Current methods of epitope identification for cancer vaccine design

The importance of the immune system in tumor development and progression has been emerging in many cancers. Previous cancer vaccines have not shown long-term clinical benefit possibly because were not designed to avoid eliciting regulatory T-cell responses that inhibit the anti-tumor immune response. This review will examine different methods of identifying epitopes derived from tumor associated antigens suitable for immunization and the steps used to design and validate peptide epitopes to improve efficacy of anti-tumor peptide-based vaccines. Focusing on in silico prediction algorithms, we survey the advantages and disadvantages of current cancer vaccine prediction tools.     

Using quantitative structure–activity relationship modeling to quantitatively predict the developmental toxicity of halogenated azole compounds

Developmental toxicity is a relevant endpoint for the comprehensive assessment of human health risk from chemical exposure. However, animal developmental toxicity data remain unavailable for many environmental contaminants due to the complexity and cost of these types of analyses. Here we describe an approach that uses quantitative structure–activity relationship modeling as an alternative methodology to fill data gaps in the developmental toxicity profile of certain halogenated compounds. Chemical information was obtained and curated using the OECD Quantitative Structure–Activity Relationship Toolbox, version 3.0. Data from 35 curated compounds were analyzed via linear regression to build the predictive model, which has an R² of 0.79 and a Q² of 0.77. The applicability domain (AD) was defined by chemical category and structural similarity. Seven halogenated chemicals that fit the AD but are not part of the training set were employed for external validation purposes. Our model predicted lowest observed adverse effect level values with a maximal threefold deviation from the observed experimental values for all chemicals that fit the AD. The good predictability of our model suggests that this method may be applicable to the analysis of qualifying compounds whenever developmental toxicity information is lacking or incomplete for risk assessment considerations. Copyright © 2013 John Wiley & Sons, Ltd.     

A European perspective on alternatives to animal testing for environmental hazard identification and risk assessment

Tests with vertebrates are an integral part of environmental hazard identification and risk assessment of chemicals, plant protection products, pharmaceuticals, biocides, feed additives and effluents. These tests raise ethical and economic concerns and are considered as inappropriate for assessing all of the substances and effluents that require regulatory testing. Hence, there is a strong demand for replacement, reduction and refinement strategies and methods. However, until now alternative approaches have only rarely been used in regulatory settings. This review provides an overview on current regulations of chemicals and the requirements for animal tests in environmental hazard and risk assessment. It aims to highlight the potential areas for alternative approaches in environmental hazard identification and risk assessment. Perspectives and limitations of alternative approaches to animal tests using vertebrates in environmental toxicology, i.e. mainly fish and amphibians, are discussed. Free access to existing (proprietary) animal test data, availability of validated alternative methods and a practical implementation of conceptual approaches such as the Adverse Outcome Pathways and Integrated Testing Strategies were identified as major requirements towards the successful development and implementation of alternative approaches. Although this article focusses on European regulations, its considerations and conclusions are of global relevance.     

Target-specific anti-fungal discovery by targeting Geotrichum candidum histidinol dehydrogenase: a hybrid approach

This study describes a hybrid approach of screening substrate analogue inhibitors of histidinol dehydrogenase. Imidazole derivative library of approximately 400 compounds classified using Hierarchical cluster analysis, representative compounds of each class were tested in enzymatic assay and used for the development of quantitative structure-activity relationship models. Rest of the compounds in the library were screened using developed models and compounds predicted active were retested. 60% of the predicted compounds showed enzyme inhibition activity with IC(50) values ranged between 5.2 and 58.0 microm range and have fungistatic activity.     

Molecular structural characteristics as determinants of estrogen receptor selectivity

Recent reports that a wide variety of natural and man-made compounds are capable of competing with natural hormones for estrogen receptors serve as timely examples of the need to advance screening techniques to support human health and ascertain ecological risk. Quantitative structure-activity relationships (QSARs) can potentially serve as screening tools to identify and prioritize untested compounds for further empirical evaluations. Computer-based QSAR molecular models have been used to describe ligand-receptor interactions and to predict chemical structures that possess desired pharmacological characteristics. These have recently included combined and differential relative binding affinities of potential estrogenic compounds at estrogen receptors (ER) alpha and beta. In the present study, artificial neural network (ANN) QSAR models were developed that were able to predict differential relative binding affinities of a series of structurally diverse compounds with estrogenic activity. The models were constructed with a dataset of 93 compounds and tested with an additional dataset of 30 independent compounds. High training correlations (r(2)=0.83-0.91) were observed while validation results for the external compounds were encouraging (r(2)=0.62-0.86). The models were used to identify structural features of phytoestrogens that are responsible for selective ligand binding to ERalpha and ERbeta. Numerous structural characteristics are required for complexation with receptors. In particular, size, shape and polarity of ligands, heterocyclic rings, lipophilicity, hydrogen bonding, presence of quaternary carbon atom, presence, position, length and configuration of a bulky side chain, were identified as the most significant structural features responsible for selective binding to ERalpha and ERbeta.     

5-取代-1-正丁基-3-吡唑烷酮类化合物的合成及抗惊作用的构效关系

According to the quantitative structure-activity relationship studies of 3-pyrazolidinones with different substituent on positions 1 and 5 reported previously, the anticonvulsant activity is parabolically related with the total fragment constent (Fr hydrophobic parameter) of the 1 and 5 substituents of 3-pyrazolidion. The optimum Fr was about 5.6. Therefore, eleven new 5-substituted-3-pyrazolidinones have been synthesized. Pharmacological test showed that they are all potent anticonvulsant agents. Among them 1-n-butyl-5-(p-chlorophenyl)-3-pyrazolidinone was shown to be the most potent so far synthesized.