In silico-aided prediction of biological properties of chemicals: oestrogen receptor-mediated effects

In silico methods are a valid tool for analysing the properties of chemical compounds and interest in computational modelling techniques to predict the activity of chemicals is constantly growing. Many computational methods can be used to analyse the toxicity or biological activity of chemicals, particularly as regards their interactions with biological macromolecules (e.g. receptors) and other physico-chemical properties. An overview of these methods is provided in this tutorial review, with some examples of their application to predict oestrogen receptor (ER)-mediated effects. Nuclear receptors, particularly ER, have been studied with in silico tools since concern is growing about substances, called endocrine disrupters, that can interfere with hormone regulation. Molecular modelling techniques such as Quantitative Structure-Activity Relationships (QSAR), related methods like 3D-QSAR, and virtual docking have been used to investigate these phenomena and are described here. Implications about regulatory acceptance and use of these methods and the resulting models for identifying hazards and setting priorities are also addressed.     

Computational approaches to shed light on molecular mechanisms in biological processes

Computational approaches based on Molecular Dynamics simulations, Quantum Mechanical methods and 3D Quantitative Structure-Activity Relationships were employed by computational chemistry groups at the University of Milano-Bicocca to study biological processes at the molecular level. The paper reports the methodologies adopted and the results obtained on Aryl hydrocarbon Receptor and homologous PAS proteins mechanisms, the properties of prion protein peptides, the reaction pathway of hydrogenase and peroxidase enzymes and the defibrillogenic activity of tetracyclines.     

SAR—The U.S. Regulatory Perspective

Abstract

Structure-Activity Relationships (SAR) have been used for over a decade by the U.S. EPA's Office of Pollution Prevention and Toxics (OPPT) in their new chemicals program. The development and use of SAR resulted from the need to make rapid risk-based decisions on thousands of new chemicals per year while seldom receiving data on chemical properties, potential exposures, or hazards to humans or organisms in the environment. Qualitative SAR and quantitative SAR methods (QSAR) have been used to fill some of these data gaps by estimating the potential properties and hazards of such chemicals. SAR has been used to assess chemical hazards, identify testing needs, and set priorities. Validation of these SAR assessment tools is an ongoing process.     

Nonlinear prediction of quantitative structure-activity relationships

Predicting the log of the partition coefficient P is a long-standing benchmark problem in Quantitative Structure-Activity Relationships (QSAR). In this paper we show that a relatively simple molecular representation (using 14 variables) can be combined with leading edge machine learning algorithms to predict logP on new compounds more accurately than existing benchmark algorithms which use complex molecular representations.     

Identification,control strategies,and analytical approaches for the determination of potential genotoxic impurities in pharmaceuticals: A comprehensive review

Potential genotoxic impurities in pharmaceuticals at trace levels are of increasing concern to both pharmaceutical industries and regulatory agencies due to their possibility for human carcinogenesis. Molecular functional groups that render starting materials and synthetic intermediates as reactive building blocks for small molecules may also be responsible for their genotoxicity. Determination of these genotoxic impurities at trace levels requires highly sensitive and selective analytical methodologies, which poses tremendous challenges on analytical communities in pharmaceutical research and development. Experimental guidance for the analytical determination of some important classes of genotoxic impurities is still unavailable in the literature. Therefore, the present review explores the structural alerts of commonly encountered potential genotoxic impurities, draft guidance of various regulatory authorities in order to control the level of impurities in drug substances and to assess their toxicity. This review also describes the analytical considerations for the determination of potential genotoxic impurities at trace levels and finally few case studies are also discussed for the determination of some important classes of potential genotoxic impurities. It is the authors’ intention to provide a complete strategy that helps analytical scientists for the analysis of such potential genotoxic impurities in pharmaceuticals.     

Prediction of the rodent carcinogenicity of 60 pesticides by the DEREKfW expert system

The two-year rodent bioassay represents the golden standard for evaluating the carcinogenicity of chemicals. Because of practical and ethical reasons, alternative approaches have been investigated for many years. Among these approaches, the (quantitative) structure-activity relationships [(Q)SARs] offer promising perspectives for quickly screening a large number of chemicals. To increase the acceptance of (Q)SARs among the regulators, their predictive power needs to be scientifically validated. In this article, we tested the capacity of the DEREKfW expert system to qualitatively predict the rodent carcinogenicity and the genotoxic potential of 60 pesticides recently registered in Switzerland. The percentage of false negatives was found to be 31% for carcinogenicity. The associated sensitivity of 69% indicates that most of the pesticides with positive rodent bioassay results were detected by DEREKfW. On the other hand, the low specificity of 47% indicates that many pesticides may be flagged as carcinogenic while rodent bioassays would not confirm this potential. This may lead to unnecessary testing or the unnecessary restriction of a chemical.     

