Inhibition of the sodium−glucose cotransporter 2 (SGLT2) by canagliflozin in type 2 diabetes mellitus results in large between-patient variability in clinical response. To better understand this variability, the positron emission tomography (PET) tracer [18F]canagliflozin was developed via a Cu-mediated 18F-fluorination of its boronic ester precursor with a radiochemical yield of 2.0 ± 1.9% and a purity of >95%. The GMP automated synthesis originated [18F]canagliflozin with a yield of 0.5−3% (n = 4) and a purity of >95%. Autoradiography showed [18F]canagliflozin binding in human kidney sections containing SGLT2. Since [18F]canagliflozin is the isotopologue of the extensively characterized drug canagliflozin and thus shares its toxicological and pharmacological characteristics, it enables its immediate use in patients.
Many origin of life theories argue that molecular self-organization explains the spontaneous emergence of structural and dynamical constraints. However, the preservation of these constraints over time is not well-explained because ofthe self-undermining and self-limiting nature of these same processes. A process called autogenesis has been proposed in which a synergetic coupling between self-organized processes preserves the constraints thereby accumulated. Thispaper presents a computer simulation of this process (the AutogenicAutomaton) and compares its behavior to the same self-organizing processes when uncoupled. We demonstrate that this coupling produces a second-order constraint that can both resist dissipation and become replicated in new substrates over time.
MULTIFILE
Adverse Outcome Pathways (AOPs) are conceptual frameworks that tie an initial perturbation (molecular initiat- ing event) to a phenotypic toxicological manifestation (adverse outcome), through a series of steps (key events). They provide therefore a standardized way to map and organize toxicological mechanistic information. As such, AOPs inform on key events underlying toxicity, thus supporting the development of New Approach Methodologies (NAMs), which aim to reduce the use of animal testing for toxicology purposes. However, the establishment of a novel AOP relies on the gathering of multiple streams of evidence and infor- mation, from available literature to knowledge databases. Often, this information is in the form of free text, also called unstructured text, which is not immediately digestible by a computer. This information is thus both tedious and increasingly time-consuming to process manually with the growing volume of data available. The advance- ment of machine learning provides alternative solutions to this challenge. To extract and organize information from relevant sources, it seems valuable to employ deep learning Natural Language Processing techniques. We review here some of the recent progress in the NLP field, and show how these techniques have already demonstrated value in the biomedical and toxicology areas. We also propose an approach to efficiently and reliably extract and combine relevant toxicological information from text. This data can be used to map underlying mechanisms that lead to toxicological effects and start building quantitative models, in particular AOPs, ultimately allowing animal-free human-based hazard and risk assessment.
