Persistent organic pollutants (POPs) present in foods have been a major concern for food safety due to their persistence and toxic effects. To ensure food safety and protect human health from POPs, it is critical to achieve a better understanding of POP pathways into food and develop strategies to reduce human exposure. POPs could present in food in the raw stages, transferred from the environment or artificially introduced during food preparation steps. Exposure to these pollutants may cause various health problems such as endocrine disruption, cardiovascular diseases, cancers, diabetes, birth defects, and dysfunctional immune and reproductive systems. This review describes potential sources of POP food contamination, analytical approaches to measure POP levels in food and efforts to control food contamination with POPs.

Drug-induced liver injury (DILI) is a leading cause of drug failure in clinical trials and a major reason for drug withdrawals from the market. Although there is evidence that dosages of ≥100 mg/day are associated with increased risk for hepatotoxicity, many drugs are safe at such dosages. There is an unmet need to predict risk for DILI more reliably, and lipophilicity might be a contributing factor. We analyzed the combined factors of daily dose and lipophilicity for 164 US Food and Drug Administration–approved oral medications and observed high risk for hepatotoxicity (odds ratio [OR], 14.05; P < 0.001) for drugs given at dosages ≥100 mg/day and octanol-water partition coefficient (logP) ≥3. This defined the “rule-of-two.” Similar results were obtained for an independent set of 179 oral medications with 85% of the rule-of-two positives being associated with hepatotoxicity (OR, 3.89; P < 0.01). Using the World Health Organization's Anatomical Therapeutic Chemical classification system, the rule-of-two performed best in predicting DILI in seven therapeutic categories. Among 15 rule-of-two positives, 14 were withdrawn from hepatotoxic drugs, and one was over-the-counter medication labeled for liver injury. We additionally examined drug pairs that have similar chemical structures and act on the same molecular target but differ in their potential for DILI. Again, the rule-of-two predicted hepatotoxicity reliably. Finally, the rule-of-two was applied to clinical case studies to identify hepatotoxic drugs in complex comedication regimes to further demonstrate its use. Conclusion: Apart from dose, lipophilicity contributes significantly to risk for hepatotoxicity. Applying the rule-of-two is an appropriate means of estimating risk for DILI compared with dose alone. (HEPATOLOGY 2013)

The number of Individuals that use dietary supplements and herbal medicine products are continuous to increase in many countries. The context of usage of a dietary supplement varies widely from country-to-country; in some countries supplement use is just limited to general health and well-being while others permit use for medicinal purposes. To date, there is little consensus from country to country on the scope, requirements, definition, or even the terminology in which dietary supplement and herbal medicines categories could be classified. Transparent science-based quality standards for the ingredients across these regulatory frameworks/definitions becomes even more important given the international supply chain. Meanwhile, there has been a rapid advancement in emerging technologies and data science applied to the field. This review was conceived at the Global Summit on Regulatory Sciences that took place in Beijing on September 2018 (GSRS2018) which is organized by Global Coalition for Regulatory Science Research (GCRSR) that consists of the global regulatory agencies from over ten countries including the European Union. This review summarizes a significant portion of discussions relating to a longitudinal comparison of the status for dietary supplements and herbal medicines among the different national jurisdictions and to the extent of how new tools and methodologies can improve the regulatory application.

Reproducibility is a fundamental requirement in scientific experiments. Some recent publications have claimed that microarrays are unreliable because lists of differentially expressed genes (DEGs) are not reproducible in similar experiments. Meanwhile, new statistical methods for identifying DEGs continue to appear in the scientific literature. The resultant variety of existing and emerging methods exacerbates confusion and continuing debate in the microarray community on the appropriate choice of methods for identifying reliable DEG lists.

Background: Gene expression microarray has been the primary biomarker platform ubiquitously applied in biomedical research, resulting in enormous data, predictive models, and biomarkers accrued. Recently, RNA-seq has looked likely to replace microarrays, but there will be a period where both technologies co-exist. This raises two important questions: Can microarray-based models and biomarkers be directly applied to RNA-seq data? Can future RNA-seq-based predictive models and biomarkers be applied to microarray data to leverage past investment? Results: We systematically evaluated the transferability of predictive models and signature genes between microarray and RNA-seq using two large clinical data sets. The complexity of cross-platform sequence correspondence was considered in the analysis and examined using three human and two rat data sets, and three levels of mapping complexity were revealed. Three algorithms representing different modeling complexity were applied to the three levels of mappings for each of the eight binary endpoints and Cox regression was used to model survival times with expression data. In total, 240,096 predictive models were examined. Conclusions: Signature genes of predictive models are reciprocally transferable between microarray and RNA-seq data for model development, and microarray-based models can accurately predict RNA-seq-profiled samples; while RNA-seq-based models are less accurate in predicting microarray-profiled samples and are affected both by the choice of modeling algorithm and the gene mapping complexity. The results suggest continued usefulness of legacy microarray data and established microarray biomarkers and predictive models in the forthcoming RNA-seq era.