Several scientific areas have benefited significantly from the introduction of nanotechnology and the respective evolution. This is especially noteworthy in the development of new drug substances and products. This review focuses on the introduction of nanomedicines in the pharmaceutical market, and all the controversy associated to basic concepts related to these nanosystems, and the numerous methodologies applied for enhanced knowledge. Due to the properties conferred by the nanoscale, the challenges for nanotechnology implementation, specifically in the pharmaceutical development of new drug products and respective regulatory issues are critically discussed, mainly focused on the European Union context. Finally, issues pertaining to the current applications and future developments are presented.

Endocrine disrupting chemicals are a group of pollutants that can affect the endocrine system and lead to diseases and dysfunctions across the lifespan of organisms. They are omnipresent. They are in the air we breathe, in the food we eat and in the water we drink. They can be found in our everyday lives through personal care products, household cleaning products, furniture and in children’s toys. Every year, hundreds of new chemicals are produced and released onto the market without being tested, and they reach our bodies through everyday products. Permanent exposure to those chemicals may intensify or even become the main cause for the development of diseases such as type 2 diabetes, obesity, cardiovascular diseases and certain types of cancer. In recent years, legislation and regulations have been implemented, which aim to control the release of potentially adverse endocrine disrupting chemicals, often invoking the precautionary principle. The objective of this review is to provide an overview of research on environmental aspects of endocrine disrupting chemicals and their effects on human health, based on evidence from animal and human studies. Emphasis is given to three ubiquitous and persistent groups of chemicals, polychlorinated biphenyls, polybrominated diphenyl ethers and organochlorine pesticides, and on two non-persistent, but ubiquitous, bisphenol A and phthalates. Some selected historical cases are also presented and successful cases of regulation and legislation described. These led to a decrease in exposure and consequent minimization of the effects of these compounds. Recommendations from experts on this field, World Health Organization, scientific reports and from the Endocrine Society are included.

Computational Chemistry is currently a synergistic assembly between ab initio calculations, simulation, machine learning (ML) and optimization strategies for describing, solving and predicting chemical data and related phenomena. These include accelerated literature searches, analysis and prediction of physical and quantum chemical properties, transition states, chemical structures, chemical reactions, and also new catalysts and drug candidates. The generalization of scalability to larger chemical problems, rather than specialization, is now the main principle for transforming chemical tasks in multiple fronts, for which systematic and cost-effective solutions have benefited from ML approaches, including those based on deep learning (e.g. quantum chemistry, molecular screening, synthetic route design, catalysis, drug discovery). The latter class of ML algorithms is capable of combining raw input into layers of intermediate features, enabling bench-to-bytes designs with the potential to transform several chemical domains. In this review, the most exciting developments concerning the use of ML in a range of different chemical scenarios are described. A range of different chemical problems and respective rationalization, that have hitherto been inaccessible due to the lack of suitable analysis tools, is thus detailed, evidencing the breadth of potential applications of these emerging multidimensional approaches. Focus is given to the models, algorithms and methods proposed to facilitate research on compound design and synthesis, materials design, prediction of binding, molecular activity, and soft matter behavior. The information produced by pairing Chemistry and ML, through data-driven analyses, neural network predictions and monitoring of chemical systems, allows (i) prompting the ability to understand the complexity of chemical data, (ii) streamlining and designing experiments, (ii) discovering new molecular targets and materials, and also (iv) planning or rethinking forthcoming chemical challenges. In fact, optimization engulfs all these tasks directly.