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

The German Federal Institute for Risk Assessment (BfR) has developed a Decision Support System (DSS) to assess certain hazardous properties of pure chemicals, including skin and eye irritation/corrosion. The BfR–DSS is a rule-based system that could be used for the regulatory classification of chemicals in the European Union. The system is based on the combined use of two predictive approaches: exclusion rules based on physicochemical cut-off values to identify chemicals that do not exhibit a certain hazard (e.g., skin irritation/corrosion), and inclusion rules based on structural alerts to identify chemicals that do show a particular toxic potential. The aim of the present study was to evaluate the structural inclusion rules implemented in the BfR–DSS for the prediction of skin irritation and corrosion. The following assessments were performed: (a) a confirmation of the structural rules by rederiving them from the original training set (1358 substances), and (b) an external validation by using a test set of 200 chemicals not used in the derivation of the rules. It was found as a result that the test data set did not match the training set relative to the inclusion of structural alerts associated with skin irritation/corrosion, albeit some skin irritants were in the test set.     

Development of an information-intensive structure–activity relationship model and its application to human respiratory chemical sensitizers

Structure–activity relationship (SAR) models are recognized as powerful tools to predict the toxicologic potential of new or untested chemicals and also provide insight into possible mechanisms of toxicity. Models have been based on physicochemical attributes and structural features of chemicals. We describe herein the development of a new SAR modeling algorithm called cat-SAR that is capable of analyzing and predicting chemical activity from divergent biological response data. The cat-SAR program develops chemical fragment-based SAR models from categorical biological response data (e.g. toxicologically active and inactive compounds). The database selected for model development was a published set of chemicals documented to cause respiratory hypersensitivity in humans. Two models were generated that differed only in that one model included explicate hydrogen containing fragments. The predictive abilities of the models were tested using leave-one-out cross-validation tests. One model had a sensitivity of 0.94 and specificity of 0.87 yielding an overall correct prediction of 91%. The second model had a sensitivity of 0.89, specificity of 0.95 and overall correct prediction of 92%. The demonstrated predictive capabilities of the cat-SAR approach, together with its modeling flexibility and design transparency, suggest the potential for its widespread applicability to toxicity prediction and for deriving mechanistic insight into toxicologic effects.     

Improving (Q)SAR predictions by examining bias in the selection of compounds for experimental testing

ABSTRACT

Existing data on structures and biological activities are limited and distributed unevenly across distinct molecular targets and chemical compounds. The question arises if these data represent an unbiased sample of the general population of chemical-biological interactions. To answer this question, we analyzed ChEMBL data for 87,583 molecules tested against 919 protein targets using supervised and unsupervised approaches. Hierarchical clustering of the Murcko frameworks generated using Chemistry Development Toolkit showed that the available data form a big diffuse cloud without apparent structure. In contrast hereto, PASS-based classifiers allowed prediction whether the compound had been tested against the particular molecular target, despite whether it was active or not. Thus, one may conclude that the selection of chemical compounds for testing against specific targets is biased, probably due to the influence of prior knowledge. We assessed the possibility to improve (Q)SAR predictions using this fact: PASS prediction of the interaction with the particular target for compounds predicted as tested against the target has significantly higher accuracy than for those predicted as untested (average ROC AUC are about 0.87 and 0.75, respectively). Thus, considering the existing bias in the data of the training set may increase the performance of virtual screening.     

二级热脱附-气相色谱-质谱联用测定大气可吸入颗粒物中的16种多环芳烃

建立了二级热脱附-气相色谱-质谱联用技术测定大气可吸入颗粒物PM10中16种多环芳烃的分析方法。对二级热脱附和色谱-质谱条件进行了优化。实验结果表明,方法的检出限为0.14～0.42 ng/m3,平均加标回收率为52.7%～97.9%,相对标准偏差(RSD)为8.0%～18.4%。与传统方法相比,该方法的样品前处理时间短、有机溶剂的使用量少,是对人体及环境友善的检测技术。该方法已应用于32份实际大气颗粒物样品的分析。     

Physicochemical and Graph Theoretical Descriptors in Developmental Toxicity SAR: A Comparative Study