Topological structural alerts modulations of mammalian cell mutagenicity for halogenated derivatives

Genotoxicity is a key toxicity endpoint for current regulatory requirements regarding new and existing chemicals. However, genotoxicity testing is time-consuming and costly, and involves the use of laboratory animals. This has motivated the development of computational approaches, designed to predict genotoxicity without the need to conduct laboratory tests. Currently, many existing computational methods, like quantitative structure–activity relationship (QSAR) models, provide limited information about the possible mechanisms involved in mutagenicity or predictions based on structural alerts (SAs) do not take statistical models into account. This paper describes an attempt to address this problem by using the TOPological Substructural MOlecular Design (TOPS-MODE) approach to develop and validate improved QSAR models for predicting the mutagenicity of a range of halogenated derivatives. Our most predictive model has an accuracy of 94.12%, exhibits excellent cross-validation and external set statistics. A reasonable interpretation of the model in term of SAs was achieved by means of bond contributions to activity. The results obtained led to the following conclusions: primary halogenated derivatives are more mutagenic than secondary ones; and substitution of chlorine by bromine increases mutagenicity while polyhalogenation decreases activity. The paper demonstrates the potential of the TOPS-MODE approach in developing QSAR models for identifying structural alerts for mutagenicity, combining high predictivity with relevant mechanistic interpretation.     

药物中磺酸酯类基因毒性杂质研究进展

该文概述了近10年来有关药物中基因毒性杂质监管指南的完善历程与相关检测方法的研究进展。介绍了基因毒性杂质从早期的完全避免到目前的阶段化毒理学关注阈值（TTC）的风险控制理念以及各主流监管机构的具体要求。作为一类重要的基因毒性杂质,磺酸酯主要来源于磺酸及衍生物与低级醇（如甲醇、乙醇、异丙醇等）之间发生的副反应,具有化学结构类型多样化的特点。该文较为详尽地介绍了磺酸酯的形成机理和文献所采用的液相色谱法和气相色谱法,并对色谱方法的选择、预处理方式、衍生化方法及相应痕量水平的灵敏度和回收率等进行了评述。由此期望为合理控制药物中磺酸酯类基因毒性杂质,为保证药物的质量安全性提供有益的指导意见。     

A novel synthesis of arylpyrrolo[1,2-a]pyrazinone derivatives

Some aryl-2-methyl-1-pyrrolo[1,2-a]pyrazinones were designed and prepared to study the Structure-Activity Relationships (SAR) of pyrrolo[1,2-a]pyrazinone derivatives. With methyl pyrrole-2-carboxylate as the starting material, the title compounds were prepared through N-alkylation and two novel cyclizations. Eleven aryl-2-methyl-1- pyrrolo[1,2-a]pyrazinone derivatives not previously reported in the literature are presented in this paper. Some of them show potent anti-inflammatory and analgesic activities.     

Struktur/wirkung-Beziehungen bei Geruchsstoffen mit dem Bicyclo[2.2.2.]octan-Gerüst

Structure-Activity Relationships of Odorants with a Bicyclo[2.2.2]octane System The synthesis of the olfactory interesting homonojigikualcohol 2 and of its epimer 10 leads to the keto alcohols 6a, b and 7a, b as intermediates. The latter have OH and C?O groups (an AH/B system) fixed at the rigid bicyclic nucleus at different distances (2.8 and 4.7 Å) and confirm Ohloff's rule for odoriferous properties which is based on this distance.     

Uses and limitations of quantitative structure-activity relationships (QSARs) to categorize substances on the Canadian domestic substance list as persistent and/or bioaccumulative,and inherently toxic to non-human organisms

Under sections 73 and 74 of the revised Canadian Environmental Protection Act (CEPA 1999), Environment Canada and Health Canada must "categorize" and "screen" about 23,000 substances on the Domestic Substances List (DSL) for persistence (P), bioaccumulation (B), and inherently toxic (iT) properties. Since experimental data for P, B and iT are only available for a few DSL substances, a workshop was held to address issues associated with the use of Quantitative Structure-Activity Relationships (QSARs) to categorize these substances. This paper describes the results of an 11-12 November 1999 International Workshop sponsored by Environment Canada to discuss potential uses and limitations of QSARs to categorize DSL substances as either persistent or bioaccumulative and iT to non-human organisms and to recommend future research needed to develop methods for predicting the P, B and iT of difficult-to-model substances.     

