A categorical structure–activity relationship analysis of GPR119 ligands

The categorical structure–activity relationship (cat-SAR) expert system has been successfully used in the analysis of chemical compounds that cause toxicity. Herein we describe the use of this fragment-based approach to model ligands for the G protein-coupled receptor 119 (GPR119). Using compounds that are known GPR119 agonists and compounds that we have confirmed experimentally that are not GPR119 agonists, four distinct cat-SAR models were developed. Using a leave-one-out validation routine, the best GPR119 model had an overall concordance of 99%, a sensitivity of 99%, and a specificity of 100%. Our findings from the in-depth fragment analysis of several known GPR119 agonists were consistent with previously reported GPR119 structure–activity relationship (SAR) analyses. Overall, while our results indicate that we have developed a highly predictive cat-SAR model that can be potentially used to rapidly screen for prospective GPR119 ligands, the applicability domain must be taken into consideration. Moreover, our study demonstrates for the first time that the cat-SAR expert system can be used to model G protein-coupled receptor ligands, many of which are important therapeutic agents.     

Comparative PBT screening using (Q)SAR tools within REACH legislation

Small to medium sized enterprises (SMEs) in the EU are facing challenges due to the introduction of new legislation designed to protect consumers and the environment, REACH (Registration, Evaluation, Authorisation and Restriction of CHemicals). There can be high costs associated with implementing REACH because data on mammalian toxicity, environmental toxicity and environmental fate properties is required and if this data is obtained experimentally the cost is significant. These costs can be reduced if reliable quantitative structure–activity relationships ((Q)SAR) models are instead used to obtain the required information. In this paper we investigate how easily freely available (Q)SAR models can be applied for persistent, bioaccumulative and toxic (PBT) screening of 17 chemicals of interest to SMEs. In this study the PBT predictions obtained from the more user-friendly PBT Profiler and the Danish(Q)SAR database for the chemicals were compared with the results taken directly from the EPI Suite software. It was found that these widely used (Q)SAR databases might have some errors and examples are provided. It was concluded that extra care must be taken when considering the use of these databases for PBT screening. In addition, to increase the likelihood of a correct prediction, data estimates from various (Q)SAR models relevant to the PBT endpoints must be compared.     

Books Section

Abstract

The availability of validated and characterized SAR models of toxicological phenomena provides a method to apply SAR technology to a variety of environmental, public health and industrial situations. These include (i) the prioritization of environmental pollutants for control and or regulation, (ii) the design of multi-action optimized therapeutics from which the potential for unwanted side-effects have been engineered out, (iii) the development of SAR-based computer-driven screening procedure to identify candidate therapeutics based upon combinatorial chemistry or compilations of molecular structures, (iv) the generation of toxicological profiles to be used in the selection of benign chemicals in the early stages of product development.     

Validity and validation of expert (Q)SAR systems

At a recent workshop in Setubal (Portugal) principles were drafted to assess the suitability of (quantitative) structure–activity relationships ((Q)SARs) for assessing the hazards and risks of chemicals. In the present study we applied some of the Setubal principles to test the validity of three (Q)SAR expert systems and validate the results. These principles include a mechanistic basis, the availability of a training set and validation. ECOSAR, BIOWIN and DEREK for Windows have a mechanistic or empirical basis. ECOSAR has a training set for each QSAR. For half of the structural fragments the number of chemicals in the training set is >4. Based on structural fragments and log Kow, ECOSAR uses linear regression to predict ecotoxicity. Validating ECOSAR for three ‘valid’ classes results in predictivity of ?≥?64%. BIOWIN uses (non-)linear regressions to predict the probability of biodegradability based on fragments and molecular weight. It has a large training set and predicts non-ready biodegradability well. DEREK for Windows predictions are supported by a mechanistic rationale and literature references. The structural alerts in this program have been developed with a training set of positive and negative toxicity data. However, to support the prediction only a limited number of chemicals in the training set is presented to the user. DEREK for Windows predicts effects by ‘if-then’ reasoning. The program predicts best for mutagenicity and carcinogenicity. Each structural fragment in ECOSAR and DEREK for Windows needs to be evaluated and validated separately.     

Structure-Activity Relationships for Non-Congeneric Structures. Application of Substructural Balance Method for Antiallergic Activity

Abstract

This paper presents a new research method of structure-activity relationships (SAR) based on the concept of substructural balance. By using antiallergic activity (PCA, rat, iv) of a non-congeneri set of 267 structures, the structural feature of active group is expressed in terms of substructural balanance. Each structure was expressed with 100 new substructures and the number of each substructure in a molecule was counted. The substructural balance was expressed as their ratio. Structures were classified into three groups based on their potencies (ED₅₀), active (44), median (33) and inactactive (190) group. Using two substructural ratios, 80.53% of inactive and 57.58% of median structures were excluded from those that were active. Common features of active structures were shown as a zone indicating the optimal ranges of two substructural ratios. Two substructural ratios were determined out of 4950 substructural ratios, all possible combinations of 100 substructures (₁₀₀C₂), by selecting the greatest discriminatory power of inactive from active structures. The substructures used in this work include: the number of bonds comprising of the longest conjugate system, the number of skeletal atoms and the numbers of electron-donor pairs at certain distances in the molecule.     