Abstract

Chemical insults to the developing fetus can lead to growth retardation, malformation, death, and functional deficits. The present study seeks to determine if physicochemical and/or graph theoretical parameters can be used to determine a structure-activity relationship (SAR) for developmental toxicity, and if consistency is observed among the selected features. The biological data utilized consists of a diverse series of compounds evaluated within the Chernoff-Kavlock in vivo mouse assay. Physicochemical parameters calculated correspond to electronic, steric, and transport properties. Graph theoretical parameters calculated include the simple, valence, and kappa indices. Both sets of parameters were independently applied to derive SARs in order to compare the quality of the respective models. Multiple random sampling, without replacement, was utilized to obtain ten training/test partitions. Models were built by linear discriminant analysis, decision trees, and neural networks respectively. Comparisons on identical sets of data were carried out to determine if any of the model building procedures had a significant advantage in terms of predictive performance. Furthermore, comparison of the features selected within and across the model building processes led to the determination of model consistency. Our results indicate that consistent features related to developmental toxicity are observed and that both physicochemical and graph theoretical parameters have equal utility.     

Synthesis,biological evaluation and SAR studies of benzimidazole derivatives as H1-antihistamine agents

A series of benzimidazole derivatives have been designed,synthesized and evaluated for H₁ antihistamine activity.Six compounds have showed potent antihistamine H₁ activity.The primary SAR analysis indicated that benzyl or benzylidinyl substituted on the exo-nitrogen atom and C2 of the benzimidazole were significant.Further experiments indicated that compound 17d displayed excellent activity to reduce mast cell degranulation,moderate anti-PAF activity and decreased potency on hERG compared to astermizole.Hence compound 17d could serve as anti-allergic agent for further development.     

Antibacterial activity of N-quaternary chitosan derivatives: Synthesis, characterization and structure activity relationship (SAR) investigations

Quaternary N-(2-(N,N,N-tri-alkyl ammoniumyl and 2-pyridiniumyl) acetyl) derivatives of chitosan polymer, chitooligomer, and glucosamine (monomer) were synthesized for the purpose of investigating the structure activity relationship (SAR) for the antibacterial effect. Novel methods were used in the synthesis. The final chitosan and chitooligomer derivatives could thus be obtained in two steps without prior protection of the hydroxyl groups. However, in order to obtain chitosan derivatives with the bulky N,N-dimethyl-N-dodecyl- and N,N-dimethyl-N-butyl side chains three steps were needed, starting from 3,6-O-di-tert-butyldimethylsilyl chitosan (3,6-O-di-TBDMS chitosan) as the key intermediate. The quaternary ammoniumyl acetyl derivatives of glucosamine were synthesized from glucosamine or tetra-O-acetylglucosamine. N,N,N-trimethyl chitosan (TMC) was used as reference compound for investigation of antibacterial activity. Clinical Laboratory Standard Institute (CLSI) protocols were used to determine MIC and MLC for activity against clinically important Gram-positive strains Staphylococcus aureus (ATCC 25923), and S. aureus (MRSA) (ATCC 43300), and Gram-negative strains of Escherichia coli (ATCC 25922), P. aeriginosa (ATCC 27853) and Enterococcus facialis (ATCC 29212). The MIC values for the compounds ranged from 8 to ?8192 mg/L. In general the N-(2-(N,N-dimethyl-N-dodecyl ammoniumyl) acetyl) derivatives of chitooligomer and glucosamine monomer were more active against bacteria than derivatives with shorter alkyl chains. In contrast the N-(2-(N,N-dimethyl-N-dodecyl ammoniumyl) acetyl) derivatives of chitosan were less active than derivatives with N-(2-N,N,N-trimetylammoniumyl) acetyl or N-(2-(N-pyridiniumyl) acetyl) quaternary moiety. N,N,N-trimethyl chitosan (TMC) was the most active compound in this study.     

机载SAR有源干扰分析与比较

机载合成孔径雷达的干扰是当前电子对抗领域关注的焦点问题.在首先介绍了几种常用的SAR有源干扰方法的基础上,重点阐述了相干干扰的工作原理.然后,从压制系数出发,推算出了达到SAR压制系数条件下,不同干扰方式的功率表达式.结合仿真结果,对不同干扰方法的干扰性能进行了评估和比较.最后,针对各种干扰方法的性能优缺点,提出了综合运用多种方法对机载SAR进行有源干扰的作战策略.对机载SAR有源压制干扰的作战应用具备一定的指导意义.