Integrated approach to assess the domain of applicability of some commercial (Q)SAR models1

An integrated framework of data analysis has been proposed to systematically address the determination of the domain of applicability (DA) of some commercial Quantitative Structure Activity Relationship ((Q)SAR) models based on the structure of test chemicals. This framework forms one of the important steps in dealing with the growing concerns on reliability of model-based predictions on toxicity of chemicals specifically in the regulatory context. The present study uses some of the well-known mutagenicity and carcinogenicity models that are available within the Casetox (MultiCASE Inc.) and TOPKAT (Accelrys Software Inc.) programs. The approach enumerated in this paper employs chemoinformatics tools that facilitate comparisons of key structural features as well as application of cluster analysis techniques. The approach has been illustrated using a set of eleven chemical structures selected from the Canadian Domestic Substances List (DSL) that are not present in the model training sets, and the efficacy of the approach has also been assessed using seven chemicals with known toxicities. The methodologies presented here could help address the issue of DA of complex (Q)SAR models and at the same time, serve as useful tools for regulators to make a preliminary assessment of (Q)SAR based systems thereby helping the process of hazard-based regulatory assessments of chemicals.     

Integrated approach to assess the domain of applicability of some commercial (Q)SAR models

An integrated framework of data analysis has been proposed to systematically address the determination of the domain of applicability (DA) of some commercial Quantitative Structure Activity Relationship ((Q)SAR) models based on the structure of test chemicals. This framework forms one of the important steps in dealing with the growing concerns on reliability of model-based predictions on toxicity of chemicals specifically in the regulatory context. The present study uses some of the well-known mutagenicity and carcinogenicity models that are available within the Casetox (MultiCASE Inc.) and TOPKAT (Accelrys Software Inc.) programs. The approach enumerated in this paper employs chemoinformatics tools that facilitate comparisons of key structural features as well as application of cluster analysis techniques. The approach has been illustrated using a set of eleven chemical structures selected from the Canadian Domestic Substances List (DSL) that are not present in the model training sets, and the efficacy of the approach has also been assessed using seven chemicals with known toxicities. The methodologies presented here could help address the issue of DA of complex (Q)SAR models and at the same time, serve as useful tools for regulators to make a preliminary assessment of (Q)SAR based systems thereby helping the process of hazard-based regulatory assessments of chemicals.     

QSARs for identifying and prioritizing substances with persistence and bioconcentration potential

From the 8511 chemicals with 1998 production volumes reported to the U.S. Environmental Protection Agency (U.S. EPA), the TSCA Interagency Testing Committee's (ITC's) Degradation Effects Bioconcentration Information Testing Strategies (DEBITS) was used to identify 56 chemicals. The DEBITS Quantitative Structure-Activity Relationships (QSARs) and the U.S. EPA's PBT profiler QSARs were used to predict the persistence and bioconcentration factors of these 56 chemicals. Partial order ranking was used to prioritise the chemicals based on persistence and bioconcentration potential.     

Uses and limitations of quantitative structure-activity relationships (QSARs) to categorize substances on the Canadian domestic substance list as persistent and/or bioaccumulative,and inherently toxic to non-human organisms

Under sections 73 and 74 of the revised Canadian Environmental Protection Act (CEPA 1999) , Environment Canada and Health Canada must "categorize" and "screen" about 23,000 substances on the Domestic Substances List (DSL) for persistence (P), bioaccumulation (B), and inherently toxic (iT) properties. Since experimental data for P, B and iT are only available for a few DSL substances, a workshop was held to address issues associated with the use of Quantitative Structure-Activity Relationships (QSARs) to categorize these substances. This paper describes the results of an 11-12 November 1999 International Workshop sponsored by Environment Canada to discuss potential uses and limitations of QSARs to categorize DSL substances as either persistent or bioaccumulative and iT to non-human organisms and to recommend future research needed to develop methods for predicting the P, B and iT of difficult-to-model substances.