Els Braeken

Bio: Els Braeken is an academic researcher from Thomas More College. The author has contributed to research in topics: Context (language use). The author has an hindex of 3, co-authored 3 publications receiving 43 citations.

Evaluation of in silico tools to predict the skin sensitization potential of chemicals.

Abstract: Public domain and commercial in silico tools were compared for their performance in predicting the skin sensitization potential of chemicals. The packages were either statistical based (Vega, CASE ...

In Silico Prediction of Chemical Toxicity for Drug Design Using Machine Learning Methods and Structural Alerts.

Abstract: During drug development, safety is always the most important issue, including a variety of toxicities and adverse drug effects, which should be evaluated in preclinical and clinical trial phases. This review article at first simply introduced the computational methods used in prediction of chemical toxicity for drug design, including machine learning methods and structural alerts. Machine learning methods have been widely applied in qualitative classification and quantitative regression studies, while structural alerts can be regarded as a complementary tool for lead optimization. The emphasis of this article was put on the recent progress of predictive models built for various toxicities. Available databases and web servers were also provided. Though the methods and models are very helpful for drug design, there are still some challenges and limitations to be improved for drug safety assessment in the future.

Computational Approaches in Preclinical Studies on Drug Discovery and Development.

Abstract: Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.

Review of analytical approaches for the identification of non-intentionally added substances in paper and board food contact materials

Abstract: Background Food contact materials (FCM) may contain non-intentionally added substances (NIAS) as a result of reaction by-products, oligomers, degradation processes, chemical reactions between packaging materials and foodstuff, or as impurities from the raw materials used for their production. Scope and approach In this review, current approaches for the detection and identification of NIAS from paper and board FCM are presented. Reviewed are the definition of NIAS, approaches for NIAS identification and quantification, the comprehensive analysis of NIAS and the role of in silico tools and bioassays. Key Findings and Conclusions NIAS in paper and board are mostly components from printing inks, adhesives, sizing agents and surface coatings. Recycled paper contains overall more NIAS than fresh paper. Targeted analysis is generally performed for predicted NIAS, whereas an untargeted, or full-scan screening method is applied to detect and identify unpredicted NIAS. Sample preparation and contact conditions fall in two categories; migration and extraction. Migration studies are performed with food simulants while extraction studies are Soxhlet or ultrasound assisted solvent extraction. In untargeted analysis in silico tools are gaining importance in the identification of NIAS. Bioassays are used to determine the bioactivity of extracts or fractions in order to assess the potential toxicity of NIAS present in the mixture. A combination of bioassays and chemical analysis is used to direct the identification of unknown bioactive NIAS in complex mixtures like those from paper and board FCM. However, future research is required into the selection of bioassays since these should not only be sensitive enough for detecting all compounds of concern but should also have a relevance with human health.