alkaline earth metals and noble gases appear not related to the rest of the elements. Also, we found that the boundary of non-metals are the semimetals with a stair-shape on the periodic table     

The safety assessment of herbals with a new and ethical approach

The increasing use of plant medicines (herbals) in Europe needs a shared methodology to determine the toxicity and the daily exposure level to these drugs. For this reason, the European regulatory agencies have undertaken a study that could meet popular uses and toxicological research in different countries of the Union. Here we list some examples of the most used herbal drug classes and we propose a decision-making process based on their characteristics, their content in active principles and on the basis of the present scientific pharmacological and toxicological literature. The proposed decision tree actually makes easier for the assessor to quickly and accurately evaluate the accredited indexes for risk and toxicity assessment based on the preclinical literature data and using the correct classification that some of them may have because they are already present in medicinal products or used as food.     

Application of belief theory to similarity data fusion for use in analog searching and lead hopping

A wide variety of computational algorithms have been developed that strive to capture the chemical similarity between two compounds for use in virtual screening and lead discovery. One limitation of such approaches is that, while a returned similarity value reflects the perceived degree of relatedness between any two compounds, there is no direct correlation between this value and the expectation or confidence that any two molecules will in fact be equally active. A lack of a common framework for interpretation of similarity measures also confounds the reliable fusion of information from different algorithms. Here, we present a probabilistic framework for interpreting similarity measures that directly correlates the similarity value to a quantitative expectation that two molecules will in fact be equipotent. The approach is based on extensive benchmarking of 10 different similarity methods (MACCS keys, Daylight fingerprints, maximum common subgraphs, rapid overlay of chemical structures (ROCS) shape similarity, and six connectivity-based fingerprints) against a database of more than 150,000 compounds with activity data against 23 protein targets. Given this unified and probabilistic framework for interpreting chemical similarity, principles derived from decision theory can then be applied to combine the evidence from different similarity measures in such a way that both capitalizes on the strengths of the individual approaches and maintains a quantitative estimate of the likelihood that any two molecules will exhibit similar biological activity.