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.     

Endocrine disruption profile analysis of 11,416 chemicals from chemometrical tools⊥

A number of chemicals released into the environment have the potential to disturb the normal functioning of the endocrine system. These chemicals termed endocrine disruptors (EDs) act by mimicking or antagonizing the normal functions of natural hormones and may pose serious threats to the reproductive capability and development of living species. Batteries of laboratory bioassays exist for detecting these chemicals. However, due to time and cost limitations, they cannot be used for all the chemicals which can be found in the ecosystems. SAR and QSAR models are particularly suited to overcome this problem but they only deal with specific targets/endpoints. The interest to account for profiles of endocrine activities instead of unique endpoints to better gauge the complexity of endocrine disruption is discussed through a SAR study performed on 11,416 chemicals retrieved from the US-NCI database and for which 13 different PASS (Prediction of Activity Spectra for Substances) endocrine activities were available. Various multivariate analyses and graphical displays were used for deriving structure-activity relationships based on specific structural features.     

The Influence of Non-Engineered Municipal Landfills on Groundwater Chemistry and Quality in Bloemfontein,South Africa

This study assessed the groundwater quality around two municipal solid waste landfill sites, in the city of Bloemfontein, Free State Province, South Africa. The two landfill sites are located in two contrasting geological terrains, with both lacking some basic facilities found in a well-designed landfill. A total of eight groundwater samples were collected from pollution monitoring boreholes near the two landfill sites, with five samples representing the northern landfill site and three samples representing the southern landfill site. The samples were collected in the autumn and winter seasons to assess any possible seasonal variations. They were analysed for physicochemical (pH, electrical conductivity (EC), total dissolve solids (TDS), chemical oxygen demand (COD) and total organic carbon (TOC)) and microbiological parameters (Escherichia coli, total coliform). The results of the analysis showed that the waters from both landfills were generally dominated by Ca, Mg, SO4, and HCO3 ions. Some of the major anions and cations in the water samples were above the South African National Standard (SANS241:2015) and World Health Organisation (WHO) permissible limits for drinking water. Majority of the boreholes had total dissolved solids and electrical conductivity values exceeding the SANS 241:2015 and WHO permissible limits. Piper trilinear plots for the two landfill sites showed that Ca(Mg)HCO3 water type predominates, but Ca(Mg)SO4 and Ca(Mg)Cl were also found. These water types were further confirmed with expanded Durov diagrams, indicating that that the boreholes represented a water type that is seldom found which is undergoing ion exchange, typical of sulphate contamination. From the SAR diagrams, boreholes in the northern landfill site had a high salinity hazard with only one borehole in the southern landfill site having a high salinity hazard. The geology was found to play a significant role in the distribution of contaminants into the groundwater systems in the study area. The study concluded that the northern landfill site had a poorer water quality in comparison to the southern landfill site based on the analysed physicochemical parameters. However, the southern landfill site showed significant microbial contamination, due to the elevated amount of E. coli and total coliform concentrations. The high permeability of the weathered dolerites in the northern landfill site might have enabled the percolation of contaminants into the groundwater resulting in the poorer water quality.     

一种SAR成像快速算法及优化实现

针对实时SAR成像处理中相关运算量大的特点,提出了新的频域分解快速算法和优化设计方法.根据参数优化准则,选择合适的填零个数和DFT点数分解方式,分析出利用频域分解和频域分段两种算法,可以大大减少相关运算中FFT变换的运算量.在不同情况下,详细分析了多种算法的速度性能和适用性.在并行多处理器上利用此算法,降低了SAR实时成像的运算量和成像延迟,显著提高了SAR实时成像的处理速度.     

抗差自适应模型预测滤波及其在组合导航中的应用

为了提高捷联惯导(SINS)/天文导航(CNS)/合成孔径雷达(SAR)组合导航系统的定位精度,在吸收模型预测滤波和抗差自适应滤波算法优点的基础上,提出了一种新的抗差自适应模型预测滤波算法。该算法首先利用模型预测滤波估计出系统模型误差,并对其进行实时修正,以抑制系统模型误差对导航解算精度的影响;然后利用抗差自适应因子控制观测异常,抑制观测噪声对导航解算精度的影响。将提出的算法应用于SINS/CNS/SAR组合导航系统进行仿真验证,并与抗差自适应滤波进行比较,结果表明,提出的算法得到的姿态误差、速度误差和位置误差分别在[0.2,0.2]、[0.3m/s,0.3m/s]和[6 m,6 m]以内,滤波性能明显优于抗差自适应滤波算法,说明该算法能有效抑制系统模型误差及观测异常对导航解的影响,提高组合导航的解算精度。